Method, apparatus, and program for providing stress information

Abstract

An information providing method according to an embodiment of the present disclosure includes the steps of: generating a stress prediction model trained on stress information according to a schedule of a patient; confirming a schedule of a target patient recorded in a mobile device or a wearable device of the target patient; generating diagnostic data for the target patient from the confirmed schedule; and displaying the diagnostic data.

Description

Methods, devices, and programs for providing stress information

[0001] The present disclosure relates to a method, device, and program for providing stress information.

[0002] Stress represents the feelings of anxiety and threat felt by humans in response to their surroundings. When stressed, the sympathetic nervous system becomes activated, leading to physiological changes such as increased heart rate and breathing. This, in turn, triggers adaptation or resistance to external stimuli.

[0003] (Patent Document 1) Korean Patent Publication No. 10-2525599

[0004] Smart devices or wearable devices input the device user's schedule, and we propose a method to provide the device user's stress information using this schedule and artificial intelligence.

[0005] A method for providing stress information according to one embodiment of the present disclosure may include the steps of: generating a stress prediction model that learns stress information according to a patient's schedule; confirming a schedule of a target patient recorded on a mobile device or wearable device of the target patient; generating diagnostic data for the target patient from the confirmed schedule; and displaying the diagnostic data.

[0006] A device for providing stress information according to one embodiment of the present disclosure may include a control unit that generates a stress prediction model that learns stress information according to a patient's schedule, verifies the schedule of the target patient recorded in a mobile device or wearable device of the target patient, and generates diagnostic data for the target patient from the confirmed schedule; and a display unit that displays the diagnostic data.

[0007] A program for providing stress information, stored in a computer-readable storage medium according to one embodiment of the present disclosure, may be configured to cause a control unit to perform an operation of generating a stress prediction model that learns stress information according to a patient's schedule; an operation of confirming a schedule of a target patient recorded in a mobile device or wearable device of the target patient; an operation of generating diagnostic data for the target patient from the confirmed schedule in the control unit; and an operation of displaying the diagnostic data in a display unit.

[0008] According to various embodiments of the present disclosure, a user (target patient) can easily receive diagnostic information corresponding to his / her brushing performance or dental condition.

[0009] According to various embodiments of the present disclosure, a user (a patient with temporomandibular joint disorder) can easily receive diagnostic information corresponding to his or her mouth opening pattern.

[0010] According to various embodiments of the present disclosure, records of dental examinations received in the past can be accumulated and recorded so that the device user can view them.

[0011] According to various embodiments of the present disclosure, a patient's stress situation can be predicted and related suggestion information can be provided using an application of a device carried by the patient.

[0012] Figure 1 illustrates the configuration of an information providing device according to one embodiment of the present disclosure.

[0013] FIG. 2 illustrates a flowchart of a method for providing dental information according to one embodiment of the present disclosure.

[0014] Figure 3 is a tooth condition image according to one embodiment of the present disclosure.

[0015] FIG. 4 illustrates diagnostic data displayed according to one embodiment of the present disclosure.

[0016] FIG. 5 is a tooth-related image according to one embodiment of the present disclosure.

[0017] FIG. 6 illustrates a flowchart of a method for providing temporomandibular joint disease-related information according to one embodiment of the present disclosure.

[0018] FIG. 7 illustrates a flowchart of a method for providing stress information according to one embodiment of the present disclosure.

[0019] Hereinafter, some embodiments of the present disclosure will be described in detail with reference to exemplary drawings. When adding reference numerals to components in each drawing, identical components may have the same numerals as much as possible even if they are shown in different drawings. In addition, when describing the present embodiments, if it is determined that a detailed description of a related known configuration or function may obscure the gist of the technical idea of ​​the present invention, the detailed description may be omitted. When "includes," "has," "consists of," etc. are used in this specification, other parts may be added unless "only" is used. When a component is expressed in the singular, it may include a case in which the plural is included unless specifically stated otherwise.

[0020] Additionally, terms such as first, second, A, B, (a), (b), etc. may be used to describe components of the present disclosure. These terms are only intended to distinguish the components from other components, and the nature, order, sequence, or number of the components are not limited by the terms.

[0021] In a description of the positional relationship of components, when it is described that two or more components are "connected," "combined," or "connected," it should be understood that the two or more components may be directly "connected," "combined," or "connected," but that the two or more components may also be further "interposed" with another component to be "connected," "combined," or "connected." Here, the other component may be included in one or more of the two or more components that are "connected," "combined," or "connected" to each other.

[0022] In the description of the temporal flow relationship related to components, operation methods, or manufacturing methods, for example, when the temporal or flow relationship is described as “after”, “following”, “next to”, “before”, etc., it may also include cases where it is not continuous, unless “immediately” or “directly” is used.

[0023] Meanwhile, when numerical values ​​or corresponding information for components are mentioned, even without separate explicit description, the numerical values ​​or corresponding information may be interpreted as including an error range that may occur due to various factors.

[0024] Figure 1 illustrates the configuration of an information providing device according to one embodiment of the present disclosure.

[0025] In the embodiment of Fig. 1, the information providing device includes a voice input unit (110), a control unit (120), a display unit (130), a communication unit (140), and a storage unit (150).

[0026] The voice input unit (110) can detect the voice spoken by the target patient. The voice input unit (110) may correspond to, for example, the microphone of a mobile phone (smartphone) or the microphone of a voice examination room. The input unit (110) may also correspond to a bone conduction microphone. When a bone conduction microphone is used alone or in conjunction with a general microphone, noise removal becomes easier.

[0027] The control unit (120) performs the overall control functions of the information providing device. The control unit (120) may be, for example, a processor (CPU or GPU) or an engine. In various embodiments of the present disclosure, the control unit (120) may be located in an external device (e.g., a server). The control unit (120) may perform various operations of the information providing device using programs and data stored in the storage unit (150).

[0028] The display unit (130) can display various contents using the user interface and / or graphical user interface stored in the storage unit (150) under the control of the control unit (120). Here, the contents displayed on the display unit (130) can include various text or image data (including various information data) and a menu screen including data such as icons, list menus, and combo boxes. In addition, the display unit (130) can be a touch screen.

[0029] The display unit (130) may include at least one of a liquid crystal display (LCD), a thin film transistor-liquid crystal display (TFT LCD), an organic light-emitting diode (OLED), a flexible display, a 3D display, and an e-ink display.

[0030] The communication unit (140) can communicate with any internal component or at least one external device through a wired / wireless communication network. Here, wireless Internet technologies include Wireless LAN (WLAN), Digital Living Network Alliance (DLNA), Wireless Broadband (Wibro), World Interoperability for Microwave Access (Wimax), High Speed ​​Downlink Packet Access (HSUPA), High Speed ​​Uplink Packet Access (HSUPA), IEEE 802.16, Long Term Evolution (LTE), Long Term Evolution-Advanced (LTE-A), Wireless Mobile Broadband Service (WMBS), 5G mobile communication service, Bluetooth, Radio Frequency Identification (RFID), Infrared Data Association (IrDA), Ultra Wide Band (UWB), ZigBee, Near Field Communication (NFC), Ultra Sound Communication (USC), Visible Light Communication (VLC), and Wi-Fi. Wi-Fi, Wi-Fi Direct, LoRa (Long Range), etc. may be included, and the technology used in the communication unit (140) is not limited to those exemplified above. Meanwhile, wired communication technologies may include Power Line Communication (PLC), USB communication, Ethernet, serial communication, optical / coaxial cables, etc.

[0031] The storage unit (150) can store programs and data according to various embodiments of the present disclosure. Specifically, the storage unit (150) can store a number of application programs, data for operation, and commands running on the information providing device. At least some of the application programs can be downloaded from an external device via wireless communication. Furthermore, at least some of these application programs may be stored on the information providing device from the time of shipment.

[0032] In addition, the storage unit (150) may include at least one storage medium among a Flash Memory Type, a Hard Disk Type, a Multimedia Card Micro Type, a card type memory (e.g., an SD or XD memory, etc.), a magnetic memory, a magnetic disk, an optical disk, a Random Access Memory (RAM), a Static Random Access Memory (SRAM), a Read-Only Memory (ROM), an Electrically Erasable Programmable Read-Only Memory (EEPROM), and a Programmable Read-Only Memory (PROM).

[0033] In various embodiments of the present disclosure, the information providing device may not include at least some of the components of FIG. 1, and may also further include components(s) not illustrated in FIG. 1. For example, when the information providing device provides tooth-related information, it may further include a voice output unit. The voice output unit may correspond to at least one of a speaker, a buzzer, or a receiver. According to various embodiments of the present disclosure, the information providing device of FIG. 1 may correspond to at least one of a device for providing tooth-related information, a device for providing temporomandibular joint disease-related information, and a device for providing stress information. According to various embodiments of the present disclosure, the information providing device of FIG. 1 may perform at least one of a method for providing tooth-related information, a method for providing temporomandibular joint disease-related information, or a method for providing stress information, which will be described later.

[0034] FIG. 2 illustrates a flowchart of a method for providing dental information according to one embodiment of the present disclosure.

[0035] In step 210, the control unit of the device providing dental information directly creates a diagnostic prediction model based on diagnostic information learned from tooth brushing performance or dental condition, or the communication unit receives this model from a server containing the model via wired or wireless communication. This diagnostic prediction model can be trained using artificial intelligence, such as machine learning or deep learning. This training can be performed by inputting brushing performance information or dental condition images along with corresponding diagnostic data.

[0036] In step 220, the control unit inputs tooth brushing performance information or a tooth condition image of the target patient into the diagnostic prediction model. Here, the tooth brushing performance information may include at least one of tooth brushing duration information and tooth brushing frequency information.

[0037] Figure 3 is a tooth condition image according to one embodiment of the present disclosure.

[0038] The above tooth condition image may include one or more photographs. The above tooth condition image may include one or more videos. The above tooth condition image may include information about residual plaque.

[0039] When a tooth condition image is stored, the diagnostic data may include an image with graphic effects indicating diagnostic content added to the pre-stored image.

[0040] In various embodiments of the present disclosure, toothbrushing performance information or dental condition images can be input through the operation of a first device pre-positioned at a toothbrushing location used by the subject patient and a second device carried by the subject patient. In various embodiments of the present disclosure, the second device may correspond to a device that provides tooth-related information. In various embodiments of the present disclosure, the second device may correspond to a mobile phone, a smartwatch, or a wearable device.

[0041] In one embodiment, the first device may include at least one of an Internet of Things sensor pre-installed on a toothbrush used by the subject patient, a QR code attached to a place where the subject patient brushes his / her teeth, and an NFC sticker. The Internet of Things sensor may include at least one of a button, a microphone, a camera, and a keyboard. Even when brushing his / her teeth with a regular toothbrush without a smart function, information about the brushing performance can be input through the first device. In one embodiment, the second device may include at least one of a mobile device or a wearable device including a display.

[0042] In various embodiments of the present disclosure, when the subject patient tags the second device to the first device, the number of brushing strokes and / or the starting and stopping of a stopwatch may be performed. Tagging the second device to the first device may include pressing a button of the first device and / or a button (start button or end button) of the second device. The start button and the end button may be physical buttons of the first device or the second device, or may be buttons displayed on a screen of the first device or the second device. Tagging the second device to the first device may include inputting a predetermined voice (e.g., “start” or “end”) into a microphone of the first device or the second device.

[0043] In step 230, diagnostic data is generated from the toothbrushing performance information or dental condition images of the target patient using the diagnostic prediction model. The process of generating the diagnostic data may include analyzing the dental condition images.

[0044] In various embodiments of the present disclosure, the toothbrushing performance information or dental condition image may be encrypted. This encrypted toothbrushing performance information or dental condition image may be transmitted to a server storing a diagnostic prediction model. Blockchain technology may be used for this encryption. The server, which generates diagnostic data using the diagnostic prediction model, may encrypt the diagnostic data and transmit it to a device that provides dental information.

[0045] In step 240, the diagnostic data is displayed. The diagnostic data may include a diagnostic result regarding at least one of the duration of the brushing and the number of times the brushing was performed. If the display of the diagnostic data is guided by voice, guidance messages such as “Please brush the upper right molars more,” “Please brush the upper front teeth,” “It has been 3 minutes since you started brushing,” “The time spent brushing the front teeth is too short,” “The total brushing time is 1 minute and 50 seconds. Let’s brush more thoroughly,” and “The left side is brushed thoroughly without missing anything, but the time spent brushing the inside of the right molars is a bit short.” may be output through the screen or speaker. The diagnostic data may include information regarding the direction of improvement derived from the analysis of the tooth condition image.

[0046] According to various embodiments of the present disclosure, the displayed diagnostic data can be changed in real time based on the subject patient's brushing. For example, a stopwatch can be operated to continuously guide the duration of brushing.

[0047] FIG. 4 illustrates diagnostic data displayed according to one embodiment of the present disclosure.

[0048] The diagnostic data displayed in the example of FIG. 4 is a tooth condition image, and characters are superimposed on each tooth with dental plaque. According to various embodiments of the present disclosure, the characters for teeth where dental plaque has disappeared through brushing can be changed to disappear in real time according to brushing. In one embodiment, when real-time brushing information is collected through a camera of the first device and transmitted through a communication unit of the second device, or when collected directly through the camera of the second device, the diagnostic data can be displayed in real time on the display unit of the second device.

[0049] FIG. 5 is a tooth-related image according to one embodiment of the present disclosure.

[0050] In various embodiments of the present disclosure, a tooth-related image may be displayed by the display unit of FIG. 1. In various embodiments of the present disclosure, the tooth-related image may include one or more photographs or one or more videos.

[0051] When a user of the device selects (e.g., clicks) a tooth in a dental image, the display unit of FIG. 1 may additionally display information (e.g., history) about the tooth. The treatment may include, for example, root canal treatment or tooth extraction. The information about the tooth may include, for example, the type of treatment, the time of treatment, the design and / or manufacturer of the medical device attached to the treatment, and whether the treatment is covered by insurance. The medical device may include, for example, an implant, a gold crown, or a denture. The information about the tooth may be entered directly by the patient or may be entered by at least one of the hospitals visited by the patient.

[0052] By displaying additional information about the tooth, the device user can conveniently view information entered by others. For example, this can allow the user to check the post-care status of a patient with mild symptoms after being referred to a higher-level hospital. Furthermore, in cases where a patient with a systemic disease is referred to a higher-level hospital for invasive treatment, such as tooth extraction, the user can view the treatment records and follow-up on subsequent non-invasive treatments, such as periodontal care or simple restorative treatment, at their personal dental clinic.

[0053] FIG. 6 illustrates a flowchart of a method for providing temporomandibular joint disease-related information according to one embodiment of the present disclosure.

[0054] In step 610, a diagnostic prediction model that has learned diagnostic information according to temporomandibular joint disorder may be generated or received. Specifically, a device that provides information related to temporomandibular joint disorder directly generates a diagnostic prediction model that has learned diagnostic information according to the mouth opening pattern of a target patient, or receives the model from a server that holds the model through wired or wireless communication. This diagnostic prediction model may be learned using artificial intelligence such as machine learning or deep learning. This learning may be performed by inputting images of the mouth opening pattern (multiple photos or videos) together with corresponding diagnostic data. The mouth opening pattern may include information on whether the mandible returns to the central position when the mouth is opened, whether there is interference with condyle movement, and signs of anterior disc displacement (ADD). Here, ADD may correspond to either with reduction or without reduction.

[0055] At step 620, information on the temporomandibular joint status of the target patient (e.g., an image of the mouth opening pattern) can be input into the above diagnostic prediction model.

[0056] In step 630, diagnostic data for the temporomandibular joint condition image of the target patient can be generated using the above diagnostic prediction model. The process of generating the diagnostic data may include analyzing the mouth opening pattern.

[0057] At step 640, the above diagnostic data can be displayed. In one embodiment, an alarm (first alarm) such as “You relaxed your jaw even this morning, right?” or “It’s time for physical therapy” can be delivered via voice or screen output. Instead of or in addition to delivering the alarm, the control unit can control the playback of at least one of the predetermined videos (e.g., a physical therapy training video). Based on events such as playing the video, playing it for a certain amount of time, or playing it to the end, it can be determined whether the user has performed the exercise. If the control unit determines that the user has not performed the exercise, it can deliver an alarm (second alarm) to remind the user of this.

[0058] The process of displaying the above diagnostic data may include a process of displaying an alarm (e.g., “It is time for temporomandibular joint exercise”) so that the patient can perform temporomandibular joint exercise at a certain time. The process of displaying the above diagnostic data may include a process of guiding isometric exercises. The isometric exercises may include jaw lateral deviation and / or jaw protrusion. The jaw lateral deviation may include, for example, an exercise of placing the fingers on the sides of the jaw and gently moving the jaw toward the fingers while providing slight resistance with the fingers. The jaw protrusion may include, for example, an exercise of placing the fingers in front of the jaw and pulling the jaw forward while providing resistance with the fingers. The process of guiding the isometric exercises may include a process of guiding the number of repetitions, the holding time, the number of sets, and the number of times performed per day.

[0059] FIG. 7 illustrates a flowchart of a method for providing stress information according to one embodiment of the present disclosure.

[0060] At step 710, the control unit may generate or receive a stress prediction model that has learned stress information based on the patient's schedule. The patient's schedule may, for example, include exercise, events, meetings, holidays, thesis preparation, or assignments. Specifically, a device providing stress-related information directly generates a stress prediction model that has learned stress information based on one or more patients' schedules, or receives the model from a server containing the model via wired or wireless communication. This stress prediction model may be learned using artificial intelligence, such as machine learning or deep learning.

[0061] Learning of stress information according to a patient's schedule can be performed by inputting each schedule of the patient along with corresponding stress information. In various embodiments of the present disclosure, the patient's schedule can be extracted by performing natural language processing on the contents (including words, phrases, or sentences) of one or more applications (e.g., a schedule management APP or a memo APP) running on a mobile device (e.g., a smartphone) or wearable device (e.g., a smartwatch) used by the patient. In various embodiments of the present disclosure, the stress prediction model can be learned using each schedule and its corresponding category as input data.

[0062] The above categories may be categorized based on one or more criteria. In one embodiment, they may be categorized based on stress level or stress type, and in another embodiment, they may be categorized based on the time at which stress level and diagnostic data are displayed (e.g., one hour before the start of the event). Each category may have one or more corresponding words, phrases, and / or sentences. For example, Category 1 may correspond to "Health," "Strength," and "Anaerobic."

[0063] At step 720, the control unit can check the schedule of the target patient recorded in one or more applications of the target patient's mobile device or wearable device.

[0064] At step 730, the control unit can input the confirmed schedule into the generated or received stress prediction model to generate diagnostic data for the target patient.

[0065] At step 740, the display unit may display the diagnostic data. This may include, in various embodiments of the present disclosure, providing at least one of one or more different alarms corresponding to the diagnostic data. Each alarm may correspond to one or more categories.

[0066] In various embodiments of the present disclosure, the diagnostic data may include information related to the suggested behavior for the subject patient. For example, if the identified schedule is a test, the diagnostic data may include a link to a specific video. Alternatively, if the identified schedule is swimming, the diagnostic data may include a list of activities required for swimming.

[0067] The above category may include information regarding the timing of displaying the diagnostic data. In this case, displaying the diagnostic data may be performed based on information regarding the timing of displaying the diagnostic data, which is input data for learning the stress prediction model. If displaying the diagnostic data provides two or more alarms, the timing of providing each alarm may be the same or may be handled differently depending on the timing of displaying the diagnostic data.

[0068] In various embodiments of the present disclosure, the information providing device may be a single stand-alone device, or may include multiple computing devices operating in a distributed environment comprising multiple computing devices cooperating with each other via a communications network.

[0069] Meanwhile, the information provider may be a quantum computing device, rather than a classical computing device. Quantum computing devices perform calculations on qubits, not bits. Qubits can be in a superposition of 0 and 1 simultaneously, and with M qubits, they can express 2^M states simultaneously.

[0070] Quantum computing devices can use various types of quantum gates (e.g., Pauli / Rotation / Hadamard / CNOT / SWAP / Toffoli) that input one or more qubits to perform quantum operations and perform designated operations, and can combine quantum gates to form quantum circuits that perform special functions.

[0071] Quantum computing devices can use quantum artificial neural networks (e.g. QCNN, QGRNN) that can perform functions performed by conventional artificial neural networks (e.g. CNN, RNN) at a faster speed while using fewer parameters.

[0072] The artificial intelligence model described in these embodiments may be a current or future machine learning model, such as a model that performs algorithm-based machine learning operating on the aforementioned computing device or a model that performs artificial neural network-based learning.

[0073] Models that perform algorithm-based machine learning can be classical machine learning models such as tree-based models, k-Nearest Neighbors, k-Means Clustering, Principal Component Analysis (PCA), and support vector machines (SVM).

[0074] A tree-based model can be, for example, a decision tree model, a regression model, or a random tree model.

[0075] Meanwhile, an artificial intelligence model can be an ensemble model that solves problems by training and combining multiple models rather than using just one trained model.

[0076] Ensemble models combine multiple individually trained models to prevent overfitting and improve generalization performance. Ensemble models can be helpful in improving performance when the performance of individual models is not sufficient.

[0077] Ensemble models can be broadly divided into voting and boosting methods.

[0078] Voting methods derive a final result through voting on the results generated by multiple models. Examples include bagging, which combines algorithms of the same type but trains them on different data sets, and voting, which combines different types of algorithms.

[0079] Boosting is a method of combining weak machine learning models to create a more accurate and powerful model. Boosting involves sequentially performing tasks on each weak machine learning model, with subsequent models exploring additional areas missed by the previous models. Examples of boosting methods include random forests, gradient boosting, and XGBoost (eXtra Gradient Boost).

[0080] An artificial neural network (ANN) is a machine learning algorithm that analyzes and learns complex data based on a large number of interconnected artificial neurons, mimicking the operating principles of the human brain. An ANN can be any type of ANN, including the multilayer perceptron (MLP), the most basic ANN structure consisting of an input layer, a hidden layer, and an output layer; a convolutional neural network (CNN), which performs convolution operations to extract image features and reduces dimensionality through pooling operations; and a recurrent neural network (RNN), an ANN structure used to process ordered data. These ANNs can be modified in various ways depending on the complexity and diversity of the data.

[0081] A model that has undergone learning based on an artificial neural network can also be an ensemble model that solves problems by learning multiple models and combining them rather than learning just one model.

[0082] Meanwhile, algorithm-based machine learning models and models trained using artificial neural networks can be used complementarily. For example, an algorithm-based machine learning model can use the results of an artificial neural network-based model, and vice versa. An ensemble model combining algorithm-based machine learning models and artificial neural network-based models can also be used.

[0083] The embodiments described above may be implemented through various means. For example, the embodiments may be implemented through hardware, firmware, software, or a combination thereof.

[0084] In the case of hardware implementation, the method for providing tooth-related information, the method for providing temporomandibular joint disease-related information, or the method for providing stress information according to the present embodiments may be implemented by one or more ASICs (Application Specific Integrated Circuits), DSPs (Digital Signal Processors), DSPDs (Digital Signal Processing Devices), PLDs (Programmable Logic Devices), FPGAs (Field Programmable Gate Arrays), processors, controllers, microcontrollers, or microprocessors.

[0085] Artificial intelligence learning according to various embodiments of the present disclosure can be implemented using an artificial intelligence semiconductor device in which neurons and synapses of a deep neural network are implemented using semiconductor devices. The semiconductor devices may be currently used semiconductor devices, such as SRAM, DRAM, or NAND, or may be next-generation semiconductor devices, such as RRAM, STT MRAM, or PRAM, or may be a combination thereof.

[0086] When implementing artificial intelligence learning according to the embodiments using a semiconductor device, the results (weights) of learning a deep learning model using software can be transferred to synapse-mimicking elements arranged in an array, or learning can be performed on the semiconductor device.

[0087] In the case of implementation by firmware or software, the method for providing dental information, the method for providing temporomandibular joint disease-related information, or the method for providing stress information according to the present embodiments may be implemented in the form of a device, procedure, or function that performs the functions or operations described above. The software code may be stored in a memory unit and executed by a processor. The memory unit may be located inside or outside the processor and may exchange data with the processor by various means already known.

[0088] Additionally, terms such as "system," "processor," "controller," "component," "module," "interface," "model," or "unit" as described above may generally refer to a computer-related entity, such as hardware, a combination of hardware and software, software, or software in execution. For example, the aforementioned components may be, but are not limited to, a process driven by a processor, a processor, a controller, a control processor, an object, a thread of execution, a program, and / or a computer. For example, both an application running on a controller or a processor and the controller or the processor may be components. One or more components may be within a process and / or thread of execution, and the components may be located on a single device (e.g., a system, a computing device, etc.) or distributed across two or more devices.

[0089] Meanwhile, another embodiment provides a computer program stored on a computer storage medium that performs the method for providing dental information, the method for providing temporomandibular joint disease-related information, or the method for providing stress information. Furthermore, another embodiment provides a computer-readable storage medium storing a program for implementing the method for providing dental information, the method for providing temporomandibular joint disease-related information, or the method for providing stress information. The program recorded on the storage medium can be read, installed, and executed by a computer, thereby executing the steps described above.

[0090] In this way, in order for a computer to read a program recorded on a recording medium and execute functions implemented as a program, the above-mentioned program may include code coded in a computer language such as Python, C, C++, JAVA, or machine language that can be read by the computer's processor (CPU) through the computer's device interface.

[0091] Such code may include functional code related to functions defining the aforementioned functions, and may also include control code related to execution procedures required for the computer's processor to execute the aforementioned functions according to a predetermined procedure.

[0092] Additionally, such code may further include memory reference related code regarding where in the internal or external memory of the computer the additional information or media required for the computer's processor to execute the aforementioned functions should be referenced.

[0093] Additionally, if the computer's processor needs to communicate with another computer or server located remotely in order to execute the functions described above, the code may further include communication-related code regarding how the computer's processor should communicate with another computer or server located remotely using the computer's communication module, and what information or media should be sent and received during the communication.

[0094] The computer-readable recording medium that records the program as described above includes, for example, ROM, RAM, CD-ROM, magnetic tape, floppy disk, optical media storage device, etc., and may also include one implemented in the form of a carrier wave (e.g., transmission via the Internet).

[0095] Additionally, computer-readable recording media can be distributed across network-connected computer systems, allowing computer-readable code to be stored and executed in a distributed manner.

[0096] In addition, the functional program for implementing the present invention and the code and code segments related thereto may be easily inferred or changed by programmers in the technical field to which the present invention belongs, taking into consideration the system environment of the computer that reads the recording medium and executes the program.

[0097] The method for providing dental information, the method for providing temporomandibular joint disease-related information, or the method for providing stress information described above may also be implemented in the form of a recording medium including computer-executable instructions, such as an application or program module executed by a computer. The computer-readable medium may be any available medium that can be accessed by a computer, and includes both volatile and nonvolatile media, removable and non-removable media. In addition, the computer-readable medium may include all computer storage media. The computer storage media includes both volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storing information, such as computer-readable instructions, data structures, program modules, or other data.

[0098] The method for providing the aforementioned dental information, the method for providing temporomandibular joint disease-related information, or the method for providing stress information may be executed by an application that is installed by default on the terminal (which may include a program included in a platform or operating system installed by default on the terminal), or may be executed by an application (i.e., a program) directly installed on the master terminal by the user through an application providing server such as an application store server, an application, or a web server related to the service. In this sense, the method for providing the aforementioned dental information, the method for providing temporomandibular joint disease-related information, or the method for providing stress information may be implemented by an application (i.e., a program) that is installed by default on the terminal or directly installed by the user, and may be recorded on a computer-readable recording medium such as on the terminal.

[0099] The foregoing description of the present invention is for illustrative purposes only, and those skilled in the art will readily appreciate that the present invention can be readily modified into other specific forms without altering the technical spirit or essential characteristics of the present invention. Therefore, the embodiments described above should be understood as illustrative in all respects and not restrictive. For example, each component described as a single entity may be implemented in a distributed manner, and similarly, components described as distributed may be implemented in a combined manner.

[0100] The scope of the present invention is indicated by the claims described below rather than the detailed description above, and all changes or modifications derived from the meaning and scope of the claims and their equivalent concepts should be interpreted as being included in the scope of the present invention.

[0101] The above description is merely an illustrative example of the technical idea of ​​the present disclosure, and those skilled in the art to which the present disclosure pertains will appreciate that various modifications and variations can be made without departing from the essential characteristics of the technical idea of ​​the present disclosure. In addition, the present embodiments are not intended to limit the technical idea of ​​the present disclosure but rather to explain it, and therefore the scope of the technical idea of ​​the present disclosure is not limited by these embodiments. The scope of protection of the present disclosure should be interpreted by the claims below, and all technical ideas within a scope equivalent thereto should be interpreted as being included within the scope of the rights of the present disclosure.

[0102]

[0103] CROSS-REFERENCE TO RELATED APPLICATION

[0104] This patent application claims priority under 35 USC §119(a) to Korean Patent Application No. 10-2023-0148172, filed in Korea on October 31, 2023, and Korean Patent Application No. 10-2024-0087281, filed in Korea on July 3, 2024, the entire contents of which are incorporated herein by reference. In addition, this patent application claims priority in countries other than the United States for the same reasons, the entire contents of which are incorporated herein by reference.

Claims

1. In a method performed by a device providing stress information, In the control unit, a step of creating a stress prediction model that learns stress information according to the patient's schedule; In the above control unit, a step of checking the schedule of the target patient recorded in the mobile device or wearable device of the target patient; In the above control unit, a step of generating diagnostic data for the target patient from the confirmed schedule; and A method for providing stress information, comprising: a step of displaying the diagnostic data in a display unit; 2. In claim 1, A method for providing stress information, wherein the patient's schedule is extracted through natural language processing for one or more applications performed on a mobile device or wearable device used by the patient.

3. In claim 1, A method for providing stress information, wherein displaying the above diagnostic data is performed based on information about the time at which the above diagnostic data is displayed, which is input data for learning the above stress prediction model.

4. In claim 1, A method for providing stress information, wherein the above diagnostic data includes information related to actions suggested to the target patient.

5. In claim 1, A method for providing stress information, wherein the step of displaying the diagnostic data comprises providing one of one or more different alarms corresponding to the diagnostic data.

6. In a device that provides stress information, A control unit that creates a stress prediction model that learns stress information according to the patient's schedule, checks the schedule of the target patient recorded in the target patient's mobile device or wearable device, and generates diagnostic data for the target patient from the checked schedule; and A stress information providing device including a display unit for displaying the above diagnostic data.

7. In claim 6, The control unit is a stress information providing device that extracts the patient's schedule through natural language processing for one or more applications performed on a mobile device or wearable device used by the patient.

8. In claim 6, A stress information providing device, wherein the display unit displays the diagnostic data based on information about the time at which the diagnostic data, which is input data for learning the stress prediction model, is displayed.

9. In claim 6, A stress information providing device, wherein the above diagnostic data includes information related to actions suggested to the target patient.

10. In claim 6, A stress information providing device, wherein the display unit provides one or more different alarms corresponding to the diagnostic data.

11. In a program recorded on a computer-readable storage medium and providing stress information, the program, An action to create a stress prediction model that learns stress information according to the patient's schedule; The action of checking the schedule of the target patient recorded on the target patient's mobile device or wearable device; An operation of generating diagnostic data for the target patient from the above-mentioned confirmed schedule; and A program recorded on a computer-readable storage medium that executes an action of displaying the above diagnostic data.

Images