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; 4, 5, 6, 9, 10, 11, 14, 15, 16, 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 Bright et al. U.S. Pub. No. 2025/0252665.
Re: claim 1, Azernikov teaches
1. 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 GUI 1500 can provide one or control features to adjust the 3D digital dental prosthesis model 1504... For example... the QC process can provide controls to adjust contact points the automatically generated 3D digital dental prosthesis has with neighboring digital teeth in the 3D digital dental model.”; Azernikov, [0130])
The QC process provides control features to adjust the 3D digital dental prosthesis model (receive the modifying information about the dental prosthesis to be modeled).
(“As illustrated in the figure, GUI 1520 can display at least a portion of the 3D digital dental model along with a mesial side of a generated 3D digital dental prosthesis model 1522. The GUI 1520 can display a mesial contact surface region 1524 between the 3D digital dental prosthesis model 1522 and its mesial neighboring tooth (not shown) in the 3D digital dental model. The GUI 1520 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, for example.... the adjustment can decrease the size of the adjustment surface region such as adjustment surface region 1526... the adjustment can increase the size of the adjustment surface region 1526. FIG. 15(c) illustrates an example of adjusting distal contact points...”; Azernikov, [0131], Fig. 15b)
Fig. 15b illustrates that the GUI provides an adjustment tool that allows a user to select a mesial adjustment surface region that is to be reduced and then decreases the size of the adjustment surface region (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, Bright teaches
(“... 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 (in this case text), and engages in an iterative dialogue with the user to gather information (modification information) about the 3D model (the information for modifying the initial model of the dental prosthesis in the form of voice or text). Bright is combined with Azernikov 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 information for modifying the initial model of the dental prosthesis in the form of voice or text. in order to gather additional context or constraints that enhance the accuracy and relevance of the generated content, as taught by Bright ([0082]).
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. 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, Bright teaches
a receiving unit configured to receive information in the form of voice or text 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 (in this case receives text), and engages in an iterative dialogue with the user to gather information about the 3D model (the information for modifying the initial model of the dental prosthesis in the form of voice or text). Bright is combined with Azernikov 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 information for modifying the initial model of the dental prosthesis in the form of voice or text. in order to gather additional context or constraints that enhance the accuracy and relevance of the generated content, as taught by Bright ([0082]).
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. 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. 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 4 and 9 (which are rejected under the same rationale), Azernikov and Bright teach
4. 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 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 and Bright teach
5. 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 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: claims 6 and 11 (which are rejected under the same rationale), Azernikov and Bright teach
6. 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 10, Azernikov and Bright teach
10. 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 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 and Bright teach
14. 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 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 15, Azernikov is silent regarding providing on the display a request for clarification regarding the information about the dental prosthesis to be modeled, however, Bright teaches
15. 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 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]).
Re: claim 16, Azernikov is 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. 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 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]).
Re: claim 17, Azernikov and Bright teach
17. 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 and Bright teach
18. 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) 2, 3, 8, 13 and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Azernikov in view of Bright 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. 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 prosthesis. (“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 and Bright 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 and Bright 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. 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 and Bright 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]).
Conclusion
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).
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Devona Faulk can be reached on 571-272-7515. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Donna J. Ricks/Examiner, Art Unit 2618
/DEVONA E FAULK/Supervisory Patent Examiner, Art Unit 2618
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