Electronic device for monitoring health condition of user, control method thereof, and storage medium

Abstract

An electronic device is disclosed. The electronic device of the present disclosure comprises: a microphone; at least one sensor including a sensing circuit; a display; at least one processor including a processing circuit; and a memory for storing instructions. The instructions, when executed individually or collectively by the at least one processor, may cause the electronic device to: detect an abnormal behavior on the basis of at least one of user speech acquired through the microphone and sensing data acquired by the at least one sensor; identify an expected disease corresponding to the abnormal behavior on the basis of the detection of the abnormal behavior; in order to determine whether the expected disease has occurred, acquire a question related to the expected disease and a correct answer corresponding to the question as output data of an artificial intelligence model stored in the memory, by using, as input data for the artificial intelligence model, data on the expected disease and personal data including at least one of the user speech and the sensing data; provide the question; receive a user answer to the question; determine whether the expected disease has occurred, on the basis of a comparison between the user answer and the correct answer; and provide a determination result of whether the expected disease has occurred, wherein the artificial intelligence model is trained to use diagnostic test forms of a disease and personal data as input data to acquire a question and a correct answer for diagnosing the disease, as output data on the basis of the personal data.

Description

Electronic device for monitoring a user's health status, a method for controlling the same, and a storage medium

[0001] Embodiments of the present disclosure relate to an electronic device for monitoring a user's health condition, a method for controlling the same, and a storage medium.

[0002] Various services and additional functions provided through electronic devices, such as portable electronic devices like smartphones, are gradually increasing. To enhance the utility value of these devices and satisfy the needs of diverse users, telecommunications service providers or electronic device manufacturers are competitively developing devices to offer various functions and differentiate themselves from competitors. Consequently, the various functions provided through electronic devices are also becoming increasingly sophisticated.

[0003] Various configurations and methods are possible for detecting a user's illness through various sensors in electronic devices. Furthermore, it is possible to identify not only signs of physical illness but also signs of mental illness, such as dementia; as the accuracy and variety of sensors increase, the types of diseases that can be identified have also expanded.

[0004] The information described above may be provided as related art for the purpose of aiding understanding of the present disclosure. No claim or determination is made as to whether any of the foregoing may be applied as prior art related to the present disclosure.

[0005] According to one embodiment, the electronic device may include a microphone, at least one sensor including a sensing circuit, a display, at least one processor including a processing circuit, and a memory for storing instructions.

[0006] According to one embodiment, when the instructions are executed individually or collectively by the at least one processor, the electronic device may detect abnormal behavior based on at least one of user voice obtained through the microphone or sensing data obtained by the at least one sensor.

[0007] According to one embodiment, when the instructions are executed individually or collectively by the at least one processor, the electronic device may identify a predicted disease corresponding to the abnormal behavior based on the detection of the abnormal behavior.

[0008] According to one embodiment, when the instructions are executed individually or collectively by the at least one processor, the electronic device may use personal data including at least one of the user voice or the sensing data, and data regarding the expected disease, as input data for an artificial intelligence model stored in the memory to determine whether the expected disease has occurred, thereby obtaining a question related to the expected disease and a correct answer corresponding to the question as output data of the artificial intelligence model.

[0009] According to one embodiment, when the instructions are executed individually or collectively by the at least one processor, the electronic device may provide the question.

[0010] According to one embodiment, when the instructions are executed individually or collectively by the at least one processor, the electronic device may receive a user answer to the question.

[0011] According to one embodiment, when the instructions are executed individually or collectively by the at least one processor, the electronic device may determine whether the expected disease has occurred based on a comparison of the user's answer and the correct answer.

[0012] According to one embodiment, when the instructions are executed individually or collectively by the at least one processor, the electronic device may provide a result of determining whether the expected disease has occurred.

[0013] According to one embodiment, the artificial intelligence model may be trained to use disease diagnostic test forms and personal data as input data, and to obtain questions and answers for diagnosing the disease based on the personal data as output data.

[0014] According to one embodiment, a control method for an electronic device may include an operation to detect abnormal behavior based on at least one of a user voice obtained through a microphone of the electronic device or sensing data obtained by at least one sensor of the electronic device.

[0015] According to one embodiment, a control method for an electronic device may include an operation to identify a predicted disease corresponding to the abnormal behavior based on the detection of the abnormal behavior.

[0016] According to one embodiment, a control method for an electronic device may include, for determining whether the expected disease has occurred, using personal data including at least one of the user voice or the sensing data, and data regarding the expected disease as input data for an artificial intelligence model stored in the memory of the electronic device, and obtaining a question related to the expected disease and a correct answer corresponding to the question as output data of the artificial intelligence model.

[0017] According to one embodiment, a control method for an electronic device may include an operation of providing the question.

[0018] According to one embodiment, a method for controlling an electronic device may include receiving a user's answer to the question.

[0019] According to one embodiment, a control method for an electronic device may include an operation of determining whether the expected disease has occurred based on a comparison between the user's answer and the correct answer.

[0020] According to one embodiment, the control method of an electronic device may include an operation of providing a result of determining whether the expected disease has occurred.

[0021] According to one embodiment, the artificial intelligence model may be trained to use disease diagnostic test forms and personal data as input data, and to obtain questions and answers for diagnosing the disease based on the personal data as output data.

[0022] According to one embodiment, in a non-transient computer-readable storage medium storing one or more programs, the one or more programs may include instructions that cause an electronic device to detect abnormal behavior based on at least one of user voice acquired through the microphone or sensing data acquired by the at least one sensor.

[0023] According to one embodiment, the one or more programs may include instructions that cause an electronic device to identify a predicted disease corresponding to the abnormal behavior based on the detection of the abnormal behavior.

[0024] According to one embodiment, the one or more programs may include instructions for an electronic device to determine whether the expected disease has occurred, by using personal data including at least one of the user voice or the sensing data, and data regarding the expected disease as input data for an artificial intelligence model stored in the memory, and obtaining a question related to the expected disease and a correct answer corresponding to the question as output data for the artificial intelligence model.

[0025] According to one embodiment, the one or more programs may include instructions that cause an electronic device to provide the question.

[0026] According to one embodiment, the one or more programs may include instructions that cause an electronic device to receive a user answer to the question.

[0027] According to one embodiment, the one or more programs may include instructions that cause an electronic device to determine whether the expected disease has occurred based on a comparison of the user's answer and the correct answer.

[0028] According to one embodiment, the one or more programs may include instructions that cause an electronic device to provide a result of determining whether the expected disease has occurred.

[0029] According to one embodiment, the artificial intelligence model may be trained to use disease diagnostic test forms and personal data as input data, and to obtain questions and answers for diagnosing the disease based on the personal data as output data.

[0030] FIG. 1 is a block diagram of an electronic device in a network environment according to one embodiment.

[0031] FIG. 2 is a diagram illustrating a generative artificial intelligence system according to one embodiment.

[0032] FIG. 3 is a flowchart illustrating the operation of determining whether a disease has occurred through monitoring abnormal user behavior of an electronic device according to one embodiment.

[0033] FIG. 4 is a diagram illustrating the operation of determining whether a disease has occurred through monitoring abnormal user behavior of an electronic device according to one embodiment.

[0034] FIG. 5 is a diagram illustrating the operation of generating a questionnaire to determine whether a disease has occurred through monitoring abnormal user behavior of an electronic device according to one embodiment.

[0035] FIG. 6 is a diagram illustrating the operation of determining whether a disease has occurred through monitoring abnormal user behavior of an electronic device according to one embodiment.

[0036] FIG. 7 is a diagram illustrating the operation of identifying an expected disease through monitoring user abnormal behavior of an electronic device according to one embodiment.

[0037] FIG. 8 is a diagram illustrating the operation of generating a questionnaire to determine whether a disease has occurred through monitoring abnormal user behavior of an electronic device according to one embodiment.

[0038] FIG. 9 is a diagram illustrating the operation of collecting additional information when it is difficult to generate a questionnaire of an electronic device according to one embodiment.

[0039] FIG. 10 is a diagram illustrating the operation of generating questions and answers using personal data of an electronic device according to one embodiment.

[0040] FIG. 11 is a diagram illustrating the operation of collecting additional information when it is difficult to generate a questionnaire of an electronic device according to one embodiment.

[0041] FIG. 12 is a diagram illustrating the operation of configuring a user database of an electronic device according to one embodiment.

[0042] FIG. 13 is a diagram illustrating the operation of determining whether a disease has occurred in an electronic device according to one embodiment.

[0043] FIG. 14 is a diagram illustrating an operation of an electronic device according to one embodiment that proposes performing a health check when abnormal behavior of a user is detected.

[0044] FIG. 15a is a diagram illustrating the operation of an electronic device according to one embodiment that provides a question to check the user's health.

[0045] FIG. 15b is a diagram illustrating the operation of an electronic device according to one embodiment that provides a question to check the user's health.

[0046] FIG. 16 is a diagram illustrating the operation of providing a user's health check result of an electronic device according to one embodiment.

[0047] FIG. 17 is a diagram illustrating the operation of requesting additional information of an electronic device according to one embodiment.

[0048] FIG. 18 is a diagram illustrating the operation of requesting additional information of an electronic device according to one embodiment.

[0049] FIG. 1 is a block diagram of an electronic device (101) in a network environment (100) according to one embodiment. Referring to FIG. 1, in the network environment (100), the electronic device (101) may communicate with an electronic device (102) through a first network (198) (e.g., a short-range wireless communication network) or may communicate with at least one of an electronic device (104) or a server (108) through a second network (199) (e.g., a long-range wireless communication network). According to one embodiment, the electronic device (101) may communicate with the electronic device (104) through a server (108). According to one embodiment, the electronic device (101) may include a processor (120), memory (130), input module (150), sound output module (155), display module (160), audio module (170), sensor module (176), interface (177), connection terminal (178), haptic module (179), camera module (180), power management module (188), battery (189), communication module (190), subscriber identification module (196), or antenna module (197). In some embodiments, at least one of these components (e.g., connection terminal (178)) may be omitted from the electronic device (101), or one or more other components may be added. In some embodiments, some of these components (e.g., sensor module (176), camera module (180), or antenna module (197)) may be integrated into a single component (e.g., display module (160)).

[0050] The processor (120) can control at least one other component (e.g., hardware or software component) of the electronic device (101) connected to the processor (120) by executing software (e.g., program (140)), for example, and can perform various data processing or operations. According to one embodiment, as at least part of the data processing or operations, the processor (120) can store commands or data received from other components (e.g., sensor module (176) or communication module (190)) in volatile memory (132), process the commands or data stored in volatile memory (132), and store the resulting data in non-volatile memory (134). According to one embodiment, the processor (120) may include a main processor (121) (e.g., central processing unit or application processor) or an auxiliary processor (123) that can operate independently or together with it (e.g., graphics processing unit, neural processing unit (NPU), image signal processor, sensor hub processor, or communication processor). For example, if the electronic device (101) includes a main processor (121) and an auxiliary processor (123), the auxiliary processor (123) may be configured to use lower power than the main processor (121) or to be specialized for a designated function. The auxiliary processor (123) may be implemented separately from the main processor (121) or as part thereof.

[0051] The auxiliary processor (123) may control at least some of the functions or states associated with at least one component of the electronic device (101) (e.g., display module (160), sensor module (176), or communication module (190)) on behalf of the main processor (121) while the main processor (121) is in an inactive (e.g., sleep) state, or together with the main processor (121) while the main processor (121) is in an active (e.g., application execution) state. According to one embodiment, the auxiliary processor (123) (e.g., image signal processor or communication processor) may be implemented as part of another functionally related component (e.g., camera module (180) or communication module (190)). According to one embodiment, the auxiliary processor (123) (e.g., neural network processing unit) may include a hardware structure specialized for processing an artificial intelligence model. The artificial intelligence model may be generated through machine learning. Such learning may be performed, for example, on the electronic device (101) itself where the artificial intelligence model is executed, or through a separate server (e.g., server (108)). The learning algorithm may include, for example, supervised learning, unsupervised learning, semi-supervised learning, or reinforcement learning, but is not limited to the examples described above. The artificial intelligence model may include a plurality of artificial neural network layers.An artificial neural network may be a deep neural network (DNN), a convolutional neural network (CNN), a recurrent neural network (RNN), a restricted Boltzmann machine (RBM), a deep belief network (DBN), a bidirectional recurrent deep neural network (BRDNN), a deep Q-network, or a combination of two or more of the above, but is not limited to the examples described above. In addition to the hardware structure, the artificial intelligence model may include a software structure, either additionally or substantially.

[0052] The memory (130) can store various data used by at least one component of the electronic device (101) (e.g., processor (120) or sensor module (176)). The data may include, for example, input data or output data for software (e.g., program (140)) and related commands. The memory (130) may include volatile memory (132) or non-volatile memory (134).

[0053] The program (140) may be stored as software in memory (130) and may include, for example, an operating system (142), middleware (144), or an application (146).

[0054] The input module (150) can receive commands or data to be used for a component of the electronic device (101) (e.g., processor (120)) from outside the electronic device (101) (e.g., user). The input module (150) may include, for example, a microphone, a mouse, a keyboard, a key (e.g., a button), or a digital pen (e.g., a stylus pen).

[0055] The sound output module (155) can output a sound signal to the outside of the electronic device (101). The sound output module (155) may include, for example, a speaker or a receiver. The speaker may be used for general purposes, such as multimedia playback or recording playback. The receiver may be used to receive incoming calls. According to one embodiment, the receiver may be implemented separately from the speaker or as part thereof.

[0056] The display module (160) can visually provide information to an external (e.g., user) of the electronic device (101). The display module (160) may include, for example, a display, a holographic device, or a projector and a control circuit for controlling said device. According to one embodiment, the display module (160) may include a touch sensor configured to detect a touch, or a pressure sensor configured to measure the intensity of the force generated by said touch.

[0057] The audio module (170) can convert sound into an electrical signal or, conversely, convert an electrical signal into sound. According to one embodiment, the audio module (170) can acquire sound through the input module (150) or output sound through the sound output module (155) or an external electronic device (e.g., electronic device (102)) (e.g., speaker or headphones) connected directly or wirelessly to the electronic device (101).

[0058] The sensor module (176) can detect the operating state of the electronic device (101) (e.g., power or temperature) or the external environmental state (e.g., user state) and generate an electrical signal or data value corresponding to the detected state. According to one embodiment, the sensor module (176) may include, for example, a gesture sensor, a gyroscope sensor, a barometric pressure sensor, a magnetic sensor, an accelerometer sensor, a grip sensor, a proximity sensor, a color sensor, an IR (infrared) sensor, a biosensor, a temperature sensor, a humidity sensor, or an illuminance sensor.

[0059] The interface (177) may support one or more specified protocols that can be used for the electronic device (101) to be connected directly or wirelessly to an external electronic device (e.g., electronic device (102)). According to one embodiment, the interface (177) may include, for example, a high definition multimedia interface (HDMI), a universal serial bus (USB) interface, an SD card interface, or an audio interface.

[0060] The connection terminal (178) may include a connector through which the electronic device (101) can be physically connected to an external electronic device (e.g., electronic device (102)). According to one embodiment, the connection terminal (178) may include, for example, an HDMI connector, a USB connector, an SD card connector, or an audio connector (e.g., a headphone connector).

[0061] The haptic module (179) can convert an electrical signal into a mechanical stimulus (e.g., vibration or movement) or an electrical stimulus that the user can perceive through tactile or kinesthetic senses. According to one embodiment, the haptic module (179) may include, for example, a motor, a piezoelectric element, or an electric stimulation device.

[0062] The camera module (180) can capture still images and video. According to one embodiment, the camera module (180) may include one or more lenses, image sensors, image signal processors, or flashes.

[0063] The power management module (188) can manage the power supplied to the electronic device (101). According to one embodiment, the power management module (188) can be implemented, for example, as at least part of a power management integrated circuit (PMIC).

[0064] The battery (189) can supply power to at least one component of the electronic device (101). According to one embodiment, the battery (189) may include, for example, a non-rechargeable primary battery, a rechargeable secondary battery, or a fuel cell.

[0065] The communication module (190) can support the establishment of a direct (e.g., wired) communication channel or a wireless communication channel between an electronic device (101) and an external electronic device (e.g., electronic device (102), electronic device (104), or server (108)), and the performance of communication through the established communication channel. The communication module (190) may include one or more communication processors that operate independently of the processor (120) (e.g., application processor) and support direct (e.g., wired) communication or wireless communication. According to one embodiment, the communication module (190) may include a wireless communication module (192) (e.g., cellular communication module, short-range wireless communication module, or GNSS (global navigation satellite system) communication module) or a wired communication module (194) (e.g., LAN (local area network) communication module, or power line communication module). The corresponding communication module among these communication modules can communicate with an external electronic device (104) through a first network (198) (e.g., a short-range communication network such as Bluetooth, WiFi (wireless fidelity) direct, or IrDA (infrared data association)) or a second network (199) (e.g., a legacy cellular network, a 5G network, a next-generation communication network, the Internet, or a computer network (e.g., a LAN or WAN)). These various types of communication modules may be integrated into a single component (e.g., a single chip) or implemented as multiple separate components (e.g., multiple chips). The wireless communication module (192) can identify or authenticate the electronic device (101) within a communication network such as the first network (198) or the second network (199) using subscriber information (e.g., International Mobile Subscriber Identifier (IMSI)) stored in the subscriber identification module (196).

[0066] The wireless communication module (192) can support 5G networks and next-generation communication technologies following 4G networks, for example, new radio access technology. NR access technology can support high-speed transmission of high-capacity data (enhanced mobile broadband (eMBB)), minimization of terminal power and connection of multiple terminals (massive machine type communications (mMTC)), or high reliability and low latency (ultra-reliable and low-latency communications (URLLC)). The wireless communication module (192) can support a high-frequency band (e.g., mmWave band) to achieve a high data transmission rate, for example. The wireless communication module (192) can support various technologies for securing performance in the high-frequency band, such as beamforming, massive MIMO (multiple-input and multiple-output), full-dimensional MIMO (FD-MIMO), array antenna, analog beam-forming, or large-scale antenna. The wireless communication module (192) can support various requirements specified in the electronic device (101), external electronic device (e.g., electronic device (104)), or network system (e.g., second network (199)). According to one embodiment, the wireless communication module (192) can support a Peak data rate (e.g., 20 Gbps or more) for realizing eMBB, loss coverage (e.g., 164 dB or less) for realizing mMTC, or U-plane latency (e.g., downlink (DL) and uplink (UL) each 0.5 ms or less, or round trip 1 ms or less) for realizing URLLC.

[0067] An antenna module (197) can transmit a signal or power to or from an external source (e.g., an external electronic device). According to one embodiment, the antenna module (197) may include an antenna comprising a radiator made of a conductor or a conductive pattern formed on a substrate (e.g., a PCB). According to one embodiment, the antenna module (197) may include a plurality of antennas (e.g., an array antenna). In this case, at least one antenna suitable for a communication method used in a communication network, such as a first network (198) or a second network (199), may be selected from the plurality of antennas, for example, by a communication module (190). A signal or power may be transmitted or received between the communication module (190) and an external electronic device through the selected at least one antenna. According to some embodiments, in addition to the radiator, other components (e.g., a radio frequency integrated circuit (RFIC)) may be additionally formed as part of the antenna module (197).

[0068] According to one embodiment, the antenna module (197) may form a mmWave antenna module. According to one embodiment, the mmWave antenna module may include a printed circuit board, an RFIC disposed on or adjacent to a first surface (e.g., bottom surface) of the printed circuit board and capable of supporting a specified high frequency band (e.g., mmWave band), and a plurality of antennas (e.g., array antennas) disposed on or adjacent to a second surface (e.g., top surface or side surface) of the printed circuit board and capable of transmitting or receiving a signal of the specified high frequency band.

[0069] At least some of the above components can be connected to each other via a communication method between peripheral devices (e.g., bus, GPIO (general purpose input and output), SPI (serial peripheral interface), or MIPI (mobile industry processor interface)) and exchange signals (e.g., commands or data) with each other.

[0070] According to one embodiment, commands or data may be transmitted or received between the electronic device (101) and an external electronic device (104) through a server (108) connected to a second network (199). Each of the external electronic devices (102, or 104) may be the same or a different type of device as the electronic device (101). According to one embodiment, all or part of the operations performed on the electronic device (101) may be performed on one or more of the external electronic devices (102, 104, or 108). For example, if the electronic device (101) needs to perform a function or service automatically or in response to a request from a user or another device, the electronic device (101) may request one or more external electronic devices to perform at least part of the function or service instead of performing the function or service itself or additionally. One or more external electronic devices that receive the above request may execute at least part of the requested function or service, or additional function or service related to the request, and transmit the result of the execution to the electronic device (101). The electronic device (101) may provide the result as is or additionally processed as at least part of the response to the request. For this purpose, for example, cloud computing, distributed computing, mobile edge computing (MEC), or client-server computing technology may be used. The electronic device (101) may provide ultra-low latency services using, for example, distributed computing or mobile edge computing. In another embodiment, the external electronic device (104) may include an Internet of Things (IoT) device. The server (108) may be an intelligent server using machine learning and / or neural networks. According to one embodiment, the external electronic device (104) or the server (108) may be included within a second network (199).The electronic device (101) can be applied to intelligent services (e.g., smart home, smart city, smart car, or healthcare) based on 5G communication technology and IoT-related technology.

[0071] FIG. 2 is a diagram illustrating a generative artificial intelligence system according to one embodiment.

[0072] Referring to FIG. 2, the user query / response interface (210) can receive user input. The user input may be in the form of natural language, images, and / or videos, but there are no limitations. Additionally, context information may be transmitted along with the user input. The context information may include various additional information at the time of user input. For example, the additional information may include information about the application currently being used by the user or the user's location information. Furthermore, the user input may be in a mixed form of the aforementioned natural language, images, sounds, and context information. Additionally, the user input may be in a non-natural language form, such as selecting a menu. The user query / response interface (210) can output results from a generative artificial intelligence system to the user. The output may be in the form of natural language or specific content, and may also be provided in the form of actions requested by the user. The user query / response interface (210) can output results from a generative artificial intelligence system to the user. The output can be in the form of natural language or specific content, and it may also be provided in the form of actions requested by the user.

[0073] The AI ​​framework (240) can receive input from the user and coordinate and control each component necessary to perform the user's intent based on the user's query.

[0074] User input received from the user query / response interface (210) can be transmitted to a prompt design component (241). The prompt design component (241) can be used to generate prompts suitable for inputting user input into a large language model (LLM) or a large multimodal model (LMM). The prompt design component (241) may be an AI component that uses machine learning algorithms or neural networks to develop better prompts over time. The prompt design component (241) can generate prompts by accessing a knowledge component containing user preference data, a prompt library, and prompt examples based on user input, and can transmit the generated prompts to the LLM or LMM.

[0075] The API / Plug-in management component (242) can perform the role of communicating with external information when there is a request for additional information when user input is passed as input to a generative model. The API / Plug-in management component (242) establishes a channel to communicate with the outside of the AI ​​Interface via the API, and through the established channel, it can enable access to various data sources (e.g., knowledge repository (220)). Additionally, the API / Plug-in management component (242) can request the application / service component (230) via the API to perform an action that ultimately executes the user input, rather than an intermediate result, in the case where the application or service needs to perform such action. The information obtained from the outside may be used to generate a prompt in the prompt design component (241) along with the user input, or it may be passed as input to the generative model.

[0076] The output modification component (or refiner component) (243) can fine-tune the output of the generative model. For example, the output modification component (243) can verify whether the content generated through the LLM and / or LMM is irrelevant, contains biased content, or contains harmful content. Additionally, the output modification component (243) can determine the extent to which the output matches the desired result and, if additional processing is required, proceed with that process. Furthermore, the output modification component (243) can configure and provide hints to the user to avoid unwanted output.

[0077] A generative AI model (260) generally refers to an artificial intelligence neural network that generates new forms of data based on user input information. A generative AI model (260) may include a model that generates images and / or a model that generates language. Models that generate images include, but are not limited to, GANs (generative adversarial networks) and VAEs (variational auto encoders), and examples include Diffusion-based generative models that use VAEs and Transformer structures. Models that generate language are models trained to output the most statistically appropriate output value based on input values, and examples include models such as CHAT-GPT 3 and CHAT-GPT 4. There are also LMMs (large multimodal models) that can recognize various forms of data input, such as text, images, and voice, and generate new data corresponding to them.

[0078] FIG. 3 is a flowchart illustrating the operation of determining whether a disease has occurred through monitoring abnormal user behavior of an electronic device according to one embodiment.

[0079] Referring to FIG. 3, in operation 310, an electronic device (e.g., the electronic device (101) of FIG. 1 or the processor (120) of FIG. 1) can detect abnormal behavior based on at least one of user voice obtained through a microphone (e.g., the input module (150) of FIG. 1) or sensing data obtained by at least one sensor (e.g., the sensor module (176) of FIG. 1).

[0080] According to one embodiment, the electronic device may collect a user's personal data. For example, the user's personal data may include at least one of voice data, sensing data, usage history data, or stored content.

[0081] According to one embodiment, the electronic device can collect the user's voice obtained through a microphone during a voice call of the electronic device.

[0082] According to one embodiment, the electronic device can acquire sensing data from at least one of a GPS, an accelerometer, a barometric pressure sensor, a fingerprint sensor, a gyroscope, a geomagnetic sensor, a heart rate sensor, an iris recognition sensor, a pressure sensor, a proximity sensor, a light sensor, a temperature sensor, or an oxygen saturation (SpO2) sensor.

[0083] According to one embodiment, the electronic device may acquire at least one of exercise data, sleep data, or stress data analyzed based on sensing data acquired from at least one sensor.

[0084] According to one embodiment, the electronic device can obtain usage history data stored in the electronic device. For example, the electronic device can obtain usage history data from at least one of a phone application, a messaging application (e.g., text or chat), an SNS application, and a note application.

[0085] According to one embodiment, the electronic device can acquire at least one of an image, video, or log data stored in memory as storage content.

[0086] According to one embodiment, the electronic device periodically monitors the acquired data and can detect changes in each data. According to one embodiment, the operation of detecting abnormal behavior of a user based on the acquired data will be described below with reference to FIG. 8.

[0087] According to one embodiment, in operation 320, the electronic device can identify an expected disease corresponding to the abnormal behavior based on the detection of abnormal behavior.

[0088] According to one embodiment, the electronic device can identify a disease that maps to abnormal behavior among disease-specific behavioral characteristics by comparing data detected as abnormal behavior among acquired data with disease-specific behavioral characteristics. For example, the electronic device can identify a disease as a predicted disease that includes behavioral characteristics with high similarity to the detected data among a plurality of disease-specific behavioral characteristics.

[0089] According to one embodiment, the behavioral characteristics for each disease may be stored in memory. According to one embodiment, the electronic device may receive data regarding the behavioral characteristics for each disease from an external server via a communication circuit (e.g., the communication module (190) of FIG. 1) and store the received information in memory. According to one embodiment, the external server may include a database that stores data (e.g., related factors) regarding the behavioral characteristics for each disease.

[0090] According to one embodiment, there may be one or more expected diseases. According to one embodiment, if there are two or more expected diseases, the electronic device may obtain a list of expected diseases. According to one embodiment, the operation of obtaining a list of expected diseases will be further explained below with reference to FIG. 7.

[0091] According to one embodiment, in operation 330, the electronic device can use personal data including at least one of user voice or sensing data, and data regarding the expected disease as input data for an artificial intelligence model stored in memory (e.g., memory (130) of FIG. 1) to determine whether the expected disease has occurred, and obtain a question related to the expected disease and a correct answer corresponding to the question as output data of the artificial intelligence model.

[0092] According to one embodiment, the artificial intelligence model may be trained to use disease diagnostic test forms and personal data as input data to obtain questions and answers for diagnosing a disease based on personal data as output data.

[0093] According to one embodiment, the artificial intelligence model may be stored on a server. If the artificial intelligence model is stored on a server, the electronic device can transmit personal data to the server and receive questions and answers generated based on the personal data from the server.

[0094] According to one embodiment, the questions and answers obtained from the artificial intelligence model are generated based on the user's personal data, and if the user's personal data changes, the questions and answers obtained may also change.

[0095] According to one embodiment, when usage history data is further used as input data for an artificial intelligence model, the electronic device can obtain questions and answers generated based on the usage history data as output data for the artificial intelligence model.

[0096] According to one embodiment, when stored content is further used as input data for an artificial intelligence model, the electronic device can obtain a question and a correct answer generated based on the stored content as output data for the artificial intelligence model.

[0097] According to one embodiment, the operation of obtaining questions and answers using the user's personal data will be explained below with reference to FIG. 10.

[0098] According to one embodiment, the electronic device can determine whether the generated question is sufficient to determine whether a disease has developed or whether the correct answer to the generated question can be obtained based on personal data.

[0099] According to one embodiment, the electronic device may display a message for obtaining additional information when it is necessary to generate additional questions to determine whether a disease has occurred, or when it is difficult to obtain the correct answer to the generated questions based on personal data. For example, the message for obtaining additional information may include a message for requesting additional personal data.

[0100] For example, the message may include at least one of a request message to enable a disabled sensor among at least one sensor, a message to change the sensitivity of at least one sensor, or a request message to add a user action.

[0101] According to one embodiment, the electronic device may further use personal data obtained after a message display as input data to obtain questions and answers from an artificial intelligence model. According to one embodiment, the operation of requesting and obtaining additional information will be explained below with reference to FIGS. 9 and FIGS. 11.

[0102] According to one embodiment, in operation 340, the electronic device may provide a question through a display (e.g., the display module (160) of FIG. 1). However, the method by which the electronic device provides the question is not limited to the examples described above. For example, the electronic device may provide the question in the form of voice through a speaker in addition to or in place of the display.

[0103] According to one embodiment, the electronic device may sequentially provide generated questions and request a user's answer.

[0104] According to one embodiment, in operation 350, the electronic device may receive a user answer to a question. According to one embodiment, the electronic device may receive an answer to a question provided through user input such as voice, text, touch, or drawing.

[0105] According to one embodiment, the operation of providing a question and receiving an answer will be explained below with reference to FIGS. 15a and FIGS. 15b.

[0106] According to one embodiment, in 360 operation, the electronic device can determine whether an expected disease has occurred based on a comparison of the user's answer and the correct answer.

[0107] According to one embodiment, the electronic device can determine whether an expected disease will occur through the similarity between the answer received via user input and the generated correct answer.

[0108] According to one embodiment, the electronic device can improve the accuracy of the predicted onset of disease by transmitting the answer received through user input and the generated correct answer to an external expert, and by receiving feedback from the external expert regarding whether the predicted disease will occur.

[0109] According to one embodiment, in operation 370, the electronic device may provide a result of determining whether an expected disease has occurred through a display. However, the method by which the electronic device provides a result of determining whether an expected disease has occurred is not limited to the examples described above. For example, the electronic device may provide a result of determining whether an expected disease has occurred in the form of voice through a speaker, either additionally to the display or in place of the display.

[0110] According to one embodiment, the electronic device may notify the user of whether a disease has occurred and provide results. For example, the electronic device may provide a message suggesting hospital treatment and detailed results indicating that the onset of a disease is suspected. According to one embodiment, the operation of providing results on determining whether a disease has occurred will be described below with reference to FIG. 16.

[0111] According to one embodiment, the memory may store a user database that stores personal data. According to one embodiment, the electronic device may store in the user database at least one of a question generated based on personal data, a correct answer, an answer received through user input, or a result of determining whether an expected disease has occurred. According to one embodiment, the operation of configuring the user database will be described with reference to FIG. 12.

[0112] According to one embodiment, the electronic device may exclude questions stored in a user database from among the questions obtained as output data of an artificial intelligence model based on the detection of new abnormal behavior related to an expected disease. For example, questions identical to those previously provided to determine whether an expected disease has occurred may be excluded, and new questions related to the new abnormal behavior may be provided to the user.

[0113] According to one embodiment, the electronic device may receive update data related to an expected disease from an external server. For example, the external server may be a server storing medical specialized data. According to one embodiment, if an update of information, such as a new paper related to the disease, is performed on the external server, the electronic device may receive the update data from the external server.

[0114] According to one embodiment, the electronic device can further use the received update data to train an artificial intelligence model.

[0115] According to one embodiment, the electronic device may transmit received update data to an external server that stores disease-specific behavioral characteristics. According to one embodiment, the external server that stores disease-specific behavioral characteristics may update disease-specific behavioral characteristics using the update data received from the electronic device.

[0116] According to one embodiment, the operation of updating an artificial intelligence model or disease-specific behavioral characteristics using update data related to expected diseases will be explained below with reference to FIG. 13.

[0117] In this way, suspected symptoms of a user's disease can be recognized early by utilizing personal data acquired through electronic devices, and the user's health can be monitored by determining whether a disease has developed through questions generated based on personal data.

[0118] FIG. 4 is a diagram illustrating the operation of determining whether a disease has occurred through monitoring abnormal user behavior of an electronic device according to one embodiment.

[0119] Referring to FIG. 4, an electronic device (e.g., the electronic device (101) of FIG. 1 or the processor (120) of FIG. 1) may acquire personal data of a user (10) for the detection of abnormal behavior. For example, the personal data may include user voice data (401), sensor data (402) acquired from at least one sensor of the electronic device (e.g., the sensor module (176) of FIG. 1), and usage history data and / or stored content such as photos, videos, and log data acquired by the user (10) using the electronic device.

[0120] According to one embodiment, the electronic device can use user voice data (401) to analyze the emotions or mood of the user (10) or to check whether the speech is unclear.

[0121] According to one embodiment, the electronic device can convert user voice data (401) into text through an automatic speech recognition (ASR) module (403). According to one embodiment, the electronic device can use the converted text as input data for anomaly detection.

[0122] According to one embodiment, sensor data (402) may be obtained from at least one of a GPS, an accelerometer, a barometric pressure sensor, a fingerprint sensor, a gyroscope, a geomagnetic sensor, a heart rate sensor, an iris recognition sensor, a pressure sensor, a proximity sensor, a light sensor, a temperature sensor, or an oxygen saturation (SpO2) sensor.

[0123] According to one embodiment, the electronic device may acquire at least one of exercise data, sleep data, or stress data analyzed based on sensor data (402) acquired from at least one sensor.

[0124] According to one embodiment, the electronic device can obtain usage history data stored in the electronic device. For example, the electronic device can obtain usage history data from at least one of a phone application, a messaging application (e.g., text or chat), an SNS application, and a note application.

[0125] According to one embodiment, the electronic device can acquire at least one of an image, video, or log data stored in memory as storage content.

[0126] According to one embodiment, the electronic device can perform abnormal behavior detection (410) using at least one of text data, sensor data (402), usage history data, or stored content obtained through the ARS module (403).

[0127] For example, the electronic device can detect abnormalities in the user's (10) activity pattern by using GPS sensor data. According to one embodiment, the electronic device can detect abnormalities in the user's (10) mental or emotional state based on call content or conversation content from a message application. According to one embodiment, the electronic device can detect abnormalities in the user's (10) gait by using accelerometer sensor data or gyroscope sensor data.

[0128] According to one embodiment, the electronic device may perform anomaly detection (410) by monitoring the personal data of the user (10) based on disease-specific related factor data received from a disease-specific related factor database (DB) (411). According to one embodiment, the disease-specific related factor database (411) may include a database defining the correlation of related factors for detecting anomalies in user behavior associated with a disease. Through the disease-specific related factor database (411), the disease corresponding to the abnormal behavior can be identified. For example, the related factors may include at least one of activity patterns related to the user's movement, emotions related to the content of conversation (e.g., calls or texts), gait patterns, or sleep patterns. According to one embodiment, the abnormal behavior detection (410) operation will be described below with reference to FIG. 5.

[0129] According to one embodiment, the electronic device may generate a query (e.g., a question and an answer) (420). For example, the electronic device may generate a user-customized question using an artificial intelligence model to further examine whether there is an onset of a disease expected through abnormal behavior detection (410). For example, the electronic device may generate a user-customized question using the user's personal data.

[0130] According to one embodiment, when generating a user-customized question, the electronic device may select a questionnaire type corresponding to an expected disease from among questionnaire types corresponding to each disease. According to one embodiment, if a question based on personal data is required for the selected questionnaire type, the electronic device may generate a question based on the personal data and generate a correct answer for the generated question.

[0131] According to one embodiment, the electronic device can generate a correct answer to a generated question based on the generated question and personal data obtained from the electronic device. According to one embodiment, the electronic device can generate a user-customized question and correct answer using the user's personal data stored in the user information database (421).

[0132] According to one embodiment, if there is insufficient personal data required for generating a question or a correct answer, the electronic device may generate a question and a correct answer after obtaining additional data required for generating the question and a correct answer through sensor control (450). For example, the electronic device may perform sensor control (450) by activating a deactivated sensor or by increasing the sensitivity of an activated sensor. According to one embodiment, the electronic device may induce the user to perform sensor control (450) by displaying a request to activate a deactivated sensor or a request to increase the sensitivity of an activated sensor to the user.

[0133] According to one embodiment, the electronic device may perform a user query (430) based on a generated question. For example, the electronic device may provide the generated question to the user and receive a user answer to the question.

[0134] According to one embodiment, the electronic device may perform symptom determination (440) based on the received user response and the generated correct answer. For example, it may determine whether the user exhibits symptoms of an expected disease based on the similarity between the user response and the generated correct answer.

[0135] According to one embodiment, the electronic device may adjust the disease-specific related factor database (411) based on the results of symptom identification (440). For example, the electronic device may adjust the relationship between the disease and the related factor when the correlation between abnormal behavior and user responses is low. For example, the electronic device may adjust the reference values ​​related to the disease-specific related factor.

[0136] According to one embodiment, the electronic device may correct user information stored in the user information database (421) based on the results of symptom determination (440). For example, the electronic device may store information regarding whether a disease has occurred in relation to the user's personal data in the user information database (421). For example, information regarding abnormal behavior related to the user's voice data, sensor data, usage history data, or stored content, respectively, may be stored in the user information database (421). According to one embodiment, the electronic device may store information regarding whether a disease has occurred for each piece of information on abnormal behavior in the user information database (421).

[0137] According to one embodiment, the medical professional data database (460) may be used for error review regarding the criteria and judgment content used when determining whether a disease has occurred. For example, the medical professional data database (460) may perform error review through a group of experts or medical professional data and provide feedback on the symptom identification (440) action and / or feedback on disease-specific related factors.

[0138] According to one embodiment, the electronic device may notify the user of the symptom determination result and issue a report (470) regarding the symptom determination result. The electronic device may provide a message and detailed results suggesting hospital treatment, indicating that the onset of a disease is suspected. According to one embodiment, the operation of providing the determination result of whether the expected disease has occurred will be explained below with reference to FIG. 16.

[0139] FIG. 5 is a diagram illustrating the operation of generating a questionnaire to determine whether a disease has occurred through monitoring abnormal user behavior of an electronic device according to one embodiment.

[0140] Referring to FIG. 5, an electronic device (e.g., the electronic device (101) of FIG. 1 or the processor (120) of FIG. 1) can perform abnormal behavior detection (410) using collected input data (501) and monitoring factor information (502) received from a disease-specific related factor database (DB) (411). According to one embodiment, the monitoring factor information (502) may include disease-specific trends or amounts of change.

[0141] According to one embodiment, the electronic device monitors input data (501) and, when abnormal behavior is detected (510), can identify a predicted disease by determining which disease the abnormal behavior is highly associated with. For example, if a specific input data among the input data (501) deviates from a reference range, the electronic device can classify it as a factor associated with abnormal behavior.

[0142] According to one embodiment, the electronic device may select at least one predicted disease (520) by comparing the disease-specific related factors received from the disease-specific related factor database (411) with input data related to abnormal behavior. According to one embodiment, the input data related to abnormal behavior and / or information related to at least one predicted disease may be used to generate a question (420) for determining whether a disease has occurred. According to one embodiment, the electronic device may add, exclude, and / or adjust the correlation coefficients of related factors in the disease-specific related factor database (411) based on the selected predicted disease results.

[0143] FIG. 6 is a diagram illustrating the operation of determining whether a disease has developed through monitoring abnormal user behavior of an electronic device according to one embodiment. For example, FIG. 6 may be an example of determining panic disorder through GPS data monitoring.

[0144] Referring to FIG. 6, an electronic device (e.g., the electronic device (101) of FIG. 1 or the processor (120) of FIG. 1) can obtain information (610) related to a disease through an expert or related database that is an external server. For example, the electronic device can obtain information (610) related to the cause or symptoms of a disease through an expert or a related database containing research or papers. For example, in the case of panic disorder, information (610) related to panic disorder can be obtained, stating that if autonomic nervous system symptoms persist and anxiety increases, the user with the disease will have their living radius narrowed and become restricted.

[0145] According to one embodiment, the electronic device can detect abnormal behavior by utilizing a GPS sensor to collect user activity data (620) and monitoring whether a change in the range of movement occurs based on the acquired activity data (622). For example, the electronic device can calculate the average daily travel distance using GPS sensor data and exclude values ​​that confuse the average value, such as the minimum value (e.g., the bottom approximately 20%) and the maximum value (e.g., the top approximately 20%). According to one embodiment, if the average daily travel distance decreases by about 30% or less compared to the existing average daily travel distance when comparing the currently monitored GPS sensor data with the average daily travel distance, the average daily travel distance can be selected as an abnormal behavior factor for screening. If symptoms of a decrease of about 30% are confirmed for 5 or more days out of 10, an abnormal behavior factor related to the range of movement can be determined.

[0146] According to one embodiment, the electronic device can detect diseases related to abnormal behavior-related factors and additionally review several other factors to suspect a panic disorder (622).

[0147] According to one embodiment, the electronic device may generate a user query (e.g., a question and / or a correct answer) related to panic disorder and provide the generated question to the user.

[0148] According to one embodiment, a question may be provided to the user, and then the occurrence of a disease may be determined based on the received answer (623). According to one embodiment, if it is difficult to determine whether a disease has occurred due to reasons such as a lack of data, the electronic device may determine whether a disease has occurred (623) by adding expert feedback to the question and the received answer.

[0149] According to one embodiment, the electronic device can notify the user of the test results related to whether a disease has occurred and issue a report (624).

[0150] According to one embodiment, the electronic device can update the judgment criteria for the disease through expert feedback.

[0151] FIG. 7 is a diagram illustrating the operation of identifying an expected disease through monitoring user abnormal behavior of an electronic device according to one embodiment.

[0152] Referring to FIG. 7, an electronic device (e.g., the electronic device (101) of FIG. 1 or the processor (120) of FIG. 1) can acquire GPS data (710), gyroscope and accelerometer sensor data (720), voice / chat data (730) and / or sleep data (740) as personal data.

[0153] According to one embodiment, the electronic device can detect abnormal behavior using acquired personal data. For example, the electronic device can detect abnormalities (711) in activity patterns through GPS data (710). For example, the electronic device can detect abnormal behaviors such as the user wandering aimlessly, going to places not usually visited, or narrowing or widening the radius of daily life using GPS data (710).

[0154] According to one embodiment, the electronic device can detect abnormalities in gait (721) through gyroscope and accelerometer sensor data (720). For example, the electronic device can detect abnormal behavior such as when the stride length becomes smaller, when the speed of the gait becomes slower, and / or when the left and right gait performance differs.

[0155] According to one embodiment, the electronic device can detect conversation anomalies (731) through voice / chat data (730). For example, the electronic device can detect abnormal behavior such as when the speed of conversation changes during a call, when the response speed to the other party's conversation changes, and / or when the typing speed slows down during a messenger conversation.

[0156] According to one embodiment, the electronic device can detect sleep abnormalities (741) through sleep data (740). For example, the electronic device can detect abnormal behaviors such as changes in sleep duration, bedtime, irregular REM / non-REM sleep, and / or a drop in oxygen saturation levels.

[0157] According to one embodiment, the electronic device can list expected diseases (750) based on the detection of abnormalities in activity patterns (711), abnormalities in gait (721), abnormalities in conversation (731) and / or abnormalities in sleep (741).

[0158] FIG. 8 is a diagram illustrating the operation of generating a questionnaire to determine whether a disease has occurred through monitoring abnormal user behavior of an electronic device according to one embodiment.

[0159] Referring to FIG. 8, an electronic device (e.g., the electronic device (101) of FIG. 1 or the processor (120) of FIG. 1) can detect an abnormality in at least one factor while monitoring a plurality of factors (810) which are input data. For example, the electronic device can define a threshold value for each factor in which a disease is suspected and identify at least one factor that exceeds the threshold as an abnormal factor.

[0160] According to one embodiment, the electronic device may classify at least one abnormal factor into at least one abnormal behavior factor group (820, 821). For example, the abnormal behavior factor group (820, 821) may be related to different symptoms (e.g., cognitive ability, memory, language ability, attention span), and the same factor may be included in each of the different groups.

[0161] According to one embodiment, the electronic device may select the suspected behavior (831) of the first disease (e.g., dementia) that is most highly associated with the data included in the first abnormal behavior factor group (820) and the multiple diseases stored in the disease-specific related factor database (830). According to one embodiment, the electronic device may select the suspected behavior (832) of the second disease (e.g., depression) that is most highly associated with the data included in the second abnormal behavior factor group (821) and the multiple diseases stored in the disease-specific related factor database (830).

[0162] According to one embodiment, if the electronic device is identified as a suspected behavior (831) of a first disease (e.g., dementia), it may generate a questionnaire (840) for testing for a first disease (e.g., dementia). For example, the electronic device may generate a user-customized question to determine whether the first disease has developed, based on a plurality of factors (810) which are input data and a first disease diagnostic test form.

[0163] According to one embodiment, if the electronic device is identified as a suspected behavior (832) of a second disease (e.g., depression), it may generate a questionnaire (841) for testing for the second disease (e.g., depression). For example, the electronic device may generate a user-customized question to determine whether the second disease has developed, based on a plurality of factors (810) which are input data and a second disease diagnostic test form.

[0164] FIG. 9 is a diagram illustrating the operation of collecting additional information when it is difficult to generate a questionnaire of an electronic device according to one embodiment.

[0165] Referring to FIG. 9, an electronic device (e.g., the electronic device (101) of FIG. 1 or the processor (120) of FIG. 1) can generate a questionnaire (930) and an answer sheet (931) for disease diagnosis based on expected disease information (910), personal data stored in a user database (911), and / or a diagnostic test form (912) according to the disease, using an artificial intelligence model, a screening question generation model (920). According to one embodiment, the answer sheet (931) is intended to determine the user's answer to the questionnaire (930) and may be generated based on the user's personal data.

[0166] According to one embodiment, the diagnostic test form (912) for a disease may include a questioning method and certified and standardized basic question information for diagnosis for each disease. For example, for dementia and cognitive impairment tests, diagnostic tests such as CERAD, BRSD, MMSE, ADAS, SBT, HDS, FAB, TICS, SIRQD, SMCQ, SCIRS, GDS, and PHQ may be used. According to one embodiment, some parts of the questionnaire (930) may be generated as personalized questions using personal data, while others may include general questions related to disease diagnosis.

[0167] For example, when the Mini-Mental State Exam (MMSE) for the diagnosis of dementia and cognitive impairment is used as a diagnostic test form (912) for a disease, examples of the generated questionnaire and answer sheet are as shown in [Table 1] below.

[0168] Category No Point Evaluation Item Questionnaire Generation Answer Key Generation 1. Orientation 10~5 Check today's date What is today's date? What is the current time? Utilizing device time May 29, 2024 7:20 20~4 Check (home) address 1. If home address is in personal information - Please tell me your home address? 2. Utilizing device information (Example) - What is your phone number? - Is it AM or PM? - Who did you call yesterday evening? - Where did you go last Saturday? 129beon-gil, Samseong-ro, Yeongtong-gu, Suwon-si, Gyeonggi-do Device Question 010-5383-0000 PM Sonju Jeju-do 30~1 Evaluation location (living room, house, apartment, etc.) (Generate query based on device information) Country currently living (GPS / WIFI based) Korea 2. Memory Registration & Recall 40~3 Memory Recall Test - Repeating 3 words (e.g., tree, car, hat) (Based on user voice (call, message, etc.)) These are three words you used a lot yesterday. Please repeat them. Subway, noodles, hat Subway, noodles, hat 50~3 3~5 minutes later Try to remember the three words mentioned earlier Say the three words mentioned a moment ago. Subway, noodles, hat 3. Attention & Calculation 60~5 Subtract 7 from 100, repeat 4 times What is 100 minus 7? What is 7 minus 7 again? What is 7 minus 7 again? What is 7 minus 7 again? 9 3 8 6 7 9 7 2 4. Language Function 70~2 Presenting an object and asking what it is (Presenting general household items) (Analyzing an object in a gallery photo) Who is the person / place in the photo? Son, Jeju Island 80~3 1 sheet of paper, speaking and writing words (Utilizing words frequently used by the user) Write down the word OOOO OOOO 90~15 Drawing two overlapping polygons Draw the following picture. 100~1 Reading along with "Soy sauce factory manager" Read along with the following: "Soy sauce factory manager" Soy sauce factory manager 5. Comprehension & Judgment 110~1 "Why do we wash our clothes before wearing them?" Answer the following questions."Why do you wash your clothes before wearing them?" "Because they are dirty." 120~1 If you find someone else's ID card on the street, how can you easily return it to the owner? Please tell me your thoughts on the following question. "If you find an ID card on the street, how can you easily return it to the owner?" "I should go to the police station and report it."

[0169] Referring to [Table 1], the electronic device can generate questionnaires and answer sheets based on personal data collected from the electronic device for some items of orientation, memory registration and recall, and language function among the five classification items.

[0170] For example, regarding information that is unlikely to be stored in the electronic device, such as the home address of item 2 for testing orientation, the electronic device can change the type of problem for the item and generate a questionnaire and answer sheet as shown in FIG. 10 below based on personal data stored in the electronic device.

[0171] According to one embodiment, the electronic device may request a user examination (940) using a questionnaire (930) output from an examination question generation model (920). For example, the electronic device may provide the questionnaire (930) to the user and receive a response from the user.

[0172] According to one embodiment, the electronic device may perform a user evaluation (950) regarding the onset of a disease by comparing the received user answer with the answer sheet (931) output from the examination question generation model (920). According to one embodiment, the electronic device may provide the user evaluation results to the user. For example, the electronic device may provide whether the disease has occurred, detailed examination results, and a suggestion for visiting a hospital.

[0173] According to one embodiment, the screening question generation model (920) may output additional question generation shortage items (932) as output data when there is insufficient input data for generating a questionnaire (930) and / or an answer sheet (931). According to one embodiment, the electronic device may perform a request for necessary information collection (941) to obtain additional input data related to the question generation shortage items (932). For example, the electronic device may perform a request to activate a deactivated sensor, a request to increase the sensitivity of the sensor, a request for additional data input, and / or a request for additional data measurement.

[0174] According to one embodiment, when additional information is collected (960) in accordance with a request for collection of necessary information (941), the electronic device can generate a questionnaire (930) and an answer sheet (931) by updating the user database (911) based on the additional information and inputting the information of the updated user database (911) into a screening question generation model (920).

[0175] FIG. 10 is a diagram illustrating the operation of generating questions and answers using personal data of an electronic device according to one embodiment.

[0176] Referring to FIG. 10, an electronic device (e.g., the electronic device (101) of FIG. 1 or the processor (120) of FIG. 1) can generate a user-customized questionnaire (1020) and an answer sheet (1030) based on usage data (1010) that can be obtained using the electronic device. According to one embodiment, the usage data (1010) may include personal data stored in the electronic device (e.g., phone number, call log, image, sensor data) and data that can be obtained from the electronic device (e.g., current time).

[0177] For example, an electronic device can generate a question such as "What is your (or a specific person's) phone number?" and a corresponding answer such as "010-5383-0000" based on a phone number.

[0178] According to one embodiment, the electronic device can generate a question such as "Is it AM or PM now?" and a corresponding answer such as "PM" based on current time information.

[0179] According to one embodiment, the electronic device can generate a question such as "Who did you call last night?" and a corresponding answer such as "grandchild" based on a call record.

[0180] According to one embodiment, the electronic device can generate a question such as "Where did you go last Saturday?" and a corresponding answer such as "Jeju Island" based on personal data (e.g., GPS data).

[0181] In this way, the electronic device can generate user-customized questions and answers by generating questions and answers based on the user's personal data.

[0182] FIG. 11 is a diagram illustrating the operation of collecting additional information when it is difficult to generate a questionnaire of an electronic device according to one embodiment.

[0183] Referring to FIG. 11, in operation 1110, an electronic device (e.g., the electronic device (101) of FIG. 1 or the processor (120) of FIG. 1) can determine whether there is a lack of information for generating a questionnaire. According to one embodiment, the electronic device can determine that there is a lack of information not only for generating a questionnaire but also for generating an answer sheet corresponding to the questionnaire.

[0184] According to one embodiment, in operation 1120, the electronic device may select a request method for obtaining additional information. For example, the electronic device may request sensor control or request user action to obtain additional information. According to one embodiment, the electronic device may request sensor control or user action through a message or voice.

[0185] According to one embodiment, in operation 1130, the electronic device may request sensor control. For example, the electronic device may request the activation of a deactivated sensor or request a change in settings related to the measurement cycle or sensitivity of the sensor. According to one embodiment, the electronic device may request sensor control from the user or directly perform the necessary sensor control. For example, the electronic device may request the activation of a deactivated GPS sensor to check the user's range of motion or change the heart rate measurement cycle from 10 minutes to 1 minute to closely observe changes in heart rate.

[0186] According to one embodiment, in the operation of 1131, the electronic device may request user action. For example, the electronic device may request the user to input specific information, take measurements, and / or confirm. For example, it may request the user to input their date of birth to determine the average step count by age group, or to activate the BIA module and perform measurements to check the BIA score.

[0187] According to one embodiment, the electronic device may request sensor control and user behavior together. According to one embodiment, if the electronic device needs to quantify sensor data for questionnaire generation, it may request to acquire sensor data and request the input of related numerical, quantitative, or indicative data. For example, after requesting the activation of a pulse sensor, if the sensed pulse is irregular, the electronic device may request the user to input their mood state based on this, thereby quantifying emotion-related data such as the user's depression.

[0188] FIG. 12 is a diagram illustrating the operation of configuring a user database of an electronic device according to one embodiment.

[0189] Referring to FIG. 12, an electronic device (e.g., the electronic device (101) of FIG. 1 or the processor (120) of FIG. 1) may collect acquired personal data (1201). According to one embodiment, the personal data (1201) may be raw data acquired from the electronic device during daily life. For example, the personal data (1201) may include voice data, text data and / or sensor data (e.g., GPS, gyroscope, accelerometer, sleep, heart rate and / or BIA).

[0190] According to one embodiment, the electronic device may convert each collected raw data into a metadata format (1211) defined for each factor (1210). According to one embodiment, the electronic device may analyze and calculate the correlation between the converted data, which is a personal factor, and the disease using a predefined disease-specific related factor relationship database (1221). For example, the electronic device may calculate the incidence rate of the disease based on the personal factor. According to one embodiment, the electronic device may store the disease-specific correlation obtained based on the personal factor in a user database (1230). According to one embodiment, the user database (1230) may store factor-specific metadata, factor-specific related disease correlations, and numerical values ​​of major diseases (e.g., incidence rate) in the user's general state.

[0191] According to one embodiment, the user database (1230) is important information for generating questions and determining diseases, and can determine early whether a disease has occurred by managing major diseases highly associated with the user and monitoring the incidence rate.

[0192] FIG. 13 is a diagram illustrating the operation of determining whether a disease has occurred in an electronic device according to one embodiment.

[0193] Referring to FIG. 13, an electronic device (e.g., the electronic device (101) of FIG. 1 or the processor (120) of FIG. 1) may receive feedback regarding whether the user is expected to develop a disease through an external server (1310). For example, the external server (1310) may be related to medical professional materials and may be a server associated with professional materials related to a group of experts or research and papers.

[0194] According to one embodiment, the electronic device may update the disease-specific related factor relationship database (1320) based on feedback from an external server (1310) to update the screening method. For example, if the correlation between abnormal behavior and the query result is low, the relationship between the expected disease and the factor used for disease monitoring may be adjusted. This allows for grouping factors associated with a specific disease and improving the accuracy of monitoring abnormal behavior by correcting the reference values ​​defined for each factor.

[0195] According to one embodiment, the electronic device can correct the result through the symptom determination (1330) operation through feedback from an external server (1310).

[0196] According to one embodiment, the electronic device can correct user information related to the expected disease (e.g., factors related to abnormal behavior and / or correlation with the onset of the disease) by correcting the result through a symptom determination (1330) operation, and can update the user information database (1340) based on the corrected user information.

[0197] FIG. 14 is a diagram illustrating an operation of an electronic device according to one embodiment that proposes performing a health check when abnormal behavior of a user is detected.

[0198] Referring to FIG. 14, an electronic device (e.g., the electronic device (101) of FIG. 1 or the processor (120) of FIG. 1) may provide a notification (1410) requesting a survey to check for the onset of a suspected disease associated with the suspected disease when abnormal behavior is detected based on collected personal data. For example, if an abnormality in the user's cognitive behavior is detected through GPS sensor data or application usage history, or if the range of motion is reduced, the electronic device may identify symptoms of dementia or cognitive impairment as a suspected disease and request a health check to determine whether the suspected disease has developed.

[0199] According to one embodiment, when the electronic device receives user input selecting a UI (1411) to perform a health check, it may sequentially provide generated questions as illustrated in FIG. 15a and FIG. 15b and request answers to the questions.

[0200] FIG. 15a is a diagram illustrating the operation of an electronic device according to one embodiment that provides a question to check the user's health.

[0201] FIG. 15b is a diagram illustrating the operation of an electronic device according to one embodiment that provides a question to check the user's health.

[0202] Referring to FIGS. 15a and 15b, an electronic device (e.g., the electronic device (101) of FIG. 1 or the processor (120) of FIG. 1) can display a plurality of generated questions one by one and receive answers to the displayed questions.

[0203] According to one embodiment, screens (1510, 1520, 1530, 1540, 1550, 1560) containing each question (1511, 1521, 1531, 1541, 1551, 1561) may include a UI (1512) for entering an answer and a UI (1513) for displaying the next question.

[0204] For example, the first screen (1510) may include a first question (1511) generated based on the date information of the electronic device (e.g., What is today's date?). According to one embodiment, the electronic device may receive user voice as an answer to the first question (1511) after touching or while holding the UI (1512) for entering an answer. According to one embodiment, when user input selecting the UI (1512) for entering an answer is received, the electronic device may display a text input window and a soft keyboard for entering an answer as text.

[0205] According to one embodiment, the electronic device may display a second screen (1520) when user input is received selecting a UI (1513) to display the next question after entering an answer. For example, the second screen (1520) may include a second question (1521) generated based on GPS sensor data of the electronic device (e.g., What country do you currently live in?).

[0206] According to one embodiment, a third screen (1530) displayed after the second screen (1520) may include a third question (1531) generated based on an image stored in an electronic device (e.g., "It is a gallery photo. Who is it?") and one image (1532) containing a person among a plurality of images stored in the gallery.

[0207] According to one embodiment, a fourth screen (1534) displayed after a third screen (1530) may include a fourth question (1541) generated based on general items for disease diagnosis (e.g., "Draw the following shape"), a shape image (1542), and a UI (1543) for tracing the shape image (1542).

[0208] According to one embodiment, when the electronic device receives user input selecting a UI (1543) for drawing along a shape image (1542), it may display a fifth screen (1550) for drawing along the shape image (1542). For example, the fifth screen (1550) may include a shape image and a drawing pad (1551) for drawing the shape image. According to one embodiment, the electronic device may analyze the user's response by comparing the drawing image (1552) and the shape image entered on the drawing pad (1551).

[0209] According to one embodiment, a sixth screen (1560) displayed after the fifth screen (1550) may include a fifth question (1561) generated based on general items for disease diagnosis (e.g., I found a passport. How can I easily return it to the owner?).

[0210] In this way, by utilizing the user's personal data to generate questions and answers that elicit various types of responses, the electronic device becomes able to determine whether a disease has occurred based on the correct answers and the user's responses without the judgment of external experts.

[0211] FIG. 16 is a diagram illustrating the operation of providing a user's health check result of an electronic device according to one embodiment.

[0212] Referring to FIG. 16, an electronic device (e.g., the electronic device (101) of FIG. 1 or the processor (120) of FIG. 1) can determine whether an expected disease has occurred after a survey including multiple questions as shown in FIG. 15a and FIG. 15b is completed and provide the determination result to the user.

[0213] According to one embodiment, the electronic device may display a screen (1610) containing a judgment result on a display (e.g., the display module (160) of FIG. 1). For example, the screen (1610) containing the judgment result may include a UI for checking a disease-related score, whether a disease has occurred, a hospital visit suggestion message and / or details.

[0214] According to one embodiment, the electronic device may provide the judgment result to the user as voice.

[0215] According to one embodiment, when the electronic device receives user input selecting a UI to check details, it may display a screen (1620) containing detailed results for each question. For example, detailed results for each question may include the correct answer for each question, the user's response, and a score based on the correct answer and the response.

[0216] FIG. 17 is a diagram illustrating the operation of requesting additional information of an electronic device according to one embodiment.

[0217] Referring to FIG. 17, an electronic device (e.g., the electronic device (101) of FIG. 1 or the processor (120) of FIG. 1) may provide a request (1710) for obtaining additional information when there is insufficient data for generating a question and / or an answer. For example, the request (1710) may include a request for sensor control. For example, the electronic device may request (1711) to enable a disabled sensor or to change settings related to the sensor's measurement cycle or sensitivity.

[0218] FIG. 18 is a diagram illustrating the operation of requesting additional information of an electronic device according to one embodiment.

[0219] Referring to FIG. 18, an electronic device (e.g., the electronic device (101) of FIG. 1 or the processor (120) of FIG. 1) may provide a request (1810) for obtaining additional information when there is insufficient data for generating questions and / or answers. For example, the request (1810) may include a request for user behavior. For example, if the electronic device has not stored blood pressure measurement data for the previous two months, it may induce the user to measure their blood pressure to use as data for identifying abnormal behavior and generating questions / answers.

[0220] According to one embodiment, when the electronic device selects a UI to perform a user action (e.g., blood pressure measurement) through a request (1810) to obtain additional information, the user action can be performed through at least one sensor of the electronic device.

[0221] According to one embodiment, when the electronic device selects a UI for performing a user action (e.g., blood pressure measurement) through a request (1810) to obtain additional information, the user action can be induced through a sensor of an connected external electronic device. For example, the external electronic device can display a UI (1820) for the blood pressure measurement process.

[0222] According to one embodiment, if the electronic device is a VST (video see-through) device, the electronic device can detect abnormal user behavior by using video information in addition to existing sensors. According to one embodiment, the electronic device can provide questions and receive answers via video and / or audio by diversifying the form of the questions.

[0223] According to one embodiment, if the electronic device is a robot-shaped device rather than a wearable device, it can check the user's movements from a second-person perspective looking at the user and detect whether there is abnormal behavior. According to one embodiment, the electronic device can detect abnormal behavior through sensors included in a mobile robot, or change the method of questioning, such as in the form of a conversation or a reaction and movement when the user answers.

[0224] According to one embodiment, as remote medical services are expected to expand in the future, various wearable robot devices are expected to be used to monitor the user's life (Life Logging). By using the user's life log information, the user's dietary habits and / or exercise habits can be verified, and appropriate prescriptions can be given after detecting abnormal behavior. In the case of a diagnosed disease, it can act as a health assistant to manage health status through sensing data, and it can also be used for care services to support the daily lives of the elderly.

[0225] For example, electronic device settings can be customized to suit specific diseases, and detailed configurations can be performed to enhance monitoring of relevant factors. In this way, the electronic device can diagnose diseases based on monitoring data and provide user-customized settings and services for lifestyle improvement and disease treatment through additional questioning and data acquisition. In the case of helper robots, they can assist users or adjust the level of intervention through learning from personal data, allowing for mutual complementarity rather than one-sided service by adjusting the level required by the user. By acquiring health data and predicting future health beyond just personalized services, they can provide customized services tailored to the user's needs.

[0226] According to one embodiment, the electronic device can create a database of users based on collected sensor data, and use information stored in the database to detect abnormal behavior and generate questions and answers capable of identifying related diseases. According to one embodiment, by comparing the user's answer to the question with the correct answer, appropriate treatment suggestions and / or prescriptions can be issued. Additionally, personal data can be utilized to predict expected diseases using an artificial intelligence model.

[0227] According to one embodiment, the electronic device of the present disclosure may provide customized settings and services for improvement and / or treatment after a disease is identified by linking with a hospital. For example, when it is confirmed that an anticipated disease has developed, it may provide a hospital appointment and / or a prescription after a medical consultation.

[0228] According to one embodiment, the electronic device may include a microphone, at least one sensor including a sensing circuit, a display, at least one processor including a processing circuit, and a memory for storing instructions.

[0229] According to one embodiment, when the instructions are executed individually or collectively by the at least one processor, the electronic device may detect abnormal behavior based on at least one of user voice obtained through the microphone or sensing data obtained by the at least one sensor.

[0230] According to one embodiment, when the instructions are executed individually or collectively by the at least one processor, the electronic device may identify a predicted disease corresponding to the abnormal behavior based on the detection of the abnormal behavior.

[0231] According to one embodiment, when the instructions are executed individually or collectively by the at least one processor, the electronic device may use personal data including at least one of the user voice or the sensing data, and data regarding the expected disease, as input data for an artificial intelligence model stored in the memory to determine whether the expected disease has occurred, thereby obtaining a question related to the expected disease and a correct answer corresponding to the question as output data of the artificial intelligence model.

[0232] According to one embodiment, when the instructions are executed individually or collectively by the at least one processor, the electronic device may provide the question.

[0233] According to one embodiment, when the instructions are executed individually or collectively by the at least one processor, the electronic device may receive a user answer to the question.

[0234] According to one embodiment, when the instructions are executed individually or collectively by the at least one processor, the electronic device may determine whether the expected disease has occurred based on a comparison of the user's answer and the correct answer.

[0235] According to one embodiment, when the instructions are executed individually or collectively by the at least one processor, the electronic device may provide a result of determining whether the expected disease has occurred.

[0236] According to one embodiment, the artificial intelligence model may be trained to use disease diagnostic test forms and personal data as input data, and to obtain questions and answers for diagnosing the disease based on the personal data as output data.

[0237] According to one embodiment, the personal data may further include usage history data including at least one of calls or text messages of the electronic device.

[0238] According to one embodiment, when the instructions are executed individually or collectively by the at least one processor, the electronic device may obtain the question and the answer generated based on the usage history data as output data of the artificial intelligence model.

[0239] According to one embodiment, the personal data may further include storage content comprising at least one of an image or a video stored in the memory.

[0240] According to one embodiment, when the instructions are executed individually or collectively by the at least one processor, the electronic device may obtain the question and the answer generated based on the stored content as output data of the artificial intelligence model.

[0241] According to one embodiment, when the instructions are executed individually or collectively by the at least one processor, the electronic device may display a message through the display requesting additional personal data based on the determination that it is difficult to obtain the correct answer based on the input data.

[0242] According to one embodiment, the message may include at least one of a request message to activate a disabled sensor among the at least one sensor, a message to change the sensitivity of the at least one sensor, or a request message to add a user action.

[0243] According to one embodiment, when the instructions are executed individually or collectively by the at least one processor, the electronic device may further utilize the personal data obtained after the message display as the input data to obtain the question and the answer from the artificial intelligence model.

[0244] According to one embodiment, the electronic device may further include a communication circuit.

[0245] According to one embodiment, when the instructions are executed individually or collectively by the at least one processor, the electronic device may receive data regarding disease-specific behavioral characteristics from a first external server through the communication circuit.

[0246] According to one embodiment, when the instructions are executed individually or collectively by the at least one processor, the electronic device may identify the disease corresponding to the behavioral characteristic mapped to the abnormal behavior among the disease-specific behavioral characteristics as the expected disease.

[0247] According to one embodiment, the memory may store a user database that stores the personal data.

[0248] According to one embodiment, when the instructions are executed individually or collectively by the at least one processor, the electronic device may store at least one of the question, the correct answer, the user answer, or the result of determining whether the expected disease has occurred in the user database.

[0249] According to one embodiment, when the instructions are executed individually or collectively by the at least one processor, the electronic device may exclude questions stored in the user database from among the questions obtained as output data of the artificial intelligence model based on the detection of new abnormal behavior related to the expected disease.

[0250] According to one embodiment, when the instructions are executed individually or collectively by the at least one processor, the electronic device may receive update data related to the expected disease from a second external server through the communication circuit.

[0251] According to one embodiment, when the instructions are executed individually or collectively by the at least one processor, the electronic device may be able to learn the artificial intelligence model using the update data further.

[0252] According to one embodiment, when the instructions are executed individually or collectively by the at least one processor, the electronic device may receive update data related to the expected disease from a second external server through the communication circuit.

[0253] According to one embodiment, when the instructions are executed individually or collectively by the at least one processor, the electronic device may transmit the update data to the first external server through the communication circuit to update the disease-specific behavioral characteristics stored in the first external server.

[0254] According to one embodiment, a control method for an electronic device may include an operation to detect abnormal behavior based on at least one of a user voice obtained through a microphone of the electronic device or sensing data obtained by at least one sensor of the electronic device.

[0255] According to one embodiment, a control method for an electronic device may include an operation to identify a predicted disease corresponding to the abnormal behavior based on the detection of the abnormal behavior.

[0256] According to one embodiment, a control method for an electronic device may include, for determining whether the expected disease has occurred, using personal data including at least one of the user voice or the sensing data, and data regarding the expected disease as input data for an artificial intelligence model stored in the memory of the electronic device, and obtaining a question related to the expected disease and a correct answer corresponding to the question as output data of the artificial intelligence model.

[0257] According to one embodiment, a control method for an electronic device may include an operation of providing the question.

[0258] According to one embodiment, a method for controlling an electronic device may include receiving a user's answer to the question.

[0259] According to one embodiment, a control method for an electronic device may include an operation of determining whether the expected disease has occurred based on a comparison between the user's answer and the correct answer.

[0260] According to one embodiment, the control method of an electronic device may include an operation of providing a result of determining whether the expected disease has occurred.

[0261] According to one embodiment, the artificial intelligence model may be trained to use disease diagnostic test forms and personal data as input data, and to obtain questions and answers for diagnosing the disease based on the personal data as output data.

[0262] According to one embodiment, the control method of an electronic device may further include usage history data in which the personal data includes at least one of a call or a text message of the electronic device.

[0263] According to one embodiment, the operation of obtaining the question related to the expected disease and the answer corresponding to the question may include the operation of obtaining the question and the answer generated based on the usage history data as output data of the artificial intelligence model.

[0264] According to one embodiment, the control method of the electronic device may further include storage content in which the personal data includes at least one of an image or a video stored in the memory.

[0265] According to one embodiment, the operation of obtaining the question related to the expected disease and the correct answer corresponding to the question may include the operation of obtaining the question and the correct answer generated based on the stored content as output data of the artificial intelligence model.

[0266] According to one embodiment, the control method of an electronic device may further include the operation of displaying a message through the display to request additional personal data based on the finding that it is difficult to obtain the correct answer based on the input data.

[0267] According to one embodiment, the message may include at least one of a request message to activate a disabled sensor among the at least one sensor, a message to change the sensitivity of the at least one sensor, or a request message to add a user action.

[0268] According to one embodiment, the operation of obtaining the question related to the expected disease and the correct answer corresponding to the question may include the operation of obtaining the question and the correct answer from the artificial intelligence model by further utilizing the personal data obtained after the message display as the input data.

[0269] According to one embodiment, the control method of an electronic device may further include the operation of receiving data regarding disease-specific behavioral characteristics from a first external server through a communication circuit of the electronic device.

[0270] According to one embodiment, the operation of identifying the expected disease corresponding to the abnormal behavior may include identifying the disease corresponding to the behavioral characteristic mapped to the abnormal behavior among the behavioral characteristics of the disease as the expected disease.

[0271] According to one embodiment, the memory may store a user database that stores the personal data.

[0272] According to one embodiment, the control method of an electronic device may further include the operation of storing at least one of the question, the correct answer, the user answer, or the result of determining whether the expected disease has occurred in the user database.

[0273] According to one embodiment, the control method of an electronic device may further include the operation of excluding questions stored in the user database among the questions obtained as output data of the artificial intelligence model based on the detection of new abnormal behavior related to the expected disease.

[0274] According to one embodiment, the control method of an electronic device may further include the operation of receiving update data related to the expected disease from a second external server through a communication circuit of the electronic device.

[0275] According to one embodiment, the control method of an electronic device may further include the operation of learning the artificial intelligence model using the update data.

[0276] According to one embodiment, the control method of an electronic device may further include the operation of receiving update data related to the expected disease from a second external server through the communication circuit.

[0277] According to one embodiment, the control method of an electronic device may further include the operation of transmitting the update data to the first external server through the communication circuit to update the disease-specific behavioral characteristics stored in the first external server.

[0278] According to one embodiment, in a non-transient computer-readable storage medium storing one or more programs, the one or more programs may include instructions that cause an electronic device to detect abnormal behavior based on at least one of user voice acquired through the microphone or sensing data acquired by the at least one sensor.

[0279] According to one embodiment, the one or more programs may include instructions that cause an electronic device to identify a predicted disease corresponding to the abnormal behavior based on the detection of the abnormal behavior.

[0280] According to one embodiment, the one or more programs may include instructions for an electronic device to determine whether the expected disease has occurred, by using personal data including at least one of the user voice or the sensing data, and data regarding the expected disease as input data for an artificial intelligence model stored in the memory, and obtaining a question related to the expected disease and a correct answer corresponding to the question as output data for the artificial intelligence model.

[0281] According to one embodiment, the one or more programs may include instructions that cause an electronic device to provide the question.

[0282] According to one embodiment, the one or more programs may include instructions that cause an electronic device to receive a user answer to the question.

[0283] According to one embodiment, the one or more programs may include instructions that cause an electronic device to determine whether the expected disease has occurred based on a comparison of the user's answer and the correct answer.

[0284] According to one embodiment, the one or more programs may include instructions that cause an electronic device to provide a result of determining whether the expected disease has occurred.

[0285] According to one embodiment, the artificial intelligence model may be trained to use disease diagnostic test forms and personal data as input data, and to obtain questions and answers for diagnosing the disease based on the personal data as output data.

[0286] According to one embodiment, the personal data may further include usage history data including at least one of calls or text messages of the electronic device.

[0287] According to one embodiment, the one or more programs may include instructions that cause an electronic device to obtain the question and the answer generated based on the usage history data as output data of the artificial intelligence model.

[0288] According to one embodiment, the personal data may further include storage content comprising at least one of an image or a video stored in the memory.

[0289] According to one embodiment, the one or more programs may include instructions that cause an electronic device to obtain the question and the answer generated based on the stored content as output data of the artificial intelligence model.

[0290] According to one embodiment, the one or more programs may include instructions that cause an electronic device to display a message requesting additional personal data through the display based on the finding that it is difficult to obtain the correct answer based on the input data.

[0291] According to one embodiment, the message may include at least one of a request message to activate a disabled sensor among the at least one sensor, a message to change the sensitivity of the at least one sensor, or a request message to add a user action.

[0292] According to one embodiment, the one or more programs may include instructions that cause an electronic device to obtain the question and the answer from the artificial intelligence model by further using the personal data obtained after the message display as the input data.

[0293] According to one embodiment, the electronic device may further include a communication circuit.

[0294] According to one embodiment, the one or more programs may include instructions that cause an electronic device to receive data regarding disease-specific behavioral characteristics from a first external server through the communication circuit.

[0295] According to one embodiment, the one or more programs may include instructions that cause an electronic device to identify a disease corresponding to a behavioral characteristic mapped to the abnormal behavior among the disease-specific behavioral characteristics as the expected disease.

[0296] According to one embodiment, the memory may store a user database that stores the personal data.

[0297] According to one embodiment, the one or more programs may include instructions that cause an electronic device to store at least one of the question, the correct answer, the user answer, or the result of determining whether the expected disease has occurred in the user database.

[0298] According to one embodiment, the one or more programs may include instructions that cause the electronic device to exclude questions stored in the user database from among the questions obtained as output data of the artificial intelligence model based on the detection of new abnormal behavior related to the expected disease.

[0299] According to one embodiment, the one or more programs may include instructions that cause an electronic device to receive update data related to the expected disease from a second external server through the communication circuit.

[0300] According to one embodiment, the one or more programs may include instructions that cause an electronic device to learn the artificial intelligence model by further using the update data.

[0301] According to one embodiment, the one or more programs may include instructions that cause an electronic device to receive update data related to the expected disease from a second external server through the communication circuit.

[0302] According to one embodiment, the one or more programs may include instructions that cause an electronic device to transmit the update data to the first external server through the communication circuit so that the electronic device updates the disease-specific behavioral characteristics stored in the first external server.

[0303] The electronic device according to the embodiments disclosed in this document may be of various forms. The electronic device may include, for example, a portable communication device (e.g., a smartphone), a computer device, a portable multimedia device, a portable medical device, a camera, a wearable device, or a consumer electronics device. The electronic device according to the embodiments of this document is not limited to the devices described above.

[0304] The embodiments of this document and the terms used therein are not intended to limit the technical features described in this document to specific embodiments, and should be understood to include various modifications, equivalents, or substitutions of said embodiments. In connection with the description of the drawings, similar reference numerals may be used for similar or related components. The singular form of a noun corresponding to an item may include one or more of said items unless the relevant context clearly indicates otherwise. In this document, phrases such as "A or B," "at least one of A and B," "at least one of A or B," "A, B or C," "at least one of A, B and C," and "at least one of A, B, or C" each may include any one of the items listed together in the corresponding phrase, or all possible combinations thereof. Terms such as "first," "second," or "first" or "second" may be used simply to distinguish said components from other said components and do not limit said components in any other aspect (e.g., importance or order). Where any (e.g., 1st) component is referred to as “coupled” or “connected” to another (e.g., 2nd) component, with or without the terms “functionally” or “communicationly,” it means that said any component may be connected to said other component directly (e.g., via a wire), wirelessly, or through a third component.

[0305] The term “module” as used in the embodiments of this document may include a unit implemented in hardware, software, or firmware, and may be used interchangeably with terms such as logic, logic block, component, or circuit, for example. A module may be a component formed integrally, or a minimum unit of said component or a part thereof that performs one or more functions. For example, according to one embodiment, a module may be implemented in the form of an application-specific integrated circuit (ASIC).

[0306] One embodiment of the present document may be implemented as software (e.g., program (140)) comprising one or more instructions stored in a storage medium (e.g., internal memory (136) or external memory (138)) readable by a machine (e.g., electronic device (101)). For example, a processor (e.g., processor (120)) of the machine (e.g., electronic device (101)) may call at least one of the one or more instructions stored in the storage medium and execute it. This enables the machine to be operated to perform at least one function according to the at least one called instruction. The one or more instructions may include code generated by a compiler or code that can be executed by an interpreter. The storage medium readable by the machine may be provided in the form of a non-transitory storage medium. Here, 'non-temporary' simply means that the storage medium is a tangible device and does not contain a signal (e.g., electromagnetic waves), and the term does not distinguish between cases where data is stored semi-permanently and cases where it is stored temporarily.

[0307] According to one embodiment, the method according to the embodiments disclosed herein may be provided by being included in a computer program product. The computer program product may be traded between a seller and a buyer as a product. The computer program product may be distributed in the form of a device-readable storage medium (e.g., compact disc read-only memory (CD-ROM)), or distributed online (e.g., download or upload) through an application store (e.g., Play Store™) or directly between two user devices (e.g., smartphones). In the case of online distribution, at least a portion of the computer program product may be temporarily stored or temporarily created on a device-readable storage medium, such as the memory of a manufacturer's server, an application store's server, or a relay server.

[0308] According to one embodiment, each component (e.g., module or program) of the components described above may include a singular or multiple entities, and some of the multiple entities may be separated and placed in other components. According to one embodiment, one or more of the components or operations of the aforementioned components may be omitted, or one or more other components or operations may be added. Generally or additionally, multiple components (e.g., module or program) may be integrated into a single component. In this case, the integrated component may perform one or more functions of each of the components of the multiple components in the same or similar manner as those performed by the corresponding components among the multiple components prior to integration. According to one embodiment, operations performed by the module, program, or other components may be executed sequentially, in parallel, iteratively, or heuristically, or one or more of the operations may be executed in a different order, omitted, or one or more other operations may be added.

Claims

1. In an electronic device, mike; At least one sensor including a sensing circuit; display; At least one processor including a processing circuit; and Includes memory for storing instructions; and When the above instructions are executed individually or collectively by the at least one processor, the electronic device: Detecting abnormal behavior based on at least one of user voice acquired through the microphone or sensing data acquired by at least one sensor, and Based on the detection of the above abnormal behavior, identify the expected disease corresponding to the above abnormal behavior, and To determine whether the above-mentioned expected disease has occurred, personal data including at least one of the user voice or the sensing data, and data regarding the above-mentioned expected disease are used as input data for an artificial intelligence model stored in the memory, and a question related to the above-mentioned expected disease and a correct answer corresponding to the question are obtained as output data of the artificial intelligence model. Providing the above question, Receive user responses to the above question, and Determining whether the above-mentioned expected disease will occur based on a comparison of the above-mentioned user answer and the above-mentioned correct answer, and Provides a result of determining whether the above-mentioned expected disease will occur, and The above artificial intelligence model is, An electronic device trained to use disease diagnostic test forms and personal data as input data, and to obtain questions and answers for the diagnosis of the disease based on the personal data as output data.

2. In Paragraph 1, The above personal data further includes usage history data comprising at least one of calls or text messages of the electronic device, and When the above instructions are executed individually or collectively by the at least one processor, the electronic device, An electronic device that obtains the question and the answer generated based on the usage history data as output data of the artificial intelligence model.

3. In Paragraph 2, The above personal data further includes stored content comprising at least one of an image or video stored in the memory, and When the above instructions are executed individually or collectively by the at least one processor, the electronic device, An electronic device that obtains the question and the answer generated based on the stored content using the output data of the artificial intelligence model.

4. In Paragraph 2, When the above instructions are executed individually or collectively by the at least one processor, the electronic device, An electronic device that displays a message requesting additional personal data through the display based on the finding that it is difficult to obtain the correct answer based on the input data.

5. In Paragraph 4, The above message includes at least one of a request message to activate a disabled sensor among the at least one sensor, a message to change the sensitivity of the at least one sensor, or a request message to add a user action. When the above instructions are executed individually or collectively by the at least one processor, the electronic device, An electronic device that further utilizes personal data obtained after displaying the above message as input data to obtain the above question and the above answer from the above artificial intelligence model.

6. In Paragraph 1, Including a communication circuit; further When the above instructions are executed individually or collectively by the at least one processor, the electronic device, Receiving data on disease-specific behavioral characteristics from a first external server through the above communication circuit, and An electronic device that identifies a disease corresponding to a behavioral characteristic mapped to the abnormal behavior among the behavioral characteristics of the above disease as the above-mentioned expected disease.

7. In Paragraph 1, The above memory stores a user database that stores the above personal data, and When the above instructions are executed individually or collectively by the at least one processor, the electronic device, An electronic device that stores at least one of the above question, the above correct answer, the above user answer, or the result of determining whether the above expected disease has occurred in the above user database.

8. In Paragraph 7, When the above instructions are executed individually or collectively by the at least one processor, the electronic device, An electronic device that excludes questions stored in the user database from among the questions obtained as output data of the artificial intelligence model, based on the detection of new abnormal behavior related to the above-mentioned expected disease.

9. In Paragraph 1, Including a communication circuit; further When the above instructions are executed individually or collectively by the at least one processor, the electronic device, Receive update data related to the expected disease from a second external server through the above communication circuit, and An electronic device that further utilizes the above update data to train the above artificial intelligence model.

10. In Paragraph 1, Including a communication circuit; further When the above instructions are executed individually or collectively by the at least one processor, the electronic device, Receive update data related to the predicted disease from a second external server through the above communication circuit, and An electronic device that transmits the update data to the first external server through the communication circuit to update the disease-specific behavioral characteristics stored in the first external server.

11. In a method for controlling an electronic device, An operation to detect abnormal behavior based on at least one of user voice acquired through a microphone of the electronic device or sensing data acquired by at least one sensor of the electronic device; An action of identifying an expected disease corresponding to the above abnormal behavior based on the detection of the above abnormal behavior; To determine whether the above-mentioned expected disease has occurred, the operation of using personal data including at least one of the user voice or the sensing data, and data regarding the above-mentioned expected disease as input data for an artificial intelligence model stored in the memory of the electronic device, and obtaining a question related to the above-mentioned expected disease and a correct answer corresponding to the question as output data of the artificial intelligence model; Action providing the above question; Action of receiving a user response to the above question; An operation to determine whether the above-mentioned expected disease occurs based on a comparison of the above-mentioned user answer and the above-mentioned correct answer; and The operation of providing a result of determining whether the above-mentioned expected disease has occurred; is included, The above artificial intelligence model is, A method for controlling an electronic device that is learned to use disease diagnostic test forms and personal data as input data, and to obtain questions and answers for the diagnosis of the disease based on the personal data as output data.

12. In Paragraph 11, The above personal data further includes usage history data comprising at least one of calls or text messages of the electronic device, and The operation of obtaining the above question related to the above-mentioned expected disease and the correct answer corresponding to the above question is, A method for controlling an electronic device, comprising the operation of obtaining the question and the correct answer generated based on the usage history data as output data of the artificial intelligence model.

13. In Paragraph 12, The above personal data further includes stored content comprising at least one of an image or video stored in the memory, and The operation of obtaining the above question related to the above-mentioned expected disease and the correct answer corresponding to the above question is, A method for controlling an electronic device, comprising the operation of obtaining the question and the correct answer generated based on the stored content as output data of the artificial intelligence model.

14. In Paragraph 12, A control method for an electronic device further comprising: an operation of displaying a message requesting additional personal data through the display of the electronic device based on the confirmation that it is difficult to obtain the correct answer based on the input data above.

15. In a non-transient computer-readable storage medium storing one or more programs, said one or more programs are: an electronic device: Detecting abnormal behavior based on at least one of user voice acquired through the microphone of the electronic device or sensing data acquired by at least one sensor of the electronic device, and Based on the detection of the above abnormal behavior, identify the expected disease corresponding to the above abnormal behavior, and To determine whether the above-mentioned expected disease has occurred, personal data including at least one of the user voice or the sensing data, and data regarding the above-mentioned expected disease are used as input data for an artificial intelligence model stored in the memory of the electronic device, and a question related to the above-mentioned expected disease and a correct answer corresponding to the question are obtained as output data of the artificial intelligence model. Providing the above question, Receive user responses to the above question, and Determining whether the above-mentioned expected disease will occur based on a comparison of the above-mentioned user answer and the above-mentioned correct answer, and Includes instructions that provide a result of determining whether the above-mentioned expected disease has occurred, and The above artificial intelligence model is, A storage medium that is trained to use disease diagnostic test forms and personal data as input data, and to obtain questions and answers for the diagnosis of the disease based on the personal data as output data.

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