Electronic device for managing health of user by using lifelog information, and control method therefor

Abstract

Provided are an electronic device and a control method therefor. The electronic device comprises: a display; a memory for storing instructions; and at least one processor. The instructions, when executed individually or collectively by at least one processor, cause the electronic device to: acquire first biometric information associated with a disease of interest of a user and lifelog information of the user; acquire information about a correlation between the first biometric information and the lifelog information by using the first biometric information and the lifelog information; when new lifelog information is acquired, identify whether a health risk event occurs, through the new lifelog information on the basis of the information about the correlation; and when it is identified that the health risk event has occurred, provide a guidance message corresponding to the health risk event on the basis of the new lifelog information.

Description

Electronic device for managing user health using lifelog information and method for controlling the same

[0001] The present disclosure relates to an electronic device and a method for controlling the same, and more specifically, to an electronic device and a method for controlling the same that can detect a user's health event using lifelog information and provide a guidance message to the user.

[0002] Recently, health management services are being provided to users using electronic devices such as smartphones. Meanwhile, patients with chronic diseases, particularly those suffering from hypertension or diabetes, must manage their conditions through daily routines. For example, they must measure their blood pressure or check their blood sugar and take their morning medication immediately upon waking up. These activities are crucial because they are closely linked to the management of the patient's condition. However, there is a problem in that when new changes occur in daily routines—such as the addition of schedules or unexpected environmental shifts—in addition to fixed daily activities, it is difficult to predict how these changes will affect the management of the chronic disease.

[0003] Meanwhile, 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.

[0004] According to one embodiment of the present disclosure, an electronic device comprises: a display; a memory for storing instructions; and at least one processor. When the instructions are executed individually or collectively by the at least one processor, the electronic device acquires first biometric information and a user’s lifelog information associated with a disease of interest to the user, and acquires information regarding the correlation between the first biometric information and the lifelog information using the first biometric information and the lifelog information. When new lifelog information is acquired, the device identifies whether a health risk event occurs through the new lifelog information based on the information regarding the correlation, and when it is identified that a health risk event has occurred, the device provides a guidance message corresponding to the health risk event based on the new lifelog information.

[0005] The above lifelog information includes a plurality of types of information, and when the instructions are executed individually or collectively by the at least one processor, the electronic device may identify a first type of lifelog information that has a high correlation with the first biometric information when it is determined that a health risk event related to the user's disease of interest occurs among the plurality of types of lifelog information.

[0006] When the above instructions are executed individually or collectively by the at least one processor, the electronic device may identify whether a health risk event occurs by identifying whether the new lifelog information of the first type matches the acquired lifelog information when it is determined that a health risk event related to the user's disease of interest has occurred.

[0007] The above lifelog information includes second biometric information different from the first biometric information, and when the instructions are executed individually or collectively by the at least one processor, the electronic device may identify whether a health risk event occurs by identifying whether the sensing value of the new second biometric information matches the sensing value of the second biometric information obtained when it is determined that a health risk event related to the user's disease of interest has occurred, if the first type of lifelog information is the second biometric information.

[0008] If the above-mentioned first type of lifelog information is the above-mentioned second biometric information, the guidance message may include at least one of information regarding the user's disease of interest, information regarding the correlation between the above-mentioned first biometric information and the above-mentioned second biometric information, information regarding the sensing value of the above-mentioned new second biometric information, and health management information related to the disease of interest.

[0009] The above lifelog information includes at least one of image information, consumption history information, schedule information, application usage information, location information, mail information, message information, contact information, and search information, and when the instructions are executed individually or collectively by the at least one processor, the electronic device acquires a new context text from the first type of lifelog information when the first type of lifelog information is one of image information, consumption history information, schedule information, application usage information, location information, mail information, message information, contact information, and search information, and can identify whether a health risk event occurs by identifying whether the new context text matches the acquired context text when it is determined that a health risk event related to the user's disease of interest is occurring.

[0010] If the above-mentioned first type of lifelog information is one of image information, consumption history information, schedule information, application usage information, location information, mail information, message information, contact information, and search information, the above-mentioned guidance message may include at least one of information regarding the user's disease of interest, information regarding the correlation between the first biometric information and the first type of lifelog information, information regarding the new context text, and health management information related to the disease of interest.

[0011] When the above instructions are executed individually or collectively by the at least one processor, the electronic device may acquire information about the user's disease of interest according to user input received through the execution screen of the health management application, or acquire information about the user's disease of interest based on usage information of the electronic device.

[0012] When the above instructions are executed individually or collectively by the at least one processor, if the electronic device is configured to obtain the lifelog information through the execution screen of a health management application, the lifelog information can be collected through a plurality of applications installed on the electronic device.

[0013] Meanwhile, a control method for an electronic device according to one embodiment of the present disclosure comprises: a step of acquiring first biometric information and a user’s lifelog information associated with a disease of interest to the user; a step of acquiring information regarding the correlation between the first biometric information and the lifelog information using the first biometric information and the lifelog information; a step of identifying whether a health risk event occurs through the new lifelog information based on the information regarding the correlation when new lifelog information is acquired; and a step of providing a guidance message corresponding to the health risk event based on the new lifelog information when it is identified that the health risk event has occurred.

[0014] The above lifelog information includes a plurality of types of information, and the step of obtaining information regarding the correlation can identify a first type of lifelog information that has a high correlation with the first biometric information when it is determined that a health risk event related to the user's disease of interest occurs among the plurality of types of lifelog information.

[0015] The above identifying step can identify whether a health risk event occurs by identifying whether the new lifelog information of the first type matches the acquired lifelog information when it is determined that a health risk event related to the user's disease of interest occurs, once the new lifelog information is acquired.

[0016] The above lifelog information includes second biometric information that is different from the first biometric information, and the identifying step can identify whether a health risk event occurs by identifying whether the sensing value of the new second biometric information matches the sensing value of the second biometric information obtained when it is determined that a health risk event related to the user's disease of interest has occurred, when the first type of lifelog information is the second biometric information.

[0017] If the above-mentioned first type of lifelog information is the above-mentioned second biometric information, the guidance message may include at least one of information regarding the user's disease of interest, information regarding the correlation between the above-mentioned first biometric information and the above-mentioned second biometric information, information regarding the sensing value of the above-mentioned new second biometric information, and health management information related to the disease of interest.

[0018] The above lifelog information includes at least one of image information, consumption history information, schedule information, application usage information, location information, mail information, message information, contact information, and search information, and the identifying step may include: a step of obtaining a new context text from the first type of lifelog information when the first type of lifelog information is one of image information, consumption history information, schedule information, application usage information, location information, mail information, message information, contact information, and search information; and a step of identifying whether a health risk event occurs by identifying whether the new context text matches the obtained context text when it is determined that a health risk event related to the user's disease of interest is occurring.

[0019] If the above-mentioned first type of lifelog information is one of image information, consumption history information, schedule information, application usage information, location information, mail information, message information, contact information, and search information, the above-mentioned guidance message may include at least one of information regarding the user's disease of interest, information regarding the correlation between the first biometric information and the first type of lifelog information, information regarding the new context text, and health management information related to the disease of interest.

[0020] The above control method may include the step of obtaining information about a disease of interest to the user based on user input received through the execution screen of a health management application, or obtaining information about a disease of interest to the user based on usage information of the electronic device.

[0021] The step of acquiring the first biometric information and the lifelog information may include the step of collecting the lifelog information through a plurality of applications installed on the electronic device when a user permission to acquire the lifelog information is set through the execution screen of the health management application.

[0022] Meanwhile, in a non-transient computer-readable medium storing instructions for executing a method of controlling an electronic device according to one embodiment of the present disclosure, the method of controlling the electronic device comprises: a step of acquiring first biometric information associated with a disease of interest of a user and a user's lifelog information; a step of acquiring information regarding the correlation between the first biometric information and the lifelog information using the first biometric information and the lifelog information; a step of identifying whether a health risk event occurs through the new lifelog information based on the information regarding the correlation when new lifelog information is acquired; and a step of providing a guidance message corresponding to the health risk event based on the new lifelog information when it is identified that the health risk event has occurred.

[0023] In relation to the description of the drawings, the same or similar reference numerals may be used for identical or similar components.

[0024] FIG. 1 is a block diagram briefly illustrating the configuration of an electronic device according to one embodiment of the present disclosure.

[0025] FIG. 2 is a flowchart illustrating a method for guiding health risk events using lifelog information according to one embodiment of the present disclosure.

[0026] FIGS. 3a and 3b are flowcharts for explaining the correlation between first biological information and lifelog information according to one embodiment of the present disclosure.

[0027] FIGS. 4a to 5 are drawings for explaining an embodiment of a correlation analysis module learning and providing a guidance message using the learned correlation analysis module according to one embodiment of the present disclosure.

[0028] FIGS. 6a to 8d are drawings for explaining embodiments of providing guidance messages corresponding to health risk events using various lifelog information according to various embodiments of the present disclosure.

[0029] FIG. 9 is a drawing illustrating an example of lifelog information related to a disease according to one embodiment of the present disclosure.

[0030] FIG. 10 is a block diagram illustrating in detail the configuration of an electronic device according to one embodiment of the present disclosure.

[0031] The present disclosure will be described in detail below with reference to the attached drawings.

[0032] The terms used in the embodiments of this disclosure have been selected to be as widely used as possible, taking into account their functions within this disclosure; however, these terms may vary depending on the intent of those skilled in the art, case law, the emergence of new technologies, etc. Additionally, in specific cases, terms may be arbitrarily selected by the applicant, and in such cases, their meanings will be described in detail in the description section of the disclosure. Therefore, terms used in this disclosure should be defined not merely by their names, but based on their meanings and the overall content of this disclosure.

[0033] In this specification, expressions such as “have,” “may have,” “include,” or “may include” indicate the presence of the above features (e.g., numerical values, functions, actions, or components such as parts) and do not exclude the presence of additional features.

[0034] The expression "at least one of A or / and B" should be understood as representing either "A" or "B" or "A and B".

[0035] Expressions such as "first," "second," "first," or "second" used in this specification may modify various components regardless of order and / or importance, and are used only to distinguish one component from another and do not limit said components.

[0036] Where it is stated that a component (e.g., a first component) is "(operatively or communicatively) coupled with / to" or "connected to" another component (e.g., a second component), it should be understood that the component may be directly connected to the other component or connected through the other component (e.g., a third component).

[0037] The singular expression includes the plural expression unless the context clearly indicates otherwise. In this application, terms such as “comprising” or “consisting of” are intended to specify the existence of the features, numbers, steps, actions, components, parts, or combinations thereof described in the specification, and should be understood as not precluding the existence or addition of one or more other features, numbers, steps, actions, components, parts, or combinations thereof.

[0038] In the embodiments, a "module" or "part" performs at least one function or operation and may be implemented in hardware or software, or a combination of hardware and software. Additionally, a plurality of "modules" or a plurality of "parts" may be integrated into at least one module and implemented by at least one processor, except for a "module" or "part" that needs to be implemented in specific hardware.

[0039] In the present disclosure, the term "user" may refer to a person using an electronic device or a device using an electronic device (e.g., an artificial intelligence electronic device).

[0040] In the present disclosure, the term "user input" refers to user input for controlling an electronic device and may be referred to by various terms such as user command, user interaction, user touch, etc.

[0041] In the present disclosure, the UI is a visual and functional layer that enables interaction between an electronic device (100) and a user, and can provide information or control the functions of the electronic device (100). UI elements are components that make up the UI and can be used to interact with the user. In this case, UI elements may be referred to by various terms such as icons, indicators, objects, etc.

[0042] The various elements and areas in the drawings are depicted schematically. Accordingly, the technical concept of the present invention is not limited by the relative sizes or spacing depicted in the attached drawings.

[0043] Embodiments of the present disclosure will be described in more detail below with reference to the attached drawings.

[0044] FIG. 1 illustrates an example of a block diagram of an electronic device according to one embodiment.

[0045] In one embodiment, in terms of being owned by a user, the electronic device (100) may be referred to as a terminal (or user terminal). The terminal may include, for example, a personal computer (PC) such as a laptop and a desktop. The terminal may include, for example, a smartphone, a smartpad, and / or a tablet PC. The terminal may include smart accessories such as a smartwatch and / or a head-mounted device (HMD). According to one embodiment, the electronic device (100) may include a deformable housing. Based on the deformability, the housing of the electronic device (100) may be divided into a plurality of parts.

[0046] According to one embodiment, the electronic device (100) may include at least one of a processor (110), a memory (120), a display (130), a communication circuit (140), or a camera (150). The processor (110), the memory (120), the display (130), the communication circuit (140), and the camera (150) may be electrically and / or operably coupled with each other by an electronic component such as a communication bus.

[0047] In one embodiment, the hardware of the electronic device (100) being operatively coupled may mean that a direct or indirect connection between the hardware is established via wired or wireless means so that the second hardware is controlled by the first hardware among the hardware. Although illustrated based on different blocks, the embodiment is not limited thereto, and some of the hardware of FIG. 1 (e.g., at least a portion of the processor (110), memory (120), and communication circuit (140)) may be included in a single integrated circuit, such as a system on a chip (SoC). The hardware of the electronic device (100) divided into blocks may be located within a first housing (161), a second housing (162), and / or a hinge housing (163). The type and / or number of hardware included in the electronic device (100) is not limited to that illustrated in FIG. 1. For example, the electronic device (100) may include only some of the hardware components illustrated in FIG. 1.

[0048] According to one embodiment, a processor (110) of an electronic device (100) may include hardware for processing data based on one or more instructions. The hardware for processing data may include, for example, an arithmetic and logic unit (ALU), a floating point unit (FPU), a field programmable gate array (FPGA), a central processing unit (CPU), and / or an application processor (AP). The number of processors (110) may be one or more. For example, the processor (110) may have the structure of a multi-core processor such as a dual core, a quad core, or a hexa core.

[0049] According to one embodiment, the memory (120) of the electronic device (100) may include a hardware component for storing data and / or instructions that are input and / or output to the processor (110). The memory (120) may include, for example, volatile memory such as random-access memory (RAM) and / or non-volatile memory such as read-only memory (ROM). Volatile memory may include, for example, at least one of dynamic RAM (DRAM), static RAM (SRAM), cache RAM, and pseudo SRAM (PSRAM). Non-volatile memory may include, for example, programmable ROM (PROM), erasable PROM (EPROM), electrically erasable PROM (EEPROM), flash memory, hard disk, compact disk, solid state drive (SSD), and embedded multimedia card (eMMC).

[0050] According to one embodiment, within the memory (120) of the electronic device (100), one or more instructions (or commands) representing operations and / or operations to be performed on data by the processor (110) may be stored. A set of one or more instructions may be referred to as firmware, an operating system, a process, a routine, a sub-routine, and / or an application. For example, the electronic device (100) and / or the processor (110) may perform various operations when a set of a plurality of instructions distributed in the form of an operating system, firmware, a driver, and / or an application is executed. In the following, the statement that an application is installed on an electronic device (100) means that one or more instructions provided in the form of an application are stored in the memory (120) of the electronic device (100), and that the one or more applications are stored in an executable format (e.g., a file having an extension specified by the operating system of the electronic device (100)) that is executable by the processor (110) of the electronic device (100).

[0051] One or more processors (110) control input data to be processed according to a predefined operation rule or AI model (artificial-intelligence model) stored in memory (120). The predefined operation rule or AI model is characterized by being created through learning. Being created through learning means that a predefined operation rule or AI model with desired characteristics is created by applying a learning algorithm to a number of learning data. Such learning may be performed on the device itself where the artificial intelligence according to the present disclosure is performed, or it may be performed through a separate server / system.

[0052] An AI model may be composed of multiple neural network layers. At least one layer has at least one weight value and performs the layer's operation through the result of the operation of the previous layer and at least one defined operation. Examples of neural networks include Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Deep Neural Networks (DNN), Restricted Boltzmann Machines (RBM), Deep Belief Networks (DBN), Bidirectional Recurrent Deep Neural Networks (BRDNN), Deep Q-Networks, and Transformers; however, the neural networks in this disclosure are not limited to the aforementioned examples except where specified.

[0053] A learning algorithm is a method of training a specific target device (e.g., a robot) using a number of learning data to enable the target device to make decisions or predictions on its own. Examples of learning algorithms include supervised learning, unsupervised learning, semi-supervised learning, or reinforcement learning, and the learning algorithms in this disclosure are not limited to the aforementioned examples except where specified.

[0054] According to one embodiment, a display (130) of an electronic device (100) can output visualized information to a user. For example, the display (130) can be controlled by a controller, such as a GPU (graphic processing unit), to output visualized information to a user. The display (130) may include an OLED (Organic Light Emitting Diodes) display, an LED (Light Emitting Diodes), a micro LED, a Mini LED, a PDP (Plasma Display Panel), a QD (Quantum dot) display, a QLED (Quantum dot light-emitting diodes) display, and / or an e-ink display or / and an e-paper display.

[0055] According to one embodiment, the display (130) may include a first display and a second display. The first display may have at least a partially curved shape and / or a deformable shape. The second display may be implemented as an e-ink display or / and an e-paper display (hereinafter, an e-ink display). According to one example, the second display implemented as an e-ink display may receive power from a power management integrated circuit (PMIC).

[0056] The other display (130) may further include a cover display. The cover display may be placed on one side of the housing that is viewable within a state where the first display is obscured (e.g., folded state). For example, the cover display may be placed in one area of ​​the first housing. The size of the cover display may vary depending on the embodiment.

[0057] According to one embodiment, an electronic device (100) may provide visualized information to a user using a second display. The second display may be placed on a side of the electronic device (100) opposite to one side on which the first display is placed. The second display may be placed in a second housing (162) different from the first housing (161) on which the cover display is placed. The second display may be implemented as an electronic ink display. Unlike outputting visualized information by light, an electronic ink display may provide visualized information by changing the position of particles (e.g., particles having different charges) contained within the electronic ink display through a power signal. For example, each particle may be distinguished by a different color.

[0058] According to one example, the electronic device (100) may temporarily refrain from transmitting a power signal to the second display after providing visualized information (e.g., image content) on the second display. Even after temporarily refraining from transmitting a power signal to the second display, the electronic device (100) may continue to provide the visualized information (e.g., image content) on the second display. While providing visualized information to a user using the second display, the electronic device (100) may reduce power loss of a battery (not shown).

[0059] A communication circuit (140) of an electronic device (100) according to one embodiment may include hardware for supporting the transmission and / or reception of electrical signals between the electronic device (100) and an external device (e.g., a server). The communication circuit (140) may include, for example, at least one of a modem, an antenna, and an optic / electronic converter. The communication circuit (140) may support the transmission and / or reception of electrical signals based on various types of protocols such as Ethernet, LAN (local area network), WAN (wide area network), WiFi (wireless fidelity), NFC (near field communication), Bluetooth, BLE (bluetooth low energy), ZigBee, LTE (long term evolution), 5G NR (new radio), and / or 6G.

[0060] According to one example, the electronic device (100) may be connected to a server based on a wired network and / or a wireless network. The wired network may include a network such as the Internet, a LAN (local area network), a WAN (wide area network), Ethernet, or a combination thereof. The wireless network may include a network such as LTE (long term evolution), 5g NR (new radio), WiFi (wireless fidelity), Zigbee, NFC (near field communication), Bluetooth, BLE (bluetooth low-energy), or a combination thereof. According to one example, the electronic device (100) and the server may be connected indirectly through an intermediate node within the network.

[0061] According to one embodiment, the camera (150) of the electronic device (100) may include one or more light sensors (e.g., a CCD (charged coupled device) sensor, a CMOS (complementary metal oxide semiconductor) sensor) that generate an electrical signal indicating the color and / or brightness of light. The plurality of light sensors included in the camera (150) may be arranged in the form of a two-dimensional grid (2 dimensional array). The camera (150) may acquire the electrical signals of each of the plurality of light sensors substantially simultaneously to generate an image comprising a plurality of pixels arranged in two dimensions corresponding to the light reaching the light sensors of the two-dimensional grid. For example, photo data captured using the camera (150) may refer to a single image acquired from the camera (150). For example, video data captured using the camera (150) may refer to a sequence of a plurality of images acquired from the camera (150) at a specified frame rate. An electronic device (100) according to one embodiment may further include a flash light for outputting light in a direction in which a camera (150) is positioned to receive light. The number of cameras (150) included in the electronic device (100) may be one or more. For example, the electronic device (100) may generate image content to be provided on a display (130) using an image obtained through the camera (150).

[0062] In particular, the processor (110) obtains first biometric information and the user's lifelog information associated with the user's interest disease, obtains information on the correlation between the first biometric information and the lifelog information using the first biometric information and the lifelog information, and when new lifelog information is obtained, identifies whether a health risk event occurs through the new lifelog information based on the information on the correlation, and if it is identified that a health risk event has occurred, provides a guidance message corresponding to the health risk event based on the new lifelog information.

[0063] In one or more embodiments, the lifelog information includes a plurality of types of information, and the processor (110) can identify a first type of lifelog information that has a high correlation with the first biometric information when it is determined that a health risk event related to the user's interest disease occurs among the plurality of types of lifelog information.

[0064] In one or more embodiments, when new lifelog information of the first type is acquired, the processor (110) can identify whether the new lifelog information matches the acquired lifelog information when it is determined that a health risk event related to the user's disease of interest is occurring, thereby identifying whether a health risk event is occurring.

[0065] In one or more embodiments, the lifelog information includes second biometric information that is different from the first biometric information, and the processor (110) can identify whether a health risk event occurs by identifying whether the sensing value of the new second biometric information matches the sensing value of the second biometric information obtained when it is determined that a health risk event related to the user's disease of interest is occurring. At this time, the guidance message may include at least one of information about the user's disease of interest, information about the correlation between the first biometric information and the second biometric information, information about the sensing value of the new second biometric information, and health management information related to the disease of interest.

[0066] In one or more embodiments, the lifelog information includes at least one of image information, consumption history information, schedule information, application usage information, location information, mail information, message information, contact information, and search information. When the first type of lifelog information is one of image information, consumption history information, schedule information, application usage information, location information, mail information, message information, contact information, and search information, the processor (110) obtains a new context text from the first type of lifelog information, and can identify whether a health risk event occurs by identifying whether the new context text matches the obtained context text when it is determined that a health risk event related to the user's disease of interest is occurring. At this time, the guidance message may include at least one of information about the user's disease of interest, information about the correlation between the first biometric information and the first type of lifelog information, information about the new context text, and health management information related to the disease of interest.

[0067] In one or more embodiments, the processor (110) may obtain information about the user's disease of interest based on user input received through the execution screen of a health management application or obtain information about the user's disease of interest based on usage information of the electronic device (100).

[0068] In one or more embodiments, the processor (110) can collect lifelog information through a plurality of applications installed on the electronic device (100) when a user permission to acquire the lifelog information is set through the execution screen of a health management application.

[0069] FIG. 2 is a flowchart illustrating a method for guiding health risk events using lifelog information according to one embodiment of the present disclosure.

[0070] In the following embodiments, each operation may be performed sequentially, but is not necessarily performed sequentially. For example, the order of each operation may be changed, and at least two operations may be performed in parallel.

[0071] According to one or more embodiments, S210 to S260 may be understood to be performed in a processor (e.g., processor (110) of FIG. 1) of an electronic device (e.g., electronic device (100) of FIG. 1).

[0072] In one or more embodiments, the electronic device (100) can obtain information about the user's disease of interest (S210). Here, the disease of interest is a disease that the user possesses or is interested in, and may be various diseases such as, for example, diabetes, hypertension, allergies, etc.

[0073] In one or more embodiments, the electronic device (100) can obtain information about the user's disease of interest based on user input received through the execution screen of a health management application. For example, when the electronic device (100) receives user input selecting one of a plurality of diseases through a UI displayed on the execution screen of a health management application, it can obtain information about the user's disease of interest based on the user input. For example, if the user sets "diabetes" as the disease of interest through the UI displayed on the execution screen of a health management application, the electronic device (100) can identify that the user's disease of interest is diabetes.

[0074] In one or more embodiments, the electronic device (100) can obtain information about a user's disease of interest based on usage information of the electronic device (100). Specifically, the electronic device (100) can obtain information about a user's disease of interest by using the user's application search information, consumption history information, schedule information, mail information, application usage information, message information, contact information, etc. For example, if the user searches for diabetes more than a preset value through a search site, purchases medication related to diabetes, has a schedule to visit a hospital related to diabetes, or receives an email related to diabetes, the electronic device (100) can identify that the user's disease of interest is diabetes.

[0075] In one or more embodiments, the electronic device (100) can obtain information about the user's disease of interest depending on the type of external device connected to the electronic device (100). For example, if the external device connected to the electronic device (100) is a continuous glucose monitoring device, the electronic device (100) can identify that the user's disease of interest is diabetes.

[0076] In one or more embodiments, the electronic device (100) can obtain information about a user's disease of interest based on a message or other contact received from another electronic device, if the message or other contact includes a mention of a specific disease or content expressing a health condition.

[0077] In one or more embodiments, for non-disease health risk information such as allergies, stress, etc., the electronic device (100) can obtain information about the user's disease of interest based on changes in the user's biometric information obtained through a sensor, even without direct user input.

[0078] The electronic device (100) can acquire first biometric information associated with the user's disease of interest and the user's lifelog information (S620). Here, the first biometric information associated with the user's disease of interest is biometric information directly related to the user's disease of interest; for example, if the disease of interest is diabetes, the first biometric information is blood sugar-related information, and if the disease of interest is hypertension, the first biometric information is blood pressure level information. Lifelog information is information recorded (or collected and stored) in the electronic device (100) while the user lives, and may include multiple types of information. For example, lifelog information may include at least one of second biometric information different from the first biometric information, image information, consumption history information, schedule information, application usage information, location information, mail information, message information, contact information, and search information. Meanwhile, if user permission to acquire lifelog information is set through the execution screen of a health management application, the electronic device (100) can collect lifelog information through multiple applications installed on the electronic device (100).

[0079] In addition, the electronic device (100) can obtain lifelog information from an external device (e.g., a smart watch) connected to the electronic device (100).

[0080] The electronic device (100) can obtain information regarding the correlation between the first biometric information and the lifelog information (S230). Here, the correlation may be a numerical value obtained by synthesizing the similarity of the signal pattern and the frequency of occurrence with the first biometric information when the first biometric information enters a section corresponding to a health risk event.

[0081] In particular, the electronic device (100) can identify a first type of lifelog information that has a high correlation with the first biometric information when it is determined that a health risk event related to the user's interest disease occurs among a plurality of types of lifelog information.

[0082] Specifically, the electronic device (100) can identify the occurrence of a health risk event through the first biometric information. In one or more embodiments, the electronic device (100) can identify that a health risk event has occurred in the interval where the first biometric information exceeds a threshold.

[0083] In one or more embodiments, in the case of second biometric information that is different from first biometric information among the lifelog information, the electronic device (100) may obtain information regarding second biometric information with a pattern similar to first biometric information in the section where the first biometric information exceeds a threshold value. That is, in the case of second biometric information with a pattern similar to first biometric information in the section where the first biometric information exceeds a threshold value, the electronic device (100) may indicate that the correlation between the first biometric information and the second biometric information is high. Furthermore, the electronic device (100) may store second biometric information having a correlation greater than or equal to a preset value with the first biometric information as similar biometric information.

[0084] In one or more embodiments, for lifelog information that is not biometric information (e.g., image information, consumption history information, schedule information, application usage information, location information, mail information, message information, contact information, and search information), the electronic device (100) can obtain context text corresponding to the lifelog information. For example, the electronic device (100) can obtain context text by inputting the lifelog information into a learned neural network model. Furthermore, the electronic device (100) can obtain lifelog information corresponding to context text whose frequency is greater than or equal to a preset value in the interval where the first biometric information exceeds a threshold. That is, in the case of lifelog information where the frequency of context text is high in the interval where the first biometric information exceeds a threshold, the electronic device (100) may indicate that the correlation between the first biometric information and the lifelog information is high. Furthermore, the electronic device (100) can store lifelog information having a correlation greater than or equal to a preset value with the first biometric information as similar lifelog information.

[0085] The electronic device (100) can acquire new lifelog information (S240). The electronic device (100) can acquire new lifelog information after storing lifelog information that has a high correlation with the first biometric information. Here, the new lifelog information may include at least one of second biometric information different from the first biometric information, image information, consumption history information, schedule information, application usage information, location information, mail information, message information, contact information, and search information.

[0086] The electronic device (100) can identify whether a health risk event occurs through new lifelog information (S250). Here, a health risk event may refer to an event in which a first biometric information related to the user's disease of interest exceeds a threshold value, causing an abnormality in the user's disease of interest or a high probability of such an event occurring.

[0087] Specifically, when new lifelog information of the first type is acquired, the electronic device (100) can identify whether a health risk event occurs by identifying whether the new lifelog information matches the acquired lifelog information when it is determined that a health risk event related to the user's interest disease occurs.

[0088] In one or more embodiments, when the first type of lifelog information (i.e., lifelog information with high correlation) is the second biometric information, the electronic device (100) can identify whether a health risk event occurs by identifying whether the sensing value of the new second biometric information matches the sensing value of the second biometric information obtained when it is determined that a health risk event related to the user's disease of interest is occurring.

[0089] In one or more embodiments, when the first type of lifelog information (i.e., lifelog information with high correlation) is one of image information, consumption history information, schedule information, application usage information, location information, mail information, message information, contact information, and search information, the electronic device (100) obtains new context text from the first type of lifelog information, and can identify whether a health risk event occurs by identifying whether the new context text matches the obtained context text when it is determined that a health risk event related to the user's disease of interest is occurring.

[0090] That is, the electronic device (100) can identify health risk events for a user's interest disease using lifelog information without a separate first biometric information sensing value.

[0091] When it is identified that a health risk event has occurred (S250-Y), the electronic device (100) may provide a guidance message corresponding to the health risk event based on new lifelog information. Here, the guidance message may provide different information depending on the type of new lifelog information. In one or more embodiments, if the first type of lifelog information is second biometric information, the guidance message may include at least one of information regarding the user's disease of interest, information regarding the correlation between the first biometric information and the second biometric information, information regarding the sensing value of the new second biometric information, and health management information related to the disease of interest. If the first type of lifelog information is one of image information, consumption history information, schedule information, application usage information, location information, mail information, message information, contact information, and search information, the guidance message may include at least one of information regarding the user's disease of interest, information regarding the correlation between the first biometric information and the first type of lifelog information, information regarding new context text, and health management information related to the disease of interest.

[0092] That is, as described above, the electronic device (100) can learn lifelog information related to the user's interest disease and detect health risk events based on the type and sensitivity (e.g., sensing value, etc.) of lifelog information specialized for the user.

[0093] The present disclosure will be described in more detail below with reference to the drawings.

[0094] FIG. 3a is a flowchart illustrating the correlation between first bio-information and second bio-information according to one embodiment of the present disclosure.

[0095] The electronic device (100) can identify second biometric information that has a high correlation with the first biometric information among the lifelog information. Specifically, the electronic device (100) can acquire first biometric information (310) and second to fourth biometric information (320, 330, 340) associated with the user's disease of interest. The electronic device (100) can identify second biometric information (320) (e.g., heart rate variability signal) that increases in a pattern similar to the first biometric information (310) (e.g., arrhythmia signal) in the first interval (300) of the sensing value of the first biometric information (310) among the second to fourth biometric information (320, 330, 340). Here, the first interval (300) is an interval in which the sensing value of the first biometric information (310) increases above a threshold value, and may be an interval for identifying that a health risk event has occurred. For example, the first section (300) may be a section where a user's disease related to arrhythmia is suspected. And, the electronic device (100) can identify the second biometric information (320) as pseudo-biometric information. The electronic device (200) can identify whether a health risk event has occurred based on the sensing value of the second biometric information (320) identified as pseudo-biometric information.

[0096] FIG. 3b is a flowchart illustrating the correlation between a first biometric information and lifelog information according to one embodiment of the present disclosure.

[0097] The electronic device (100) can identify lifelog information that has a high correlation with the first biometric information among the lifelog information. Specifically, the electronic device (100) can acquire the first biometric information (360) associated with the user's interest disease and various lifelog information. In particular, the electronic device (100) can identify context text identified in the first interval (350) for the sensing value of the first biometric information (360) (e.g., body temperature). Here, the first interval (350) is an interval in which the sensing value of the first biometric information (350) changes above a threshold value, and may be an interval for identifying that a health risk event has occurred. In particular, the first interval (350) is an interval related to the first time point (380) in which the user inputs that an allergy has occurred or detects an allergy, and may be an interval in which the body temperature, which is the first biometric signal, changes above a threshold value. In particular, during the first period (350), the electronic device (100) can obtain information regarding a first context text (370-1) corresponding to a lifelog of entering a specific place and information regarding a second context text (370-2) corresponding to a lifelog of consuming a specific food. For example, if an image or schedule of a user entering a "basement" is identified, the electronic device (100) inputs the image information or schedule information into a learned neural network model to obtain the first context text (370-1), which is "basement visit," and if an image or search result of a user consuming an "orange" is identified, the electronic device (100) inputs the image information or search information into a learned neural network model to obtain the second context text (370-2), which is "orange consumption." Furthermore, the electronic device (100) can identify the lifelog information of entering a specific place and the lifelog information of consuming a specific food as similar lifelog information. The electronic device (200) can identify whether a health risk event has occurred based on context text identified as similar lifelog information.

[0098] FIG. 4a is a diagram illustrating a method for learning a correlation analysis module according to one embodiment of the present disclosure.

[0099] The electronic device (100) can store a correlation analysis module (420). Here, the correlation analysis module (420) is an initial correlation analysis module that has not been learned by the user's lifelog information, and can store information about the type of first biometric information related to the disease of interest.

[0100] In particular, the electronic device (100) inputs the first biometric information (410) and the lifelog information (430) into the correlation analysis module (430) to identify the lifelog information (430) that has a high correlation with the first biometric information (410).

[0101] Specifically, the electronic device (100) can obtain first biometric information (410) from a sensor included in the electronic device (100) or from an external device (e.g., a blood pressure monitor) connected to the electronic device (100). For example, if the user's disease of interest is hypertension, the electronic device (100) can obtain blood pressure information, which is the first biometric information (410), through a blood pressure sensor.

[0102] The electronic device (100) can obtain at least one of other biometric information, image information, consumption history information, schedule information, application usage information, location information, mail information, message information, contact information, and search information as lifelog information (430), other than the first biometric information (410). For example, the electronic device (100) can identify various lifelog information (430), such as information on the user's heart rate variability, information on stress levels, photo information, schedule information, search information, etc., through an ECG (electrocardiogram) sensor.

[0103] The correlation analysis module (420) can receive the first biometric information (410) and the lifelog information (430). The correlation analysis module (420) can obtain information regarding the correlation between the first biometric information (410) and the lifelog information (430). In particular, the correlation analysis module (420) can analyze the correlation with the lifelog information (430) in the section where it is determined that a health risk event occurs due to the first biometric information (410). The correlation analysis module (420) can identify the first type of lifelog information that has a high correlation with the first biometric information (410) in the section where it is determined that a health risk event related to the user's disease of interest occurs among a plurality of types of lifelog information.

[0104] For example, the correlation analysis module (420) can identify heart rate variability information that shows a signal pattern similar to the first biometric information in the section where it is determined that a health risk event occurs, which is the first biometric information related to hypertension, the disease of interest to the user. Alternatively, the correlation analysis module (420) can identify high-frequency context texts such as "meeting," "presentation," and "sitting posture" in the section where it is determined that a health risk event occurs, which is the first biometric information related to hypertension, the disease of interest to the user.

[0105] That is, the correlation analysis module (420) can analyze the correlation between the first biometric information and the lifelog information to obtain lifelog information that has a high correlation with the first biometric information. By doing so, the correlation analysis module (420) can learn the first biometric information and the lifelog information.

[0106] FIG. 4b is a diagram illustrating an embodiment of providing a guidance message using a learned correlation analysis module according to one embodiment of the present disclosure.

[0107] When the correlation analysis module (420) is learned, the electronic device (100) can acquire the learned correlation analysis module (450). As new lifelog information (440), the electronic device (100) can acquire at least one of biometric information other than the first biometric information, image information, consumption history information, schedule information, application usage information, location information, mail information, message information, contact information, and search information. In particular, the electronic device (100) can acquire new lifelog information that has a high correlation with the first biometric information among a plurality of types of lifelog information.

[0108] The learned correlation analysis module (450) can acquire new lifelog information (440). The learned correlation analysis module (450) can identify whether a health risk event has occurred based on the new lifelog information (440). That is, the learned correlation analysis module (450) can identify whether a health risk event has occurred using only the new lifelog information (440) without the first biometric information (410). In one or more embodiments, the learned correlation analysis module (450) can identify whether a health risk event has occurred by identifying whether similar biometric information among the new lifelog information (440) matches the second biometric information acquired when a health risk event occurs. In one or more embodiments, the learned correlation analysis module (450) can identify whether a health risk event has occurred by identifying whether similar lifelog information among the new lifelog information (440) matches the lifelog information acquired when a health risk event occurs.

[0109] For example, the learned correlation analysis module (450) can identify whether a health risk event has occurred by identifying whether information regarding new heart rate variability among new lifelog information matches heart rate variability information obtained in a section where it is determined that a health risk event is occurring. Alternatively, the learned correlation analysis module (450) can identify whether a health risk event has occurred by identifying whether context text obtained from new lifelog information matches context text obtained in a section where it is determined that a health risk event is occurring, such as "meeting," "presentation," "sitting posture," etc.

[0110] When a health risk event is identified as having occurred, the correlation analysis module (450) may output a health risk signal (460). Here, the electronic device (100) may provide a guidance message (470) based on the health risk signal (460), but this is merely an example of an embodiment, and the guidance message (470) may be provided by transmitting the health risk signal (460) to an external device linked with the electronic device (100).

[0111] The electronic device (100) may provide (or output, display, etc.) a guidance message (470). Here, the guidance message may vary depending on the new lifelog information. In one or more embodiments, if the new lifelog information is second biometric information, the guidance message (470) may include at least one of information regarding the user's disease of interest, information regarding the correlation between the first biometric information (410) and the second biometric information, information regarding the sensing value of the new second biometric information, and health management information related to the disease of interest. In one or more embodiments, if the new lifelog information is one of image information, consumption history information, schedule information, application usage information, location information, mail information, message information, contact information, and search information, the guidance message (470) may include at least one of information regarding the user's disease of interest, information regarding the correlation between the first biometric information (410) and the first type of lifelog information, information regarding the new context text, and health management information related to the disease of interest.

[0112] At this time, the guidance message (470) may be provided through the display (130), but this is merely one embodiment, and it is obvious that it may be provided through the speaker in the form of a voice message.

[0113] Meanwhile, in FIGS. 4a and 4b, the initial correlation analysis module (420) and the learned correlation analysis module (450) are described separately, but this is merely an example, and the initial correlation analysis module (420) and the learned correlation analysis module (450) may be the same module.

[0114] FIG. 5 is a diagram illustrating a specific method for learning a correlation analysis module and providing a guidance message corresponding to a health risk event using the learned correlation analysis module, according to one embodiment of the present disclosure.

[0115] The electronic device (100) can acquire first biometric information (510). Here, the first biometric information may be first biometric information associated with the user's disease of interest. For example, the electronic device (100) can acquire the user's blood pressure information at 6:00 PM and can identify whether the blood pressure information has risen compared to the previous check time at 9:00 AM.

[0116] The correlation analysis module (520) can search for lifelog information when the threshold of the first biometric information is exceeded (520-1). Here, the threshold may be a value identified as the occurrence of a health risk event. For example, the electronic device (100) identifies the search period from 9:00 AM to 6:00 PM, which is the previous check time, as the search period and can search for lifelog information during the search period.

[0117] The correlation analysis module (520) can acquire second bio-information with a pattern similar to the first bio-information (520-2). If the first bio-information is data that is measured continuously (e.g., blood glucose data), the correlation analysis module (520) can acquire second bio-information that rises or falls in the same manner as the rising section of the first bio-signal. If the first bio-information is data that is measured sporadically (or discontinuously) (e.g., blood pressure), the correlation analysis module (520) can determine whether the second bio-signal measured immediately before the recent data measurement period within the search section is periodically repeated.

[0118] The correlation analysis module (520) can designate the second biometric information as similar biometric information (520-3). That is, the correlation analysis module (520) can designate the second biometric information, which has a pattern similar to the first biometric information, as similar biometric information by determining that it has a high correlation with the first biometric information.

[0119] The correlation analysis module (520) can extract repetitive context text from lifelog information (520-4). For example, the correlation analysis module (520) can extract context text corresponding to lifelog information measured within a search interval and identify whether the extracted context text is extracted more than a threshold number of times. For example, the correlation analysis module (520) can obtain context text related to food or place through restaurant payment information and can obtain context text related to food through food image information.

[0120] The correlation analysis module (520) can store the repeatedly extracted context text as similar lifelog information (520-5). That is, the correlation analysis module (520) can store the context text corresponding to the lifelog information having a high correlation with the first biometric information as similar lifelog information.

[0121] The electronic device (100) can store highly correlated lifelog information (530). That is, the electronic device (100) can store similar biometric information and similar lifelog information. Here, the electronic device (100) can store information about the type or numerical value of highly correlated lifelog information.

[0122] The electronic device (100) can acquire new lifelog information of a highly correlated type (540). For example, the electronic device (100) can acquire new lifelog information related to the user's restaurant visit (e.g., payment information, schedule information, etc.).

[0123] The correlation analysis module (520) can determine a first biosignal that is highly correlated with similar biosignal information or similar lifelog (520-6). For example, if information such as "kimchi stew" or "increase in heart rate" is obtained through similar lifelog information, the correlation analysis module (520) can determine blood pressure information, which is the first biosignal information that is highly correlated with the similar lifelog information.

[0124] The correlation analysis module (520) can detect health risk events associated with the first biometric information (520-7). For example, the correlation analysis module (520) can detect health risk events associated with the first biometric information, such as blood pressure information (e.g., hypertension detection event).

[0125] The electronic device (100) can output a guidance message corresponding to a health risk event (550). That is, the electronic device (100) can detect a health risk event through new lifelog information obtained based on correlation information and output a guidance message corresponding to the health risk event.

[0126] FIGS. 6a to 6d are drawings for illustrating an embodiment of providing a guidance message corresponding to a health risk event related to allergies using lifelog information according to various embodiments of the present disclosure.

[0127] The electronic device (100) can provide an execution screen for a health management application as shown in FIG. 6a. The electronic device (100) can set the user's interest in diseases, external devices for acquiring sensing data, and rights to acquire lifelog information according to user input obtained through the execution screen of the health management application.

[0128] Specifically, the execution screen of the health management application may include an area (610) for setting user information, an area (620) for setting a disease of interest (or existing disease), an area (630) for setting an external device for acquiring sensing data, an area (640) for setting a lifelog information linkage permission, and an area (650) for setting a real-time biometric information linkage permission. For example, the user can set allergies as an existing disease through the execution screen of the health management application.

[0129] In particular, when a user command to set the lifelog information linkage authority is entered, the electronic device (100) can provide a UI (660) for setting the access authority to the lifelog information on the execution screen of a health management application, as shown in FIG. 6b. The electronic device (100) can set the access authority to the lifelog information according to the user input entered through the UI (660).

[0130] In one or more embodiments, for a disease of interest (e.g., allergy, stress, etc.) for which the first biological information cannot be automatically measured, the electronic device (100) can receive information about the disease of interest through a UI (670) displayed on the execution screen of a health application as shown in FIG. 6c.

[0131] The electronic device (100) can learn the disease of interest and lifelog information in the manner described in FIGS. 2 to 5 and obtain lifelog information that has a high correlation with the disease of interest.

[0132] When the electronic device (100) detects that a health risk event has occurred based on new lifelog information, it may provide a guidance message (680) corresponding to the health risk event, as shown in FIG. 6d. In particular, the guidance message (680) may include information about context text such as "The ambient temperature has suddenly risen," information about a disease of interest such as "There is a high possibility of allergic symptoms appearing," and information to induce health management behavior such as "Please maintain an appropriate body temperature," as shown in FIG. 6d.

[0133] FIGS. 7a to 7d are drawings illustrating an embodiment of providing a guidance message corresponding to a health risk event related to hypertension using lifelog information according to an embodiment of the present disclosure. Meanwhile, since FIGS. 7a and 7b are identical to FIGS. 6a and 6b, a redundant description will be omitted.

[0134] As shown in FIG. 7a, the electronic device (100) can set hypertension as a disease of the user based on user input entered through the execution screen of the health management application.

[0135] The electronic device (100) can learn the disease of interest and the lifelog information in the manner described in FIGS. 2 to 5 to obtain lifelog information that has a high correlation with the disease of interest. For example, the electronic device (100) can store "noodles, salt, etc." as context text corresponding to similar lifelog information.

[0136] The electronic device (100) can acquire a food image (770) as shown in FIG. 7c as new lifelog information. Here, the electronic device (100) can transmit the acquired food image (770) to a health management application or a cloud server. The electronic device (100) can acquire context text corresponding to the food image (770) by inputting the food image (770) into a trained neural network model. For example, the electronic device (100) can acquire "buckwheat noodles, dinner, nutritional information of buckwheat noodles" as context text.

[0137] The electronic device (100) can identify whether the context text corresponding to the new lifelog information matches the context text obtained when a health risk event occurs.

[0138] The electronic device (100) can provide a guidance message (780) that responds to a health risk event based on the matching result, as shown in Fig. 7d. In particular, the guidance message (780) may provide information regarding context text such as "Buckwheat noodles contain a lot of salt," information regarding a disease of interest such as "It may have a bad effect on high blood pressure," and information to induce health management behavior such as "We recommend eating only 2 / 3."

[0139] FIGS. 8a to 8d are drawings illustrating an embodiment of providing a guidance message corresponding to a health risk event related to hypertension using lifelog information according to another embodiment of the present disclosure. Meanwhile, since FIGS. 8a and 8b are identical to FIGS. 6a and 6b, a redundant description will be omitted.

[0140] As shown in FIG. 8a, the electronic device (100) can set hypertension as a disease of the user based on user input entered through the execution screen of the health management application.

[0141] The electronic device (100) can learn the disease of interest and lifelog information in the manner described in FIGS. 2 to 5 and obtain lifelog information that has a high correlation with the disease of interest. For example, the electronic device (100) can store heart rate information, stress change information, etc., as similar biometric information.

[0142] As illustrated in FIG. 8c, the electronic device (100) can acquire heart rate information and stress change information as new lifelog information when a user performs a task (870). The electronic device (100) can identify whether the sensing value of the new biometric information corresponding to the new lifelog information matches the sensing value of the second biometric information acquired when a health risk event occurs.

[0143] The electronic device (100) can provide a guidance message (880) corresponding to a health risk event based on the matching result, as shown in FIG. 8d. In particular, the guidance message (880) may include information on the correlation between first biometric information and second biometric information, such as “I recently discovered that the pulse and stress index increase when blood pressure rises,” as shown in FIG. 8d, and information to induce health management behavior, such as “Caution is needed.”

[0144] FIG. 9 is a drawing illustrating an example of lifelog information related to a disease according to one embodiment of the present disclosure.

[0145] FIG. 9 is a table matching examples of diseases, symptoms, biometric information, and lifelog information. That is, when information regarding a disease of interest to the user is obtained, the electronic device (100) can set one of the biometric information shown in FIG. 9 as the first biometric information. Also, the electronic device (100) can obtain lifelog information as shown in FIG. 9 and train a correlation analysis module. Furthermore, when a health risk event is detected, the electronic device (100) can guide the user to symptoms as shown in FIG. 9 and guide health management behaviors. However, FIG. 9 is merely an example of an embodiment, and it goes without saying that the electronic device (100) can learn other biometric information or other lifelog information.

[0146]

[0147] FIG. 10 is a block diagram of an electronic device (1001) in a network environment (1000) according to various embodiments. The electronic device (1001) may be implemented as the electronic device (100) shown in FIG. 1 according to one example.

[0148] Referring to FIG. 10, in a network environment (1000), an electronic device (1001) may communicate with an electronic device (1002) through a first network (1098) (e.g., a short-range wireless communication network) or with at least one of an electronic device (1004) or a server (1008) through a second network (1099) (e.g., a long-range wireless communication network). According to one embodiment, the electronic device (1001) may communicate with the electronic device (1004) through a server (1008). According to one embodiment, the electronic device (1001) may include a processor (1020), memory (1030), input module (1050), sound output module (1055), display module (1060), audio module (1070), sensor module (1076), interface (1077), connection terminal (1078), haptic module (1079), camera module (1080), power management module (1088), battery (1089), communication module (1090), subscriber identification module (1096), or antenna module (1097). In some embodiments, at least one of these components (e.g., connection terminal (1078)) may be omitted from the electronic device (1001), or one or more other components may be added. In some embodiments, some of these components (e.g., sensor module (1076), camera module (1080), or antenna module (1097)) may be integrated into a single component (e.g., display module (1060)).

[0149] The processor (1020) can, for example, execute software (e.g., program (1040)) to control at least one other component (e.g., hardware or software component) of the electronic device (1001) connected to the processor (1020) and perform various data processing or operations. According to one embodiment, as at least part of the data processing or operations, the processor (1020) can store commands or data received from other components (e.g., sensor module (1076) or communication module (1090)) in volatile memory (1032), process the commands or data stored in volatile memory (1032), and store the resulting data in non-volatile memory (1034). According to one embodiment, the processor (1020) may include a main processor (1021) (e.g., a central processing unit or an application processor) or an auxiliary processor (1023) that can operate independently or together with it (e.g., a graphics processing unit, a neural processing unit (NPU), an image signal processor, a sensor hub processor, or a communication processor). For example, if the electronic device (1001) includes a main processor (1021) and an auxiliary processor (1023), the auxiliary processor (1023) may be configured to use lower power than the main processor (1021) or to be specialized for a specified function. The auxiliary processor (1023) may be implemented separately from the main processor (1021) or as part thereof.

[0150] The auxiliary processor (1023) may control at least some of the functions or states associated with at least one component of the electronic device (1001) (e.g., display module (1060), sensor module (1076), or communication module (1090)) on behalf of the main processor (1021) while the main processor (1021) is in an inactive (e.g., sleep) state, or together with the main processor (1021) while the main processor (1021) is in an active (e.g., application execution) state. According to one embodiment, the auxiliary processor (1023) (e.g., image signal processor or communication processor) may be implemented as part of another functionally related component (e.g., camera module (1080) or communication module (1090)). According to one embodiment, the auxiliary processor (1023) (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 (1001) itself where the artificial intelligence model is executed, or through a separate server (e.g., server (1008)). 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.

[0151] The memory (1030) can store various data used by at least one component of the electronic device (1001) (e.g., processor (1020) or sensor module (1076)). The data may include, for example, input data or output data for software (e.g., program (1040)) and related commands. The memory (1030) may include volatile memory (1032) or non-volatile memory (1034).

[0152] The program (1040) may be stored as software in memory (1030) and may include, for example, an operating system (1042), middleware (1044), or an application (1046).

[0153] The input module (1050) can receive commands or data to be used for a component of the electronic device (1001) (e.g., processor (1020)) from outside the electronic device (1001) (e.g., user). The input module (1050) 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).

[0154] The sound output module (1055) can output a sound signal to the outside of the electronic device (1001). The sound output module (1055) 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.

[0155] The display module (1060) can visually provide information to an external (e.g., user) of the electronic device (1001). The display module (1060) 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 (1060) 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.

[0156] The audio module (1070) can convert sound into an electrical signal or, conversely, convert an electrical signal into sound. According to one embodiment, the audio module (1070) can acquire sound through the input module (1050) or output sound through the sound output module (1055) or an external electronic device (e.g., electronic device (1002)) (e.g., speaker or headphones) that is directly or wirelessly connected to the electronic device (1001).

[0157] The sensor module (1076) can detect the operating state of the electronic device (1001) (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 (1076) 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.

[0158] The interface (1077) may support one or more specified protocols that can be used for the electronic device (1001) to be connected directly or wirelessly to an external electronic device (e.g., electronic device (1002)). According to one embodiment, the interface (1077) may include, for example, a high definition multimedia interface (HDMI), a universal serial bus (USB) interface, an SD card interface, or an audio interface.

[0159] The connection terminal (1078) may include a connector through which the electronic device (1001) can be physically connected to an external electronic device (e.g., electronic device (1002)). According to one embodiment, the connection terminal (1078) may include, for example, an HDMI connector, a USB connector, an SD card connector, or an audio connector (e.g., a headphone connector).

[0160] The haptic module (1079) can convert an electrical signal into a mechanical stimulus (e.g., vibration or movement) or an electrical stimulus that can be perceived by the user through tactile or kinesthetic senses. According to one embodiment, the haptic module (1079) may include, for example, a motor, a piezoelectric element, or an electric stimulation device.

[0161] The camera module (1080) can capture still images and video. According to one embodiment, the camera module (1080) may include one or more lenses, image sensors, image signal processors, or flashes.

[0162] The power management module (1088) can manage power supplied to the electronic device (1001). According to one embodiment, the power management module (1088) can be implemented, for example, as at least part of a power management integrated circuit (PMIC).

[0163] The battery (1089) can supply power to at least one component of the electronic device (1001). According to one embodiment, the battery (1089) may include, for example, a non-rechargeable primary battery, a rechargeable secondary battery, or a fuel cell.

[0164] The communication module (1090) can support the establishment of a direct (e.g., wired) communication channel or a wireless communication channel between an electronic device (1001) and an external electronic device (e.g., electronic device (1002), electronic device (1004), or server (1008)), and the performance of communication through the established communication channel. The communication module (1090) may include one or more communication processors that operate independently of the processor (1020) (e.g., application processor) and support direct (e.g., wired) communication or wireless communication. According to one embodiment, the communication module (1090) may include a wireless communication module (1092) (e.g., cellular communication module, short-range wireless communication module, or GNSS (global navigation satellite system) communication module) or a wired communication module (1094) (e.g., LAN (local area network) communication module, or power line communication module). Among these communication modules, the communication module described above can communicate with an external electronic device (1004) through a first network (1098) (e.g., a short-range communication network such as Bluetooth, WiFi (wireless fidelity) direct, or IrDA (infrared data association)) or a second network (1099) (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 (1092) can identify or authenticate the electronic device (1001) within a communication network such as the first network (1098) or the second network (1099) using subscriber information (e.g., International Mobile Subscriber Identifier (IMSI)) stored in the subscriber identification module (1096).

[0165] The wireless communication module (1092) 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 (1092) can support a high-frequency band (e.g., mmWave band) to achieve a high data transmission rate, for example. The wireless communication module (1092) 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 (1092) can support various requirements specified in the electronic device (1001), external electronic device (e.g., electronic device (1004)), or network system (e.g., second network (1099)). According to one embodiment, the wireless communication module (1092) 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.

[0166] An antenna module (1097) 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 (1097) 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 (1097) 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 (1098) or a second network (1099), may be selected from the plurality of antennas, for example, by a communication module (1090). A signal or power may be transmitted or received between the communication module (1090) 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 (1097).

[0167] According to various embodiments, the antenna module (1097) 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.

[0168] 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.

[0169] According to one embodiment, commands or data may be transmitted or received between the electronic device (1001) and an external electronic device (1004) through a server (1008) connected to a second network (1099). Each of the external electronic devices (1002, or 904) may be the same or a different type of device as the electronic device (1001). According to one embodiment, all or part of the operations performed on the electronic device (1001) may be performed on one or more of the external electronic devices (1002, 904, or 908). For example, if the electronic device (1001) needs to perform a function or service automatically or in response to a request from a user or another device, the electronic device (1001) 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 (1001). The electronic device (1001) 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 (1001) may provide ultra-low latency services using, for example, distributed computing or mobile edge computing. In another embodiment, the external electronic device (1004) may include an Internet of Things (IoT) device. The server (1008) may be an intelligent server using machine learning and / or neural networks.According to one embodiment, an external electronic device (1004) or server (1008) may be included within the second network (1099). The electronic device (1001) may be applied to intelligent services (e.g., smart home, smart city, smart car, or healthcare) based on 5G communication technology and IoT-related technology.

[0170] The methods according to the various embodiments of the present disclosure described above may be implemented in the form of an application that can be installed on an existing electronic device. Alternatively, the methods according to the various embodiments of the present disclosure described above may be performed using a deep learning-based artificial neural network (or deep artificial neural network), that is, a learning network model.

[0171]

[0172] The methods according to the various embodiments of the present disclosure described above can be implemented by software upgrades or hardware upgrades alone for existing electronic devices.

[0173] The various embodiments of the present disclosure described above may also be performed through an embedded server equipped in an electronic device or an external server of the electronic device.

[0174] According to a specific example of the present disclosure, the various embodiments described above may be implemented as software comprising instructions stored on a machine-readable storage medium (e.g., a computer). The machine may include an electronic device (e.g., electronic device (A)) according to the disclosed embodiments, which is a device capable of calling instructions stored from the storage medium and operating according to the called instructions. When instructions are executed by a processor, the processor may perform a function corresponding to the instructions directly or by using other components under the control of the processor. Instructions may include code generated or executed by a compiler or an interpreter. The machine-readable storage medium may be provided in the form of a non-transitory storage medium. Here, "non-transitory" means only that the storage medium does not contain a signal and is tangible, and does not distinguish whether data is stored semi-permanently or temporarily in the storage medium.

[0175] Additionally, according to one embodiment of the present disclosure, the method according to the various embodiments described above may be provided as 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 online through an application store (e.g., Play Store™). In the case of online distribution, at least a portion of the computer program product may be temporarily stored or temporarily created in a storage medium such as the memory of a manufacturer's server, an application store's server, or a relay server.

[0176] Additionally, each component (e.g., module or program) according to the various embodiments described above may be composed of a single or multiple entities, and some of the aforementioned sub-components may be omitted, or other sub-components may be further included in the various embodiments. Generally or additionally, some components (e.g., module or program) may be integrated into a single entity to perform the functions performed by each of the respective components prior to integration in the same or similar manner. The operations performed by the module, program, or other components according to the various embodiments may be executed sequentially, in parallel, iteratively, or heuristically, or at least some operations may be executed in a different order, omitted, or other operations added.

[0177] Although preferred embodiments of the present disclosure have been illustrated and described above, the present disclosure is not limited to the specific embodiments described above. It is understood that various modifications can be made by those skilled in the art without departing from the essence of the present disclosure as claimed in the claims, and such modifications should not be understood individually from the technical spirit or perspective of the present disclosure.

Claims

1. In an electronic device, display; Memory for storing instructions; and It includes at least one processor, When the above instructions are executed individually or collectively by the at least one processor, the electronic device, Acquire first biometric information and user's lifelog information related to the user's disease of interest, and Information regarding the correlation between the first biometric information and the lifelog information is obtained using the first biometric information and the lifelog information, and When new lifelog information is obtained, based on information regarding the correlation, it identifies whether a health risk event occurs through the new lifelog information, and An electronic device that, when the above health risk event is identified as having occurred, provides a guidance message corresponding to the health risk event based on the new lifelog information.

2. In Paragraph 1, The above lifelog information includes multiple types of information, When the above instructions are executed individually or collectively by the at least one processor, the electronic device, An electronic device that identifies a first type of lifelog information with a high correlation to the first biometric information when it is determined that a health risk event related to the user's interest disease occurs among the plurality of types of lifelog information.

3. 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, when new lifelog information of the first type is acquired, identifies whether the new lifelog information matches the acquired lifelog information when it is determined that a health risk event related to the user's disease of interest is occurring, thereby identifying whether a health risk event is occurring.

4. In Paragraph 3, The above lifelog information includes second biometric information different from the first biometric information, and When the above instructions are executed individually or collectively by the at least one processor, the electronic device, An electronic device that identifies whether a health risk event occurs by identifying whether the sensing value of the new second biometric information matches the sensing value of the second biometric information obtained when it is determined that a health risk event related to the user's disease of interest occurs, when the first type of lifelog information is the second biometric information.

5. In Paragraph 4, If the above-mentioned first type of lifelog information is the above-mentioned second biometric information, The above guidance message is, An electronic device comprising at least one of information regarding the disease of interest of the user, information regarding the correlation between the first biometric information and the second biometric information, information regarding the sensing value of the novel second biometric information, and health management information related to the disease of interest.

6. In Paragraph 3, The above lifelog information includes at least one of image information, consumption history information, schedule information, application usage information, location information, mail information, message information, contact information, and search information. When the above instructions are executed individually or collectively by the at least one processor, the electronic device, If the above-mentioned first type of lifelog information is one of image information, consumption history information, schedule information, application usage information, location information, mail information, message information, contact information, and search information, a new context text is obtained from the above-mentioned first type of lifelog information, and An electronic device that identifies whether a health risk event occurs by identifying whether the new context text matches the acquired context text when it is determined that a health risk event related to the user's interest disease is occurring.

7. In Paragraph 6, If the above-mentioned first type of lifelog information is one of image information, consumption history information, schedule information, application usage information, location information, mail information, message information, contact information, and search information, The above guidance message is, An electronic device comprising at least one of information regarding the user’s interest in a disease, information regarding the correlation between the first biometric information and the first type of lifelog information, information regarding the novel context text, and health management information related to the interest in a disease.

8. In Paragraph 1, When the above instructions are executed individually or collectively by the at least one processor, the electronic device, An electronic device that obtains information about a disease of interest to the user based on user input received through the execution screen of a health management application, or obtains information about a disease of interest to the user based on usage information of the electronic device.

9. In Paragraph 1, When the above instructions are executed individually or collectively by the at least one processor, the electronic device, An electronic device that collects the lifelog information through a plurality of applications installed on the electronic device when user permission to acquire the lifelog information is set through the execution screen of a health management application.

10. In a method for controlling an electronic device, A step of acquiring first biometric information and user's lifelog information associated with the user's disease of interest; A step of obtaining information regarding the correlation between the first biometric information and the lifelog information using the first biometric information and the lifelog information; When new lifelog information is obtained, a step of identifying whether a health risk event occurs through the new lifelog information based on information regarding the correlation; and A control method comprising the step of providing a guidance message corresponding to the health risk event based on the new lifelog information when it is identified that the above health risk event has occurred.

11. In Paragraph 10, The above lifelog information includes multiple types of information, The step of obtaining information regarding the above correlation is, A control method for identifying a first type of lifelog information that has a high correlation with the first biometric information when it is determined that a health risk event related to the user's interest disease occurs among the plurality of types of lifelog information.

12. In Paragraph 11, The above identification step is, A control method for identifying whether a health risk event occurs by identifying whether the new life log information of the first type is matched with the acquired life log information when it is determined that a health risk event related to the user's interest disease occurs, when the new life log information is acquired.

13. In Paragraph 12, The above lifelog information includes second biometric information different from the first biometric information, and The above identification step is, A control method for identifying whether a health risk event occurs by identifying whether the sensing value of the new second biometric information matches the sensing value of the second biometric information obtained when it is determined that a health risk event related to the user's interest disease occurs, wherein the first type of lifelog information is the second biometric information.

14. In Paragraph 13, If the above-mentioned first type of lifelog information is the above-mentioned second biometric information, The above guidance message is, A control method comprising at least one of information regarding the user’s interest in a disease, information regarding the correlation between the first biometric information and the second biometric information, information regarding the sensing value of the novel second biometric information, and health management information related to the interest in a disease.

15. In Paragraph 12, The above lifelog information includes at least one of image information, consumption history information, schedule information, application usage information, location information, mail information, message information, contact information, and search information. The above identification step is, If the above-mentioned first type of lifelog information is one of image information, consumption history information, schedule information, application usage information, location information, mail information, message information, contact information, and search information, a step of obtaining a new context text from the above-mentioned first type of lifelog information; and A control method comprising the step of identifying whether a health risk event occurs by identifying whether the new context text matches the acquired context text when it is determined that a health risk event related to the user's interest disease occurs.

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