KR102580825B1 - System for providing educational data related to dental cad and cam using a neural network - Google Patents

Abstract

Embodiments present a dental education system in which an education server provides education data related to dental computer aided design (CAD) and computer aided manufacturing (CAM) using a neural network. The dental education system according to one embodiment includes a plurality of learning data including a plurality of oral scan data and data related to a plurality of dental work product production requests from a plurality of dentistry-related servers or a plurality of dental laboratory-related servers. A training data determination module that receives and determines training data related to dental CAD and CAM according to learning difficulty through a training model using a neural network based on the plurality of learning data, and training related to dental CAD and CAM A CAD education module that provides lectures on dental CAD programs created based on data, and a CAM education module that provides lectures on dental CAM programs produced based on educational data related to the dental CAD and CAM. Equipped with an education server; And it may include a learner terminal that applies for and receives lectures on the dental CAD program and lectures on the dental CAM program through the education server, and shares information and history information about dentistry and dental labs. For example, the education server obtains a plurality of keywords related to a plurality of dental CAD programs and a plurality of dental CAM programs based on web crawling of websites related to a plurality of dental labs, and the plurality of keywords and a trend detection module that determines the dental CAD program and the dental CAM program based on the first test result based on the dental CAD program and the second test result based on the dental CAM program. Equipped with an education update module that updates education data related to dental CAD and CAM, and a grade management module that determines a grade for the learner terminal based on the first test result and the second test result, A recruitment module may be provided to match the learner terminal with at least one of a dental clinic or dental laboratory recruiting dental technicians according to history information and the grade of the learner terminal.

Description

A system that provides educational data related to dental CAD and CAM using a neural network {SYSTEM FOR PROVIDING EDUCATIONAL DATA RELATED TO DENTAL CAD AND CAM USING A NEURAL NETWORK}

Embodiments of the present disclosure relate to technology for providing training data related to dental CAD (computer aided design) and CAM (computer aided manufacturing), and provide training data related to dental CAD and CAM using a neural network. It's about technology.

The conventional method of manufacturing dental workpieces is to first make an impression of the patient's mouth using impression material at a dentist's office, and then send the impression body made with the impression to a dental laboratory (hereinafter referred to as a dental laboratory), where the dental laboratory creates a negative impression. Pour plaster into the impression and make the shape into a positive shape. Then, the worker creates the wax coping directly on the molded shape. Coping refers to the substructure that serves as the basis before building up the final porcelain in the production of dental prosthesis. This substructure is made of strong metal or ceramic to increase chewing force and tooth strength in the oral cavity. To manufacture such a coping, a coping is made using wax on a prepared (tooth prepared) tooth. When the manufactured wax coping is placed in an investment agent and heated to a high temperature, the wax is burned and disappears, creating a space. do. Coping is completed by injecting metal into the created space using a casting machine. In this way, various processes are performed on the completed coping, and the porcelain prosthesis, which is the final upper structure, is completed by layering the powder and baking it in a ceramic furnace.

On the other hand, when an impression material is used to create an impression of the patient's oral cavity using the analog method described above, if the impression material is deformed, errors may occur in the final dental prosthesis, which may not fit the patient's oral cavity. Additionally, when producing using an analog method, numerous additional tasks are required, and since these tasks are performed manually, it can take a considerable amount of time.

To improve this analog method, a method of digitally manufacturing dental prosthesis using CAD programs and CAM programs has recently been used. In other words, the patient's oral condition can be acquired in a digitized three-dimensional shape through an oral scan, and a dental prosthesis can be manufactured through a CAD program and a CAM program based on the acquired three-dimensional shape.

Meanwhile, dental prosthesis milling equipment is a device that processes and manufactures medical materials or processes base materials such as dental prostheses according to information on three-dimensional sample teeth to replace missing teeth. am. Such dental prosthesis milling equipment may include a workpiece, which is a device that processes a base material such as a dental prosthesis using a bur and provides power for movement and rotation to the base material. Here, dental prosthesis, also called dentures or artificial teeth, refers to a prosthesis that artificially replaces natural teeth and their associated tissues when they are missing. Types of these prosthetics include Inlay, Onlay, Crown, Laminate, Bridge, Coping, Implant, Denture, or Surgical. Surgical guides, etc. may be included. Until now, prosthetics were manufactured entirely by the manual labor of dental technicians, but today, dental prosthesis milling equipment, an artificial tooth processing device, is used to perform prosthesis manufacturing work more efficiently and precisely.

Meanwhile, various CAD programs and CAM programs exist, and the CAD programs and CAM programs used in dentistry or dental labs are not standardized. Additionally, systematic training on CAD programs and CAM programs is not provided to those who manufacture dental prosthesis, so access to dental CAD programs and dental CAM programs may be difficult.

Accordingly, the education server uses a neural network to collect educational data related to dental CAD and CAM, provides lectures on dental CAD programs and dental CAM programs, and provides evaluation of dental CAD programs and dental CAM programs. By performing evaluations on dental CAM programs, a dental education system may be needed to improve performance on dental CAD programs and dental CAM programs.

KR 10-2232777 B1 KR 10-2019-0088580 A J.P. 2022-13898 A KR 10-2019-0059111 A KR 10-2325409 B1

Embodiments of the present disclosure can provide a system that provides educational data related to dental CAD and CAM using a neural network.

The technical challenges to be achieved in the embodiments are not limited to the matters mentioned above, and other technical challenges not mentioned may be considered by those skilled in the art from the various embodiments described below. You can.

A dental education system in which an education server provides education data related to dental CAD (computer aided design) and CAM (computer aided manufacturing) using a neural network according to an embodiment includes a plurality of dental offices. Receive a plurality of learning data including a plurality of oral scan data and data related to a plurality of dental work production requests from a server related to or a server related to a plurality of dental labs, and create a neural network based on the plurality of learning data. An educational data determination module that determines educational data related to dental CAD and CAM according to the learning difficulty level through the educational model used, and a lecture on a dental CAD program created based on the educational data related to dental CAD and CAM. An education server including a CAD education module and a CAM education module that provides lectures on a dental CAM program created based on educational data related to the dental CAD and CAM; And it may include a learner terminal that applies for and receives lectures on the dental CAD program and lectures on the dental CAM program through the education server, and shares information and history information about dentistry and dental labs. For example, the education server obtains a plurality of keywords related to a plurality of dental CAD programs and a plurality of dental CAM programs based on web crawling of websites related to a plurality of dental labs, and the plurality of keywords and a trend detection module that determines the dental CAD program and the dental CAM program based on the first test result based on the dental CAD program and the second test result based on the dental CAM program. Equipped with an education update module that updates education data related to dental CAD and CAM, and a grade management module that determines a grade for the learner terminal based on the first test result and the second test result, A recruitment module may be provided to match the learner terminal with at least one of a dental clinic or dental laboratory recruiting dental technicians according to history information and the grade of the learner terminal.

For example, the lectures for the dental CAD program include multiple lectures on crown dentures, multiple lectures on partial dentures, multiple lectures on complete dentures, multiple lectures on implants, and multiple lectures on orthodontic devices. , may include multiple lectures on laminate and multiple lectures on after sales service (A/S). For example, the dental CAM program may be linked with the dental CAD program. For example, a lecture on the dental CAM program may be linked to a lecture on the dental CAD program. For example, the education server includes a CAD evaluation module that tests dental CAD performance ability based on the dental CAD program and a CAM evaluation module that tests dental CAM performance ability based on lectures on the dental CAM program. More can be provided. For example, the first test result may be determined by comparing a first output file generated based on the dental CAD program and a first output file with correct answers. For example, the second test result may be determined by comparing a second output file generated based on the dental CAM program with a correct answer second output file. For example, the first output file and the second output file may be received from the learner terminal to the education server.

The dental education system may further include a lecturer terminal that performs a non-face-to-face real-time lecture through a remote connection program with the learner terminal. The education server further includes a one-on-one education module that matches the learner terminal and the instructor terminal based on the first message and the history information as the learner terminal transmits a first message requesting a one-on-one lecture to the education server. can do.

The education server may further include a VR education module that implements a plurality of milling equipment in VR (virtual reality) and provides it to the learner terminal. For example, a second message requesting VR education may be received by the education server from the learner terminal. For example, VR information about the plurality of milling equipment may be transmitted to the learner terminal through the VR education module. For example, one of the plurality of milling equipment may be virtually operated by the learner terminal based on the VR information. For example, the plurality of milling equipment may include a 3-axis milling equipment, a 4-axis milling equipment, a 5-axis milling equipment, and a 6-axis milling equipment.

For example, the plurality of keywords may include names of the plurality of dental CAD programs, names of the plurality of dental CAM programs, and a plurality of words related to evaluation. For example, the plurality of words related to the evaluation may include a plurality of words related to a positive evaluation, a plurality of words related to a neutral evaluation, and a plurality of words related to a negative evaluation. For example, awareness of the plurality of dental CAD programs may be determined by the following equation.

In the above equation, a is the awareness, k is the number of the plurality of dental CAD programs, t _x is the number of collected words related to the evaluation of the dental CAD program, and g _x is the number of collected words related to positive evaluation of the dental CAD program, b _x is the number of collected words related to negative evaluation of the dental CAD program, and n _x is the number of collected names of the dental CAD program, t _i is the number of plural words related to the evaluation of the ith dental CAD program, and g _i is the number of words related to the evaluation of the ith dental CAD program. b i is the number of collected words related to the positive evaluation, b _i is the number of collected words related to the negative evaluation of the ith dental CAD program, and n _i is the number of words related to the i th dental CAD program. It may be the number of names collected.

For example, the neural network may include an input layer, one or more hidden layers, and an output layer. For example, each learning data consisting of a plurality of learning data and training data related to dental CAD and CAM is input to the input layer of the neural network, passes through the one or more hidden layers and the output layer, and is converted into an output vector. output, the output vector is input to a loss function layer connected to the output layer, and the loss function layer outputs a loss value using a loss function that compares the output vector with the correct vector for each training data, The training model may be created in which the parameters of the neural network are learned in a direction that reduces the loss value.

Additionally, based on the first similarity between the first output file and the correct answer first output file being less than a preset first similarity, the first test result may be determined to pass. For example, the first similarity may be determined by the following equation.

In the above equation, s ₁ is the first similarity, n _a is the number of shapes constituting the 3D object of the first output file, and n _r is the 3D object of the first output file. is the number of shapes constituting, k is the number of shapes constituting the three-dimensional object of the first output file, and d _i is the center of gravity of the figure constituting the three-dimensional object of the ith first output file. It is the distance between the centers of gravity of the shapes constituting the 3D object of the first output file, and x _a , y _a and z _a are 3D coordinate values of the center of gravity for the 3D object of the first output file. , and the x _r , y _r , and z _r may be 3D coordinate values of the center of gravity of the 3D object of the first output file.

Additionally, based on the second similarity between the second output file and the correct answer second output file being less than a preset second similarity, the second test result may be determined to pass. For example, the second similarity may be determined by the following equation.

In the above equation, s ₂ is the second similarity, l _a is the total length of the tool's movement trajectory in the second output file, and l _r is the tool's movement trajectory in the second output file. is the total length, z is the number of time sections set in the dental CAM program, r _at is the number of rotations of the tool in the t section of the second output file with the correct answer, and r _rt is the second output The number of rotations of the tool in the t section of the file, v _at is the moving speed of the tool in the t section of the second output file, and v _rt is the moving speed of the tool in the t section of the second output file. It can be.

According to embodiments, the education server receives a plurality of learning data including a plurality of oral scan data and data related to a plurality of dental work product production requests from a plurality of dentistry-related servers or a plurality of dental laboratory-related servers, , By determining training data related to dental CAD and CAM according to learning difficulty through a training model using a neural network based on the plurality of learning data, it is possible to reflect the latest data currently being used in a dental office or dental laboratory, Depending on the level of learning difficulty, we can provide lectures on more effective CAD programs and CAM programs.

According to embodiments, the education server acquires a plurality of keywords related to a plurality of dental CAD programs and a plurality of dental CAM programs based on web crawling for websites related to a plurality of dental labs, and the plurality of keywords By determining the dental CAD program and the dental CAM program based on the above, lectures on the dental CAD program and dental CAM program currently needed in the dental laboratory are provided to the learner terminal. can do.

The effects that can be obtained from the examples are not limited to the effects mentioned above, and other effects not mentioned can be clearly derived and understood by those skilled in the art based on the detailed description below. It can be.

The accompanying drawings, which are included as part of the detailed description to aid understanding of the embodiments, provide various embodiments and together with the detailed description describe technical features of the various embodiments.
1 is a diagram showing the configuration of an electronic device according to an embodiment.
Figure 2 is a diagram showing the configuration of a program according to one embodiment.
Figure 3 shows a dental education system that provides education data related to dental CAD and CAM using a neural network according to an embodiment.
Figure 4 shows an example of the use of a dental education system that provides education data related to dental CAD and CAM using a neural network according to the present disclosure.
Figure 5 is a flowchart of a method in which an education server provides lectures on a dental CAD program and a lecture on a dental CAM program to a learner terminal in a dental education system according to an embodiment, and matches the learner terminal with a dentist or dental laboratory. represents.
Figure 6 is a block diagram showing the configuration of an education server according to an embodiment.

The following embodiments combine elements and features of the embodiments in a predetermined form. Each component or feature may be considered optional unless explicitly stated otherwise. Each component or feature may be implemented in a form that is not combined with other components or features. Additionally, various embodiments may be configured by combining some components and/or features. The order of operations described in various embodiments may change. Some features or features of one embodiment may be included in other embodiments or may be replaced with corresponding features or features of other embodiments.

In the description of the drawings, procedures or steps that may obscure the gist of the various embodiments are not described, and procedures or steps that can be understood at the level of a person with ordinary knowledge in the relevant technical field are not described. did.

Throughout the specification, when a part is said to “comprise or include” a certain element, this means that it does not exclude other elements but may further include other elements, unless specifically stated to the contrary. do. In addition, terms such as "... unit", "... unit", and "module" used in the specification refer to a unit that processes at least one function or operation, which refers to hardware, software, or a combination of hardware and software. It can be implemented as: Additionally, the terms “a or an,” “one,” “the,” and similar related terms are used herein in the context of describing various embodiments (particularly in the context of the claims below). Unless otherwise indicated or clearly contradicted by context, it may be used in both singular and plural terms.

Hereinafter, embodiments according to various embodiments will be described in detail with reference to the attached drawings. The detailed description set forth below in conjunction with the accompanying drawings is intended to illustrate exemplary embodiments of various embodiments and is not intended to represent the only embodiment.

In addition, specific terms used in various embodiments are provided to aid understanding of the various embodiments, and the use of such specific terms may be changed to other forms without departing from the technical spirit of the various embodiments. .

1 is a diagram showing the configuration of an electronic device according to an embodiment.

1 is a block diagram of an electronic device 101 in a network environment 100, according to various embodiments. Referring to FIG. 1, in the network environment 100, the electronic device 101 communicates with the electronic device 102 through a first network 198 (e.g., a short-range wireless communication network) or a second network 199. It is possible to communicate with at least one of the electronic device 104 or the server 108 through (e.g., a long-distance wireless communication network). According to one embodiment, the electronic device 101 may communicate with the electronic device 104 through the server 108. According to one embodiment, the electronic device 101 includes a processor 120, a memory 130, an input module 150, an audio output module 155, a display module 160, an audio module 170, and a sensor module ( 176), interface 177, connection terminal 178, haptic module 179, camera module 180, power management module 188, battery 189, communication module 190, subscriber identification module 196 , or may include an antenna module 197. In some embodiments, at least one of these components (eg, the connection terminal 178) may be omitted or one or more other components may be added to the electronic device 101. In some embodiments, some of these components (e.g., sensor module 176, camera module 180, or antenna module 197) are integrated into one component (e.g., display module 160). It can be. The electronic device 101 may also be referred to as a client, terminal, or peer.

The processor 120, for example, executes software (e.g., program 140) to operate at least one other component (e.g., hardware or software component) of the electronic device 101 connected to the processor 120. It can be controlled and various data processing or calculations can be performed. According to one embodiment, as at least part of data processing or computation, the processor 120 stores commands or data received from another component (e.g., sensor module 176 or communication module 190) in volatile memory 132. The commands or data stored in the volatile memory 132 can be processed, and the resulting data can be stored in the non-volatile memory 134. According to one embodiment, the processor 120 includes a main processor 121 (e.g., a central processing unit or an application processor) or an auxiliary processor 123 that can operate independently or together (e.g., a graphics processing unit, a neural network processing unit ( It may include a neural processing unit (NPU), an image signal processor, a sensor hub processor, or a communication processor). For example, if the electronic device 101 includes a main processor 121 and a secondary processor 123, the secondary processor 123 may be set to use lower power than the main processor 121 or be specialized for a designated function. You can. The auxiliary processor 123 may be implemented separately from the main processor 121 or as part of it.

The auxiliary processor 123 may, for example, act on behalf of the main processor 121 while the main processor 121 is in an inactive (e.g., sleep) state, or while the main processor 121 is in an active (e.g., application execution) state. ), together with the main processor 121, at least one of the components of the electronic device 101 (e.g., the display module 160, the sensor module 176, or the communication module 190) At least some of the functions or states related to can be controlled. According to one embodiment, co-processor 123 (e.g., image signal processor or communication processor) may be implemented as part of another functionally related component (e.g., camera module 180 or communication module 190). there is. According to one embodiment, the auxiliary processor 123 (eg, neural network processing unit) may include a hardware structure specialized for processing artificial intelligence models.

Artificial intelligence models can be created through machine learning. For example, such learning may be performed in the electronic device 101 itself on which the artificial intelligence model is performed, or may be performed through a separate server (e.g., server 108). Learning algorithms may include, for example, supervised learning, unsupervised learning, semi-supervised learning, or reinforcement learning, but It is not limited. An artificial intelligence model may include multiple artificial neural network layers. Artificial neural networks include deep neural network (DNN), convolutional neural network (CNN), recurrent neural network (RNN), restricted boltzmann machine (RBM), belief deep network (DBN), bidirectional recurrent deep neural network (BRDNN), It may be one of deep Q-networks or a combination of two or more of the above, but is not limited to the examples described above. In addition to hardware structures, artificial intelligence models may additionally or alternatively include software structures.

The memory 130 may store various data used by at least one component (eg, the processor 120 or the sensor module 176) of the electronic device 101. Data may include, for example, input data or output data for software (e.g., program 140) and instructions related thereto. Memory 130 may include volatile memory 132 or non-volatile memory 134.

The program 140 may be stored as software in the memory 130 and may include, for example, an operating system 142, middleware 144, or application 146.

The input module 150 may receive commands or data to be used in a component of the electronic device 101 (e.g., the processor 120) from outside the electronic device 101 (e.g., a user). The input module 150 may include, for example, a microphone, mouse, keyboard, keys (eg, buttons), or digital pen (eg, stylus pen).

The sound output module 155 may output sound signals to the outside of the electronic device 101. The sound output module 155 may include, for example, a speaker or a receiver. Speakers can be used for general purposes such as multimedia playback or recording playback. The receiver can be used to receive incoming calls. According to one embodiment, the receiver may be implemented separately from the speaker or as part of it.

The display module 160 can visually provide information to the outside of the electronic device 101 (eg, a user). The display module 160 may include, for example, a display, a hologram device, or a projector, and a control circuit for controlling the device. According to one embodiment, the display module 160 may include a touch sensor configured to detect a touch, or a pressure sensor configured to measure the intensity of force generated by the touch.

The audio module 170 can convert sound into an electrical signal or, conversely, convert an electrical signal into sound. According to one embodiment, the audio module 170 acquires sound through the input module 150, the sound output module 155, or an external electronic device (e.g., directly or wirelessly connected to the electronic device 101). Sound may be output through the electronic device 102 (e.g., speaker or headphone).

The sensor module 176 detects the operating state (e.g., power or temperature) of the electronic device 101 or the external environmental state (e.g., user state) and generates an electrical signal or data value corresponding to the detected state. can do. According to one embodiment, the sensor module 176 includes, for example, a gesture sensor, a gyro sensor, an air pressure sensor, a magnetic sensor, an acceleration sensor, a grip sensor, a proximity sensor, a color sensor, an IR (infrared) sensor, a biometric sensor, It may include a temperature sensor, humidity sensor, or light sensor.

The interface 177 may support one or more designated protocols that can be used to connect the electronic device 101 directly or wirelessly with an external electronic device (eg, the electronic device 102). According to one embodiment, the interface 177 may include, for example, a high definition multimedia interface (HDMI), a universal serial bus (USB) interface, an SD card interface, or an audio interface.

The connection terminal 178 may include a connector through which the electronic device 101 can be physically connected to an external electronic device (eg, the electronic device 102). According to one embodiment, the connection terminal 178 may include, for example, an HDMI connector, a USB connector, an SD card connector, or an audio connector (eg, a headphone connector).

The haptic module 179 can convert electrical signals into mechanical stimulation (e.g., vibration or movement) or electrical stimulation that the user can perceive through tactile or kinesthetic senses. According to one embodiment, the haptic module 179 may include, for example, a motor, a piezoelectric element, or an electrical stimulation device.

The camera module 180 can capture still images and moving images. According to one embodiment, the camera module 180 may include one or more lenses, image sensors, image signal processors, or flashes.

The power management module 188 can manage power supplied to the electronic device 101. According to one embodiment, the power management module 188 may be implemented as at least a part of, for example, a power management integrated circuit (PMIC).

The battery 189 may supply power to at least one component of the electronic device 101. According to one embodiment, the battery 189 may include, for example, a non-rechargeable primary battery, a rechargeable secondary battery, or a fuel cell.

Communication module 190 is configured to provide a direct (e.g., wired) communication channel or wireless communication channel between electronic device 101 and an external electronic device (e.g., electronic device 102, electronic device 104, or server 108). It can support establishment and communication through established communication channels. Communication module 190 operates independently of processor 120 (e.g., an application processor) and may include one or more communication processors that support direct (e.g., wired) communication or wireless communication. According to one embodiment, the communication module 190 is a wireless communication module 192 (e.g., a cellular communication module, a short-range wireless communication module, or a global navigation satellite system (GNSS) communication module) or a wired communication module 194 (e.g., : LAN (local area network) communication module, or power line communication module) may be included. Among these communication modules, the corresponding communication module is a first network 198 (e.g., a short-range communication network such as Bluetooth, wireless fidelity (WiFi) direct, or infrared data association (IrDA)) or a second network 199 (e.g., legacy It may communicate with an external electronic device 104 through a telecommunication network such as a cellular network, a 5G network, a next-generation communication network, the Internet, or a computer network (e.g., LAN or WAN). These various types of communication modules may be integrated into one component (e.g., a single chip) or may be implemented as a plurality of separate components (e.g., multiple chips). The wireless communication module 192 uses subscriber information (e.g., International Mobile Subscriber Identifier (IMSI)) stored in the subscriber identification module 196 within a communication network such as the first network 198 or the second network 199. The electronic device 101 can be confirmed or authenticated.

The wireless communication module 192 may support 5G networks after 4G networks and next-generation communication technologies, for example, NR access technology (new radio access technology). NR access technology provides high-speed transmission of high-capacity data (eMBB (enhanced mobile broadband)), minimization of terminal power and access to multiple terminals (mMTC (massive machine type communications)), or high reliability and low latency (URLLC (ultra-reliable and low latency). -latency communications)) can be supported. The wireless communication module 192 may support high frequency bands (eg, mmWave bands), for example, to achieve high data rates. The wireless communication module 192 uses various technologies to secure performance in high frequency bands, for example, beamforming, massive array multiple-input and multiple-output (MIMO), and full-dimensional multiplexing. It can support technologies such as input/output (FD-MIMO: full dimensional MIMO), array antenna, analog beam-forming, or large scale antenna. The wireless communication module 192 may support various requirements specified in the electronic device 101, an external electronic device (e.g., electronic device 104), or a network system (e.g., second network 199). According to one embodiment, the wireless communication module 192 supports Peak data rate (e.g., 20 Gbps or more) for realizing eMBB, loss coverage (e.g., 164 dB or less) for realizing mmTC, or U-plane latency (e.g., 164 dB or less) for realizing URLLC. Example: Downlink (DL) and uplink (UL) each of 0.5 ms or less, or round trip 1 ms or less) can be supported.

The antenna module 197 may transmit or receive signals or power to or from the outside (eg, an external electronic device). According to one embodiment, the antenna module 197 may include an antenna including a radiator made of a conductor or a conductive pattern formed on a substrate (eg, PCB). According to one embodiment, the antenna module 197 may include a plurality of antennas (eg, an array antenna). In this case, at least one antenna suitable for a communication method used in a communication network such as the first network 198 or the second network 199 is connected to the plurality of antennas by, for example, the communication module 190. can be selected Signals or power may be transmitted or received between the communication module 190 and an external electronic device through the at least one selected antenna. According to some embodiments, in addition to the radiator, other components (eg, radio frequency integrated circuit (RFIC)) may be additionally formed as part of the antenna module 197.

According to various embodiments, the antenna module 197 may form a mmWave antenna module. According to one embodiment, a mmWave antenna module includes: a printed circuit board, an RFIC disposed on or adjacent to a first side (e.g., bottom side) of the printed circuit board and capable of supporting a designated high frequency band (e.g., mmWave band); And a plurality of antennas (e.g., array antennas) disposed on or adjacent to the second side (e.g., top or side) of the printed circuit board and capable of transmitting or receiving signals in the designated high frequency band. can do.

At least some of the components are connected to each other through a communication method between peripheral devices (e.g., bus, general purpose input and output (GPIO), serial peripheral interface (SPI), or mobile industry processor interface (MIPI)) and signal ( (e.g. commands or data) can be exchanged with each other.

According to one embodiment, commands or data may be transmitted or received between the electronic device 101 and the external electronic device 104 through the server 108 connected to the second network 199. Each of the external electronic devices 102 or 104 may be of the same or different type as the electronic device 101. According to one embodiment, all or part of the operations performed in the electronic device 101 may be executed in one or more of the external electronic devices 102, 104, or 108. For example, when the electronic device 101 needs to perform a certain function or service automatically or in response to a request from a user or another device, the electronic device 101 may perform the function or service instead of executing the function or service on its own. Alternatively, or additionally, one or more external electronic devices may be requested to perform at least part of the function or service. One or more external electronic devices that have received the request may execute at least part of the requested function or service, or an additional function or service related to the request, and transmit the result of the execution to the electronic device 101. The electronic device 101 may process the result as is or additionally and provide it as at least part of a response to the request. For this purpose, for example, cloud computing, distributed computing, mobile edge computing (MEC), or client-server computing technology can be used. The electronic device 101 may provide an ultra-low latency service using, for example, distributed computing or mobile edge computing. In another embodiment, the external electronic device 104 may include an Internet of Things (IoT) device. Server 108 may be an intelligent server using machine learning and/or neural networks. According to one embodiment, the external electronic device 104 or server 108 may be included in the second network 199. The electronic device 101 may be applied to intelligent services (e.g., smart home, smart city, smart car, or healthcare) based on 5G communication technology and IoT-related technology.

The server 108 is connected to the electronic device 101 and can provide services to the connected electronic device 101. In addition, the server 108 may perform a membership registration process, store and manage various information of users who have registered as members, and provide various purchase and payment functions related to the service. Additionally, the server 108 may share execution data of service applications running on each of the plurality of electronic devices 101 in real time so that services can be shared between users. This server 108 may have the same hardware configuration as a typical web server or service server. However, in terms of software, it may be implemented through any language such as C, C++, Java, Python, Golang, and Kotlin and may include program modules that perform various functions. In addition, the server 108 is generally connected to an unspecified number of clients and/or other servers through an open computer network such as the Internet, and receives work performance requests from clients or other servers and derives and provides work results in response. It refers to a computer system and the computer software (server program) installed for it. In addition, in addition to the server program described above, the server 108 includes a series of application programs running on the server 108 and, in some cases, various databases (DBs) built internally or externally, hereinafter " It should be understood as a broad concept including “DB”). Accordingly, the server 108 classifies membership registration information and various information and data about games, stores them in a DB, and manages this DB, which may be implemented inside or outside the server 108. In addition, the server 108 can be implemented using a variety of server programs provided on general server hardware and operating systems such as Windows, Linux, UNIX, and Macintosh. , Representative examples include IIS (Internet Information Server) used in a Windows environment and CERN, NCSA, APPACH, and TOMCAT used in a Unix environment, etc., to implement web services. Additionally, the server 108 may be linked with an authentication system and payment system for user authentication of the service or payment for purchases related to the service.

The first network 198 and the second network 199 are a connection structure that allows information exchange between each node, such as terminals and servers, or a network connecting the server 108 and the electronic devices 101 and 104. It means (Network). The first network 198 and the second network 199 are the Internet, LAN (Local Area Network), Wireless LAN (Wireless Local Area Network), WAN (Wide Area Network), PAN (Personal Area Network), and 3G. , 4G, LTE, 5G, Wi-Fi, etc., but are not limited to these. The first network 198 and the second network 199 may be closed, such as a LAN or WAN, but are preferably open, such as the Internet. The Internet includes protocols such as TCP/IP protocol, TCP, and UDP (user datagram protocol), as well as various services that exist at the upper layer, such as HTTP (HyperText Transfer Protocol), Telnet, FTP (File Transfer Protocol), and DNS (Domain Name System). ), a worldwide open computer primary network (198) and secondary network (199) that provides Simple Mail Transfer Protocol (SMTP), Simple Network Management Protocol (SNMP), Network File Service (NFS), and Network Information Service (NIS). ) refers to the structure.

A database can have a general data structure implemented in the storage space (hard disk or memory) of a computer system using a database management program (DBMS). A database may have a data storage format that allows for free search (extraction), deletion, editing, addition, etc. of data. Databases are relational database management systems (RDBMS) such as Oracle, Infomix, Sybase, and DB2, or object-oriented database management such as Gemston, Orion, and O2. It can be implemented according to the purpose of an embodiment of the present disclosure using a system (OODBMS) and an XML native database such as Excelon, Tamino, and Sekaiju, and has its own functions. To achieve this, you can have appropriate fields or elements.

Figure 2 is a diagram showing the configuration of a program according to one embodiment.

Figure 2 is a block diagram 200 illustrating program 140 according to various embodiments. According to one embodiment, the program 140 includes an operating system 142, middleware 144, or an application 146 executable on the operating system 142 for controlling one or more resources of the electronic device 101. It can be included. Operating system 142 may include, for example, AndroidTM, iOSTM, WindowsTM, SymbianTM, TizenTM, or BadaTM. At least some of the programs 140 are preloaded into the electronic device 101, for example, at the time of manufacture, or are stored in an external electronic device (e.g., the electronic device 102 or 104, or a server) when used by a user. It can be downloaded or updated from 108)). All or part of the program 140 may include a neural network.

The operating system 142 may control management (eg, allocation or retrieval) of one or more system resources (eg, process, memory, or power) of the electronic device 101 . Operating system 142 may additionally or alternatively operate on other hardware devices of electronic device 101, such as input module 150, audio output module 155, display module 160, and audio module 170. , sensor module 176, interface 177, haptic module 179, camera module 180, power management module 188, battery 189, communication module 190, subscriber identification module 196, or It may include one or more driver programs for driving the antenna module 197.

The middleware 144 may provide various functions to the application 146 so that functions or information provided from one or more resources of the electronic device 101 can be used by the application 146. The middleware 144 includes, for example, an application manager 201, a window manager 203, a multimedia manager 205, a resource manager 207, a power manager 209, a database manager 211, and a package manager 213. ), connectivity manager (215), notification manager (217), location manager (219), graphics manager (221), security manager (223), call manager (225), or voice recognition manager (227). You can.

The application manager 201 may, for example, manage the life cycle of the application 146. The window manager 203 may, for example, manage one or more GUI resources used on the screen. For example, the multimedia manager 205 identifies one or more formats required for playing media files, and encodes or decodes the corresponding media file using a codec suitable for the selected format. It can be done. The resource manager 207 may, for example, manage the source code of the application 146 or the memory space of the memory 130. The power manager 209 manages, for example, the capacity, temperature, or power of the battery 189, and may use this information to determine or provide related information necessary for the operation of the electronic device 101. . According to one embodiment, the power manager 209 may interface with a basic input/output system (BIOS) (not shown) of the electronic device 101.

Database manager 211 may create, search, or change a database to be used by application 146, for example. The package manager 213 may, for example, manage the installation or update of applications distributed in the form of package files. The connectivity manager 215 may manage, for example, a wireless connection or direct connection between the electronic device 101 and an external electronic device. For example, the notification manager 217 may provide a function for notifying the user of the occurrence of a designated event (eg, an incoming call, message, or alarm). The location manager 219 may, for example, manage location information of the electronic device 101. The graphics manager 221 may, for example, manage one or more graphic effects to be provided to the user or a user interface related thereto.

Security manager 223 may provide, for example, system security or user authentication. The telephony manager 225 may manage, for example, a voice call function or a video call function provided by the electronic device 101. For example, the voice recognition manager 227 transmits the user's voice data to the server 108 and provides a command corresponding to a function to be performed in the electronic device 101 based at least in part on the voice data, Alternatively, text data converted based at least in part on the voice data may be received from the server 108. According to one embodiment, the middleware 244 may dynamically delete some existing components or add new components. According to one embodiment, at least a portion of the middleware 144 may be included as part of the operating system 142 or may be implemented as separate software different from the operating system 142.

The application 146 includes, for example, home 251, dialer 253, SMS/MMS (255), instant message (IM) 257, browser 259, camera 261, and alarm 263. , Contacts (265), Voice Recognition (267), Email (269), Calendar (271), Media Player (273), Album (275), Watch (277), Health (279) (such as exercise amount or blood sugar) It may include applications that measure biometric information) or environmental information 281 (e.g., measure atmospheric pressure, humidity, or temperature information). According to one embodiment, the application 146 may further include an information exchange application (not shown) that can support information exchange between the electronic device 101 and an external electronic device. The information exchange application may include, for example, a notification relay application configured to deliver designated information (e.g., calls, messages, or alarms) to an external electronic device, or a device management application configured to manage the external electronic device. there is. The notification relay application, for example, transmits notification information corresponding to a specified event (e.g., mail reception) generated in another application (e.g., email application 269) of the electronic device 101 to an external electronic device. You can. Additionally or alternatively, the notification relay application may receive notification information from an external electronic device and provide it to the user of the electronic device 101.

The device management application, for example, controls the power (e.g., turn-on or turn-off) of an external electronic device or some component thereof (e.g., a display module or camera module of the external electronic device) that communicates with the electronic device 101. ) or functions (such as brightness, resolution, or focus). A device management application may additionally or alternatively support installation, deletion, or update of applications running on external electronic devices.

Throughout this specification, neural network, neural network, and network function may be used with the same meaning. A neural network may consist of a set of interconnected computational units, which may generally be referred to as “nodes.” These “nodes” may also be referred to as “neurons.” A neural network is composed of at least two or more nodes. Nodes (or neurons) that make up neural networks may be interconnected by one or more “links.”

Within a neural network, two or more nodes connected through a link can relatively form a relationship as an input node and an output node. The concepts of input node and output node are relative, and any node in an output node relationship with one node may be in an input node relationship with another node, and vice versa. As described above, input node to output node relationships can be created around links. One or more output nodes can be connected to one input node through a link, and vice versa.

In a relationship between an input node and an output node connected through one link, the value of the output node may be determined based on data input to the input node. Here, nodes connecting the input node and the output node may have weights. Weights may be variable and may be varied by a user or algorithm in order for the neural network to perform a desired function. For example, when one or more input nodes are connected to one output node by respective links, the output node is set to the values input to the input nodes connected to the output node and the links corresponding to each input node. The output node value can be determined based on the weight.

As described above, in a neural network, two or more nodes are interconnected through one or more links to form an input node and output node relationship within the neural network. The characteristics of the neural network may be determined according to the number of nodes and links in the neural network, the correlation between the nodes and links, and the value of the weight assigned to each link. For example, if there are two neural networks with the same number of nodes and links and different weight values between the links, the two neural networks may be recognized as different from each other.

Figure 3 shows a dental education system that provides education data related to dental CAD and CAM using a neural network according to an embodiment. One embodiment of FIG. 3 may be combined with various embodiments of the present disclosure.

Referring to FIG. 3, the dental education system 300 may include an education server 310, a dentistry-related server 320, a dental laboratory-related server 330, a lecturer terminal 340, and a learner terminal 350. You can.

The education server 310 provides lectures on dental CAD programs and lectures on dental CAM programs to a plurality of learner terminals, and provides evaluation of dental CAD programs and dental CAM programs for a plurality of learner terminals. It may be a server that performs evaluation and manages multiple dental clinics, multiple dental labs, and multiple learner terminals in connection. Here, the learner may be a student or the general public who needs education on dental CAD programs and dental CAM programs.

The dentist-related server 320 may be a server that manages the dentistry. Here, the dental office may be a dental office that requests a membership application from the education server 310 and is approved as a member. For example, the dentistry-related server 320 may transmit dentistry-related information to the education server 310. For example, information related to dentistry may include information about the dentist, employment information about dental technicians needed by the dentist, and dental treatment data. For example, dental care data may include a patient's oral scan data and data related to a request for manufacturing a dental workpiece. Here, employment information may include information about salary, information about career, and information about working conditions.

The server 330 related to the dental laboratory may be a server that manages the dental laboratory. Here, the dental laboratory may be a dental laboratory that requests a membership application from the education server 310 and is approved as a member. For example, the server 330 related to the dental laboratory may transmit information related to the dental laboratory to the education server 310. For example, information related to a dental laboratory may include information about the dental laboratory, employment information on dental technicians needed by the dental laboratory, and data on the production of dental equipment by the dental laboratory. Here, employment information may include information about salary, information about career, and information about working conditions.

For example, the education server 310, the server 320 related to dentistry, and the server 330 related to dental labs may be the server 108 of FIG. 1 described above.

The lecturer terminal 340 provides a lecture on at least one of a dental CAD program or a dental CAM program to the education server 310, and provides dental education to the learner terminal 350 matched by the education server 310. It may be a terminal that provides one-on-one training on a dental CAD program or a dental CAM program. For example, the lecturer terminal 340 may be a terminal related to a dental technician with a certain level of experience or a dentist with a certain level of experience.

The learner terminal 350 may be a terminal associated with a learner taking at least one of a lecture on a dental CAD program or a lecture on a dental CAM program provided by the education server 310. For example, the learner terminal 350 may transmit a message to the education server 310 requesting registration for at least one of a lecture on a dental CAD program or a lecture on a dental CAM program. For example, the education server 310 may transmit an approval message to the learner terminal 350 based on a message requesting course registration. For example, a message requesting course registration may include information and history information about the course being applied for.

For example, the lecturer terminal 340 and the learner terminal 350 may be the electronic device 101 of FIG. 1 described above. For example, the instructor terminal 340 and the learner terminal 350 include various communication means such as desktops, tablets, netbooks, personal digital assistants (PDAs), portable multimedia players (PMPs), smartphones, and wearable smart devices. It can be a device, and various functions provided by the education server can be executed through web-based or separate software/applications.

According to one embodiment, the training server 310 includes a training data determination module, a trend identification module, a CAD training module, a CAM training module, a training update module, a CAD evaluation module, a CAM evaluation module, a one-on-one training module, a VR training module, and a member It may include a management module, grade management module, purchase module, SNS (social network service) management module, and recruitment module.

The education server 310 receives a plurality of oral scan data and a plurality of data related to a request for manufacturing a plurality of dental workpieces from the dentistry-related server 320 or the dental laboratory-related server 330 through the education data determination module. Learning data can be received. The training server 310 may determine training data related to dental CAD and CAM according to learning difficulty through a training model using a neural network based on the plurality of training data through the training data determination module. For example, oral scan data may be data obtained by scanning the patient's oral cavity using various 3D intraoral scanners. For example, intraoral scan data may be in any of the following file formats: STL, PLY, OBJ, 3MF, DICOM, and JPEG, including point cloud data. For example, data related to a request for manufacturing a dental workpiece may include data requesting a dental laboratory to manufacture a dental workpiece at a dental office. For example, data related to a request for manufacturing a dental work product may include information about the work type, information about the material, information about the area, and information about the tooth color. Here, educational data related to dental CAD and CAM may include oral scan data (eg, STL files).

For example, the neural network may include an input layer, one or more hidden layers, and an output layer. For example, each learning data consisting of a plurality of learning data and training data related to dental CAD and CAM is input to the input layer of the neural network, passes through the one or more hidden layers and the output layer, and is output as an output vector. The output vector is input to a loss function layer connected to the output layer, and the loss function layer outputs a loss value using a loss function that compares the output vector with the correct answer vector for each training data. The training model may be created in which the parameters of the neural network are learned in a direction that reduces the loss value.

Additionally, for example, a plurality of learning data may be converted into input vectors. The education server converts a plurality of learning data into [values corresponding to the type of technique, number of types of technique, value corresponding to material, value corresponding to area, value corresponding to tooth color] for each oral scan data. It can be vectorized into a composed vector. Here, dental equipment items may include inlays, onlays, crowns, implants, laminates, partial dentures, complete dentures, copings, surgical guides, and orthodontic devices. For example, values corresponding to qigong items may be preset in the education server. Here, the material may include ceramic, resin, gold, porcelain fused to metal (PFM), porcelain fused gold (PFG), and zirconia. For example, values corresponding to materials may be preset in the training server. Here, the area can be indicated through the tooth number. For example, values corresponding to regions may include 11 to 18, 21 to 28, 31 to 38, and 41 to 48, and may be values corresponding to each number. For example, values corresponding to tooth color may include values for brightness, saturation, and hue. For example, brightness, saturation, and hue may each correspond to numbers 1 through 5.

For example, the learning difficulty level may vary depending on at least one of the type of pore item, the number of pore items, a value corresponding to a material, a value corresponding to a part, or a value corresponding to a tooth color. For example, learning difficulty levels may include basic, intermediate, and advanced. For example, educational data related to dental CAD and CAM may be pre-stored in the education server according to the learning difficulty level.

That is, the education server can determine oral scan data to be used as samples for dental CAD and CAM programs according to learning difficulty level through an education model learned based on a plurality of learning data. Therefore, the education server can acquire oral scan data and dental work product production request data to be used as samples of dental CAD and CAM programs for each learning difficulty level, and the oral scan data and dental work product production request data are currently used by dental or dental equipment. By reflecting the latest data being used in public offices, we can provide more effective lectures on CAD programs and CAM programs.

The education server 310 can acquire a plurality of keywords related to a plurality of dental CAD programs and a plurality of dental CAM programs based on web crawling of websites related to a plurality of dental labs through a trend detection module, A dental CAD program and a dental CAM program can be determined based on the plurality of keywords. Here, web crawling may be an operation to collect data from web pages on the Internet. For example, the plurality of keywords may include names of the plurality of dental CAD programs, names of the plurality of dental CAM programs, and a plurality of words related to evaluation. For example, the plurality of words related to the evaluation may include positive evaluation type words, neutral evaluation type words, and negative evaluation type words.

For example, awareness of the plurality of dental CAD programs may be determined by Equation 1 below.

In Equation 1, a is the awareness, k is the number of the plurality of dental CAD programs, and t _x is the number of words collected related to the evaluation of the dental CAD program, The g _x is the number of words of a positive evaluation type for the corresponding dental CAD _program collected, the _b The name of the dental CAD program is the number collected, t _i is the number of plural words related to the evaluation of the ith dental CAD program, and g _i is the positive number for the ith dental CAD program. where b i is the number of collected words of the evaluation type, where b _i is the number of negative type words for the ith dental CAD program, and where n _i is the number of collected names of the ith dental CAD program. there is.

For example, words related to evaluation may be words located within a preset range from the location of the name of the dental CAD program.

For example, there are a large number of names of dental CAD programs collected, a large number of words with a positive evaluation type for the dental CAD program, and a large number of related words with a negative evaluation type for the dental CAD program. The smaller the number collected, the greater the awareness can be determined.

Through this, the education server can determine the most recognized dental CAD program and provide lectures on the dental CAD program currently needed in dental labs to the learner terminal.

Additionally, for example, a preset number of CAD programs may be determined in order of increasing recognition. That is, lectures on multiple CAD programs may be provided.

For example, the recognition of a dental CAM program may be determined in the same way as the dental CAD program.

Additionally, for example, a plurality of words related to the evaluation in the plurality of keywords may be determined into a positive evaluation type, a neutral evaluation type, and a negative evaluation type through an analysis model using a neural network.

The analysis model may be a multi-channel CNN using three different convolutional layers. One or more hidden layers of the analysis model include three different convolutional layers, and based on the three different convolutional layers, morphemes, syllables, and graphemes can all be set as input values. Here, a morpheme refers to the smallest unit of speech that cannot be further analyzed as a meaningful element. Here, a syllable means a word of sound. Here, a grapheme refers to the basic unit of a character. Therefore, by setting all morphemes, syllables, and graphemes as input values, words that have not been learned can be supplemented through feature vectors for syllables and graphemes, and proper nouns or technical terms can be supplemented through feature vectors for morphemes. You can.

For example, an analysis model can use different types of word embeddings simultaneously, such as Word2vec, Glove, Syntactic, etc.

For example, the input layer of an analysis model may include three input channels, one sentence in a reply post is classified into morphemes, syllables, and graphemes, and each classified morpheme, syllable, and grapheme is divided into three input channels. can be entered.

For example, the server analyzes sentences containing the names of multiple dental CAD programs or the names of multiple dental CAM programs into morphemes, syllables, and graphemes, and for each morpheme, syllable, and grapheme, Word2vec, Different types of word embedding can be performed, such as Glove, Syntactic, etc. For example, each learning data consisting of vectors and correct answer evaluation types for morphemes, syllables, and graphemes on which the word embedding was performed is input to the input layer of the neural network, and passes through the one or more hidden layers and the output layer. outputs an output vector, and the output vector is input to a loss function layer connected to the output layer, and the loss function layer calculates a loss value using a loss function that compares the output vector with the correct vector for each training data. can be output. For example, the analysis model may be created in which parameters of the neural network are learned in a direction that reduces the loss value.

Therefore, the server labels data as one of a plurality of evaluation types for words related to evaluation of the plurality of dental CAD programs or the plurality of dental CAM programs through the analysis model learned using the neural network. can be performed.

The education server 310 may provide a lecture on a dental CAD program created based on educational data related to the dental CAD and CAM to the learner terminal 350 through a CAD education module. For example, lectures on dental CAD programs created based on educational data related to dental CAD and CAM may be pre-stored in the education server 310. For example, a lecture on a dental CAD program created based on educational data related to the dental CAD and CAM may be received from the lecturer terminal 340 and pre-stored in the education server 310.

The education server 310 may provide the learner terminal 350 with a lecture on a dental CAM program created based on educational data related to the dental CAD and CAM through the CAM education module. For example, lectures on dental CAM programs created based on educational data related to dental CAD and CAM may be pre-stored in the education server 310. For example, a lecture on a dental CAM program created based on educational data related to the dental CAD and CAM may be received from the lecturer terminal 340 and pre-stored in the education server 310.

For example, the lectures for the dental CAD program include multiple lectures on crown dentures, multiple lectures on partial dentures, multiple lectures on complete dentures, multiple lectures on implants, and multiple lectures on orthodontic devices. , may include multiple lectures on laminate and multiple lectures on after sales service (A/S). Here, lectures on crown dentures may include lectures on inlay, onlay, crown, and bridge technologies.

For example, each lecture covers the transformation tool, morphing tool, wax tool, attachment tool, and smoothing tool included in dental CAD programs. It may include a method of using a plurality of tools such as for each purpose.

For example, the dental CAM program may be linked with the dental CAD program. That is, the dental CAM program is compatible with files output through the dental CAD program.

For example, a lecture on the dental CAM program may be linked to a lecture on the dental CAD program. That is, if the lecture on the dental CAD program is a lecture on coffin bridge dentures, the dental CAM program may be a lecture on manufacturing coffin bridge dentures designed by the dental CAD program. For example, a lecture on the dental CAM program may include a method of determining a tool path for a dental workpiece to be manufactured by adjusting the basic values of each interface included in the dental CAM program.

The education server 310 may test the learner terminal 350's dental CAD performance ability based on the dental CAD program through a CAD evaluation module. The education server 310 receives the first output file generated based on the dental CAD program from the learner terminal 350, and compares the first output file with the correct answer first output file through the CAD evaluation module. 1 Test results can be determined. For example, the first output file may be an STL file format or a 3MF file format. The STL file format is the de facto standard data transfer format in the rapid prototyping industry. For example, the STL file format may be a format that performs approximate calculations using triangles on the surface of a 3D object. For example, an STL file may contain information about the triangles that make up the surface of a three-dimensional object. The 3MF file format is a file format associated with applications used to design 3D models. For example, a 3MF file may contain information such as color, material, materials, and mesh that make up a 3D object.

The education server 310 may test the learner terminal 350's dental CAM performance ability based on the dental CAM program through the CAM evaluation module. The education server 310 receives the second output file generated based on the dental CAM program from the learner terminal 350, and compares the second output file with the correct answer second output file through the CAM evaluation module. 2 Test results can be determined. For example, the second output file may be a numerical control (NC) file format. The NC file format is a format for the manufacturing process, and the NC file may be a file containing information for the manufacturing process of milling equipment. For example, an NC file may contain information about the tool's movement trajectory, number of tool rotations, and movement speed. For example, information on the tool's movement trajectory, number of rotations and movement speed of the tool is information on the horizontal plane, vertical plane, inclined plane, horizontal corner, vertical corner, inclined corner, and convex (Convex) for the processing target determined based on the STL file. ) It can be determined through surface analysis of corners and freeform surfaces.

Additionally, the education server 310 may transmit a first evaluation message to the learner terminal 350 for evaluating the learner terminal 350 that has taken a lecture on a dental CAD program. Here, the first evaluation message may include first oral scan data (eg, STL file) and first dental work product production request data. For example, the first oral scan data and the first dental work product production request data may be different for each learner terminal 350. At this time, the first evaluation message may be different depending on the grade of the learner terminal 350 and the type of lecture the learner terminal 350 completed. For example, the first evaluation message may be transmitted to the learner terminal 350 who has completed a specific course included in the lecture on the dental CAD program. For example, the first evaluation message is at least one of an evaluation message for a crown denture, an evaluation message for a partial denture, an evaluation message for a complete denture, an evaluation message for an implant, an evaluation message for an orthodontic device, or an evaluation message for a laminate. may include. For example, the learner terminal 350 may transmit the first output file output through the dental CAD program to the education server 310 based on the first oral scan data and the first dental work product production request data. there is. The education server 310 may determine the first test result by comparing the first output file and the first output file with correct answers.

Additionally, based on the first similarity between the first output file and the correct answer first output file being less than a preset first similarity, the first test result may be determined to pass.

For example, the first similarity may be determined by Equation 2 below.

In Equation 2, s ₁ is the first similarity, n _a is the number of shapes constituting the 3D object of the first output file, and n _r is the 3D object of the first output file. is the number of shapes constituting the object, k is the number of shapes constituting the three-dimensional object of the first output file, and d _i is the weight of the shape constituting the three-dimensional object of the i th answer first output file. It is the distance between the center and the center of gravity of the figure constituting the three-dimensional object of the first output file, and x _a , y _a and z _a are the three-dimensional center of gravity of the three-dimensional object of the first output file. It is a coordinate value, and x _r , y _r , and z _r may be 3D coordinate values of the center of gravity of the 3D object of the first output file.

The shapes constituting the 3D object of the first output file and the shapes constituting the 3D object of the first correct answer output file may be ordered from 1st to kth according to 3D coordinates. At this time, the number of shapes constituting the 3D object in the first output file with correct answers may be less than k. For example, if the number of shapes constituting the 3D object of the first output file with the correct answer is k-1, the center of gravity of the kth shape constituting the 3D object of the first output file with the correct answer is determined to be 0. You can.

For example, the smaller the value for the first similarity, the more similar the first output file and the first output file with the correct answer may be. For example, the difference value for the number of shapes constituting the three-dimensional object between the first output file and the first output file with the correct answer is small, and the value for the distance between the centers of gravity of the shapes constituting the three-dimensional object is small. , the smaller the value of the distance between the center of gravity of the 3D object between the first output file and the first output file with the correct answer, the more similar the first output file may be to the first output file with the correct answer.

Through this, if the first similarity value is smaller than the preset first similarity value, the education server can determine the first test result as passing, and the first evaluation message is determined differently for each learner terminal, more effectively. The performance ability of the learner terminal for the dental CAD program can be evaluated.

For example, when there are a plurality of 3D objects in the first output file, a first similarity may be determined for each 3D object.

Additionally, for example, the coordinates of the center of gravity of the above-described three-dimensional object may be determined as follows. First, for example, if the 3D object in the first output file is a polygon mesh, calculate the distance between all polygon meshes on the surface of the 3D object for points included inside the polygon mesh, and calculate the calculated distance values The smallest value among them may be determined as the first value for each point included within the polygon mesh.

In addition, the weight used in the preprocessing process of the 3D object is a weight calculated from the 3D object under a specific condition, and the specific condition can be determined based on the result calculated by clustering all 3D points. At this time, specific conditions can be calculated by using rate distortion theory and K-means clustering algorithm. To cluster 3D points, the number of clusters can be calculated using rate distortion theory. According to the calculated number of clusters, the distance between the centers of each clustered cluster is calculated using the K-means clustering algorithm, and spheres with the center of each cluster as the origin are formed using half of the smallest value among the distance values as the radius. You can. And, the number of points in each sphere can be the weight of the points in that cluster. Therefore, points within the same cluster may each have the same weight.

Second values can be generated by multiplying the first values by the calculated weight, and values in a small range for the second values can be quantized by expressing them as assigned values in a specific range. In this way, among the generated second values, 3-dimensional points corresponding to the second value greater than 0 are selected, and SVD (singular value decomposition), which is a method of decomposing the matrix into a specific structure as singular value decomposition, is used to decompose the matrix into three main components. Axes, i.e. x, y, and z axes can be obtained. Also, the center of gravity of the 3D object can be determined by dividing the sum of the quantized second values by the sum of the product of the quantized second values based on the three principal component axes and each 3D point. For example, the x-coordinate of the center of gravity can be determined by dividing the sum of the quantized second values by the sum of the products of the quantized second values with respect to the x-axis and each 3-dimensional point.

The education server 310 may transmit a second evaluation message to the learner terminal 350 for evaluating the learner terminal 350 that has taken a lecture on the dental CAM program. Here, the second evaluation message may include a file (eg, STL file) output by a dental CAD program and second dental work product production request data. For example, the file output by the dental CAD program and the second dental work product production request data may be different for each learner terminal 350. At this time, the second evaluation message may be different depending on the grade of the learner terminal 350 and the type of lecture the learner terminal 350 completed. For example, the second evaluation message may be transmitted to the learner terminal 350 who has completed a specific course included in the lecture on the dental CAM program. For example, the second evaluation message is at least one of an evaluation message for a crown denture, an evaluation message for a partial denture, an evaluation message for a complete denture, an evaluation message for an implant, an evaluation message for an orthodontic device, or an evaluation message for a laminate. may include. For example, the learner terminal 350 sends the second output file output through the dental CAM program to the education server 310 based on the file output by the dental CAD program and the second dental workpiece production request data. can be sent to. The education server 310 may determine the second test result by comparing the second output file with the correct answer second output file. For example, the second output file and the correct answer The second output file may include machining data (e.g., NC file). For example, machining data may include information about the tool's movement trajectory, number of rotations and movement speed of the tool.

Additionally, based on the second similarity between the second output file and the correct answer second output file being less than a preset second similarity, the second test result may be determined to pass.

For example, the second similarity may be determined by Equation 3 below.

In Equation 3, s ₂ is the second similarity, l _a is the total length of the movement trajectory of the tool in the second output file, and l _r is the movement of the tool in the second output file. is the total length for the trajectory, z is the number of time sections set in the dental CAM program, r _at is the number of rotations of the tool in the t section of the second output file with the correct answer, and r _rt is the number of time sections set in the dental CAM program. 2 The number of rotations of the tool in the t section of the output file, v _at is the moving speed of the tool in the t section of the second output file, and v _rt is the speed of the tool in the t section of the second output file. It could be movement speed.

For example, the smaller the value for the second similarity, the more similar the second output file and the second output file with the correct answer may be. For example, the smaller the difference between the components included in the second output file and the second output file with the correct answer, the more similar the second output file may be to the second output file with the correct answer.

Through this, if the second similarity value is smaller than the preset second similarity value, the education server can determine the second test result as passing, and the second evaluation message is determined differently for each learner terminal, more effectively. The performance ability of the learner terminal for the dental CAM program can be evaluated.

For example, when there are a plurality of 3D objects in the second output file, a second similarity may be determined for each 3D object.

The education server 310 can update education data related to the dental CAD and CAM according to the first test result based on the dental CAD program and the second test result based on the dental CAM program through the education update module. there is. For example, based on the first test result and the second test result, the course in which the learner terminal 350 failed the test the most number of times may be updated. That is, the education server 310 may transmit an update request message to the instructor terminal 340 that created the lecture for the course in which the learner terminal 350 failed the test the most times. Alternatively, the education server 310 may re-determine education data related to dental CAD and CAM used in the lecture for the course in which the learner terminal 350 failed the test the most times. In addition, the education server 310 may transmit the determined education data related to dental CAD and CAM to the lecturer terminal 340.

The education server 310 can provide non-face-to-face real-time lectures through a remote connection program to the learner terminal 350 by connecting the lecturer terminal 340 and the learner terminal 350 through a one-on-one education module. For example, the education server 310 may receive a first message requesting a one-on-one lecture from the learner terminal 350, and based on the first message and the history information received from the learner terminal 350, the learner The terminal 350 and the lecturer terminal 340 can be matched. Here, the history information may include information about the learner's age, gender, address, and career.

The education server 310 may implement a plurality of milling equipment in VR through a virtual reality (VR) education module and provide it to the learner terminal 350. For example, the education server 310 may receive a second message requesting VR education from the learner terminal 350, and send VR information about the plurality of milling equipment to the learner terminal 350 through a VR education module. can be sent to. For example, one of the plurality of milling equipment may be virtually operated by the learner terminal 350 based on the VR information. For example, the plurality of milling equipment may include a 3-axis milling equipment, a 4-axis milling equipment, a 5-axis milling equipment, and a 6-axis milling equipment.

The education server 310 can manage dentists and dental labs approved as members through the member management module. For example, the education server 310 may receive a message requesting membership registration from a server 320 related to dentistry or a server 330 related to a dental laboratory, and the education server 310 may receive a message requesting membership registration through a member management module. A message requesting approval may be transmitted to the server 320 related to dentistry or the server 330 related to dental lab. For example, the membership level for the dental office or dental laboratory approved as a member may be determined according to the amount of data provided to the education server 310 and the fee paid at the time of membership registration. For example, the scope of services provided to the dentist or dental laboratory may differ depending on the membership level. In other words, the greater the amount of data provided to the education server 310 by a dental office or dental lab approved as a member and the more the fee paid when signing up as a member, the higher the membership level for the dental clinic or dental lab can be determined. And, the higher the membership level, the wider the range of services provided to dentists and dental labs. For example, membership levels may include bronze, silver, gold, and crown levels.

The education server 310 may manage the grade for the learner terminal 350 that has applied for a lecture on a dental CAD program or a lecture on a dental CAM program through the grade management module. For example, the grade for the learner terminal 350 may be changed based on the first test result and the second test result. For example, the levels for the learner terminal 350 may include beginner, intermediate, and advanced for a dental CAD program and beginner, intermediate, and advanced for a dental CAM program.

Through the purchase module, the education server 310 may provide information for dentists and dental labs approved as members to purchase materials necessary for manufacturing dental workpieces or products for manufacturing dental workpieces. For example, the scope of information provided to the dentist or dental laboratory may differ depending on the membership level.

The education server 310 provides information and advertisements related to lectures on dental CAD programs and lectures on dental CAM programs, evaluations of dental CAD programs, and dental CAM programs through a SNS (social network service) management module. You can post information and advertisements related to the evaluation, or communicate with the learner terminal 340 and the lecturer terminal 350 through posts. Here, SNS may be a media service that exchanges information through electronic networks such as the Internet or intranet, such as Facebook or Instagram.

The education server 310 matches the learner terminal 350 with at least one of a dental clinic or dental laboratory recruiting dental technicians according to the history information received from the learner terminal 350 through the recruitment module and the grade for the learner terminal 350. You can do it. For example, the education server 310 is based on the history information received from the learner terminal 350 and the grade for the learner terminal 350, and the grade, region, age, and The learner terminal 350 matching the gender can be determined.

Figure 4 shows an example of the use of a dental education system that provides education data related to dental CAD and CAM using a neural network according to the present disclosure. One embodiment of FIG. 4 may be combined with various embodiments of the present disclosure.

Referring to FIG. 4, the education server can determine educational data related to dental CAD and CAM according to learning difficulty level, and determine the currently most recognized dental CAD program and dental CAM program.

The education server may provide lectures on the determined dental CAD program and dental CAM program to the learner terminal, and provide one-on-one education through matching between the instructor terminal and the learner terminal for the determined dental CAD program and dental CAM program. It can be provided based on a remote control program, and VR education can be provided by transmitting information on a plurality of milling equipment related to the determined dental CAD program and dental CAM program in VR to the learner terminal.

The education server compares the evaluation of the determined dental CAD program and dental CAM program with the first output file and the second output file received from the learner terminal with the first output file and the second output file, and then sends the evaluation to the learner terminal. The grade can be determined. Additionally, the education server may update lectures on the determined dental CAD program and dental CAM program based on the evaluation of the determined dental CAD program and dental CAM program. Additionally, the education server may update lectures on dental CAD programs and dental CAM programs determined periodically based on web crawling for websites related to dental CAD programs and dental CAM programs.

The education server can match the user of the learner terminal with the dentist and dental laboratory registered as a member based on the grade of the learner terminal, the membership grade and employment information for the dental clinic and dental laboratory registered as a member.

The education server may deliver guidance and advertisements for the purchase of dental CAD programs, dental CAM programs, dental milling equipment, and materials for manufacturing dental workpieces, depending on the membership level for registered dentists and dental labs. You can communicate with dentists and dental labs, learner terminals, and lecturer terminals through SNS.

Figure 5 is a flowchart of a method in which an education server provides lectures on a dental CAD program and a lecture on a dental CAM program to a learner terminal in a dental education system according to an embodiment, and matches the learner terminal with a dentist or dental laboratory. represents. One embodiment of FIG. 5 may be combined with various embodiments of the present disclosure.

Referring to FIG. 5, in step S501, the training server trains dental CAD and CAM according to learning difficulty through a training model using a neural network based on a plurality of learning data collected from a server related to dentistry or a server related to a dental laboratory. Training data related to can be determined.

In step S502, the education server may determine a dental CAD program and a dental CAM program based on a plurality of keywords obtained based on web crawling for websites related to a plurality of dental laboratories. For example, the education server determines the awareness of a plurality of dental CAD programs and the awareness of a plurality of dental CAM programs based on a plurality of keywords according to Equation 1 above, and selects the most recognized dental CAD program. You can decide on the program and dental CAM program.

In step S503, the education server may provide lectures on the dental CAD program and lectures on the dental CAM program to the learner terminal.

In step S504, the education server may determine whether a first message requesting one-on-one education has been received from the learner terminal. The first message may include information about at least one of a lecture selected by the learner terminal among lectures on a dental CAD program or a lecture selected by the learner terminal among lectures on a dental CAM program. For example, the first message may include a lecture on bridges among lectures on dental CAD programs.

In step S505, when the education server receives the first message from the learner terminal, the education server may match the learner terminal and the instructor terminal. For example, when a lecture on a bridge is selected among lectures on a dental CAD program by the first message, the education server selects a lecturer corresponding to the grade of the learner terminal from among a plurality of lecturer terminals matched to the lecture on the bridge. The terminal and the learner terminal can be matched. For example, lecturer terminals matching lectures on dental CAD programs and lectures on dental CAM programs may be preset in the education server for each grade of the learner terminal.

In step S506, the education server may determine whether a second message requesting VR education has been received from the learner terminal.

In step S507, when the education server receives the second message from the learner terminal, the education server may transmit VR information about a plurality of milling equipment to the learner terminal. For example, when the user of the learner terminal receives VR information from the education server, the main stream content performs the main interaction with the virtual milling equipment included in the VR information, and the plurality of contents performs a plurality of sub-interactions with the virtual milling equipment. Based on the sub-interaction content of , various interactions with virtual milling equipment can be performed through a VR device connected to the learner terminal. Here, the main stream content may be content that operates the main functions of virtual milling equipment. Here, the sub-interaction content may be content that manipulates the possibility of adding virtual milling equipment. For example, the user of the learner terminal can select any one of 3-axis milling equipment, 4-axis milling equipment, 5-axis milling equipment, and 6-axis milling equipment based on VR information, and main stream content for the selected milling equipment and The selected milling equipment can be operated virtually using a VR device connected to the learner terminal through multiple sub-interaction contents.

In step S508, the education server may perform evaluation on the learner terminal. For example, the education server may transmit at least one of a first evaluation message for the dental CAD program or a second evaluation message for the dental CAM program to a learner terminal that satisfies a specific condition. For example, when the education server transmits the first evaluation message to the learner terminal, the education server receives the first output file from the learner terminal, and the above-described equation for the first output file and the first output file with the correct answer The first test result can be determined based on 2. For example, when the education server transmits the second evaluation message to the learner terminal, the education server receives the second output file from the learner terminal, and uses the above-described equation for the second output file and the correct answer second output file. The second test result can be determined based on 3.

In step S509, the education server may transmit evaluation-related information to the instructor terminal based on the evaluation of the learner terminal. For example, the education server may transmit an update request message containing information related to evaluation to the instructor terminal that created the lecture for the course in which the learner terminal failed the test the most frequently. For example, information related to evaluation may include information about the passing rate of the learner's terminal. Thereafter, for example, the education server may determine education data related to the second dental CAD and CAM for lectures on courses in which the learner terminal has failed the highest number of times. And, the education server may transmit education data related to the second dental CAD and CAM to the lecturer terminal.

In step S510, the education server may determine a grade for the learner terminal based on the evaluation of the learner terminal. For example, the education server may change the grade for the learner terminal based on at least one of the first test result or the second test result. For example, if the first test result for intermediate level is pass, the education server may determine the level for the learner terminal from beginner to intermediate level for the dental CAD program. For example, if the second test result for advanced is passing, the education server may determine the grade for the learner terminal from intermediate to advanced for the dental CAM program.

In step S511, the education server may determine whether the learner terminal matches the recruitment information. For example, the education server may determine whether there is a match for employment information about at least one of a dental clinic or dental laboratory recruiting dental technicians according to the history information received from the learner terminal and the grade for the learner terminal. For example, the education server determines a plurality of dental clinics or a plurality of dental labs within a first preset range from the address based on the address of the learner, and ranks the learner terminal among the plurality of dental clinics or the plurality of dental labs. You can determine the dentist or dental laboratory that sent the recruitment information you included. At this time, if recruitment information including the grade of the learner terminal is not transmitted from a plurality of dentists or a plurality of dental labs within the preset first range, the education server sends a preset second range that is a larger range than the preset first range. Multiple dental clinics or multiple dental labs within the clinic can be determined. Thereafter, the education server may determine the dentist or dental laboratory that transmitted the recruitment information including the grade of the learner terminal among the plurality of dental clinics or dental labs within the second preset range.

In step S512, based on the learner terminal matching the employment information, the education server may match the learner terminal with a dentist or dental laboratory. For example, the education server may transmit employment information about the dentist or dental laboratory to the learner terminal, and transmit history information about the learner terminal and the grade of the learner terminal to the dentist or dental laboratory.

The embodiment of FIG. 5 is an example, and the order of each decision process may be different. For example, each judgment process can be performed independently. For example, an education server may perform the above judgment process in parallel.

Figure 6 is a block diagram showing the configuration of an education server according to an embodiment. One embodiment of FIG. 6 may be combined with various embodiments of the present disclosure.

As shown in FIG. 6, the education server 600 may include a processor 610, a communication unit 620, and a memory 630. However, not all of the components shown in FIG. 6 are essential components of the education server 600. The education server 600 may be implemented with more components than those shown in FIG. 6 , or the education server 600 may be implemented with fewer components than the components shown in FIG. 6 . For example, the education server 600 according to some embodiments may further include a user input interface (not shown), an output unit (not shown), etc. in addition to the processor 610, the communication unit 620, and the memory 630. there is.

The processor 610 typically controls the overall operation of the education server 600. The processor 610 may include one or more processors and control other components included in the education server 600. For example, the processor 610 can generally control the communication unit 620 and the memory 630 by executing programs stored in the memory 630. Additionally, the processor 610 may perform the functions of the education server 600 shown in FIGS. 3 to 5 by executing programs stored in the memory 630.

The communication unit 620 may include one or more components that allow the education server 600 to communicate with other devices (not shown) and servers (not shown). The other device (not shown) may be a computing device such as the education server 600 or a sensing device, but is not limited thereto. The communication unit 620 may receive a user input from another electronic device or receive data stored in an external device from an external device through a network.

For example, the communication unit 620 may transmit and receive a message to establish a connection with at least one device. The communication unit 620 may transmit information generated by the processor 610 to at least one device connected to the server. The communication unit 620 may receive information from at least one device connected to the education server. The communication unit 620 may transmit information related to the received information in response to information received from at least one device.

The memory 630 may store programs for processing and control of the processor 610. For example, the memory 630 may store information input to a server or information received from another device through a network. Additionally, the memory 630 may store data generated by the processor 610. The memory 630 may store information input to or output from the education server 600.

The memory 630 is a flash memory type, a hard disk type, a multimedia card micro type, a card type memory (for example, SD or XD memory, etc.), and RAM. (RAM, Random Access Memory) SRAM (Static Random Access Memory), ROM (Read-Only Memory), EEPROM (Electrically Erasable Programmable Read-Only Memory), PROM (Programmable Read-Only Memory), magnetic memory, magnetic disk , and may include at least one type of storage medium among optical disks.

The embodiments described above may be implemented with hardware components, software components, and/or a combination of hardware components and software components. For example, the devices, methods, and components described in the embodiments may include, for example, a processor, a controller, an arithmetic logic unit (ALU), a digital signal processor, a microcomputer, and a field programmable gate (FPGA). It may be implemented using one or more general-purpose or special-purpose computers, such as an array, programmable logic unit (PLU), microprocessor, or any other device capable of executing and responding to instructions. A processing device may execute an operating system (OS) and one or more software applications that run on the operating system. Additionally, a processing device may access, store, manipulate, process, and generate data in response to the execution of software. For ease of understanding, a single processing device may be described as being used; however, those skilled in the art will understand that a processing device includes multiple processing elements and/or multiple types of processing elements. It can be seen that it may include. For example, a processing device may include a plurality of processors or one processor and one controller. Additionally, other processing configurations, such as parallel processors, are possible.

Software may include a computer program, code, instructions, or a combination of one or more of these, which may configure a processing unit to operate as desired, or may be processed independently or collectively. You can command the device. Software and/or data may be used on any type of machine, component, physical device, virtual equipment, computer storage medium or device to be interpreted by or to provide instructions or data to a processing device. , or may be permanently or temporarily embodied in a transmitted signal wave. Software may be distributed over networked computer systems and stored or executed in a distributed manner. Software and data may be stored on one or more computer-readable recording media.

The method according to the embodiment may be implemented in the form of program instructions that can be executed through various computer means and recorded on a computer-readable medium. The computer-readable medium may include program instructions, data files, data structures, etc., singly or in combination. Program instructions recorded on the medium may be specially designed and configured for the embodiment or may be known and available to those 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 media such as CD-ROMs and DVDs, and magnetic media such as floptical disks. -Includes optical media (magneto-optical media) and hardware devices specifically configured to store and execute program instructions, such as ROM, RAM, flash memory, etc. Examples of program instructions include machine language code, such as that produced by a compiler, as well as high-level language code that can be executed by a computer using an interpreter, etc. The hardware devices described above may be configured to operate as one or more software modules to perform the operations of the embodiments, and vice versa.

Although the embodiments have been described with limited drawings as described above, those skilled in the art can apply various technical modifications and variations based on the above. For example, the described techniques are performed in a different order than the described method, and/or components of the described system, structure, device, circuit, etc. are combined or combined in a different form than the described method, or other components are used. Alternatively, appropriate results may be achieved even if substituted or substituted by an equivalent.

Therefore, other implementations, other embodiments, and equivalents of the claims also fall within the scope of the claims described below.

Claims (5)

In a dental education system in which an education server provides education data related to dental computer aided design (CAD) and computer aided manufacturing (CAM) using a neural network,
Receive a plurality of learning data including a plurality of oral scan data and data related to a plurality of dental work product production requests from a plurality of dentistry-related servers or a plurality of dental laboratory-related servers, and based on the plurality of learning data A training data determination module that determines training data related to dental CAD and CAM according to learning difficulty through a training model using a neural network, and a dental CAD program created based on the training data related to dental CAD and CAM. an education server including a CAD education module that provides lectures on dental CAD and a CAM education module that provides lectures on a dental CAM program created based on educational data related to the dental CAD and CAM; and
Includes a learner terminal that applies for and receives lectures on the dental CAD program and lectures on the dental CAM program through the education server, and shares information and history information about dentistry and dental labs,
The education server is,
A plurality of keywords related to a plurality of dental CAD programs and a plurality of dental CAM programs are obtained based on web crawling of websites related to a plurality of dental labs, and the dental CAD program and the plurality of dental CAM programs are obtained based on the plurality of keywords. Equipped with a trend detection module for determining the dental CAM program,
Equipped with a training update module that updates training data related to the dental CAD and CAM according to a first test result based on the dental CAD program and a second test result based on the dental CAM program,
Equipped with a grade management module that determines a grade for the learner terminal based on the first test result and the second test result,
Equipped with a recruitment module that matches the learner terminal with at least one of a dentistry or dental laboratory recruiting dental technicians according to the history information and the grade for the learner terminal,
Equipped with a CAD evaluation module that tests dental CAD performance ability based on the dental CAD program and a CAM evaluation module that tests dental CAM performance ability based on lectures on the dental CAM program,
Based on the fact that the first similarity between the first output file generated based on the dental CAD program and the correct answer first output file is less than a preset first similarity, the first test result is determined to pass,
Based on the second similarity between the second output file generated based on the dental CAM program and the correct answer second output file being less than a preset second similarity, the second test result is determined to pass,
The first output file and the second output file are received from the learner terminal to the education server,
The first similarity is the number of shapes constituting the three-dimensional object of the first output file with the correct answer, the number of shapes constituting the three-dimensional object of the first output file, and the three-dimensional object of the first output file with the correct answer. The distance between the center of gravity for the figure and the center of gravity for the figure constituting the three-dimensional object of the first output file and the center of gravity of the three-dimensional object of the correct answer first output file and the 3 of the first output file Determined based on the distance between the centers of gravity for dimensional objects,
The second similarity is the total length of the movement trajectory of the tool in the second output file of the correct answer, the total length of the movement trajectory of the tool in the second output file, and the second answer in the time interval set in the dental CAM program. The difference value between the number of rotations of the tool in the output file and the number of rotations of the tool in the second output file, and the movement speed of the tool in the second output file and the tool in the second output file in the time interval set in the dental CAM program is determined based on the difference in movement speed,
The plurality of keywords include names of the plurality of dental CAD programs, names of the plurality of dental CAM programs, and a plurality of words related to evaluation,
The plurality of words related to the evaluation include a plurality of words related to a positive evaluation, a plurality of words related to a neutral evaluation, and a plurality of words related to a negative evaluation,
A first recognition level for each of the plurality of dental CAD programs and the plurality of dental CAD programs based on the names of the plurality of dental CAD programs, the names of the plurality of dental CAM programs, and a plurality of words related to the evaluation. The second recognition level for each CAM program is determined,
The dental CAD program with the first highest recognition is determined as the dental CAD program, and the dental CAM program with the second highest recognition is determined as the dental CAM program,
Dental education system.
delete According to clause 1,
The lectures for the dental CAD program include multiple lectures on crown dentures, multiple lectures on partial dentures, multiple lectures on complete dentures, multiple lectures on implants, multiple lectures on orthodontic devices, and multiple lectures on laminates. Includes multiple lectures and multiple lectures on A/S (after sales service),
The dental CAM program is linked to the dental CAD program,
The lecture on the dental CAM program is linked to the lecture on the dental CAD program,
It further includes a lecturer terminal that performs a non-face-to-face real-time lecture with the learner terminal through a remote connection program,
As the learner terminal transmits a first message requesting one-on-one lecture to the education server, further comprising a one-on-one education module that matches the learner terminal and the instructor terminal based on the first message and the history information,
It further includes a lecturer terminal that performs a non-face-to-face real-time lecture with the learner terminal through a remote connection program,
As the learner terminal transmits a first message requesting one-on-one lecture to the education server, further comprising a one-on-one education module that matches the learner terminal and the instructor terminal based on the first message and the history information,
Dental education system.
According to clause 1,
It is further provided with a VR education module that implements a plurality of milling equipment in VR (virtual reality) and provides it to the learner terminal,
A second message requesting VR education from the learner terminal is received by the education server,
VR information about the plurality of milling equipment is transmitted to the learner terminal through the VR education module,
Any one of the plurality of milling equipment is virtually operated by the learner terminal based on the VR information,
The plurality of milling equipment includes 3-axis milling equipment, 4-axis milling equipment, 5-axis milling equipment, and 6-axis milling equipment.
Dental education system.
According to clause 1,
The first awareness is determined by the following equation,

In the above equation, a is the first awareness, k is the number of the plurality of dental CAD programs, and t _x is the number of collected words related to the evaluation of the dental CAD program, The g _x is the number of collected words related to a positive evaluation of the dental CAD program, and _{b n _ _} is the number of collected words related to the positive evaluation of, where b _i is the number of collected words related to the negative evaluation of the ith dental CAD program, and n _i is the number of the ith dental CAD program The name of the program is the number of collected numbers,
The second awareness is determined by an equation in which the variables for the dental CAD program are replaced with variables for the dental CAM program,
The neural network includes an input layer, one or more hidden layers, and an output layer,
Each learning data consisting of a plurality of learning data and training data related to dental CAD and CAM is input to the input layer of the neural network, passes through the one or more hidden layers and the output layer, and is output as an output vector, The output vector is input to a loss function layer connected to the output layer, and the loss function layer outputs a loss value using a loss function that compares the output vector with the correct answer vector for each training data, and the neural network Parameters are learned in the direction of decreasing the loss value, and the training model is generated.
Dental education system.

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