KR20240175303A - Method and apparatus for managing symptom in user customized manner - Google Patents

Abstract

The present disclosure relates to a user-tailored symptom management method. The user-tailored symptom management method includes a step of transmitting questionnaire data for classifying a user's mental illness to a user terminal, a step of receiving response data to the questionnaire data transmitted from the user terminal, a step of determining a mental illness code of the user based on the received response data, a step of determining an artificial intelligence model for predicting the user's symptoms based on the determined mental illness code of the user, and a step of predicting the user's symptoms using the determined artificial intelligence model.

Description

METHOD AND APPARATUS FOR MANAGING SYMPTOM IN USER CUSTOMIZED MANNER

The present disclosure relates to a user-tailored symptom management method and device, and more specifically, to a user-tailored symptom management method and device that predicts and manages a user's mental or psychological abnormalities.

Recently, the number of people suffering from mental illnesses such as depression and panic disorder is increasing due to fatigue in social life. Accordingly, various technologies are being developed to diagnose the user's condition and notify the diagnosis results as a method for managing and alleviating mental illness. However, it is difficult to immediately improve the user's condition by only notifying the diagnosis results to a professional counselor or doctor, and there is a problem that directly notifying the user of the diagnosis results may further worsen the user's unstable psychological state.

The present disclosure provides a user-tailored symptom management method, a computer program stored on a computer-readable medium, a computer-readable medium storing the computer program, and a device (system).

The problems to be solved by the present disclosure are not limited to the problems described above, and other problems not mentioned will be clearly understood by those skilled in the art from the description below.

A method for user-tailored symptom management performed by at least one processor according to one embodiment of the present disclosure includes a step of transmitting questionnaire data for classifying a mental illness of a user to a user terminal, a step of receiving response data for the questionnaire data transmitted from the user terminal, a step of determining a mental illness code of the user based on the received response data, a step of determining an artificial intelligence model for predicting the symptoms of the user based on the determined mental illness code of the user, and a step of predicting the symptoms of the user using the determined artificial intelligence model.

A method for managing user-tailored symptoms according to one embodiment of the present disclosure further includes a step of receiving user data representing changes in the user's body and activities and external environment data representing the user's surrounding environment from a user terminal. The step of predicting the user's symptoms using the determined artificial intelligence model includes a step of predicting the user's symptoms by inputting the user data and external environment data into the artificial intelligence model.

A method for managing user-tailored symptoms according to one embodiment of the present disclosure further includes a step of receiving home appliance usage data indicating whether the user uses the home appliance and the usage time from the user terminal. The step of predicting the user's symptoms using the determined artificial intelligence model includes a step of predicting the user's symptoms by inputting user data, external environment data, and home appliance usage data into the artificial intelligence model.

According to one embodiment of the present disclosure, a step of predicting a user's symptoms by inputting user data, external environment data, and home appliance usage data into an artificial intelligence model includes a step of refining the artificial intelligence model based on learning data associated with the user to determine the user's lifestyle pattern, a step of inputting the user data, external environment data, and home appliance usage data into the refined artificial intelligence model to extract outliers that do not correspond to the determined lifestyle pattern, and a step of predicting whether or not the user's symptoms will occur by using the extracted outliers.

A step of predicting whether a user's symptoms are expressed by using an extracted outlier according to one embodiment of the present disclosure includes a step of calculating a symptom score corresponding to the outlier, and a step of predicting whether the user's symptoms are expressed based on whether the calculated symptom score is equal to or greater than a first threshold.

A user-tailored symptom management method according to one embodiment of the present disclosure further includes a step of transmitting message data including data on whether a symptom has been predicted to occur to an external device when the calculated symptom score is equal to or greater than a second threshold.

A user-tailored symptom management method according to one embodiment of the present disclosure further includes a step of determining a user's preferred content type based on response data and user data, a step of extracting at least one content based on the determined user's preferred content type when symptom onset is predicted, and a step of transmitting the extracted at least one content to a user terminal.

A step of transmitting at least one extracted content according to an embodiment of the present disclosure to a user terminal includes, when an outlier that does not correspond to the user's lifestyle pattern is extracted by an artificial intelligence model, a step of calculating a time interval in which the outlier occurred, and a step of transmitting at least one content to the user terminal based on the calculated time interval.

According to one embodiment of the present disclosure, a computer program stored in a computer-readable recording medium is provided for executing the above-described method on a computer.

A computing device according to one embodiment of the present disclosure includes a communication module, a memory, and at least one processor connected to the memory and configured to execute at least one computer-readable program included in the memory. The at least one program includes commands for transmitting questionnaire data for classifying a mental illness of a user to a user terminal, receiving response data for the questionnaire data transmitted from the user terminal, determining a mental illness code of the user based on the received response data, determining an artificial intelligence model for predicting a symptom of the user based on the determined mental illness code of the user, and predicting the symptom of the user using the determined artificial intelligence model.

In various embodiments of the present disclosure, a computing device can utilize an artificial intelligence model to predict possible symptoms in advance by utilizing various data associated with a user, and indirectly provide content to stabilize the symptoms, thereby stabilizing an unstable state of the user.

In various embodiments of the present disclosure, a computing device can effectively determine a user's mental illness code for providing a user-tailored symptom management service simply by receiving a response to the questionnaire data.

In various embodiments of the present disclosure, a computing device can alleviate a user's symptoms in a natural manner by extracting and providing appropriate content that can draw the user's attention without providing the user with direct information about the symptoms.

The effects according to the present disclosure are not limited to the effects described above, and other effects not mentioned will be clearly understood by those skilled in the art from the description below.

FIG. 1 is a functional block diagram showing the internal configuration of a computing device according to one embodiment of the present disclosure.
FIG. 2 is a schematic diagram showing a configuration in which multiple user terminals using a customized symptom management service according to one embodiment of the present disclosure are connected to a computing device so that they can communicate with each other.
FIG. 3 is a block diagram showing the internal configuration of a user terminal and a computing device according to one embodiment of the present disclosure.
FIG. 4 illustrates an example of displaying questionnaire data on a display of a user terminal according to one embodiment of the present disclosure.
FIG. 5 illustrates an example of content being displayed on a display of a user terminal according to one embodiment of the present disclosure.
FIG. 6 is a flowchart illustrating an example of a computing device and a user terminal exchanging data and/or information necessary to determine an artificial intelligence model corresponding to a user's mental illness code according to one embodiment of the present disclosure.
FIG. 7 is a flowchart illustrating an example of a computing device, a user terminal, and an external device exchanging data and/or information necessary to alleviate a user's symptoms according to one embodiment of the present disclosure.
FIG. 8 is a flowchart illustrating an example of a user-customized symptom management method according to one embodiment of the present disclosure.

Hereinafter, exemplary embodiments according to the present invention will be described in detail with reference to the contents described in the attached drawings. However, the present invention is not limited or restricted by the exemplary embodiments. Unless otherwise defined, all terms (including technical and scientific terms) used in this specification will be used with a meaning that can be commonly understood by a person having ordinary knowledge in the technical field to which the present disclosure belongs, but this may vary depending on the intention or precedent of a technician engaged in the field, the emergence of new technologies, etc.

In addition, terms defined in commonly used dictionaries are not to be interpreted ideally or excessively unless explicitly specifically defined. In certain cases, there are terms that the applicant has arbitrarily selected, and in this case, the meaning thereof will be described in detail in the relevant description section. Accordingly, the terms used in this disclosure should be defined based on the meaning of the term and the overall content of this disclosure, rather than simply the name of the term.

Throughout this specification, when a part is said to "comprise" a certain element, it is intended that other elements may be included, rather than excluding other elements unless specifically stated otherwise. Furthermore, the singular forms used herein also include plural forms unless specifically stated otherwise. Furthermore, the expression "at least one of a, b, and/or c" used throughout this specification can encompass "a alone," "b alone," "c alone," "a and b," "a and c," "b and c," or "all of a, b, and c."

Meanwhile, terms such as "first and/or second" used in this specification may be used to describe various components, but this is only used for the purpose of distinguishing one component from another component, and is not intended to be limited to the component referred to by the terms. For example, without departing from the scope of the rights of the present invention, the first component may be referred to as the second component, and the second component may also be referred to as the first component.

In addition, terms such as “… unit”, “… module”, etc. described in this specification mean a unit that processes at least one function or operation, which may be implemented by hardware or software, or a combination of hardware and software. In addition, embodiments of the present disclosure in this specification may be represented by functional block configurations and various processing steps. These functional blocks may be implemented by various numbers of hardware or/and software configurations that execute specific functions. For example, embodiments of the present disclosure may employ direct circuit configurations such as memory, processing, logic, look-up tables, etc. that may execute various functions under the control of one or more microprocessors or other control devices.

In an embodiment according to the present disclosure, a function related to artificial intelligence may be implemented through a processor and a memory. At this time, the processor may be any one of a general-purpose processor such as a CPU (Center Processing Unit), an AP (Application Processor), a DSP (Digital Signal Processor), a graphic-only processor such as a GPU (Graphics Processing Unit), a VPU (Vision Processing Unit), and an artificial intelligence-only processor such as an NPU (Neural Network Processing Unit). The processor may process input data according to a predefined operation rule or artificial intelligence model stored in the memory. Alternatively, when the processor is an artificial intelligence-only processor, the artificial intelligence-only processor may be designed with a hardware structure specialized for processing a specific artificial intelligence model. In some embodiments according to the present disclosure, the function related to artificial intelligence may be implemented through a plurality of processors.

In an embodiment according to the present disclosure, a predefined operation rule or an artificial intelligence model may be configured to perform machine learning. Here, being configured to perform machine learning means that the predefined operation rule or the artificial intelligence model is configured to perform a desired characteristic (or purpose) by learning using a plurality of learning data based on a learning algorithm. Such learning may be performed in the device itself in which the artificial intelligence according to the present disclosure is implemented, or may be performed through a separate server and/or system.

The artificial intelligence model can be implemented as a neural network (or artificial neural network), and can operate based on a statistical learning algorithm that imitates biological nerves in machine learning and cognitive science. A neural network can mean a model in which artificial neurons (nodes) that form a network by combining synapses change the strength of the synapses through learning and have problem-solving capabilities. A neural network can be composed of multiple neural network layers, and for example, a neural network can include an input layer, a hidden layer, and an output layer. Each of the multiple neural network layers can include at least one node and at least one weight, and can perform neural network operations through operations between the operation results of the previous (precious) layer and the weights. At least one weight of the multiple neural network layers can be optimized by the learning results of the artificial intelligence model. For example, at least one weight can be updated so that the loss value or cost value obtained from the artificial intelligence model is reduced or minimized during the learning process. Neural networks can infer the desired outcome from arbitrary input.

The learning method of an artificial intelligence model can be divided into supervised learning, in which input data and output data are provided as training data, so that the correct answer (output data) corresponding to the problem (input data) is determined, unsupervised learning, in which only input data is provided without output data, so that the correct answer (output data) corresponding to the problem (input data) is not determined, and reinforcement learning, in which a reward is given whenever an action is taken in the current state, and learning is performed in the direction of maximizing this reward. Alternatively, they can be divided according to the architecture, which is the structure of the learning model.

In an embodiment of the present disclosure, the artificial intelligence model is a CNN (Convolution Neural Network) such as GoogleNet, AlexNet, VGG Network, R-CNN (Region with Convolution Neural Network), RPN (Region Proposal Network), RNN (Recurrent Neural Network), S-DNN (Stacking-based deep Neural Network), S-SDNN (State-Space Dynamic Neural Network), Deconvolution Network, DBN (Deep Belief Network), RBM (Restrcted Boltzman Machine), Fully Convolutional Network, LSTM (Long Short-Term Memory) Network, Classification Network, Generative Modeling, eXplainable AI, Continual AI, Representation Learning, AI for Material Design, BERT, SP-BERT, MRC/QA for natural language processing, Text Analysis, Dialog System, GPT-3, GPT-4, Visual Analytics for vision processing, Visual Understanding, Video Synthesis, ResNet for data intelligence, Anomaly Detection, Prediction, Time-Series Forecasting, Optimization, At least one of various artificial intelligence structures and algorithms such as Recommendation, Data Creation, etc. may be used, and the above-described examples are merely listing examples of artificial intelligence structures and algorithms used according to embodiments of the present disclosure, and do not limit the artificial intelligence structures and algorithms used according to embodiments of the present disclosure.

Hereinafter, various embodiments of the present disclosure will be described in detail with reference to the accompanying drawings. In describing the embodiments, descriptions of technical contents that are well known in the technical field to which the present invention belongs and are not directly related to the present invention will be omitted. This is to convey the gist of the present invention more clearly without obscuring unnecessary descriptions. For the same reason, some components in the accompanying drawings are exaggerated, omitted, or schematically illustrated. In addition, the size of each component does not entirely reflect the actual size. In this specification, the same reference numerals may refer to the same or corresponding components throughout.

FIG. 1 is a functional block diagram showing the internal configuration of a computing device (100) according to an embodiment of the present disclosure. According to an embodiment, the computing device (100) is any device for predicting a user's mental and psychological abnormalities and performing user-tailored symptom management, and may include, for example, a data communication module (102), a disease determination module (104), a symptom prediction module (106), a content determination module (108), etc. In addition, the computing device (100) may communicate with a user terminal (110) and an external device (120) and exchange any data and/or information necessary for performing user-tailored symptom management. Here, the user terminal (110) may refer to a terminal of a user who receives a service, and the external device (120) may refer to a device associated with a hospital, an emergency center, etc.

According to one embodiment, the computing device (100) can communicate with the user terminal (110), recognize the user's mental illness, and exchange data and/or information for predicting symptoms associated with the mental illness. For example, the computing device (100) can obtain, from the user terminal (110), not only the user's body data including height and weight, but also data related to mental illness such as the user's medical history, medication history, and treatment period, tendency data, health checkup data, and genetic test data, and recognize the user's mental illness based on the data.

According to one embodiment, the data communication module (102) can transmit questionnaire data for classifying the user's mental illness to the user terminal (110) and receive response data to the questionnaire data transmitted from the user terminal (110) to recognize the user's mental illness. Here, the questionnaire data may be data including a diagnostic checklist and/or questions for determining the user's prior information, and the response data may be data including the user's response to the questionnaire data. That is, when the user inputs an answer based on the questionnaire data transmitted to the user terminal (110), the data communication module (102) can receive response data.

In addition to the response data described above, the data communication module (102) may receive various data related to the user from the user terminal (110). For example, the data communication module (102) may receive user data indicating changes in the user's body and activities, such as activity data collected through a wearable device worn by the user (e.g., data indicating the user's number of steps, calories consumed, etc.), location data collected from a GPS (global positioning system) sensor embedded in the user terminal (110), and life log data collected through a search engine and/or API linkage associated with SNS, etc.

In addition, the data communication module (102) can receive external environmental data indicating the user's surrounding environment, such as weather data regarding weather and season, etc., collected based on a weather application installed on the user terminal (110). In addition, the data communication module (102) can receive home appliance usage data regarding whether the user uses home appliances and the usage time, etc., collected based on an IoT application installed on the user terminal (110). In addition, the data communication module (102) can also obtain medical data, such as prescription history and medical records, obtained from a professional medical institution and/or a medical expert from an external device (120).

According to one embodiment, the disease determination module (104) may determine a mental illness code corresponding to the user based on the received response data. For example, there may be a table in which separate mental illness codes are matched and stored for each mental illness category, such as obsessive-compulsive disorder, panic disorder, depression, and bipolar disorder, and the disease determination module (104) may recognize the user's mental illness based on the content of the response data received from the user terminal (110) and determine a mental illness code corresponding thereto using the stored table.

According to one embodiment, the computing device (100) may determine at least one artificial intelligence model for predicting the user's symptoms from among a plurality of artificial intelligence models based on the determined mental illness code of the user. For example, there may be an artificial intelligence model pre-trained to predict symptoms for each mental illness category, and the computing device (100) may extract at least one artificial intelligence model matching the mental illness code of the user and utilize it for symptom prediction. That is, since the data and learning and inference processes used to predict symptoms for each mental illness may be different, the computing device (100) may pre-train an artificial intelligence model separately for each mental illness category, and then select and utilize an artificial intelligence model optimized for the user's mental illness during inference.

According to one embodiment, the determined artificial intelligence model can be refined to a user-customized manner by using various data acquired from the user terminal (110) as learning data. For example, if user data indicating the user's physical changes and activities, external environment data such as weather data, and home appliance usage data such as whether the user uses the home appliance and the usage time are input as learning data into the artificial intelligence model, the artificial intelligence model can recognize the user's usual lifestyle pattern and symptom pattern.

According to one embodiment, the symptom prediction module (106) can predict the user's symptoms using a sophisticated artificial intelligence model. Here, the symptom indicates a disease caused by a mental illness suffered by the user or an abnormal state of the body. For example, a symptom of panic disorder may indicate a panic attack, and a symptom of depression may indicate a sign of suicide. The symptom prediction module (106) can predict the possibility of the user's symptom onset by inputting user data, external environment data, home appliance usage data, etc. into the artificial intelligence model.

According to one embodiment, the symptom prediction module (106) inputs user data, external environment data, and/or home appliance usage data into a sophisticated artificial intelligence model, extracts outliers that do not correspond to the determined symptom pattern, and predicts whether the user's symptoms will occur using the extracted outliers. That is, the symptom prediction module (106) comprehensively analyzes the user's activities, weather, whether or not the user uses the home appliance, etc., and if a pattern that is different from the user's usual lifestyle pattern and symptom pattern is detected, extracts it as an outlier and predicts whether or not the symptoms will occur.

According to one embodiment, the symptom prediction module (106) can calculate a symptom score corresponding to an outlier and predict whether the user's symptom will occur based on whether the calculated symptom score is equal to or greater than a first threshold. Here, the symptom score may refer to a probability value output by an artificial intelligence model. For example, when user data, external environment data, and/or home appliance usage data are input, the artificial intelligence model can be trained to calculate a symptom score indicating a possibility of symptom occurrence, and the symptom prediction module (106) can predict that there is a possibility of the user's symptom occurrence when the symptom score calculated by the artificial intelligence model is equal to or greater than a first threshold that is predetermined.

According to one embodiment, the symptom prediction module (106) may transmit message data including data on whether the predicted symptom occurs to the external device (120) if the calculated symptom score is equal to or greater than the second threshold. Here, the second threshold may be greater than the first threshold. That is, if the possibility of the user's symptom occurring is predicted to be significantly high, the symptom prediction module (106) may transmit message data to an external device (120) associated with a hospital, an emergency center, etc., to immediately notify the user of the user's condition. In this case, a medical institution and/or a medical professional associated with the external device (120) may check the message data and take necessary actions for the user.

When the user's symptom onset is predicted, the content determination module (108) can determine the user's preferred content type based on the response data and the user data, and extract at least one content based on the determined user's preferred content type and provide it to the user terminal (110). Here, the content can include text content, audio streaming content, video content, etc. for drawing the user's attention. For example, the content determination module (108) can determine the user's preferred content type using the response data, the user's activity data, the life log data, etc., and extract at least one content corresponding to the determined preferred content type and transmit it to the user terminal (110).

According to one embodiment, the content determination module (108) may analyze the user's activity data, life log data, etc., using any algorithm and/or machine learning model for taste analysis to determine the user's preferred content type. For example, the content determination module (108) may analyze the category, title, cover image, and/or thumbnail of the sound source and/or video used by the user based on the algorithm and/or machine learning model to determine the user's preferred content type, extract at least one content corresponding to the determined preferred content type, and provide link information of the corresponding content to the user terminal (110).

According to one embodiment, the content determination module (108) may calculate a time interval for providing at least one extracted content to the user, and transmit at least one extracted content in the determined time interval. For example, if an outlier that does not correspond to the user's lifestyle pattern is extracted by the artificial intelligence model, the content determination module (108) may calculate a time interval in which the outlier occurred, and provide content within the range of the calculated time interval. In this way, if content is provided to draw the user's attention in the time interval in which the outlier occurred, the variable causing the symptom in the corresponding time interval may be naturally removed, and the user's condition may be stabilized.

In Fig. 1, each functional configuration included in the computing device (100) is separately described, but this is only to help understanding of the invention, and two or more functions may be performed in one computing device. With this configuration, the computing device (100) can utilize an artificial intelligence model to utilize various data related to the user to predict possible symptoms in advance, and indirectly provide content to stabilize the symptoms, thereby stabilizing the user's unstable state.

FIG. 2 is a schematic diagram showing a configuration in which a plurality of user terminals (110_1, 110_2, 110_3) using a customized symptom management service according to an embodiment of the present disclosure are connected to a computing device (100) so as to be able to communicate with the computing device (100). As illustrated, the plurality of user terminals (110_1, 110_2, 110_3) may be connected to a computing device (100) capable of providing a customized symptom management service via a network (210). According to an embodiment, the computing device (100) may include one or more server devices and/or databases capable of storing, providing, and executing computer-executable programs (e.g., downloadable applications) and data related to customized symptom management services, or one or more distributed computing devices and/or distributed databases based on cloud computing services.

The user-tailored symptom management service provided by the computing device (100) may be provided to the user through an application or web browser installed on each of a plurality of user terminals (110_1, 110_2, 110_3). For example, the computing device (100) may receive response data, user data, external environment data, home appliance usage data, etc. from the user terminals (110_1, 110_2, 110_3) through an application, etc., and may determine the user's mental illness code or predict symptoms based on the same.

A plurality of user terminals (110_1, 110_2, 110_3) can communicate with the computing device (100) via the network (210). The network (210) can be configured to enable communication between the plurality of user terminals (110_1, 110_2, 110_3) and the computing device (100). Depending on the installation environment, the network (210) can be configured as, for example, a wired network such as Ethernet, a wired home network (Power Line Communication), a telephone line communication device, and RS-serial communication, a wireless network such as a mobile communication network, a Wireless LAN (WLAN), Wi-Fi, Bluetooth, and ZigBee, or a combination thereof. The communication method is not limited, and may include not only a communication method utilizing a communication network (e.g., a mobile communication network, wired Internet, wireless Internet, broadcasting network, satellite network, etc.) that the network (210) may include, but also short-range wireless communication between user terminals (110_1, 110_2, 110_3).

In FIG. 2, a mobile phone terminal (110_1), a tablet terminal (110_2), and a PC terminal (110_3) are illustrated as examples of user terminals, but are not limited thereto, and the user terminals (110_1, 110_2, 110_3) may be any device capable of wired and/or wireless communication and capable of installing and executing applications or web browsers, etc. For example, the user terminals (110_1, 110_2, 110_3) may include AI speakers, smartphones, mobile phones, computers, laptops, tablet PCs, wearable devices, IoT (internet of things) devices, VR (virtual reality) devices, AR (augmented reality) devices, etc. In addition, although FIG. 2 illustrates three user terminals (110_1, 110_2, 110_3) communicating with the computing device (100) via the network (210), this is not limited thereto, and a different number of user terminals may be configured to communicate with the computing device (100) via the network (210).

According to one embodiment, the computing device (100) may transmit questionnaire data for classifying the user's mental illness to the user terminal (110). In addition, the computing device (100) may receive response data for the questionnaire data transmitted from the user terminal (110). In this case, the computing device (100) may determine the user's mental illness code based on the received response data, and may determine an artificial intelligence model for predicting the user's symptoms based on the determined user's mental illness code, thereby predicting the user's symptoms.

FIG. 3 is a block diagram showing the internal configuration of a user terminal (110) and a computing device (100) according to one embodiment of the present disclosure. The user terminal (110) may refer to any device capable of executing an application or web browser, etc. for providing a user-customized symptom management service and capable of wired/wireless communication, and may include, for example, a mobile phone terminal (110_1), a tablet terminal (110_2), a PC terminal (110_3), etc. of FIG. 2.

As illustrated, the user terminal (110) may include a memory (312), a processor (314), a communication module (316), and an input/output interface (318). Similarly, the computing device (100) may include a memory (332), a processor (334), a communication module (336), and an input/output interface (338). As illustrated in FIG. 3, the user terminal (110) and the computing device (100) may be configured to communicate information and/or data via the network (210) using the respective communication modules (316, 336). In addition, the input/output device (320) may be configured to input information and/or data to the user terminal (110) or output information and/or data generated from the user terminal (110) via the input/output interface (318).

The memory (312, 332) may include any non-transitory computer-readable recording medium. According to one embodiment, the memory (312, 332) may include a permanent mass storage device such as a random access memory (RAM), a read only memory (ROM), a disk drive, a solid state drive (SSD), a flash memory, etc. As another example, a permanent mass storage device such as a ROM, an SSD, a flash memory, a disk drive, etc. may be included in the user terminal (110) or the computing device (100) as a separate permanent storage device distinct from the memory. In addition, the memory (312, 332) may store an operating system and at least one program code (e.g., code for a user-customized symptom management application installed and run on the user terminal (110).

These software components may be loaded from a computer-readable storage medium separate from the memory (312, 332). This separate computer-readable storage medium may include a storage medium directly connectable to the user terminal (110) and the computing device (100), for example, a computer-readable storage medium such as a floppy drive, a disk, a tape, a DVD/CD-ROM drive, a memory card, etc. As another example, the software components may be loaded into the memory (312, 332) through a communication module other than a computer-readable storage medium. For example, at least one program may be loaded into the memory (312, 332) based on a computer program that is installed by files provided by developers or a file distribution system that distributes installation files of applications through a network (210).

The processor (314, 334) may be configured to process instructions of a computer program by performing basic arithmetic, logic, and input/output operations. The instructions may be provided to the processor (314, 334) by the memory (312, 332) or the communication module (316, 336). For example, the processor (314, 334) may be configured to execute instructions received according to program code stored in a storage device such as the memory (312, 332).

The communication module (316, 336) may provide a configuration or function for the user terminal (110) and the computing device (100) to communicate with each other via the network (210), and may provide a configuration or function for the user terminal (110) and/or the computing device (100) to communicate with another user terminal or another system (for example, a separate cloud system, etc.). For example, a request or data (for example, user data, external environment data, home appliance usage data, etc.) generated by the processor (314) of the user terminal (110) according to a program code stored in a recording device such as a memory (312) may be transmitted to the computing device (100) via the network (210) under the control of the communication module (316). Conversely, a control signal or command provided under the control of the processor (334) of the computing device (100) may be received by the user terminal (110) through the communication module (316) of the user terminal (110) via the communication module (336) and the network (210).

The input/output interface (318) may be a means for interfacing with an input/output device (320). As an example, the input device may include a device such as a camera, a keyboard, a microphone, a mouse, etc., including an audio sensor and/or an image sensor, and the output device may include a device such as a display, a speaker, a haptic feedback device, etc. As another example, the input/output interface (318) may be a means for interfacing with a device that has a configuration or function integrated into it for performing input and output, such as a touch screen. For example, when the processor (314) of the user terminal (110) processes a command of a computer program loaded into the memory (312), a service screen configured using information and/or data provided by the computing device (100) or another user terminal may be displayed on the display through the input/output interface (318).

In FIG. 3, the input/output device (320) is illustrated as not being included in the user terminal (110), but is not limited thereto, and may be configured as a single device with the user terminal (110). In addition, the input/output interface (338) of the computing device (100) may be a means for interfacing with a device (not illustrated) for input or output that is connected to the computing device (100) or that the computing device (100) may include. In FIG. 3, the input/output interfaces (318, 338) are illustrated as elements configured separately from the processors (314, 334), but is not limited thereto, and the input/output interfaces (318, 338) may be configured to be included in the processors (314, 334).

The user terminal (110) and the computing device (100) may include more components than the components of FIG. 3. However, it is not necessary to clearly illustrate most of the conventional components. According to one embodiment, the user terminal (110) may be implemented to include at least some of the input/output devices (320) described above. In addition, the user terminal (110) may further include other components, such as a transceiver, a GPS sensor, a camera, various sensors, a database, etc. For example, when the user terminal (110) is a smart phone, it may include components that a smart phone generally includes, and for example, various components, such as an acceleration sensor, a gyro sensor, a camera module, various physical buttons, buttons using a touch panel, input/output ports, and a vibrator for vibration, may be implemented to be further included in the user terminal (110).

While a program for an application, etc. is running, the processor (314) can receive text, images, video, voice and/or motion, etc. input or selected through an input device, such as a camera, microphone, including a touch screen, keyboard, audio sensor and/or image sensor, connected to an input/output interface (318), and can store the received text, images, video, voice and/or motion, etc. in a memory (312) or provide it to the computing device (100) through a communication module (316) and a network (210).

The processor (314) of the user terminal (110) may be configured to manage, process, and/or store information and/or data received from the input device (320), another user terminal, the computing device (100), and/or a plurality of external systems. The information and/or data processed by the processor (314) may be provided to the computing device (100) via the communication module (316) and the network (210). The processor (314) of the user terminal (110) may transmit the information and/or data to the input/output device (320) via the input/output interface (318) and output the information and/or data. For example, the processor (314) may display survey data, content, etc. received from the computing device (100) on the screen of the user terminal (110).

The processor (334) of the computing device (100) may be configured to manage, process, and/or store information and/or data received from a plurality of user terminals (110) and/or a plurality of external systems. The information and/or data processed by the processor (334) may be provided to the user terminal (110) via the communication module (336) and the network (210). The processor (334) of the computing device (100) may be configured to output the processed information and/or data via an output device (320) such as a display output capable device (e.g., a touch screen, a display, etc.) or a voice output capable device (e.g., a speaker) of the user terminal (110).

FIG. 4 illustrates an example of displaying questionnaire data (412) on a display of a user terminal according to an embodiment of the present disclosure. As described above, the computing device (100 of FIG. 1) can transmit questionnaire data (412) for classifying a user's mental illness to the user terminal (110 of FIG. 1) and receive response data for the questionnaire data (412) transmitted from the user terminal.

According to one embodiment, the first operation (410) indicates that when the user terminal receives the questionnaire data (412), it displays it on the display. Here, the questionnaire data (412) is for checking the user's prior information, and may include questions for checking the user's preferences for a psychological diagnosis checklist, objects, people, places, music tastes, whether to receive treatment, information on medications being taken, treatment period, etc.

As illustrated, when a psychological diagnosis checklist is displayed on the display of a user terminal, the user can check the questions and input answers by using touch input, etc. For example, the user can select an area corresponding to 4 points for the questionnaire "1. I am unmotivated, depressed, and sad" by using touch input, etc., and input an answer. When the user inputs answers to all necessary questionnaires in this way, response data including answers to questionnaire data (412) can be transmitted to the computing device. In this case, the computing device can determine the user's mental illness code based on the received response data.

In Fig. 4, a psychological diagnosis checklist is shown as being displayed on the display of the user terminal as the questionnaire data (412), but it is not limited thereto, and the questionnaire data (412) may further include items for checking the user's medical history, medication history, etc. With this configuration, the computing device can effectively determine the user's mental illness code for providing a user-tailored symptom management service by only receiving a response to the questionnaire data (412).

FIG. 5 illustrates an example of displaying content on a display of a user terminal according to an embodiment of the present disclosure. As described above, the computing device (100 of FIG. 1) determines a preferred content type of the user based on the response data and the user data, and when the occurrence of the user's symptoms is predicted, at least one content can be extracted based on the determined preferred content type of the user. In this case, the computing device can transmit at least one extracted content and/or link information of the content to the user terminal (110 of FIG. 1). For example, the user terminal can provide the content to the user through the first operation (510) and the second operation (520).

According to one embodiment, the first operation (510) indicates that a notification message (512) for providing content to a user is displayed on a display. According to one embodiment, the computing device may transmit a notification message (512) to a user terminal to confirm the user's intention before providing the content. In this case, the user terminal may display the notification message (512) on the display in the form of a pop-up message or the like. For example, when music streaming content is provided, a notification message (512) such as "How about this music today? Would you like to listen to it?" may be displayed on the display.

According to one embodiment, the second operation (520) indicates that content (522) is displayed on the display in response to the user selecting the notification message (512) by a touch input or the like. For example, the notification message (512) may include link information for connecting any application (video application, audio streaming application, etc.) or web browser that provides the content (522). That is, when the user selects the notification message (512) by a touch input or the like, the user terminal may execute an associated application or connect a web page based on the link information included in the notification message (512) to provide the user with content (522) extracted by the computing device.

In Fig. 5, the audio streaming content is shown as being displayed on the user terminal, but the type of content provided is not limited thereto. For example, various types of content such as text content, image content, and video content may be provided to the user. With this configuration, the computing device can alleviate the user's symptoms in a natural manner by extracting and providing appropriate content that can draw the user's attention without providing the user with information about the symptoms directly.

FIG. 6 is a flowchart illustrating an example of a computing device (100) and a user terminal (110) exchanging data and/or information necessary to determine an artificial intelligence model corresponding to a user's mental illness code according to an embodiment of the present disclosure. As described above, the computing device (100) and the user terminal (110) may exchange data and/or information for predicting whether a user suffering from a specific mental illness will exhibit symptoms.

According to one embodiment, the computing device (100) can transmit questionnaire data (602) for classifying the user's mental illness to the user terminal (110). In this case, the user terminal (110) can display the received questionnaire data (602) on the display and obtain user input for entering an answer (604). When all user inputs are obtained or the user selects an arbitrary button for transmission by touch input, etc., the user terminal (110) can transmit response data (606) to the computing device (100).

In one embodiment, the computing device (100) may determine a mental illness code of the user based on the received response data (608). For example, the mental illness code may be assigned to a superordinate category such as generalized anxiety disorder, depression, etc. In another example, the mental illness code may be assigned to a subordinate category such as panic disorder, obsessive-compulsive disorder, bipolar disorder, postpartum depression, seasonal affective disorder, etc. In this case, the mental illness code of the user may be determined as at least one of the codes corresponding to the superordinate category and/or the subordinate category.

According to one embodiment, the computing device (100) may determine an artificial intelligence model for predicting the user's symptoms based on the determined mental illness code of the user (610). For example, since the type of symptoms, data required for symptom prediction, and processing processes may be different for each mental illness, there may be multiple artificial intelligence models corresponding to each mental illness code. In this case, the computing device (100) may extract at least one artificial intelligence model corresponding to the user's mental illness code from among multiple pre-learned artificial intelligence models.

Additionally, the extracted artificial intelligence model can be fine-tuned by utilizing user data, external environment data, home appliance usage data, etc. collected from the user terminal (110) as learning data. Here, fine-tuning may refer to a learning process of fine-tuning parameters of the artificial intelligence model to perform a specific task based on a pre-learned model. That is, the computing device can refine the extracted artificial intelligence model into a user-customized model by performing fine-tuning based on various data collected from the user terminal (110).

FIG. 7 is a flowchart illustrating an example of a computing device (100), a user terminal (110), and an external device (120) exchanging data and/or information necessary to alleviate a user's symptoms according to an embodiment of the present disclosure. As described above, the computing device (100) can predict the user's symptoms using a determined or refined artificial intelligence model. For example, the computing device (100) can predict the user's symptoms by inputting user data, external environment data, and/or home appliance usage data into the artificial intelligence model.

According to one embodiment, the user terminal (110) may provide user data (702) indicating changes in the user's body and activities to the computing device (100). For example, the user data (702) may include activity data, location data, life log data, etc. Here, the activity data is collected through a wearable device worn by the user, and may include self-recorded data such as whether the user exercises, whether the user sleeps, heart rate, pulse, respiration rate, blood pressure, body temperature, stress index, number of steps, and food intake. In addition, the life log data may include data regarding the number and time of the user's text messages, calls, SNS, etc. When such user data (702) is used for symptom prediction, the artificial intelligence model may recognize a phenomenon such as sudden unstable breathing or a significant decrease in the number of calls as an abnormality and predict whether or not the symptom will occur.

Additionally or alternatively, the user terminal (110) may provide external environmental data (704) representing the surrounding environment affecting the user to the computing device (100). For example, the external environmental data (704) may include weather data such as temperature, wind speed, amount of sunlight, fine dust, etc., and seasonal data. That is, the artificial intelligence model may analyze the user's personal activities as well as the user's psychological changes according to the weather using the user data (702) and the external environmental data (704) to predict whether symptoms will occur.

Additionally or alternatively, the user terminal (110) may provide home appliance usage data (706) indicating whether the user uses the home appliance and the usage time to the computing device (100). For example, the home appliance usage data (706) may be collected from a home appliance IoT application installed on the user terminal (110) and may include data on how much the user uses home appliances such as a washing machine, dryer, dishwasher, etc. Since a user with a mental illness such as depression may experience a phenomenon in which the home appliance usage time increases or decreases rapidly when the symptom occurs, the artificial intelligence model may predict whether the symptom occurs by using all of the user data (702), the external environment data (704), and the home appliance usage data (706).

According to one embodiment, the computing device (100) can predict the user's symptoms (708) by inputting user data (702), external environment data (704), and home appliance usage data (706) into an artificial intelligence model. For example, the computing device (100) can refine the artificial intelligence model based on learning data associated with the user to determine the user's lifestyle pattern, input the user data (702), external environment data (704), and home appliance usage data (706) into the refined artificial intelligence model, extract outliers that do not correspond to the determined lifestyle pattern, and then predict whether the user's symptoms will occur using the extracted outliers.

According to one embodiment, the computing device (100) determines the user's preferred content type based on the response data and the user data, and when the user's symptom onset is predicted, extracts at least one content based on the determined user's preferred content type. Then, the computing device (100) can transmit the extracted at least one content (712) to the user terminal (110). At this time, the time at which the content (712) is transmitted can be determined based on the time interval in which the outlier occurs. For example, the computing device (100) can determine the time interval in which the outlier mainly occurs, and transmit the content (712) in the determined time interval. In another example, the computing device (100) can determine the user's sleep time, and transmit the content (712) for relieving tension just before the sleep time.

According to one embodiment, the computing device (100) may communicate with an external device (120) and exchange data regarding the user's condition and symptoms. Here, the external device (120) may refer to a device associated with a hospital, an emergency center, etc. For example, when an outlier is detected by an artificial intelligence model, the computing device (100) may transmit data regarding the user's activity and time corresponding to the outlier to the external device (120). In addition, when the computing device (100) determines that the possibility of the user's symptom onset is higher than a threshold value, the computing device (100) may transmit message data (714) including data regarding whether the user's predicted symptom onset is present to the external device (120).

In this way, when message data (714) is transmitted, the external device (120) can store or update the received message data (714) in a database of a medical institution, etc., so that it can be used for the user's treatment, etc. In this case, the medical institution and/or medical professional associated with the external device (120) can check the message data (714) and take necessary actions for the user.

FIG. 8 is a flowchart illustrating an example of a user-tailored symptom management method (800) according to an embodiment of the present disclosure. The user-tailored symptom management method (800) may be performed by a processor (for example, at least one processor of a computing device). The user-tailored symptom management method (800) may transmit questionnaire data for classifying a user's mental illness to a user terminal (S810). In addition, the processor may receive response data to the questionnaire data transmitted from the user terminal (S820).

According to one embodiment, the processor can determine the user's mental illness code based on the received response data (S830). In addition, the processor can determine an artificial intelligence model for predicting the user's symptoms based on the determined user's mental illness code (S840). The processor can predict the user's symptoms using the determined artificial intelligence model (S850).

According to one embodiment, the processor may receive user data indicating changes in the user's body and activities and external environmental data indicating the user's surrounding environment from the user terminal. In this case, the processor may input the user data and the external environmental data into the artificial intelligence model to predict the user's symptoms. Additionally or alternatively, the processor may receive home appliance usage data indicating whether the user uses the home appliance and the usage time from the user terminal. In this case, the processor may input the user data, the external environmental data, and the home appliance usage data into the artificial intelligence model to predict the user's symptoms.

According to one embodiment, the processor may refine an artificial intelligence model based on learning data associated with the user to determine the user's lifestyle pattern, and input user data, external environment data, and home appliance usage data into the refined artificial intelligence model to extract outliers that do not correspond to the determined lifestyle pattern. In this case, the processor may use the extracted outliers to predict whether the user's symptoms will occur.

According to one embodiment, the processor can calculate a symptom score corresponding to an outlier and predict whether the user's symptom will occur based on whether the calculated symptom score is greater than or equal to a first threshold. In addition, the processor can transmit message data including data regarding whether the predicted symptom will occur to an external device if the calculated symptom score is greater than or equal to a second threshold. Here, the second threshold can be greater than the first threshold.

According to one embodiment, the processor determines the user's preferred content type based on the response data and the user data, and if the user's symptom onset is predicted, extracts at least one content based on the determined user's preferred content type. Then, the processor can transmit the extracted at least one content to the user terminal. At this time, the processor can calculate a time interval in which an outlier occurred, and transmit at least one content to the user terminal based on the calculated time interval.

Meanwhile, the embodiments disclosed in this specification may be implemented in the form of a recording medium storing instructions executable by a computer. The instructions may be stored in the form of program codes, and when executed by a processor, may generate a program module to perform the operations of the disclosed embodiments. The recording medium may be implemented as a computer-readable recording medium. The computer-readable recording medium may include all kinds of recording media storing instructions decipherable by a computer. For example, there may be ROM, RAM, magnetic tape, magnetic disk, flash memory, optical data storage devices, etc.

The above-described contents are specific embodiments for carrying out the present disclosure. The present disclosure will include not only the above-described embodiments, but also embodiments that are simply designed or can be easily changed. In addition, the present disclosure will also include techniques that can be easily modified and implemented using the above-described embodiments. Therefore, the scope of the present disclosure should not be limited to the above-described embodiments, but should be determined by the claims described below as well as the equivalents of the claims of the present disclosure.

100: Computing Device
102: Data Communication Module
104: Disease Determination Module
106: Symptom Prediction Module
108: Content Decision Module
110: User terminal
120: External Device

Claims (10)

A user-tailored symptom management method performed by at least one processor,
A step of transmitting questionnaire data for classifying a user's mental illness to a user terminal;
A step of receiving response data to the transmitted questionnaire data from the user terminal;
A step of determining a mental illness code of the user based on the received response data;
A step of determining an artificial intelligence model for predicting the user's symptoms based on the determined user's mental illness code; and
A step of predicting the user's symptoms using the above-determined artificial intelligence model;
A personalized symptom management method that includes: In the first paragraph,
A step of receiving user data representing the user's physical changes and activities and external environmental data representing the user's surrounding environment from the user terminal;
Including more,
The step of predicting the user's symptoms using the above-determined artificial intelligence model is:
A step of predicting the user's symptoms by inputting the user data and the external environment data into the artificial intelligence model;
A personalized symptom management method that includes: In the second paragraph,
A step of receiving home appliance usage data indicating whether the user uses the home appliance and the usage time from the user terminal;
Including more,
The step of predicting the user's symptoms using the above-determined artificial intelligence model is:
A step of predicting the user's symptoms by inputting the user data, the external environment data, and the home appliance usage data into the artificial intelligence model;
A personalized symptom management method that includes: In the third paragraph,
The step of predicting the user's symptoms by inputting the user data, the external environment data, and the home appliance usage data into the artificial intelligence model is as follows.
A step of refining the artificial intelligence model based on learning data associated with the user to determine the user's lifestyle pattern;
A step of inputting the user data, the external environment data and the home appliance usage data into the sophisticated artificial intelligence model to extract outliers that do not correspond to the determined lifestyle pattern; and
A step of predicting whether the user's symptoms will occur using the extracted outliers;
A personalized symptom management method that includes: In paragraph 4,
The step of predicting whether the user's symptoms will occur using the extracted outliers is as follows.
A step of calculating a symptom score corresponding to the above outlier; and
A step of predicting whether the user's symptoms will occur based on whether the calculated symptom score is greater than or equal to a first threshold;
A personalized symptom management method that includes: In paragraph 5,
A step of transmitting message data including data on whether the predicted symptom has occurred to an external device when the calculated symptom score is equal to or greater than a second threshold;
A personalized symptom management method that further includes: In the second paragraph,
A step of determining the user's preferred content type based on the response data and the user data;
If the symptom onset of the user is predicted, a step of extracting at least one content based on the determined user's preferred content type; and
A step of transmitting at least one of the extracted contents to the user terminal;
A personalized symptom management method that further includes: In Article 7,
The step of transmitting at least one of the extracted contents to the user terminal is:
When an outlier that does not correspond to the user's lifestyle pattern is extracted by the above artificial intelligence model, a step of calculating the time interval in which the outlier occurred; and
A step of transmitting at least one content to the user terminal based on the calculated time interval;
A personalized symptom management method that includes: A computer program stored on a computer-readable recording medium for executing a method according to any one of claims 1 to 8 on a computer. As a computing device,
Communication module;
memory; and
At least one processor connected to said memory and configured to execute at least one computer-readable program contained in said memory;
Including,
At least one of the above programs,
Transmitting questionnaire data to classify the user's mental illness to the user terminal,
Receive response data to the transmitted survey data from the user terminal,
Determine the user's mental illness code based on the received response data,
Based on the mental illness code of the user determined above, an artificial intelligence model is determined to predict the user's symptoms,
A computing device including commands for predicting the user's symptoms using the determined artificial intelligence model.