Method for generating prosthesis by using three-dimensional scan data, and computer-readable recording medium having recorded thereon program for executing same method on computer

Abstract

A method for generating a prosthesis by using three-dimensional scan data comprises the steps of: receiving encrypted oral data; receiving model information of an artificial intelligence neural network; determining metadata of the encrypted oral data; determining an operation prediction value on the basis of the model information and the metadata; and determining the size of a sub-operation unit on the basis of the operation prediction value. The size of the sub-operation unit is controlled on the basis of the operation prediction value.

Description

A method for generating a prosthesis using 3D scan data and a computer-readable recording medium having a program for executing the same on a computer

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

[0002] With the advancement of artificial intelligence (AI) technology, the method of generating prosthetics is gradually shifting towards using AI. When prosthetics are generated using AI, production time may be delayed depending on the user's computing environment.

[0003] One objective of the present invention is to provide a method for creating a prosthesis using three-dimensional scan data.

[0004] However, the problem to be solved by the present invention is not limited to the problem mentioned above, and may be expanded in various ways without departing from the spirit and scope of the present invention.

[0005] A method for generating a prosthesis using three-dimensional scan data according to embodiments of the present invention 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 predicted operation value based on the model information and metadata, and determining the size of a sub-operation unit based on the predicted operation value. The size of the sub-operation unit is controlled based on the predicted operation value.

[0006] In one embodiment, the encrypted oral data may be generated based on the oral data. The artificial intelligence neural network may be selected based on the features of the oral data.

[0007] In one embodiment, when the sub-operation unit is in a deactivated state, the sub-operation unit may be generated based on the size. The generated sub-operation unit may receive the artificial intelligence neural network corresponding to the model information. The generated sub-operation unit may generate prosthetic data based on the received artificial intelligence neural network and the encrypted oral data.

[0008] In one embodiment, when the sub-operation unit is in a standby state, the sub-operation unit may receive the artificial intelligence neural network corresponding to the model information. The sub-operation unit may generate prosthetic data based on the received artificial intelligence neural network and the encrypted oral data.

[0009] In one embodiment, when the sub-operation unit generates the prosthetic data, the sub-operation unit may be converted to the standby state.

[0010] In one embodiment, the metadata may include information regarding the tooth number and the number of tooth formulas of the oral data corresponding to the encrypted oral data. If the number of tooth formulas is greater than or equal to a reference number of tooth formulas, the size of the sub-operation unit may be increased.

[0011] In one embodiment, the metadata may further include prosthetic type data.

[0012] In one embodiment, the step of receiving a number of generation requests may be further included. If the number of generation requests is greater than or equal to a reference number of generation requests, the size of the activated sub-operation block containing the activated sub-operation unit may be increased.

[0013] In one embodiment, the sub-operation unit may receive the artificial intelligence neural network from the data storage unit. The data storage unit may store a plurality of artificial intelligence neural networks. The data storage unit may apply the artificial intelligence neural network corresponding to the model information to the sub-operation unit.

[0014] In one embodiment, the sub-operation unit can generate prosthetic data based on the received artificial intelligence neural network and the encrypted oral data.

[0015] In one embodiment, the step of receiving a number of generation requests may be further included. If the number of generation requests is greater than or equal to a reference number of generation requests, the size of the activated sub-operation block containing the activated sub-operation unit may be increased.

[0016] In one embodiment, the step of receiving a number of generation requests may be further included. If the number of generation requests is greater than or equal to a reference number of generation requests, the number of sub-operation units in an active state may be increased.

[0017] In one embodiment, the model information may be determined based on oral data corresponding to the encrypted oral data. The model information may be determined based on the tooth number and prosthesis type of the oral data.

[0018] In one embodiment, the encrypted oral data may be generated based on the steps of generating an encryption key for the oral data and encrypting the oral data based on the encryption key. When the encryption key matches the encrypted oral data, the encrypted oral data may be received.

[0019] In one embodiment, a computer-readable recording medium on which a program is recorded can execute any one of the above methods on a computer.

[0020] According to a method for generating a prosthesis using such 3D scan data and a computer-readable recording medium having a program for executing the method on a computer, prosthesis data can be generated using a selected artificial intelligence neural network. Accordingly, prosthesis data suitable for the user's oral data can be generated. In addition, the speed of manufacturing the prosthesis can be improved.

[0021] In addition, the size of the data processing unit where prosthetic data is generated can be varied based on the model information of the artificial intelligence neural network and the metadata of the oral data. Accordingly, the processing speed of the prosthetic data can be improved.

[0022] In addition, the data processing unit where prosthetic data is generated may be a cloud server environment. Accordingly, the processing speed of the prosthetic data can be further improved.

[0023] However, the effects of the present invention are not limited to the effects mentioned above and may be extended in various ways without departing from the spirit and scope of the present invention.

[0024] FIG. 1 is a flowchart illustrating a method for creating a prosthesis according to one embodiment of the present invention.

[0025] Figure 2 is a block diagram showing an example of a prosthesis generation system that performs the method of generating the prosthesis of Figure 1.

[0026] Figure 3 is a flowchart illustrating a method for encrypting oral data of Figure 1.

[0027] Figure 4 is a block diagram showing an example of the operation of a prosthesis generation system that performs the method of encrypting oral data of Figure 3.

[0028] Figure 5 is a block diagram showing an example of the operation of a prosthesis generation system that performs the method of encrypting oral data of Figure 3.

[0029] Figure 6 is a flowchart illustrating a method for selecting an artificial intelligence neural network corresponding to the oral data of Figure 1.

[0030] Figure 7 is a diagram showing the features of the oral data of Figure 6.

[0031] FIG. 8 is a block diagram illustrating 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 of FIG. 6.

[0032] FIG. 9 is a flowchart showing a method for determining a sub-operation unit to be computed by the selected artificial intelligence neural network of FIG. 1.

[0033] FIG. 10 is a block diagram showing an example of the operation of a data processing unit included in the prosthetic generation system of FIG. 1, which performs a method for determining a sub-processing unit to be processed by the selected artificial intelligence neural network of FIG. 9.

[0034] FIG. 11 is a flowchart illustrating an example of the operation of the data operation unit of FIG. 10.

[0035] With respect to the embodiments of the present invention disclosed in the text, specific structural or functional descriptions are provided merely for the purpose of explaining the embodiments of the present invention, and the embodiments of the present invention may be implemented in various forms and should not be interpreted as being limited to the embodiments described in the text.

[0036] The present invention is capable of various modifications and may take various forms, and specific embodiments are illustrated in the drawings and described in detail in the text. However, this is not intended to limit the invention to the specific disclosed forms, and it should be understood that the invention includes all modifications, equivalents, and substitutions that fall within the spirit and scope of the invention.

[0037] Terms such as "first," "second," etc., may be used to describe various components, but said components should not be limited by said terms. These terms may be used for the purpose of distinguishing one component from another. For example, without departing from the scope of the present invention, the first component may be named the second component, and similarly, the second component may be named the first component.

[0038] When it is stated that one component is "connected" or "connected" to another component, it should be understood that while it may be directly connected or connected to that other component, there may also be other components in between. Conversely, when it is stated that one component is "directly connected" or "directly connected" to another component, it should be understood that there are no other components in between. Other expressions describing the relationship between components, such as "between" and "exactly between," or "adjacent to" and "directly adjacent to," should be interpreted in the same way.

[0039] The terms used in this application are used merely to describe specific embodiments and are not intended to limit the invention. The singular expression includes the plural expression unless the context clearly indicates otherwise. In this application, terms such as "comprising" or "having" are intended to specify the existence of the described features, numbers, steps, actions, components, parts, or combinations thereof, and should be understood as not precluding the existence or addition of one or more other features, numbers, steps, actions, components, parts, or combinations thereof.

[0040] Unless otherwise defined, all terms used herein, including technical or scientific terms, have the same meaning as generally understood by those skilled in the art to which the present invention pertains. Terms such as those defined in commonly used dictionaries should be interpreted as having a meaning consistent with their meaning in the context of the relevant technology, and should not be interpreted in an ideal or overly formal sense unless explicitly defined in this application.

[0041] Meanwhile, if an embodiment can be implemented differently, a function or operation specified within a particular block may occur differently from the order specified in the flowchart. For example, two consecutive blocks may actually be executed substantially simultaneously, or, depending on the related function or operation, said blocks may be executed in reverse order.

[0042] Hereinafter, preferred embodiments of the present invention will be described in more detail with reference to the attached drawings. Identical components in the drawings are denoted by the same reference numerals, and redundant descriptions of identical components are omitted.

[0043] FIG. 1 is a flowchart illustrating a method for creating a prosthesis according to one embodiment of the present invention. FIG. 2 is a block diagram illustrating an example of a prosthesis creation system (1) that performs the method for creating a prosthesis of FIG. 1.

[0044] Referring to FIGS. 1 and 2, a method for generating a prosthesis may include the steps of receiving a request to generate a prosthesis (S100), encrypting oral data (DD) (S200), selecting an artificial intelligence neural network corresponding to the oral data (DD) (S300), determining a sub-operation unit to be operated on by the selected artificial intelligence neural network (S400), and generating prosthesis data (CD) based on the selected artificial intelligence neural network in the determined sub-operation unit (S500).

[0045] The method for generating the prosthesis according to the present embodiment can be performed by a computing device.

[0046] The prosthetic generation system (1) may include a data transmission unit (1000) and a data processing unit (2000).

[0047] The data transmission unit (1000) can receive oral data (DD). The data transmission unit (1000) can receive prosthetic data (CD) from the data processing unit (2000). The data transmission unit (1000) can output the prosthetic data (CD). The data transmission unit (1000) can output a request for the creation of a prosthetic to the data processing unit (2000). The data transmission unit (1000) can select an artificial intelligence neural network corresponding to the oral data (DD). The data transmission unit (1000) can output model information (AID) of the artificial intelligence neural network corresponding to the oral data (DD) to the data processing unit (2000).

[0048] The data transmission unit (1000) may request an encryption key corresponding to oral data (DD) from the data processing unit (2000). The data processing unit (2000) may output the encryption key to the data transmission unit (1000). The data transmission unit (1000) may encrypt the oral data (DD) based on the encryption key. The encrypted oral data (DD) may be encrypted oral data (PDD). The data transmission unit (1000) may output the encrypted oral data (PDD) to the data processing unit (2000). The oral data (DD) is image data including the patient's oral cavity. For example, the oral data (DD) may be three-dimensional scan data generated by scanning the patient's oral cavity with an oral scanner. For example, the oral data may be computed tomography (CT) data corresponding to the patient's oral cavity. For example, the oral data may be magnetic resonance imaging (MRI) data corresponding to the patient's oral cavity.

[0049] The data transmission unit (1000) may perform the steps of receiving a request to create a prosthesis (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) may refer to a user's computing environment.

[0050] The data processing unit (2000) can receive encrypted oral data (PDD) and model information (AID). The data processing unit (2000) can generate prosthetic data (CD) based on the encrypted oral data (PDD) using an artificial intelligence neural network corresponding to the model information (AID). The data processing unit (2000) can store multiple artificial intelligence neural networks. The data processing unit (2000) can determine the artificial intelligence neural network corresponding to the model information (AID).

[0051] For example, the data processing unit (2000) can select an artificial intelligence neural network corresponding to model information (AID). The data processing unit (2000) can output prosthetic data (CD) to the data transmission unit (1000).

[0052] The data processing unit (2000) may perform a step (S400) of determining a sub-processing unit to be processed by a selected artificial intelligence neural network, and a step (S500) of generating prosthetic data (CD) based on the selected artificial intelligence neural network in the determined sub-processing unit. In one embodiment, the data processing unit (2000) may refer to a data processing server. In one embodiment, the data processing unit (2000) may refer to a cloud server environment.

[0053] FIG. 3 is a flowchart illustrating a method for encrypting oral data (DD) of FIG. 1. FIG. 4 is a block diagram illustrating an example of the operation of a prosthetic generation system (1) that performs the method for encrypting oral data (DD) of FIG. 3. FIG. 5 is a block diagram illustrating an example of the operation of a prosthetic generation system (1) that performs the method for encrypting oral data (DD) of FIG. 3.

[0054] Referring to FIGS. 1 to 5, the step of encrypting oral data (DD) (S200) may include the step of generating an encryption key (PW) for oral data (DD) (S210), the step of encrypting oral data (DD) based on the encryption key (PW) (S220), and the step of determining whether there is a correspondence between the encrypted oral data (PDD) and the encryption key (PW) (S230).

[0055] The data transmission unit (1000) can output an encryption key operation request (PWR) to the data operation unit (2000). The data operation unit (2000) can output an encryption key (PW) to the data transmission unit (1000) in response to the encryption key operation request (PWR). The encryption key (PW) may correspond to oral data (DD) received by the data transmission unit (1000). The data transmission unit (1000) can receive the encryption key (PW). The data transmission unit (1000) can encrypt the oral data (DD) based on the encryption key (PW). The metadata of the encrypted oral data (PDD) may include the encryption key (PW).

[0056] The data transmission unit (1000) can output encrypted oral data (PDD) and an encryption key (PW) to the data operation unit (2000). The data operation unit (2000) can determine whether there is a correspondence between the encrypted oral data (PDD) and the encryption key (PW). If there is a correspondence between the encrypted oral data (PDD) and the encryption key (PW), the data operation unit (2000) can decrypt the encrypted oral data (PDD). If the encrypted oral data (PDD) is decrypted, the data operation unit (2000) can generate prosthetic data (CD) based on the decrypted oral data. If there is a correspondence between the encrypted oral data (PDD) and the encryption key (PW), the data operation unit (2000) may not generate prosthetic data (CD).

[0057] FIG. 6 is a flowchart illustrating a method for selecting an artificial intelligence neural network corresponding to the oral data (DD) of FIG. 1. FIG. 7 is a diagram illustrating a feature (DF) of the oral data (DD) of FIG. 6. FIG. 8 is a block diagram illustrating an example of the operation of a prosthesis generation system (1) that performs the method of selecting an artificial intelligence neural network corresponding to the oral data (DD) of FIG. 6.

[0058] Referring to FIGS. 1 to 8, the step (S300) of selecting an artificial intelligence neural network corresponding to oral data (DD) may include a step (S310) of determining a feature (DF) of oral data (DD), a step (S320) of determining an artificial intelligence neural network corresponding to a feature (DF) of oral data (DD), and a step (S330) of outputting model information (AID) of the artificial intelligence neural network and encrypted oral data (PDD).

[0059] The data transmission unit (1000) can determine a feature (DF) of oral data (DD). For example, the feature (DF) may represent a tooth formula of the oral data (DD). For example, the feature (DF) may represent a tooth formula requiring the creation of a prosthesis. The data transmission unit (1000) can determine an artificial intelligence neural network corresponding to the feature (DF). In one embodiment, the data transmission unit (1000) can determine an artificial intelligence neural network corresponding to the feature (DF) using an artificial intelligence neural network. The data transmission unit (1000) can output model information (AID) corresponding to the determined artificial intelligence neural network.

[0060] The data transmission unit (1000) can output encrypted oral data (PDD). The encrypted oral data (PDD) may include information about oral data (DD) and metadata (MD).

[0061] Metadata (MD) may include the tooth number, number of tooth numbers, etc. of oral data (DD). For example, the tooth number may refer to the number of a tooth number corresponding to a feature (DF). For example, if the feature (DF) corresponds to the left maxillary central incisor, the tooth number may be 21. For example, the number of tooth numbers may refer to the number of tooth numbers corresponding to the feature (DF). For example, if the feature (DF) corresponds to the left maxillary central incisor and the left maxillary lateral incisor, the number of tooth numbers may be 2. However, the present invention is not limited to the types of features of the oral data (DD) included in the metadata (MD). The metadata (MD) may further include prosthetic type data. For example, the prosthetic type data may include information indicating a crown, laminate, implant, etc.

[0062] FIG. 9 is a flowchart showing a method for determining a sub-operation unit to be computed by a selected artificial intelligence neural network of FIG. 1. FIG. 10 is a block diagram showing an example of the operation of a data operation unit (2000) included in a prosthetic generation system (1) of FIG. 1 that performs the method for determining a sub-operation unit to be computed by a selected artificial intelligence neural network of FIG. 9.

[0063] Referring to FIGS. 1 to 10, the step (S400) of determining a sub-operation unit (SC) to be operated on by a selected artificial intelligence neural network may include: receiving model information (AID) and encrypted oral data (PDD) (S410); determining metadata (MD) of the model information (AID) and encrypted oral data (PDD) (S420); determining an operation prediction value based on the model information (AID) and metadata (MD) (S430); determining the size of the sub-operation unit (SC) based on the operation prediction value (S440); determining whether the sub-operation unit (SC) is in an active state (S450); and applying an artificial intelligence neural network (CAI) corresponding to the model information (AID) to the sub-operation unit (SC) (S460).

[0064] The data operation unit (2000) may include a sub-operation management unit (2100), an active sub-operation block (2200), a waiting sub-operation block (2300), and a data storage unit (2400). Each of the active sub-operation block (2200) and the waiting sub-operation block (2300) may include a sub-operation unit (SC).

[0065] The sub-operation management unit (2100) can control the state and size of the sub-operation unit (SC). The active sub-operation block (2200) may include sub-operation units (SC) in an active state. The standby sub-operation block (2300) may include sub-operation units (SC) in a standby state. The sub-operation unit (SC) can generate 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) can store multiple artificial intelligence neural networks. The data storage unit (2400) can apply an artificial intelligence neural network (CAI) corresponding to model information (AID) to the sub-operation unit (SC).

[0066] The sub-operation management unit (2100) can control the state of the sub-operation unit (SC). When the sub-operation unit (SC) is in an active state, the sub-operation unit (SC) can generate prosthetic data (CD) based on an artificial intelligence neural network (CAI) corresponding to model information (AID). When the sub-operation unit (SC) is in a standby state, the sub-operation unit (SC) can wait while storing the artificial intelligence neural network (CAI) corresponding to model information (AID). When the sub-operation unit (SC) is in an inactive state, the sub-operation unit (SC) in the inactive state can be deleted. For example, if the sub-operation unit (SC) waits in the standby state for a reference time, the sub-operation unit (SC) can be converted to an inactive state. Accordingly, if the sub-operation unit (SC) waits in the standby state for the reference time, the sub-operation unit (SC) can be deleted.

[0067] When 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) can convert the sub-operation unit (SC) storing the artificial intelligence neural network (CAI) corresponding to the model information (AID) into an active state. When the sub-operation unit (SC) storing the artificial intelligence neural network (CAI) corresponding to the model information (AID) is in an inactive state (i.e., when 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) can create the sub-operation unit (SC). The data storage unit (2400) can apply the artificial intelligence neural network (CAI) corresponding to the model information (AID) to the created sub-operation unit (SC).

[0068] The data storage unit (2400) can store a plurality of artificial intelligence neural networks. Each of the artificial intelligence neural networks can be trained to generate prosthetic data (CD) corresponding to a feature (DF). For example, a first artificial intelligence neural network included in the artificial intelligence neural networks can be trained to generate prosthetic data (CD) corresponding to a first dental formula. For example, the first artificial intelligence neural network can be trained to generate prosthetic data (CD) corresponding to the first dental formula using information such as the first dental formula, surrounding dental formulas of the first dental formula, and opposing teeth of the first dental formula. For example, a second artificial intelligence neural network included in the artificial intelligence neural networks can be trained to generate prosthetic data (CD) corresponding to the first to third dental formulas.

[0069] The sub-operation management unit (2100) can control the size of the sub-operation unit (SC) based on the predicted operation value. For example, the size may refer to the size of the server space. The predicted operation value may be calculated based on metadata (MD) and model information (AID). For example, if the number of formulas included in the metadata (MD) is greater than or equal to the reference number of formulas, the size of the sub-operation unit (SC) may be increased. The reference number of formulas may be set by a setter. 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) may be increased. The reference capacity may be set by a setter. In one embodiment, if the size of the sub-operation unit (SC) corresponding to the number of formulas and the model information (AID) is not calculated, the sub-operation management unit (2100) may create a sub-operation unit (SC) having a reference size. The reference size may be set by a setter.

[0070] The sub-computation unit (SC) can generate prosthetic data (CD) based on oral data (DD) using an artificial intelligence neural network (CAI) corresponding to model information (AID).

[0071] FIG. 11 is a flowchart illustrating an example of the operation of the data operation unit (2000) of FIG. 10.

[0072] Referring to FIGS. 1 to 11, the data operation unit (2000) can vary the size of the activation sub-operation block (2200). A method for varying the size of the activation sub-operation block (2200) may include the step of receiving a request to create a prosthesis (S10), the step of determining the number of creation requests (S20), the step of determining the size of the activation sub-operation block (2200) (S30), and the step of varying the size of the activation sub-operation block (2200) based on the number of creation requests (S40).

[0073] The above number of generation requests may correspond to the number of prosthesis generation requests. For example, if the number of users requesting prosthesis data (CD) increases, the above number of generation requests may increase. For example, if the number of prosthesis data (CD) requested by users increases, the above number of generation requests may increase.

[0074] The sub-operation management unit (2100) can vary the size of the active sub-operation block (2200) based on the number of creation requests. For example, if the number of creation requests increases, the sub-operation management unit (2100) can increase the size of the active sub-operation block (2200). For example, if the number of creation requests is greater than or equal to the reference number of creation requests, the sub-operation management unit (2100) can increase the size of the active sub-operation block (2200). For example, the reference number of creation requests can be set by a setter.

[0075] For example, if the number of generation requests increases, the sub-operation management unit (2100) may increase the number of sub-operation management units (2100) in an active state. For example, if the number of generation requests is greater than or equal to the reference number of generation requests, the sub-operation management unit (2100) may increase the number of sub-operation management units (2100) in an active state.

[0076] In this embodiment, prosthetic data (CD) can be generated using a selected artificial intelligence neural network. Accordingly, prosthetic data (CD) suitable for the user's oral data (DD) can be generated. Additionally, the speed of manufacturing the prosthesis can be improved.

[0077] Additionally, the size of the data processing unit (2000) can be varied based on the model information (AID) of the artificial intelligence neural network and the metadata (MD) of the oral data (DD). Accordingly, the processing speed of the prosthetic data (CD) can be improved.

[0078] Additionally, the prosthetic data (CD) can be generated by the data processing unit (2000). Additionally, the data processing unit (2000) may be a cloud server environment. Accordingly, the processing speed of the prosthetic data (CD) can be further improved.

[0079] In one embodiment, a method for generating a prosthesis using three-dimensional scan data according to the above embodiments and a computer-readable recording medium having a program for executing the same on a computer may be provided. The above method may be written as a program executable on a computer and may be implemented on a general-purpose digital computer that operates the program using a computer-readable medium. In addition, the structure of the data used in the above method may be recorded on the computer-readable medium through various means. The computer-readable medium may include program instructions, data files, data structures, etc., either alone or in combination. The program instructions recorded on the medium may be those specifically designed and configured for the present invention or may be those known and available to a person skilled in the art 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 floptical disks; and hardware devices specifically configured to store and execute program instructions, such as ROM, RAM, and flash memory. Examples of program instructions include machine code, such as that generated by a compiler, as well as high-level language code that can be executed by a computer using an interpreter, etc. The above-mentioned hardware device may be configured to operate as one or more software modules to perform the operation of the present invention.

[0080] In addition, the method for creating a prosthesis using the aforementioned three-dimensional scan data can also be implemented in the form of a computer program or application executed by a computer that is stored on a recording medium.

[0081] The present invention relates to a method for creating a prosthesis using three-dimensional scan data and a computer-readable recording medium having a program for executing the method on a computer, wherein the speed of manufacturing a prosthesis can be improved by using the method for creating a prosthesis using three-dimensional scan data.

[0082] Although the invention has been described with reference to the above embodiments, a person skilled in the art will understand that various modifications and changes can be made to the invention without departing from the spirit and scope of the invention as described in the following claims.

Claims

1. A step of receiving encrypted oral data; Step of receiving model information of an artificial intelligence neural network; A step of determining the metadata of the above-mentioned encrypted oral data; A step of determining a computational prediction value based on the above model information and the above metadata; and It includes a step of determining the size of a sub-operation unit based on the above-mentioned operation prediction value, and A method for generating a prosthesis using 3D scan data, characterized in that the size of the above sub-operation unit is controlled based on the above operation prediction value.

2. In claim 1, the encrypted oral data is generated based on the oral data, and A method for generating a prosthesis using 3D scan data, characterized in that the artificial intelligence neural network is selected based on the features of the oral data.

3. In claim 2, if the sub-operation unit is in a deactivated state, the sub-operation unit is created based on the size, and The above-mentioned generated sub-operation unit receives the artificial intelligence neural network corresponding to the above-mentioned model information, and A method for generating a prosthesis using 3D scan data, characterized in that the generated sub-operation unit generates prosthesis data based on the received artificial intelligence neural network and the encrypted oral data.

4. In claim 2, when the sub-operation unit is in a waiting state, the sub-operation unit receives the artificial intelligence neural network corresponding to the model information, and A method for generating a prosthesis using 3D scan data, characterized in that the above sub-operation unit generates prosthesis data based on the received artificial intelligence neural network and the encrypted oral data.

5. A method for generating a prosthesis using 3D scan data, characterized in that, in claim 4, when the sub-operation unit generates the prosthesis data, the sub-operation unit is converted to the standby state.

6. In claim 1, the metadata includes information on the tooth number and the number of tooth formulas of the oral data corresponding to the encrypted oral data, and A method for generating a prosthesis using 3D scan data, characterized in that when the number of tooth formulas is greater than or equal to the reference number of tooth formulas, the size of the sub-operation unit increases.

7. A method for generating a prosthesis using 3D scan data, characterized in that, in claim 6, the metadata further includes prosthesis type data.

8. In Paragraph 6, It further includes a step of receiving the number of creation requests, and A method for generating a prosthesis using 3D scan data, characterized in that when the number of generation requests is greater than or equal to the reference number of generation requests, the size of the activated sub-operation block containing the activated sub-operation unit increases.

9. In claim 1, the sub-operation unit receives the artificial intelligence neural network from the data storage unit, and The above data storage unit stores a plurality of artificial intelligence neural networks, and A method for generating a prosthesis using 3D scan data, characterized in that the data storage unit applies the artificial intelligence neural network corresponding to the model information to the sub-computation unit.

10. A method for generating a prosthesis using 3D scan data, wherein, in claim 9, the sub-operation unit generates prosthesis data based on the received artificial intelligence neural network and the encrypted oral data.

11. In Paragraph 1, It further includes a step of receiving the number of creation requests, and A method for generating a prosthesis using 3D scan data, characterized in that when the number of generation requests is greater than or equal to the reference number of generation requests, the size of the activated sub-operation block containing the activated sub-operation unit increases.

12. In Paragraph 1, It further includes a step of receiving the number of creation requests, and A method for generating a prosthesis using 3D scan data, characterized in that when the number of generation requests is greater than or equal to the reference number of generation requests, the number of sub-operation units in an active state increases.

13. In claim 1, the model information is determined based on oral data corresponding to the encrypted oral data, and A method for generating a prosthesis using 3D scan data, characterized in that the above model information is determined based on the tooth number and prosthesis type of the above oral data.

14. In claim 1, the encrypted oral data is, Step of generating an encryption key for oral data; and It is generated based on the step of encrypting oral data based on the above encryption key, and A method for generating a prosthesis using 3D scan data, characterized by receiving the encrypted oral data when the encryption key and the encrypted oral data match.

15. A computer-readable recording medium having a program recorded thereon for executing the method of any one of claims 1 through 14 on a computer.

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