A computer-readable recording medium containing a method for generating prosthetics using 3D scan data, and a program for executing this method on a computer.

Abstract

The object of the present invention is to provide a method for generating prosthetic devices using three-dimensional scan data. [Solution] A method for generating a prosthesis using 3D scan data includes the steps of: receiving encrypted oral data; receiving model information of an artificial intelligence neural network; determining metadata of the encrypted oral data; determining a calculated predicted value based on the model information and the metadata; and determining the size of a sub-calculation unit based on the calculated predicted value. The size of the sub-calculation unit is controlled based on the calculated predicted value.

Description

Technical Field

[0001] The present invention relates to a prosthesis generation method using three-dimensional scan data and a computer-readable recording medium on which a program for causing a computer to execute the method is recorded. More specifically, the present invention relates to a prosthesis generation method using three-dimensional scan data and a computer-readable recording medium on which a program for causing a computer to execute the method is recorded, using a cloud environment and an artificial intelligence neural network.

Background Art

[0002] With the development of artificial intelligence (AI) technology, the method of generating prostheses using artificial intelligence has been gradually changing. When generating a prosthesis using artificial intelligence, the production time may be delayed depending on the user's computing environment.

Summary of the Invention

Problems to be Solved by the Invention

[0003] An object of the present invention is to provide a method for generating a prosthesis using three-dimensional scan data. However, the problems to be solved by the present invention are not limited to the problems mentioned above, and can be variously extended without departing from the spirit and scope of the present invention.

Means for Solving the Problems

[0004] The prosthesis generation method using three-dimensional scan data according to the present invention includes a step of receiving encrypted oral data and a step of receiving model information of an artificial intelligence neural network. The method includes the steps of determining metadata of the encrypted oral data, determining a calculated predicted value based on the model information and the metadata, and determining the size of a sub-calculation unit based on the calculated predicted value, wherein the size of the sub-calculation unit is controlled based on the calculated predicted value.

[0005] The encrypted oral data is generated based on the oral data, and the artificial intelligence neural network is selected based on the characteristics of the oral data. If the sub-operation unit is in an inactive state, the sub-operation unit is generated based on the size, the generated sub-operation unit receives the artificial intelligence neural network corresponding to the model information, and the generated sub-operation unit generates prosthetic data based on the received artificial intelligence neural network and the encrypted oral data.

[0006] When the sub-processing unit is in a standby state, the sub-processing unit receives the artificial intelligence neural network corresponding to the model information, and the sub-processing unit generates prosthetic data based on the received artificial intelligence neural network and the encrypted oral data.

[0007] When the sub-calculation unit generates the prosthetic data, the sub-calculation unit changes to the standby state. When the sub-calculation unit generates the prosthetic data, the sub-calculation unit changes to the standby state.

[0008] The metadata includes information on the tooth formula number and number of teeth of the oral data corresponding to the encrypted oral data, and if the number of teeth is greater than or equal to the standard number of teeth, the size of the sub-calculation unit increases.

[0009] The metadata further includes data on the type of prosthesis. The process further includes a step of receiving the number of generation requests, and if the number of generation requests is equal to or greater than the reference number of generation requests, the size of the activated sub-operation block containing the activated sub-operation unit increases.

[0010] The sub-operation unit receives the artificial intelligence neural network from the data storage unit, the data storage unit stores a plurality of artificial intelligence neural networks, and the data storage unit applies the artificial intelligence neural network corresponding to the model information to the sub-operation unit.

[0011] The sub-processing unit generates prosthetic data based on the received artificial intelligence neural network and the encrypted oral data. The process further includes a step of receiving the number of generation requests, and if the number of generation requests is equal to or greater than the reference number of generation requests, the size of the activated sub-operation block containing the activated sub-operation unit increases.

[0012] The process further includes a step of receiving the number of generation requests, and if the number of generation requests is equal to or greater than the reference number of generation requests, the number of activated sub-operation units increases. The model information is determined based on the oral data corresponding to the encrypted oral data, and the model information is determined based on the tooth number and prosthesis type of the oral data.

[0013] The encrypted oral data is generated by the steps of generating an encryption key for the oral data and encrypting the oral data based on the encryption key. The encrypted oral data is received when the encryption key matches the encrypted oral data.

[0014] In one embodiment, a computer-readable recording medium on which a program is recorded causes a computer to execute one of the above methods. [Effects of the Invention]

[0015] According to this method for generating prosthetics using 3D scan data, and a computer-readable recording medium on which a program for executing this method on a computer is stored, the prosthetic data is generated using a selected artificial intelligence neural network. This makes it possible to generate prosthetic data that is suitable for the user's oral data. Furthermore, it is possible to improve the speed of prosthetic production.

[0016] Furthermore, the size of the data processing unit that generates prosthetic data changes based on the model information of the artificial intelligence neural network and the metadata of the oral cavity data. This makes it possible to improve the processing speed of prosthetic data.

[0017] Furthermore, the data processing unit that generates the prosthetic data operates in a cloud server environment. This allows for a further improvement in the processing speed of the prosthetic data. However, the effects of the present invention are not limited to those mentioned above, and can be extended in various ways without departing from the spirit and scope of the present invention. [Brief explanation of the drawing]

[0018] [Figure 1] This is a flowchart showing a method for producing a prosthesis according to one embodiment of the present invention. [Figure 2] This block diagram shows an example of a prosthesis manufacturing system that performs the method for producing the prosthesis shown in Figure 1. [Figure 3] Figure 1 is a flowchart showing how to encrypt oral data. [Figure 4] Figure 3 is a block diagram showing an example of the operation of a prosthetic device generation system that encrypts oral data. [Figure 5] Figure 3 is a block diagram showing an example of the operation of a prosthetic device generation system that encrypts oral data. [Figure 6] This flowchart shows how to select an artificial intelligence neural network corresponding to the oral cavity data in Figure 1. [Figure 7]It is a diagram showing the characteristics of the oral data in FIG. 6. [Figure 8] It is a block diagram showing an example of the operation of a prosthesis generation system that performs a method of selecting an artificial intelligence neural network corresponding to the oral data in FIG. 6. [Figure 9] It is a flowchart showing a method of determining a sub-operation unit in which the selected artificial intelligence neural network in FIG. 1 is calculated. [Figure 10] It is a block diagram showing an example of the operation of a data operation unit included in the prosthesis generation system of FIG. 1 that performs a method of determining a sub-operation unit in which the selected artificial intelligence neural network in FIG. 9 is calculated. [Figure 11] It is a sequence diagram showing an example of the operation of the data operation unit in FIG. 10.

Mode for Carrying Out the Invention

[0019] Regarding the embodiments of the present invention shown in the text, the specific structural and functional explanations are merely exemplified for the purpose of explaining the embodiments of the present invention. The embodiments of the present invention can be implemented in various forms and should not be construed as being limited to the embodiments described in the text.

[0020] The present invention can be modified in various ways and can have various forms. Specific embodiments will be illustrated in the drawings and described in detail in the text. However, this is not intended to limit the present invention to a specific disclosed form, and it should be understood that all modifications, equivalents, and alternatives included within the spirit and technical scope of the present invention are included.

[0021] Terms such as first, second, etc. are used to describe various components, but the components should not be limited by the terms. The terms are used for the purpose of distinguishing one component from another. For example, without departing from the scope of the rights of the present invention, the first component can be referred to as the second component, and similarly, the second component can also be referred to as the first component.

[0022] When one component is described as being "linked" or "connected" to another component, it should be understood that this can mean that the other component is directly linked or connected to it, or that another component may exist in between. On the other hand, when one component is described as being "directly linked" or "directly connected" to another component, it should be understood that there is no other component in between. Other expressions describing the relationship between components, such as "between" and "immediately between," or "adjacent to" and "directly adjacent to," should be interpreted similarly.

[0023] The terms used in this application are used solely to describe specific embodiments and are not intended to limit the invention. Singular expressions include plural expressions unless the context clearly indicates otherwise. In this application, terms such as “includes” or “having” are intended to specify the existence of features, figures, steps, actions, components, parts, or combinations thereof described in the specification, and should be understood not to preemptively exclude the existence or possibility of adding one or more other features, figures, steps, actions, components, parts, or combinations thereof.

[0024] Unless otherwise defined, all terms used herein, including technical and scientific terms, have the same meaning as they would be generally understood by a person of ordinary skill in the art to which this invention pertains. Terms as defined in commonly used dictionaries should be understood to have the meaning consistent with their meaning in the context of the relevant art, and not in an ideal or overly formal sense unless explicitly defined herein.

[0025] On the other hand, if a certain embodiment can be realized in a different way, the functions or actions specified within a particular block may occur differently from the procedure specified in the flowchart. For example, two consecutive blocks may actually occur substantially simultaneously, and depending on the functions or actions involved, the blocks may also occur in reverse order.

[0026] Preferred embodiments of the present invention will be described in more detail below with reference to the attached drawings. Identical components in the drawings are denoted by the same reference numerals, and redundant descriptions of the same components are omitted.

[0027] Figure 1 is a flowchart showing a method for producing a prosthesis according to one embodiment of the present invention. Figure 2 is a block diagram showing an example of a prosthesis production system 1 that performs the method for producing the prosthesis shown in Figure 1.

[0028] As shown in Figures 1 and 2, the method for generating a prosthesis includes the steps of receiving a prosthesis generation request (S100), encrypting oral data (DD) (S200), selecting an artificial intelligence neural network corresponding to the oral data (DD) (S300), determining a sub-operation unit on which the selected artificial intelligence neural network is performed (S400), and generating prosthesis data (CD) based on the artificial intelligence neural network selected by the determined sub-operation unit (S500).

[0029] The method for generating the prosthesis according to this embodiment is performed by a computing device. The prosthetic device generation system 1 includes a data transmission unit 1000 and a data calculation unit 2000.

[0030] The data transmission unit 1000 receives oral cavity data (DD). The data transmission unit 1000 receives prosthetic device data (CD) from the data processing unit 2000. The data transmission unit 1000 outputs the prosthetic device data (CD). The data transmission unit 1000 outputs a prosthetic device generation request to the data processing unit 2000. The data transmission unit 1000 selects an artificial intelligence neural network corresponding to the oral cavity data (DD). The data transmission unit 1000 outputs model information (AID) of the artificial intelligence neural network corresponding to the oral cavity data (DD) to the data processing unit 2000.

[0031] The data transmission unit 1000 requests an encryption key corresponding to the oral cavity data (DD) from the data processing unit 2000. The data processing unit 2000 outputs the encryption key to the data transmission unit 1000. The data transmission unit 1000 encrypts the oral cavity data (DD) based on the encryption key. The encrypted oral cavity data (DD) is encrypted oral cavity data (PDD). The data transmission unit 1000 outputs the encrypted oral cavity data (PDD) to the data processing unit 2000. The oral cavity data (DD) is image data including the patient's oral cavity. For example, the oral cavity data (DD) is 3D scan data generated by scanning the patient's oral cavity with an oral scanner. For example, the oral cavity data is computed tomography (CT) data corresponding to the patient's oral cavity. For example, the oral cavity data is magnetic resonance imaging (MRI) data corresponding to the patient's oral cavity.

[0032] The data transmission unit 1000 performs the steps of receiving a prosthesis generation request (S100), encrypting oral data (DD) (S200), and selecting an artificial intelligence neural network corresponding to the oral data (DD) (S300). In one embodiment, the data transmission unit 1000 represents the user's computing environment.

[0033] The data processing unit 2000 receives encrypted oral cavity data (PDD) and model information (AID). The data processing unit 2000 uses an artificial intelligence neural network corresponding to the model information (AID) to generate prosthetic device data (CD) based on the encrypted oral cavity data (PDD). The data processing unit 2000 stores multiple artificial intelligence neural networks. The data processing unit 2000 determines which artificial intelligence neural network corresponds to the model information (AID).

[0034] For example, the data processing unit 2000 selects an artificial intelligence neural network corresponding to the model information (AID). The data processing unit 2000 outputs the prosthetic data (CD) to the data transmission unit 1000.

[0035] The data processing unit 2000 performs the steps of determining a sub-processing unit in which the selected artificial intelligence neural network is processed (S400), and generating prosthetic data (CD) based on the selected artificial intelligence neural network in the determined sub-processing unit (S500). In one embodiment, the data processing unit 2000 represents a data processing server. In one embodiment, the data processing unit 2000 represents a cloud server environment.

[0036] Figure 3 is a flowchart showing the method for encrypting the oral data (DD) in Figure 1. Figure 4 is a block diagram showing an example of the operation of the prosthesis production system 1 that performs the method for encrypting the oral data (DD) in Figure 3. Figure 5 is a block diagram showing an example of the operation of the prosthesis production system 1 that performs the method for encrypting the oral data (DD) in Figure 3.

[0037] Referring to Figures 1 to 5, the step of encrypting oral data (DD) (S200) includes the steps of generating an encryption key (PW) for the oral data (DD) (S210), encrypting the oral data (DD) based on the encryption key (PW) (S220), and determining whether the encrypted oral data (PDD) and the encryption key (PW) are compatible (S230).

[0038] The data transmission unit 1000 outputs an encryption key calculation request (PWR) to the data processing unit 2000. In response to the encryption key calculation request (PWR), the data processing unit 2000 outputs an encryption key (PW) to the data transmission unit 1000. The encryption key (PW) corresponds to the oral data (DD) received by the data transmission unit 1000. The data transmission unit 1000 receives the encryption key (PW). The data transmission unit 1000 encrypts the oral data (DD) based on the encryption key (PW). The metadata of the encrypted oral data (PDD) includes the encryption key (PW).

[0039] The data transmission unit 1000 outputs encrypted oral data (PDD) and encryption key (PW) to the data processing unit 2000. The data processing unit 2000 determines whether the encrypted oral data (PDD) and encryption key (PW) are compatible. If the encrypted oral data (PDD) and encryption key (PW) are compatible, the data processing unit 2000 decrypts the encrypted oral data (PDD). If the encrypted oral data (PDD) is decrypted, the data processing unit 2000 generates prosthetic data (CD) based on the decrypted oral data. If the encrypted oral data (PDD) and encryption key (PW) are compatible, the data processing unit 2000 does not generate prosthetic data (CD).

[0040] Figure 6 is a flowchart showing the method for selecting an artificial intelligence neural network corresponding to the oral data (DD) in Figure 1. Figure 7 shows the characteristics (feature, DF) of the oral data (DD) in Figure 6. Figure 8 is a block diagram showing an example of the operation of the prosthesis generation system 1, which performs the method for selecting an artificial intelligence neural network corresponding to the oral data (DD) in Figure 6.

[0041] Referring to Figures 1 to 8, the step of selecting an artificial intelligence neural network corresponding to oral cavity data (DD) (S300) includes the steps of determining the characteristics (DF) of the oral cavity data (DD) (S310), determining the artificial intelligence neural network corresponding to the characteristics (DF) of the oral cavity data (DD) (S320), and outputting the model information (AID) of the artificial intelligence neural network and the encrypted oral cavity data (PDD) (S330).

[0042] The data transmission unit 1000 determines the characteristics (DF) of the oral data (DD). For example, the characteristics (DF) represent the dental formula of the oral data (DD). For example, the characteristics (DF) represent the dental formula for which prosthesis production is required. The data transmission unit 1000 can determine the artificial intelligence neural network corresponding to the characteristics (DF). In one embodiment, the data transmission unit 1000 can use an artificial intelligence neural network to determine the artificial intelligence neural network corresponding to the characteristics (DF). The data transmission unit 1000 outputs model information (AID) corresponding to the determined artificial intelligence neural network.

[0043] The data transmission unit 1000 outputs encrypted oral data (PDD). The encrypted oral data (PDD) includes information about the oral data (DD) and metadata (MD).

[0044] Metadata (MD) includes the tooth formula number, tooth formula count, etc., of the oral data (DD). For example, the tooth formula number means the number of the tooth formula corresponding to a characteristic (DF). For example, if the characteristic (DF) corresponds to the left maxillary central incisor, the tooth formula number is 21. For example, the tooth formula count means the number of tooth formulas corresponding to the characteristic (DF). For example, if the characteristic (DF) corresponds to the left maxillary central incisor and the left maxillary lateral incisor, the tooth formula count is 2. However, the present invention is not limited to the types of features of the oral data (DD) that the metadata (MD) contains. The metadata (MD) may further include data on the type of prosthesis. For example, the data on the type of prosthesis may include information indicating crowns, laminates, implants, etc.

[0045] Figure 9 is a flowchart showing a method for determining the sub-operation unit on which the selected artificial intelligence neural network in Figure 1 is performed. Figure 10 is a block diagram showing an example of the operation of the data processing unit 2000 included in the prosthesis generation system 1 in Figure 1, which performs the method for determining the sub-operation unit on which the selected artificial intelligence neural network in Figure 9 is performed.

[0046] Referring to Figures 1 to 10, the step of determining the sub-operation unit (SC) on which the selected artificial intelligence neural network is performed (S400) includes receiving model information (AID) and encrypted oral data (PDD) (S410), determining the metadata (MD) of the model information (AID) and encrypted oral data (PDD) (S420), determining the calculation prediction value based on the model information (AID) and metadata (MD) (S430), determining the size of the sub-operation unit (SC) based on the calculation prediction value (S440), determining whether the sub-operation unit (SC) is in an activated state (S450), and applying the artificial intelligence neural network (CAI) corresponding to the model information (AID) to the sub-operation unit (SC) (S460).

[0047] The data calculation unit 2000 includes a sub-calculation management unit 2100, an active sub-calculation block 2200, a standby sub-calculation block 2300, and a data storage unit 2400. Each of the active sub-calculation block 2200 and the standby sub-calculation block 2300 includes a sub-calculation unit (SC).

[0048] The sub-calculation management unit 2100 controls the state and size of the sub-calculation units (SC). The activated sub-calculation block 2200 includes an activated sub-calculation unit (SC). The standby sub-calculation block 2300 includes a standby sub-calculation unit (SC). The sub-calculation unit (SC) generates prosthetic data (CD) corresponding to oral data (DD) using an artificial intelligence neural network (CAI) corresponding to model information (AID). The data storage unit 2400 stores multiple artificial intelligence neural networks. The data storage unit 2400 applies the artificial intelligence neural network (CAI) corresponding to the model information (AID) to the sub-calculation unit (SC).

[0049] The sub-calculation management unit 2100 controls the state of the sub-calculation unit (SC). When the sub-calculation unit (SC) is in an activated state, it generates prosthetic data (CD) based on the artificial intelligence neural network (CAI) corresponding to the model information (AID). When the sub-calculation unit (SC) is in a standby state, it waits with the artificial intelligence neural network (CAI) corresponding to the model information (AID) stored inside. When the sub-calculation unit (SC) is in an inactive state, the inactive sub-calculation unit (SC) is deleted. For example, if the sub-calculation unit (SC) waits in the standby state for a reference time, the sub-calculation unit (SC) changes to an inactive state. As a result, if the sub-calculation unit (SC) waits in the standby state for the reference time, the sub-calculation unit (SC) is deleted.

[0050] If the sub-operation unit (SC) storing the artificial intelligence neural network (CAI) corresponding to the model information (AID) is in the standby state, the sub-operation management unit 2100 changes the sub-operation unit (SC) storing the artificial intelligence neural network (CAI) corresponding to the model information (AID) to the activated state. If the sub-operation unit (SC) storing the artificial intelligence neural network (CAI) corresponding to the model information (AID) is in the deactivated state (i.e., if the sub-operation unit (SC) storing the artificial intelligence neural network (CAI) corresponding to the model information (AID) is deleted), the sub-operation management unit 2100 generates a sub-operation unit (SC). The data storage unit 2400 applies the artificial intelligence neural network (CAI) corresponding to the model information (AID) to the generated sub-operation unit (SC).

[0051] The data storage unit 2400 stores multiple artificial intelligence neural networks. Each of the artificial intelligence neural networks is trained to generate prosthetic data (CD) corresponding to characteristics (DF). For example, the first artificial intelligence neural network included in the artificial intelligence neural network is trained to generate prosthetic data (CD) corresponding to a first dental formula. For example, the first artificial intelligence neural network is trained to generate prosthetic data (CD) corresponding to a first dental formula using information such as the first dental formula, the surrounding dental formulas of the first dental formula, and the opposing teeth of the first dental formula. For example, the second artificial intelligence neural network included in the artificial intelligence neural network is trained to generate prosthetic data (CD) corresponding to the first to third dental formulas.

[0052] The sub-operation management unit 2100 controls the size of the sub-operation unit (SC) based on the predicted calculation value. For example, the size refers to the size of the server space. The predicted calculation value is calculated based on metadata (MD) and model information (AID). For example, if the number of teeth included in the metadata (MD) is greater than or equal to the reference number of teeth, the size of the sub-operation unit (SC) increases. The reference number of teeth can be set by the user. For example, if the capacity of the artificial intelligence neural network corresponding to the model information (AID) is greater than or equal to the reference capacity, the size of the sub-operation unit (SC) increases. The reference capacity is set by the user. In one embodiment, if the size of the sub-operation unit (SC) corresponding to the number of teeth and model information (AID) is not calculated, the sub-operation management unit 2100 generates a sub-operation unit (SC) having a reference size. The reference size is set by the user.

[0053] The sub-processing unit (SC) generates prosthetic data (CD) based on oral data (DD) using an artificial intelligence neural network (CAI) corresponding to the model information (AID).

[0054] Figure 11 is a sequence diagram showing an example of the operation of the data processing unit 2000 in Figure 10. Referring to Figures 1 to 11, the data calculation unit 2000 can change the size of the activation sub-calculation block 2200. The method for changing the size of the activation sub-calculation block 2200 includes the steps of receiving a prosthesis production request (S10), determining the number of production requests (S20), determining the size of the activation sub-calculation block 2200 (S30), and changing the size of the activation sub-calculation block 2200 based on the number of production requests (S40).

[0055] The number of generation requests corresponds to the number of requests for prosthetic device generation. For example, if the number of users requesting prosthetic device data (CD) increases, the number of generation requests will increase. For example, if the number of prosthetic device data (CD) requested by users increases, the number of generation requests will increase.

[0056] The sub-calculation management unit 2100 changes the size of the activated sub-calculation block 2200 based on the number of generation requests. For example, if the number of generation requests increases, the sub-calculation management unit 2100 increases the size of the activated sub-calculation block 2200. For example, if the number of generation requests is equal to or greater than the standard number of generation requests, the sub-calculation management unit 2100 increases the size of the activated sub-calculation block 2200. For example, the standard number of generation requests can be set by the user.

[0057] For example, if the number of generation requests increases, the sub-operation management unit 2100 increases the number of activated sub-operation management units 2100. For example, if the number of generation requests is equal to or greater than the standard number of generation requests, the sub-operation management unit 2100 increases the number of activated sub-operation management units 2100.

[0058] In this embodiment, prosthetic data (CD) is generated using a selected artificial intelligence neural network. This generates prosthetic data (CD) that is suitable for the user's oral data (DD). Furthermore, it is possible to improve the speed of prosthetic fabrication.

[0059] Furthermore, the size of the data processing unit 2000 changes based on the model information (AID) of the artificial intelligence neural network and the metadata (MD) of the oral cavity data (DD). This makes it possible to improve the processing speed of the prosthetic device data (CD).

[0060] Furthermore, the prosthetic data (CD) is generated by the data processing unit 2000. The data processing unit 2000 operates in a cloud server environment. This further improves the processing speed of the prosthetic data (CD).

[0061] In one embodiment, a computer-readable recording medium is provided on which a method for generating a prosthesis using three-dimensional scan data according to the above embodiment and a program for executing this method on a computer are recorded. The above-described method can be created with a computer-executable program and can be implemented on a general-purpose digital computer that runs the program using a computer-readable medium. Furthermore, the data structure used in the above-described method is recorded on the computer-readable medium by multiple means. The computer-readable medium includes program instructions, data files, data structures, etc., individually or in combination. The program instructions recorded on the medium are either specifically designed and configured for the present invention or are publicly known and usable by a person of ordinary skill in the field of computer software. Examples of computer-readable recording media include magnetic media such as hard disks, floppy disks, and magnetic tapes, optical recording media such as CD-ROMs and DVDs, magneto-optical media such as floppy disks, and hardware devices specifically configured to store and execute program instructions, such as ROMs, RAMs, and flash memory. Examples of program instructions include not only machine code, such as that produced by a compiler, but also high-level language code that is executed by a computer using an interpreter or the like. The hardware device is configured to operate as one or more software modules in order to perform the operation of the present invention.

[0062] Furthermore, the aforementioned method for generating prosthetics using 3D scan data can also be implemented in the form of a computer program or application executed by a computer recorded on a recording medium. [Industrial applicability]

[0063] The present invention relates to a method for producing prosthetics using 3D scan data, and a computer-readable recording medium on which a program for executing this method on a computer is recorded. The method for producing prosthetics using 3D scan data can improve the production speed of prosthetics.

[0064] Having been described above with reference to embodiments, a person of ordinary skill in the art will understand that the present invention can be modified and altered in various ways without departing from the spirit and scope of the invention as set forth in the following claims.

Claims

1. The steps include receiving encrypted oral data, The steps include receiving model information for an artificial intelligence neural network, The steps include determining the metadata of the encrypted oral data, The steps include determining the calculated predicted value based on the aforementioned model information and metadata, The step includes determining the size of the sub-calculation unit based on the calculated predicted value, A method for generating a prosthesis using three-dimensional scan data, characterized in that the size of the sub-calculation unit is controlled based on the calculated predicted value.

2. The encrypted oral data is generated based on the oral data. The method for generating a prosthesis using three-dimensional scan data according to claim 1, characterized in that the artificial intelligence neural network is selected based on the characteristics of the oral data.

3. When the sub-operation unit is in an inactive state, the sub-operation unit is generated based on the size, The generated sub-operation unit receives the artificial intelligence neural network corresponding to the model information, The method for generating a prosthesis using three-dimensional scan data according to claim 2, characterized in that the generated sub-calculation unit generates prosthesis data based on the received artificial intelligence neural network and the encrypted oral data.

4. When the sub-operation unit is in a standby state, the sub-operation unit receives the artificial intelligence neural network corresponding to the model information, The method for generating a prosthesis using three-dimensional scan data according to claim 2, characterized in that the sub-processing unit generates prosthesis data based on the received artificial intelligence neural network and the encrypted oral data.

5. The method for generating a prosthesis using three-dimensional scan data according to claim 4, characterized in that when the sub-calculation unit generates the prosthesis data, the sub-calculation unit changes to the standby state.

6. The method for generating a prosthesis using three-dimensional scan data according to claim 4, characterized in that when the sub-calculation unit generates the prosthesis data, the sub-calculation unit changes to the standby state.

7. The metadata includes information on the tooth formula number and number of teeth of the oral data corresponding to the encrypted oral data, The method for generating a prosthesis using three-dimensional scan data according to claim 1, characterized in that when the number of tooth formulas is equal to or greater than the number of standard tooth formulas, the size of the sub-calculation unit increases.

8. The method for generating a prosthesis using three-dimensional scan data according to claim 7, wherein the metadata further includes data on the type of prosthesis.

9. The process further includes the step of receiving the number of generation requests, The method for generating a prosthesis using three-dimensional scan data according to claim 7, characterized in that when the number of generation requests is equal to or greater than the standard number of generation requests, the size of the activated sub-calculation block, which includes the activated sub-calculation unit, increases.

10. The sub-processing unit receives the artificial intelligence neural network from the data storage unit, The aforementioned data storage unit stores multiple artificial intelligence neural networks, The method for generating a prosthesis using three-dimensional scan data according to claim 1, characterized in that the data storage unit applies the artificial intelligence neural network corresponding to the model information to the sub-operation unit.

11. The method for generating a prosthesis using three-dimensional scan data according to claim 10, characterized in that the sub-processing unit generates prosthesis data based on the received artificial intelligence neural network and the encrypted oral data.

12. The process further includes the step of receiving the number of generation requests, The method for generating a prosthesis using three-dimensional scan data according to claim 1, characterized in that when the number of generation requests is equal to or greater than the standard number of generation requests, the size of the activated sub-calculation block, which includes the activated sub-calculation unit, increases.

13. The process further includes the step of receiving the number of generation requests, The method for generating a prosthesis using three-dimensional scan data according to claim 1, characterized in that the number of activated sub-calculation units increases when the number of generation requests is equal to or greater than the standard number of generation requests.

14. The aforementioned model information is determined based on the oral data corresponding to the encrypted oral data. The method for generating a prosthesis using three-dimensional scan data according to claim 1, characterized in that the model information is determined based on the dental formula number and prosthesis type of the oral data.

15. The encrypted oral data is The steps include generating an encryption key for oral data, Based on the aforementioned encryption key, the oral data is generated by the following steps: A method for generating a prosthesis using three-dimensional scan data according to claim 1, characterized in that the encrypted oral data is received when the encryption key matches the encrypted oral data.

16. A computer-readable recording medium on which a program for causing a computer to execute the method according to any one of claims 1 to 15 is recorded.

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