DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 103
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.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1, 7, 12, 19; 6, 11, 17 and 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Azernikov et al. U.S. Pub. No. 2024/0029380 in view of Ko KR 2025-0175474 A and Thompson et al. U.S. Pub. No. 2013/0209962.
Re: claim 1, Azernikov teaches
1. (Currently Amended) A system for generating a digital model of a dental prosthesis, the system comprising: at least one processor configured to read out and execute instructions stored in at least one memory to thereby cause the system to function as: (“In the example of FIG. 1, the cloud computing environment 102 can include at least one processor in communication with at least one computer-readable storage medium which can include instructions executable by the at least one processor to perform steps... ”; Azernikov, [0040])
The cloud computing environment includes at least one processor, communication with at least one computer-readable storage medium (memory), which includes instruction executed by the processor.
a first receiving unit configured to receive information about the dental prosthesis to be modeled; (“The one or more client devices 108 can each connect with one or more scanners 109 known in the art to scan patient's dentition or a dental impression, for example, and provide a 3D digital dental model of at least a portion of a patient's dentition. ”; Azernikov, [0036])
The client devices receive information from scanners regarding the patients dentition or dental impression. (“The automated design 302 can receive a virtual 3D dental model generated by any process that scans a patient's dentition or a physical impression of the patient's dentition and generates a virtual 3D dental model of the patient's dentition. ”; Azernikov, [0048], Fig. 3)
Fig. 3 illustrates the automated design 302 (first receiving unit) receives a virtual 3D dental model generated from the scans of a patient’s dentition, which includes information about the dental prosthesis to be modeled.
an initial dental prosthesis model generation unit configured to generate an initial model of the dental prosthesis using the received information about the dental prosthesis to be modeled; (“In the example of FIG. 1, the cloud computing environment 102 can include at least one processor in communication with at least one computer-readable storage medium which can include instructions executable by the at least one processor to perform steps such as receiving a virtual 3D dental model (also referred to as a “3D digital dental model”) representing at least a portion of a patient's dentition from a client device 108... The cloud computing environment 102 can include an automated integrated restoration design service process and system 103 to automatically design and generate a virtual 3D dental prosthesis model for the virtual 3D dental model.”; Azernikov, [0040], Fig. 1)
Fig. 1 illustrates that the virtual 3D dental model is received by the cloud system, which includes an automated design 103 that generates a virtual 3D dental prosthesis model based on the 3D dental model.
(“...the virtual 3D dental model can be provided to the automated integrated restoration design service process and system 302 through a graphical user interface (“GUI”) that can be displayed on a client device by the cloud computing environment.”; Azernikov, [0052], Fig. 3)
Fig. 3 illustrates that the virtual 3D dental model is provided to the automated design 302.
(“... the cloud computing environment can receive the virtual 3D dental model from a client device and automatically determine a virtual preparation tooth (also referred to as a “digital preparation tooth”) in the virtual 3D dental model and automatically generate a virtual 3D dental prosthesis model (also referred to as a “3D digital dental prosthesis”) for the virtual preparation tooth.”; Azernikov, [0053])
Fig. 3 illustrates that when the automated design 302 receives the virtual 3D dental model from the client device, a virtual preparation tooth is determined from the virtual 3D dental model and then this information is used to generate a virtual 3D dental prosthesis model.
a second receiving unit configured to receive information for modifying the initial model of the dental prosthesis; (“... the cloud computing environment 102 can implement quality control (“QC”) features 106 to evaluate the designed virtual 3D dental prosthesis model and provide feedback to the automated integrated restoration design service process and system 103 to modify the current design and/or future designs.”; Azernikov, [0040])
The cloud computing environment implements quality control features to evaluate the virtual 3D dental prosthesis model and provide feedback to the automated design 103.
(“... the cloud computing environment can evaluate the generated 3D dental model and route the generated model optionally with the virtual 3D dental model to perform quality control (“QC”) 310 on the generated virtual 3D dental prosthesis model... the QC modifications can be provided to the design feature 302 automatically via a QC feedback loop 316. The QC feedback loop 316 can provide QC modification data to improve future designs, for example.”; Azernikov, [0053], Fig. 3)
Fig. 3 illustrates that the quality control (QC) 310 (second receiving unit) receives the 3D dental prosthesis model from the automated design 302 (receives information for modifying the initial model of the dental prosthesis) and performs QC modifications.
and a modified dental prosthesis model generation unit configured to generate a modified model of the dental prosthesis using the information for modifying the initial model of the dental prosthesis, (“The QC feedback loop 316 can provide QC modification data to improve future designs, for example... the current virtual 3D dental prosthesis model that was subject to QC can be regenerated based on the QC feedback loop 316. Once the generated virtual 3D dental prosthesis model passes QC, it can be sent to be physically generated by a CAM process 318 in some embodiments.”; Azernikov, [0053])
Fig. 3 illustrates that the QC feedback loop provides QC modification data of the virtual 3D dental prosthesis to the automated design, which regenerates the regenerates the virtual 3D dental prosthesis using the QC modification data.
wherein the system is configured to receive at least one of the modifying the information about the dental prosthesis to be modeled and the information for modifying the initial model of the dental prosthesis in the form of voice or text, the information comprising a description of a geometric change to an anatomical feature of the initial model, (“... the QC modifications can be provided to the design feature 302 automatically via a QC feedback loop 316. The QC feedback loop 316 can provide QC modification data to improve future designs, for example. In some embodiments, the current virtual 3D dental prosthesis model that was subject to QC can be regenerated based on the QC feedback loop 316. ”; Azernikov, [0053])
The QC modifications are provided to the design feature 302 (receive the modifying information about the dental prosthesis to be modeled). Azernikov is silent regarding the information for modifying the initial model of the dental prosthesis in the form of voice or text, however, Ko teaches this limitation.
(“... the control unit (150) calculates the degree of alignment between the oral model and the complete denture arrangement model when applying (or placing/implanting) the generated complete denture arrangement model to the 3D scanned oral model of the patient. At this time, if the degree of alignment between the calculated oral model and the complete denture arrangement model is less than a preset reference value, the control unit (150) outputs information indicating that the generated complete denture arrangement model is not suitable for the oral model of the patient through the display unit (130) and/or voice output unit (140), and may repeatedly perform the process of generating a complete denture arrangement model using the previously 3D scanned oral model of the patient, thereby generating a modified (or new) complete denture arrangement model.”; Ko, p. 13, 3rd para)
The control unit calculates the degree of alignment between the oral model and the complete denture arrangement model. If the degree of alignment is less than a preset value, then the control unit outputs information through the display and/or the voice output unit (the information for modifying the initial model of the dental prosthesis in the form of voice or text) indicating that the complete denture arrangement model is not suitable and needs to be regenerated using the previously scanned oral model of the patient. Azernikov and Ko are silent regarding the information comprising a description of a geometric change to an anatomical feature of the initial model, however, Thompson teaches this limitation.
(“For example, the dentist may notice that the dentures are no longer properly fitting due to wear of the denture base or the teeth or due to changes in the patient’s anatomy. This additional information may be used to change the digitized model of the denture.”; Thompson, [0033])
Additional information is generated indicating that the dentures no longer fit properly due to changes in the patient’s anatomy. This additional information is used to change the digitized model of the denture (information comprising a description of a geometric change to an anatomical feature of the initial model). Ko and Thompson are combined with Azernikov such that the voice output of Ko and the additional information of Thompson is included in the method of Azernikov. Therefore, it would have been obvious to one of ordinary skill in the art at the time of the effective filing date, to modify the method of Azernikov by adding the feature of the system is configured to receive at least one of the modifying the information about the dental prosthesis to be modeled and the information for modifying the initial model of the dental prosthesis in the form of voice or text, the information comprising a description of a geometric change to an anatomical feature of the initial model, in order to provide an optimal total denture that fits the patient’s oral structure, as taught by Ko (p. 13, 4th para) and in order to create a best fit for the duplicate denture that maintains the occlusal scheme of the existing denture while allowing changes to the denture for anatomical reasons, as taught by Thompson ([0011]).
Azernikov teaches and wherein at least one of the first receiving unit, initial dental prosthesis model generation unit, second receiving unit, and modified dental prosthesis model generation unit is configured to use artificial intelligence when performing functions. (“Some embodiments can include generating, using a trained deep neural network, a virtual 3D dental prosthesis model based on the virtual 3D dental model. Some embodiments can include automatically generating a 3D digital dental prosthesis model (the virtual 3D dental prosthesis model) in the 3D digital dental model using a trained generative deep neural network.”; Azernikov, [0100])
A deep neural network (artificial intelligence) is used to generate a virtual 3D prosthesis model (initial dental prosthesis model generation unit, modified dental prosthesis model generation unit).
(“Certain embodiments of the methods can include:... receiving, by the one or more computing devices, a patient scan data representing at least a portion of a patient's dentition; and generating, using the trained deep neural network, the first 3D dental prosthesis model based on the received patient scan data.”; Azernikov, [0101])
A deep neural network receives patient scan data (first receiving unit) and uses this information to generate the 3D dental prosthesis model (initial dental prosthesis model generation unit).
Re: claim 7, Azernikov teaches
7. (Currently Amended) A system for generating a digital model of a dental prosthesis, the system comprising: at least one processor configured to read out and execute instructions stored in at least one memory to thereby cause the system to function as: (“In the example of FIG. 1, the cloud computing environment 102 can include at least one processor in communication with at least one computer-readable storage medium which can include instructions executable by the at least one processor to perform steps... ”; Azernikov, [0040])
The cloud computing environment includes at least one processor, communication with at least one computer-readable storage medium (memory), which includes instruction executed by the processor.
Azernikov is silent regarding receive information in the form of voice or text about the dental prosthesis to be modeled, however, Ko teaches
a receiving unit configured to receive information in the form of voice or text about the dental prosthesis to be modeled, the information comprising a description of a geometric change to an anatomical feature of an initial model of the dental prosthesis; (“... the QC modifications can be provided to the design feature 302 automatically via a QC feedback loop 316. The QC feedback loop 316 can provide QC modification data to improve future designs, for example. In some embodiments, the current virtual 3D dental prosthesis model that was subject to QC can be regenerated based on the QC feedback loop 316. ”; Azernikov, [0053])
The QC modifications are provided to the design feature 302 (receiving unit configured to receive information ... about the dental prosthesis to be modeled). Azernikov is silent regarding the information in the form of voice or text, however, Ko teaches this limitation.
(“... the control unit (150) calculates the degree of alignment between the oral model and the complete denture arrangement model when applying (or placing/implanting) the generated complete denture arrangement model to the 3D scanned oral model of the patient. At this time, if the degree of alignment between the calculated oral model and the complete denture arrangement model is less than a preset reference value, the control unit (150) outputs information indicating that the generated complete denture arrangement model is not suitable for the oral model of the patient through the display unit (130) and/or voice output unit (140), and may repeatedly perform the process of generating a complete denture arrangement model using the previously 3D scanned oral model of the patient, thereby generating a modified (or new) complete denture arrangement model.”; Ko, p. 13, 3rd para)
The control unit calculates the degree of alignment between the oral model and the complete denture arrangement model. If the degree of alignment is less than a preset value, then the control unit outputs information through the display and/or the voice output unit (information in the form of voice or text about the dental prosthesis to be modeled) indicating that the complete denture arrangement model is not suitable and needs to be regenerated using the previously scanned oral model of the patient. Azernikov and Ko are silent regarding the information comprising a description of a geometric change to an anatomical feature of the initial model, however, Thompson teaches this limitation.
(“For example, the dentist may notice that the dentures are no longer properly fitting due to wear of the denture base or the teeth or due to changes in the patient’s anatomy. This additional information may be used to change the digitized model of the denture.”; Thompson, [0033])
Additional information is generated indicating that the dentures no longer fit properly due to changes in the patient’s anatomy. This additional information is used to change the digitized model of the denture (information comprising a description of a geometric change to an anatomical feature of the initial model). Ko and Thompson are combined with Azernikov such that the voice output of Ko and the additional information of Thompson is included in the method of Azernikov. Therefore, it would have been obvious to one of ordinary skill in the art at the time of the effective filing date, to modify the method of Azernikov by adding the feature of the system is configured to receive at least one of the modifying the information about the dental prosthesis to be modeled and the information for modifying the initial model of the dental prosthesis in the form of voice or text, the information comprising a description of a geometric change to an anatomical feature of the initial model, in order to provide an optimal total denture that fits the patient’s oral structure, as taught by Ko (p. 13, 4th para) and in order to create a best fit for the duplicate denture that maintains the occlusal scheme of the existing denture while allowing changes to the denture for anatomical reasons, as taught by Thompson ([0011]).
Azernikov teaches and a dental prosthesis model generation unit configured to generate a model of the dental prosthesis based on the information received by the receiving unit, with the model generation unit using artificial intelligence. (“Some embodiments can include generating, using a trained deep neural network, a virtual 3D dental prosthesis model based on the virtual 3D dental model. Some embodiments can include automatically generating a 3D digital dental prosthesis model (the virtual 3D dental prosthesis model) in the 3D digital dental model using a trained generative deep neural network.”; Azernikov, [0100])
A deep neural network (artificial intelligence) is used to generate a virtual 3D prosthesis model (generate a model of the dental prosthesis based on the information received by the receiving unit, with the model generation unit using artificial intelligence).
(“Certain embodiments of the methods can include:... receiving, by the one or more computing devices, a patient scan data representing at least a portion of a patient's dentition; and generating, using the trained deep neural network, the first 3D dental prosthesis model based on the received patient scan data.”; Azernikov, [0101])
A deep neural network receives patient scan data (information received by the first receiving unit) and uses this information to generate the 3D dental prosthesis model (generate a model of the dental prosthesis).
Claim 12 is a method analogous to the system of claim 1, is similar in scope and is rejected under the same rationale. Claim 12 has additional limitations. Re: claim 12, Azernikov teaches
12. (Currently Amended) A method for generating a digital model of a dental prosthesis performed by a computer system, the method comprising:... providing a display of the initial model of the dental prosthesis on a display device; (“The cloud computing environment 102 can output the virtual 3D dental model with the designed virtual 3D dental prosthesis... or can output the designed virtual 3D dental prosthesis only... the computing environment includes storage 14034, one or more input devices 14036, one or more output devices 14038... The output device(s) may be a display...”; Azernikov, [0041] [0043], [0045])
The cloud computing environment outputs the virtual 3D dental prosthesis model to an output device such as a display (providing a display of the initial model of the dental prosthesis on a display device).
... and providing a display of a modified model of the dental prosthesis on the display device, (“... the computer-implemented method can display at least a portion of the 3D digital model of the patient's dentition that includes the generated 3D digital dental prosthesis model on a display such as a computer screen in a Graphical User Interface (“GUI”) that can include interactive controls that can allow a dental technician, dentist, or other user to manipulate one or more features of the generated 3D digital dental prosthesis model.”; Azernikov, [0129])
The 3D digital dental prosthesis model is displayed on the GUI with interactive controls that allow a user to manipulate features of the 3D digital dental prosthesis model (providing a display of a modified model of the dental prosthesis on the display device).
Claim 19 is a medium analogous to the system of claim 1, is similar in scope and is rejected under the same rationale. Claim 19 has additional limitations. Re: claim 19, Azernikov teaches
19. (Currently Amended) A non-transitory computer readable storage medium storing computer-readable instruction that cause a processor of a computer system to:... provide a display of the initial model of the dental prosthesis on a display device; (“The cloud computing environment 102 can output the virtual 3D dental model with the designed virtual 3D dental prosthesis... or can output the designed virtual 3D dental prosthesis only... the computing environment includes storage 14034, one or more input devices 14036, one or more output devices 14038... The output device(s) may be a display...”; Azernikov, [0041] [0043], [0045])
The cloud computing environment outputs the virtual 3D dental prosthesis model to an output device such as a display (provide a display of the initial model of the dental prosthesis on a display device).
... and provide a display of a modified model of the dental prosthesis on the display device, (“... the computer-implemented method can display at least a portion of the 3D digital model of the patient's dentition that includes the generated 3D digital dental prosthesis model on a display such as a computer screen in a Graphical User Interface (“GUI”) that can include interactive controls that can allow a dental technician, dentist, or other user to manipulate one or more features of the generated 3D digital dental prosthesis model.”; Azernikov, [0129])
The 3D digital dental prosthesis model is displayed on the GUI with interactive controls that allow a user to manipulate features of the 3D digital dental prosthesis model (provide a display of a modified model of the dental prosthesis on the display device).
Re: claims 6 and 11 (which are rejected under the same rationale), Azernikov, Ko and Thompson teach
6. (Original) A system according to claim 1, wherein the first receiving unit is configured to receive a model generated by a digital impression scanner, and the initial dental prosthesis model generation unit is configured to use the model generated by the digital impression scanner to generate the initial model of the dental prosthesis. (“The automated design 302 can receive a virtual 3D dental model generated by any process that scans a patient's dentition or a physical impression of the patient's dentition and generates a virtual 3D dental model of the patient's dentition.”; Azernikov, [0048])
The automated design 302 receives a virtual 3D dental model from a scan of a patient’s dentition or a physical impression of the patient’s dentition (receive a model generated by a digital impression scanner). The automated design uses this information to generate a virtual 3D dental model of the patient’s dentition.
Re: claim 17, Azernikov, Ko and Thompson teach
17. (Original) A method of creating a dental prosthesis comprising: fabricating the dental prosthesis using the initial model of the dental prosthesis or the modified model of the dental prosthesis generated by the method according to claim 12. (“... the current virtual 3D dental prosthesis model that was subject to QC can be regenerated based on the QC feedback loop 316. Once the generated virtual 3D dental prosthesis model passes QC, it can be sent to be physically generated by a CAM process 318 in some embodiments. The CAM process 318 can include, for example, chairside milling 320 or a milling center 322, or any other CAM fabrication process known in the art.”; Azernikov, [0053], Fig. 3)
Once the virtual 3D dental prosthesis has completed the QC process and is regenerated (modified model of the dental prosthesis), the virtual 3D dental prosthesis is sent to the CAM process to be physically generated (fabricating the dental prosthesis).
Re: claim 18, Azernikov, Ko and Thompson teach
18. (Original) A dental prosthesis made according to the method of claim 17. (“... the current virtual 3D dental prosthesis model that was subject to QC can be regenerated based on the QC feedback loop 316. Once the generated virtual 3D dental prosthesis model passes QC, it can be sent to be physically generated by a CAM process 318 in some embodiments. The CAM process 318 can include, for example, chairside milling 320 or a milling center 322, or any other CAM fabrication process known in the art.”; Azernikov, [0053])
Once the virtual 3D dental prosthesis has completed the QC process and is regenerated, the virtual 3D dental prosthesis is sent to the CAM process to be physically generated (a dental prothesis made according to the method of claim 17).
Claim(s) 4, 5, 9, 10, 14, 16, is/are rejected under 35 U.S.C. 103 as being unpatentable over Azernikov in view of Ko and Thompson as applied to claims 1, 7 and 12 above, and further in view of Bright et al. U.S. Pub. No. 2025/0252665.
Re: claims 4 and 9 (which are rejected under the same rationale), Azernikov, Ko and Thompson are silent regarding at least one of the first receiving unit and the second receiving unit is configured to output a prompt requesting information to be used to generate the initial model of the dental prosthesis or information to modify the initial model of the dental prosthesis, however, Bright teaches
4. (Original) A system according to claim 1, wherein at least one of the first receiving unit and the second receiving unit is configured to output a prompt requesting information to be used to generate the initial model of the dental prosthesis or information to modify the initial model of the dental prosthesis. (“For example, a user inputs the keyword “Pizza,” the keyword “Pizza” is recognized by the digital model generation platform as a distinct thematic concept. “Pizza” is then integrated into the digital model generation platform's category repository as a standalone category, representing a unique theme for users to explore and incorporate into the user's creations. Users select the “Pizza” category from the screen interface and access a range of pre-existing templates, suggestions, and creative prompts related to Pizzas.”; Bright, [0054])
For example, when the user inputs the word “Pizza” into the digital model generation platform, it presents a screen interface such that the user can select the pizza category from the screen interface (output a prompt requesting information to be used to generate the initial model or information to modify the initial model). Bright is combined with Azernikov, Ko and Thompson such that the prompt of Bright is included in the method of Azernikov and the model of Bright is the dental prosthesis model of Azernikov. Therefore, it would have been obvious to one of ordinary skill in the art at the time of the effective filing date, to modify the method of Azernikov by adding the feature of at least one of the first receiving unit and the second receiving unit is configured to output a prompt requesting information to be used to generate the initial model of the dental prosthesis or information to modify the initial model of the dental prosthesis, in order to gather additional context or constraints that enhance the accuracy and relevance of the generated content, as taught by Bright ([0082]).
Re: claim 5, Azernikov, Ko and Thompson are silent regarding at least one of the first receiving unit and the second receiving unit is configured to output a prompt requesting further information to be used to generate the initial model of the dental prosthesis or modify the initial model of the dental prosthesis in response to the received information about the dental prosthesis to be modeled or the received information for modifying the initial model of the dental prosthesis, however, Bright teaches
5. (Original) A system according to claim 1, wherein at least one of the first receiving unit and the second receiving unit is configured to output a prompt requesting further information to be used to generate the initial model of the dental prosthesis or modify the initial model of the dental prosthesis in response to the received information about the dental prosthesis to be modeled or the received information for modifying the initial model of the dental prosthesis. (“... if the confidence score falls below the threshold, indicating a lower degree of confidence in the accuracy of the output, the digital model generation platform prompts the user for additional input or clarification (e.g., displaying “What do you see in this picture?”)... the digital model generation platform engages in an iterative dialogue with the user to gather additional context or constraints that enhance the accuracy and relevance of the generated content.”; Bright, [0125])
If the confidence score of the generated 3D model falls below the threshold, then the digital model generation platform prompts the user for additional input or clarification, and engages in an iterative dialogue with the user to gather information (modification information) about the 3D model (output a prompt requesting further information to be used to generate the initial model of the dental prosthesis or modify the initial model of the dental prosthesis in response to the received information about the dental prosthesis to be modeled). Bright is combined with Azernikov, Ko and Thompson such that the prompt of Bright is included in the method of Azernikov and the model of Bright is the dental prosthesis model of Azernikov. Therefore, it would have been obvious to one of ordinary skill in the art at the time of the effective filing date, to modify the method of Azernikov by adding the feature of at least one of the first receiving unit and the second receiving unit is configured to output a prompt requesting further information to be used to generate the initial model of the dental prosthesis or modify the initial model of the dental prosthesis in response to the received information about the dental prosthesis to be modeled or the received information for modifying the initial model of the dental prosthesis, in order to gather additional context or constraints that enhance the accuracy and relevance of the generated content, as taught by Bright ([0082]).
Re: claim 10, Azernikov, Ko and Thompson are silent regarding the prompt is a first prompt, and the receiving unit is configured to output a second prompt for further information to be used by the dental prosthesis model generation unit to generate the model of the dental prosthesis or modify the model of the dental prosthesis, however, Bright teaches
10. (Original) A system according to claim 9, wherein the prompt is a first prompt, and the receiving unit is configured to output a second prompt for further information to be used by the dental prosthesis model generation unit to generate the model of the dental prosthesis or modify the model of the dental prosthesis. (“... if the confidence score falls below the threshold, indicating a lower degree of confidence in the accuracy of the output, the digital model generation platform prompts the user for additional input or clarification (e.g., displaying “What do you see in this picture?”)... the digital model generation platform engages in an iterative dialogue with the user to gather additional context or constraints that enhance the accuracy and relevance of the generated content.”; Bright, [0125])
If the confidence score of the generated 3D model falls below the threshold, then the digital model generation platform prompts the user for additional input or clarification, and engages in an iterative dialogue (output a second prompt) with the user to gather information about the 3D model or modifications to the 3D model (output a second prompt for further information to be used by the dental prosthesis model generation unit to generate the model of the dental prosthesis or modify the model of the dental prosthesis). Bright is combined with Azernikov, Ko and Thompson such that the prompt of Bright is included in the method of Azernikov and the model of Bright is the dental prosthesis model of Azernikov. Therefore, it would have been obvious to one of ordinary skill in the art at the time of the effective filing date, to modify the method of Azernikov by adding the feature of the prompt is a first prompt, and the receiving unit is configured to output a second prompt for further information to be used by the dental prosthesis model generation unit to generate the model of the dental prosthesis or modify the model of the dental prosthesis, in order to gather additional context or constraints that enhance the accuracy and relevance of the generated content, as taught by Bright ([0082]).
Re: claim 14, Azernikov, Ko and Thompson are silent regarding providing on the display device a prompt requesting the information about the dental prosthesis to be modeled, however, Bright teaches
14. (Original) The method according to claim 12, further comprising providing on the display device a prompt requesting the information about the dental prosthesis to be modeled. (“For example, a user inputs the keyword “Pizza,” the keyword “Pizza” is recognized by the digital model generation platform as a distinct thematic concept. “Pizza” is then integrated into the digital model generation platform's category repository as a standalone category, representing a unique theme for users to explore and incorporate into the user's creations. Users select the “Pizza” category from the screen interface and access a range of pre-existing templates, suggestions, and creative prompts related to Pizzas. ”; Bright, [0054])
For example, when the user inputs the word “Pizza” into the digital model generation platform, it presents a screen interface such that the user can select the pizza category from the screen interface (providing on a display device a prompt requesting the information about the dental prosthesis to be modeled). Bright is combined with Azernikov, Ko and Thompson such that the prompt of Bright is included in the method of Azernikov and the model of Bright is the dental prosthesis model of Azernikov. Therefore, it would have been obvious to one of ordinary skill in the art at the time of the effective filing date, to modify the method of Azernikov by adding the feature of providing on the display device a prompt requesting the information about the dental prosthesis to be modeled, in order to gather additional context or constraints that enhance the accuracy and relevance of the generated content, as taught by Bright ([0082]).
Re: claim 16, Azernikov, Ko and Thompson are silent regarding providing on the display device a prompt requesting the information for modifying the initial model of the dental prosthesis, however, Bright teaches
16. (Original) The method according to claim 12, further comprising providing on the display device a prompt requesting the information for modifying the initial model of the dental prosthesis. (“... if the confidence score falls below the threshold, indicating a lower degree of confidence in the accuracy of the output, the digital model generation platform prompts the user for additional input or clarification (e.g., displaying “What do you see in this picture?”)... the digital model generation platform engages in an iterative dialogue with the user to gather additional context or constraints that enhance the accuracy and relevance of the generated content.”; Bright, [0125])
If the confidence score of the generated 3D model falls below the threshold, then the digital model generation platform prompts the user for additional input or clarification, and engages in an iterative dialogue with the user to gather information (providing on the display device a prompt requesting the information for modifying the initial model of the dental prosthesis) about modifying the 3D model. Bright is combined with Azernikov, Ko and Thompson such that the prompt of Bright is included in the method of Azernikov and the model of Bright is the dental prosthesis model of Azernikov. Therefore, it would have been obvious to one of ordinary skill in the art at the time of the effective filing date, to modify the method of Azernikov by adding the feature of providing on the display device a prompt requesting the information for modifying the initial model of the dental prosthesis, in order to gather additional context or constraints that enhance the accuracy and relevance of the generated content, as taught by Bright ([0082]).
Claim(s) 2, 3, 8, 13 and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Azernikov in view of Ko and Thompson as applied to claims 1, 7, 12 and 19 above, and further in view of Orton et al. U.S. Pub. No. 2024/0346709.
Re: claims 2, 8, 13 and 20 (which are rejected under the same rationale), Azernikov and Bright are silent regarding the artificial intelligence includes a large language model and a second model configured to produce three-dimensional models of dental prosthesis, however, Orton teaches
2. (Currently Amended) A system according to claim 1, wherein the artificial intelligence includes a large language model and a second model configured to produce three-dimensional models of dental prostheses. (“In this way, the values of the parameters produce more relevant visual content items when used to generate visual content items from a generative machine learning apparatus, such as an image generator, a video generator or a 3D model generator. The visual content is generated by inputting the computed values of the input parameters to a generative machine learning apparatus.”; Orton, [0027])
The generative machine learning apparatus (artificial intelligence) includes a 3D model generator (second model) that generates the 3D model based on input such as computed values of the input parameters.
(“... the textual information 408 is textual user input 402 which has been enhanced using a large language model 404... The term “large language model” is used to refer to a generative machine learning model which receives input comprising a text prompt and generates text in response.”; Orton, [0052], [0053], Fig. 4)
Fig. 4 illustrates a generative machine learning model that includes a large language model 404 (artificial intelligence includes a large language model) that receives input such a text prompt and generates text in response. Orton is combined with Azernikov, Ko and Thompson such that the neural network of Azernikov includes the large language model of Orton and the 3D model of Orton is the dental prosthesis model of Azernikov. Therefore, it would have been obvious to one of ordinary skill in the art at the time of the effective filing date, to modify the method of Azernikov by adding the feature of the artificial intelligence includes a large language model and a second model configured to produce three-dimensional models of dental prosthesis, in order to enhance the textual user input by generating semantic variations of the user input such as “a house” being transformed into “a villa”, as taught by Orton ([0053]).
Re: claim 3, Azernikov, Ko and Thompson are silent regarding the initial model of the dental prosthesis and the modified model of the dental prosthesis is produced by the second model of the artificial intelligence, however Orton teaches
3. (Original) A system according to claim 2, wherein at least one of the initial model of the dental prosthesis and the modified model of the dental prosthesis is produced by the second model of the artificial intelligence. (“In this way, the values of the parameters produce more relevant visual content items when used to generate visual content items from a generative machine learning apparatus, such as an image generator, a video generator or a 3D model generator. The visual content is generated by inputting the computed values of the input parameters to a generative machine learning apparatus.”; Orton, [0027])
The generative machine learning apparatus generates the 3D model based on the computed values of the input parameters (the initial model of the dental prosthesis and the modified model of the dental prosthesis is produced by the second model of the artificial intelligence). Orton is combined with Azernikov, Ko and Thompson such that the 3D model of Orton is the dental prosthesis model of Azernikov. Therefore, it would have been obvious to one of ordinary skill in the art at the time of the effective filing date, to modify the method of Azernikov by adding the feature of at least one of the initial model of the dental prosthesis and the modified model of the dental prosthesis is produced by the second model of the artificial intelligence, in order to enhance the textual user input by generating semantic variations of the user input such as “a house” being transformed into “a villa”, as taught by Orton ([0053]).
Claim(s) 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Azernikov in view of Ko, Thompson and Orton as applied to claim 13 above, and further in view of Bright.
Re: claim 15, Azernikov, Ko, Thompson and Orton are silent regarding providing on the display a request for clarification regarding the information about the dental prosthesis to be modeled, however, Bright teaches
15. (Original) The method according to claim 13, further comprising providing on the display a request for clarification regarding the information about the dental prosthesis to be modeled. (“... if the confidence score falls below the threshold, indicating a lower degree of confidence in the accuracy of the output, the digital model generation platform prompts the user for additional input or clarification (e.g., displaying “What do you see in this picture?”)... the digital model generation platform engages in an iterative dialogue with the user to gather additional context or constraints that enhance the accuracy and relevance of the generated content.”; Bright, [0125])
If the confidence score of the generated 3D model falls below the threshold, then the digital model generation platform prompts the user for additional input or clarification, and engages in an iterative dialogue with the user to gather information about the 3D model (providing on the display a request for clarification regarding the information about the dental prosthesis to be modeled). Bright is combined with Azernikov, Ko, Thompson and Orton such that the prompt of Bright is included in the method of Azernikov and the model of Bright is the dental prosthesis model of Azernikov. Therefore, it would have been obvious to one of ordinary skill in the art at the time of the effective filing date, to modify the method of Azernikov by adding the feature of providing on the display a request for clarification regarding the information about the dental prosthesis to be modeled, in order to aid users in refining the user's input and overcoming any ambiguity or misunderstanding, as taught by Bright ([0082]).
Response to Arguments
Applicant's arguments filed 8/21/2026 have been fully considered but they are not persuasive. Applicant argues:
“Applicant cannot find in the cited portions of Azernikov, Bright, Orton, or in the Office Action's reasoning, any disclosure of "wherein the system is configured to receive at least one of the information about the dental prosthesis to be modeled and the information for modifying the initial model of the dental prosthesis in the form of voice or text, the information comprising a description of a geometric change to an anatomical feature of the initial model," as claim 1 presently recites. Instead, the cited portion of Azernikov merely refers to a graphical adjustment tool.
Azernikov at paragraph [0131] describes a graphical user interface that "can provide an adjustment tool that can allow a user to select using an input device a mesial adjustment surface region 1526 that the computer-implemented method can reduce." That is the selection of a region of a displayed surface with a pointing device. It is neither voice nor text, and it is not a description of any modification or design of a prosthesis. The Office Action itself acknowledges as much, stating three separate times that "Azernikov is silent regarding the information for modifying the initial model of the dental prosthesis in the form of voice or text" (Office Action at pp. 7, 9 and 19).”
Examiner agrees. A new rejection is set forth necessitated by the amendment. Azernikov, Ko and Thompson teach this amended limitation. The QC modifications are provided to the design feature 302 (receive the modifying information about the dental prosthesis to be modeled). Azernikov is silent regarding the information for modifying the initial model of the dental prosthesis in the form of voice or text, however, Ko teaches this limitation. (Azernikov, [0053]). Ko teaches that the control unit calculates the degree of alignment between the oral model and the complete denture arrangement model. If the degree of alignment is less than a preset value, then the control unit outputs information through the display and/or the voice output unit (the information for modifying the initial model of the dental prosthesis in the form of voice or text) indicating that the complete denture arrangement model is not suitable and needs to be regenerated using the previously scanned oral model of the patient. (Ko, p. 13, 3rd para). And, Thompson teaches that additional information is generated indicating that the dentures no longer fit properly due to changes in the patient’s anatomy. This additional information is used to change the digitized model of the denture (information comprising a description of a geometric change to an anatomical feature of the initial model). (Thompson, [0033]).
Applicant's arguments filed 8/21/2026 have been fully considered but they are not persuasive. Applicant argues:
“Applicant further notes that the Office Action maps the claimed second receiving unit to the quality control feature 310 of Azernikov, stating that "the quality control (QC) 310 (second receiving unit) receives the 3D dental prosthesis model from the automated design 302" (Office Action at p. 6). What the quality control feature receives, on the Office Action's own account, is the prosthesis model. Receiving a model is not the operation of receiving information for modifying that model, and it is certainly not receiving a voice or text description of "a geometric change to an anatomical feature of the initial model" as is recited in claim 1. Azernikov's only disclosed sources of modification are the graphical region selection discussed above and an automated quality control feedback loop 316 (Azernikov at paragraph [0053]), which originates within the system rather than being received from a user.
In an effort to cure this apparent deficiency, the Office Action relies on Bright. However, Bright merely refers to a platform that displays a question to a user when it cannot interpret a photograph. The passage the Office Action quotes, Bright at paragraph [0125], describes a confidence score returned by a generative artificial intelligence model and states that "if the confidence score falls below the threshold - the digital model generation platform prompts the user for additional input or clarification (e.g., displaying "What do you see in this picture?")." The displayed question is Bright's output and prompts a user response to identify an image or a subject of an image - not a modification of the image. Bright therefore does not disclose receiving the claimed information in the form of voice or text.
Applicant further submits, as a matter of record rather than of characterization, that the word "voice" does not appear anywhere in Bright. Neither do the words "speech," "spoken," "audio," "microphone" or "utterance."
Thus, because Azernikov, Bright, Orton, and the Office Action's reasoning do not disclose, teach, or suggest all elements of claim 1, Applicant respectfully submits that a prima facie case of obviousness cannot be established. As such, Applicant respectfully requests reconsideration and withdrawal of the § 103 rejection of this claim. In view of the remarks above, claims 1, 7, 12, and 19 are allowable and Applicant respectfully requests reconsideration and withdrawal of the § 103 rejections of these claims.”
Examiner agrees. A new rejection is set forth necessitated by the amendment. Ko teaches that the control unit calculates the degree of alignment between the oral model and the complete denture arrangement model. If the degree of alignment is less than a preset value, then the control unit outputs information through the display and/or the voice output unit (the information for modifying the initial model of the dental prosthesis in the form of voice or text) indicating that the complete denture arrangement model is not suitable and needs to be regenerated using the previously scanned oral model of the patient. (Ko, p. 13, 3rd para). Claims 1, 7, 12 and 19 have been rejected. Please see the corresponding rejections.
Applicant's arguments filed 8/21/2026 have been fully considered but they are not persuasive. Applicant argues:
“Claim 15 depends from claim 13. Claim 13 was rejected under 35 U.S.C. § 103 over Azernikov in view of Bright and further in view of Orton, the Office Action having found that "Azernikov and Bright are silent regarding the artificial intelligence includes a large language model and a second model configured to produce three-dimensional models of dental prosthesis" (Office Action at p. 22). Claim 15 was nonetheless rejected in the first group, over Azernikov in view of Bright alone (Office Action at p. 19).
A claim in dependent form shall be construed to incorporate by reference all of the limitations of the claim to which it refers. 35 U.S.C. § 112(d). Claim 15 therefore includes the large language model and second model limitations of claim 13, which the Office Action expressly found absent from Azernikov and Bright and which the rejection of claim 15 does not address. The rejection of claim 15 accordingly does not account for every limitation of that claim, and Applicant respectfully requests its reconsideration and withdrawal.
Applicant respectfully submits that the dependent claims 2-6, 8-11, 13-18, and 20 depend directly or indirectly from independent claims 1, 7, 12, or 19. As such, these dependent claims incorporate all the features of their respective base claims 1, 7, 12, or 19. Accordingly, Applicant respectfully submits that these dependent claims are allowable at least by virtue of their dependency upon independent claims 1, 7, 12, or 19. Thus, Applicant respectfully requests reconsideration and withdrawal of the rejections of claims 2-6, 8-11, 13-18, and 20.”
Examiner disagrees. There was a typographical error that has been corrected for claim 15.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DONNA J RICKS whose telephone number is (571)270-7532. The examiner can normally be reached on M-F 7:30am-5pm EST (alternate Fridays off).
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Devona Faulk can be reached on 571-272-7776. 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 https://ppair-my.uspto.gov/pair/PrivatePair. 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.
/Donna J. Ricks/Examiner, Art Unit 2618
/DEVONA E FAULK/Supervisory Patent Examiner, Art Unit 2618
