Electronic device, method, and non-transitory computer-readable recording medium for tagging personalized blood glucose pattern

Abstract

This electronic device may receive continuous blood glucose data about a user. Sensing data for identifying the state of the user may be received. The state of the user may include an activity state of the user and a biometric state of the user. The sensing data may include exercise information related to the activity state and biometric information related to the biometric state. Blood glucose patterns may be identified in time intervals in which the activity state and / or the biometric state is identified on the basis of the continuous blood glucose data and the sensing data. A personalized blood glucose pattern may be identified from the blood glucose patterns in the time intervals. The personalized blood glucose pattern may include a pattern in which blood glucose is changed according to the state. A visual object indicating that the personalized blood glucose pattern is identified by the state in a graph representing the continuous blood glucose data may be displayed through a display.

Description

Electronic device, method, and non-transitory computer-readable recording medium for tagging personalized blood glucose patterns

[0001] The following descriptions relate to electronic devices, methods, and non-transitory computer-readable recording media for tagging personalized blood glucose patterns.

[0002] Invasive blood glucose measurement can be done by drawing blood and measuring the blood glucose concentration in the blood. However, the process of drawing blood involves pricking the skin, which can be painful.

[0003] Non-invasive methods of measuring blood glucose include, for example, electrochemical methods that measure the concentration of blood glucose in body fluids secreted within the skin or outside the skin, optical methods that measure the concentration of blood glucose using the optical properties of blood glucose in the body, and exhaled breath methods that measure the concentration of blood glucose using the concentration of biomarker gases in exhaled breath.

[0004] Optical methods for measuring blood glucose include near-infrared absorption spectrum analysis and Raman spectrometry. Near-infrared absorption spectrum analysis involves irradiating the skin with broadband near-infrared light and then analyzing the light re-emitted through diffuse reflection. This method calculates the amount of light absorbed by blood glucose molecules within the skin, thereby predicting blood glucose levels. Raman spectrometry involves irradiating the skin with laser light and analyzing the wavelength of the light emitted from the skin to obtain Raman shifts, thereby analyzing blood glucose levels.

[0005] The above information may be provided as background art to aid in understanding the present disclosure. No claim or determination is made as to whether any of the above-described matters constitute prior art related to the present disclosure.

[0006] An electronic device is disclosed. The electronic device may include a display, at least one processor including a processing circuit, and a memory storing instructions and including one or more storage media. The instructions, when individually or collectively executed by the at least one processor, may cause the electronic device to receive continuous blood glucose data for a user. The instructions, when individually or collectively executed by the at least one processor, may cause the electronic device to receive sensing data for identifying a state of the user. The state of the user may include an activity state of the user and the physiological state of the user. The sensing data may include exercise information related to the activity state and physiological information related to the physiological state. The instructions, when individually or collectively executed by the at least one processor, may cause the electronic device to identify blood glucose patterns during time intervals in which the activity state and / or the physiological state are identified based on the continuous blood glucose data and the sensing data. The instructions, when individually or collectively executed by the at least one processor, may cause the electronic device to identify a personalized blood glucose pattern among the blood glucose patterns during the time intervals. The personalized blood glucose pattern may include a pattern in which the blood glucose fluctuates due to the condition. The instructions, when individually or collectively executed by the at least one processor, may cause the electronic device to display, through the display, a visual object indicating that the personalized blood glucose pattern is identified by the condition within a graph representing the continuous blood glucose data.

[0007] A method is disclosed. The method may be performed by an electronic device including a display. The method may include receiving continuous blood glucose data for a user. The method may include receiving sensing data for identifying a state of the user. The state of the user may include an activity state of the user and the user's physiological state. The sensing data may include exercise information related to the activity state and physiological information related to the physiological state. The method may include identifying blood glucose patterns in time intervals in which the activity state and / or the physiological state are identified based on the continuous blood glucose data and the sensing data. The method may include identifying a personalized blood glucose pattern among the blood glucose patterns in the time intervals. The personalized blood glucose pattern may include a pattern in which the blood glucose fluctuates depending on the state. The method may include displaying, through the display, a visual object indicating that the personalized blood glucose pattern is identified by the state within a graph representing the continuous blood glucose data.

[0008] A non-transitory computer-readable storage medium is disclosed. The non-transitory computer-readable storage medium may store one or more programs including instructions. The instructions, when individually or collectively executed by at least one processor of an electronic device including a display, may cause the electronic device to receive continuous blood glucose data for a user. The instructions, when individually or collectively executed by the at least one processor, may cause the electronic device to receive sensing data for identifying a state of the user. The state of the user may include an activity state of the user and the physiological state of the user. The sensing data may include exercise information related to the activity state and physiological information related to the physiological state. The instructions, when individually or collectively executed by the at least one processor, may cause the electronic device to identify blood glucose patterns during time intervals in which the activity state and / or the physiological state are identified based on the continuous blood glucose data and the sensing data. The instructions, when individually or collectively executed by the at least one processor, may cause the electronic device to identify a personalized blood glucose pattern among the blood glucose patterns during the time intervals. The personalized blood glucose pattern may include a pattern in which the blood glucose fluctuates due to the condition. The instructions, when individually or collectively executed by the at least one processor, may cause the electronic device to display, through the display, a visual object indicating that the personalized blood glucose pattern is identified by the condition within a graph representing the continuous blood glucose data.

[0009] In connection with the description of the drawings, the same or similar reference numerals may be used for the same or similar components.

[0010] FIG. 1 is a block diagram of an electronic device within a network environment according to various embodiments.

[0011] FIG. 2A is a front perspective view of a wearable device according to one embodiment.

[0012] FIG. 2b is a rear perspective view of a wearable device according to one embodiment.

[0013] FIG. 2c is a drawing illustrating a glass cover on the back of a wearable device according to one embodiment.

[0014] FIG. 3 is a block diagram of a wearable device according to one embodiment.

[0015] FIG. 4A is a graph of sensing values ​​measured by a wearable device according to one embodiment.

[0016] FIG. 4b is a graph of sensing values ​​measured by a wearable device according to one embodiment.

[0017] FIG. 5 is a diagram showing time intervals divided based on sensing values ​​by a wearable device according to one embodiment.

[0018] FIG. 6A is a graph comparing a general blood sugar level pattern and a personalized blood sugar level pattern according to one embodiment.

[0019] Figure 6b is a graph comparing a general blood sugar level pattern and a personalized blood sugar level pattern according to one embodiment.

[0020] FIG. 7A is a diagram illustrating a graph in which information is tagged to a blood sugar level pattern analyzed by a wearable device according to one embodiment.

[0021] FIG. 7b is a diagram illustrating information tagged to a blood sugar level pattern analyzed by a wearable device according to one embodiment.

[0022] FIG. 7c is a diagram illustrating information tagged to a blood sugar level pattern analyzed by a wearable device according to one embodiment.

[0023] FIG. 7d is a diagram illustrating information tagged to a blood sugar level pattern analyzed by a wearable device according to one embodiment.

[0024] FIG. 8A is a block diagram of a wearable device according to one embodiment.

[0025] FIG. 8b is a block diagram of a wearable device according to one embodiment.

[0026] FIG. 8c is a block diagram of a wearable device according to one embodiment.

[0027] Figure 9 is a flowchart showing the operation of a wearable device according to one embodiment.

[0028] Figure 10 is a flowchart showing the operation of a wearable device according to one embodiment.

[0029] Hereinafter, embodiments of the present disclosure will be described in detail with reference to the drawings so that those skilled in the art can easily implement the present disclosure. However, the present disclosure may be implemented in various different forms and is not limited to the embodiments described herein. In connection with the description of the drawings, the same or similar reference numerals may be used for identical or similar components. Furthermore, in the drawings and related descriptions, descriptions of well-known functions and configurations may be omitted for clarity and conciseness.

[0030] FIG. 1 is a block diagram of an electronic device (101) within a network environment (100) according to various embodiments.

[0031] Referring to FIG. 1, in a network environment (100), an electronic device (101) may communicate with an electronic device (102) via a first network (198) (e.g., a short-range wireless communication network), or may communicate with at least one of an electronic device (104) or a server (108) via a second network (199) (e.g., a long-range wireless communication network). According to one embodiment, the electronic device (101) may communicate with the electronic device (104) via the server (108). According to one embodiment, the electronic device (101) may include a processor (120), a memory (130), an input module (150), an audio output module (155), a display module (160), an audio module (170), a sensor module (176), an interface (177), a connection terminal (178), a haptic module (179), a camera module (180), a power management module (188), a battery (189), a communication module (190), a subscriber identification module (196), or an antenna module (197). In some embodiments, the electronic device (101) may omit at least one of these components (e.g., the connection terminal (178)), or may have one or more other components added. In some embodiments, some of these components (e.g., the sensor module (176), the camera module (180), or the antenna module (197)) may be integrated into one component (e.g., the display module (160)).

[0032] The processor (120) may, for example, execute software (e.g., a program (140)) to control at least one other component (e.g., a hardware or software component) of the electronic device (101) connected to the processor (120) and perform various data processing or operations. According to one embodiment, as at least a part of the data processing or operations, the processor (120) may store commands or data received from other components (e.g., a sensor module (176) or a communication module (190)) in a volatile memory (132), process the commands or data stored in the volatile memory (132), and store result data in a non-volatile memory (134). According to one embodiment, the processor (120) may include a main processor (121) (e.g., a central processing unit or an application processor) or an auxiliary processor (123) (e.g., a graphics processing unit, a neural processing unit (NPU), an image signal processor, a sensor hub processor, or a communication processor) that can operate independently or together with the main processor (121). For example, when the electronic device (101) includes the main processor (121) and the auxiliary processor (123), the auxiliary processor (123) may be configured to use less power than the main processor (121) or to be specialized for a given function. The auxiliary processor (123) may be implemented separately from the main processor (121) or as a part thereof.

[0033] The auxiliary processor (123) may control at least a portion of functions or states associated with at least one component (e.g., a display module (160), a sensor module (176), or a communication module (190)) of the electronic device (101), for example, on behalf of the main processor (121) while the main processor (121) is in an inactive (e.g., sleep) state, or together with the main processor (121) while the main processor (121) is in an active (e.g., application execution) state. In one embodiment, the auxiliary processor (123) (e.g., an image signal processor or a communication processor) may be implemented as a part of another functionally related component (e.g., a camera module (180) or a communication module (190)). In one embodiment, the auxiliary processor (123) (e.g., a neural network processing unit) may include a hardware structure specialized for processing artificial intelligence models. The artificial intelligence models may be generated through machine learning. This learning can be performed, for example, on the electronic device (101) itself where the artificial intelligence model is executed, or can be performed through a separate server (e.g., server (108)). The learning algorithm can 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 can include multiple artificial neural network layers.The artificial neural network may be one of 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, or alternatively to, a hardware structure, an artificial intelligence model may include a software structure.

[0034] The memory (130) can store various data used by at least one component (e.g., processor (120) or sensor module (176)) of the electronic device (101). The data can include, for example, software (e.g., program (140)) and input data or output data for commands related thereto. The memory (130) can include volatile memory (132) or non-volatile memory (134).

[0035] The program (140) may be stored as software in the memory (130) and may include, for example, an operating system (142), middleware (144), or an application (146).

[0036] The input module (150) can receive commands or data to be used in a component of the electronic device (101) (e.g., a processor (120)) from an external source (e.g., a user) of the electronic device (101). The input module (150) can include, for example, a microphone, a mouse, a keyboard, a key (e.g., a button), or a digital pen (e.g., a stylus pen).

[0037] The audio output module (155) can output audio signals to the outside of the electronic device (101). The audio output module (155) can include, for example, a speaker or a receiver. The speaker can be used for general purposes, such as multimedia playback or recording playback. The receiver can be used to receive incoming calls. In one embodiment, the receiver can be implemented separately from the speaker or as part of the speaker.

[0038] The display module (160) can visually provide information to an external party (e.g., a user) of the electronic device (101). The display module (160) may include, for example, a display, a holographic device, or a projector and a control circuit for controlling the device. According to one embodiment, the display module (160) may include a touch sensor configured to detect a touch, or a pressure sensor configured to measure the intensity of a force generated by the touch.

[0039] The audio module (170) can convert sound into an electrical signal, or vice versa, convert an electrical signal into sound. According to one embodiment, the audio module (170) can acquire sound through the input module (150), output sound through the sound output module (155), or an external electronic device (e.g., electronic device (102)) (e.g., speaker or headphone) directly or wirelessly connected to the electronic device (101).

[0040] The sensor module (176) can detect the operating status (e.g., power or temperature) of the electronic device (101) or the external environmental status (e.g., user status) and generate an electrical signal or data value corresponding to the detected status. According to one embodiment, the sensor module (176) can include, for example, a gesture sensor, a gyro sensor, a barometric pressure sensor, a magnetic sensor, an acceleration sensor, a grip sensor, a proximity sensor, a color sensor, an IR (infrared) sensor, a biometric sensor, a temperature sensor, a humidity sensor, or an illuminance sensor.

[0041] The interface (177) may support one or more designated protocols that may be used to directly or wirelessly connect the electronic device (101) with an external electronic device (e.g., the electronic device (102)). In one embodiment, the interface (177) may include, for example, a high definition multimedia interface (HDMI), a universal serial bus (USB) interface, an SD card interface, or an audio interface.

[0042] The connection terminal (178) may include a connector through which the electronic device (101) may be physically connected to an external electronic device (e.g., electronic device (102)). According to one embodiment, the connection terminal (178) may include, for example, an HDMI connector, a USB connector, an SD card connector, or an audio connector (e.g., a headphone connector).

[0043] The haptic module (179) can convert electrical signals into mechanical stimuli (e.g., vibration or movement) or electrical stimuli that a user can perceive through tactile or kinesthetic sensations. According to one embodiment, the haptic module (179) can include, for example, a motor, a piezoelectric element, or an electrical stimulation device.

[0044] The camera module (180) can capture still images and videos. According to one embodiment, the camera module (180) may include one or more lenses, image sensors, image signal processors, or flashes.

[0045] The power management module (188) can manage power supplied to the electronic device (101). According to one embodiment, the power management module (188) can be implemented as, for example, at least a part of a power management integrated circuit (PMIC).

[0046] A battery (189) may power at least one component of the electronic device (101). In one embodiment, the battery (189) may include, for example, a non-rechargeable primary battery, a rechargeable secondary battery, or a fuel cell.

[0047] The communication module (190) may support the establishment of a direct (e.g., wired) communication channel or a wireless communication channel between the electronic device (101) and an external electronic device (e.g., electronic device (102), electronic device (104), or server (108)), and the performance of communication through the established communication channel. The communication module (190) may operate independently from the processor (120) (e.g., application processor) and may include one or more communication processors that support direct (e.g., wired) communication or wireless communication. According to one embodiment, the communication module (190) may include a wireless communication module (192) (e.g., a cellular communication module, a short-range wireless communication module, or a global navigation satellite system (GNSS) communication module) or a wired communication module (194) (e.g., a local area network (LAN) communication module, or a power line communication module). Among these communication modules, the corresponding communication module can communicate with an external electronic device (104) via a first network (198) (e.g., a short-range communication network such as Bluetooth, wireless fidelity (WiFi) direct, or infrared data association (IrDA)) or a second network (199) (e.g., a long-range communication network such as 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 can be integrated into a single component (e.g., a single chip) or implemented as multiple separate components (e.g., multiple chips). The wireless communication module (192) can verify or authenticate the electronic device (101) within a communication network such as the first network (198) or the second network (199) by using subscriber information (e.g., an international mobile subscriber identity (IMSI)) stored in the subscriber identification module (196).

[0048] The wireless communication module (192) can support 5G networks and next-generation communication technologies following the 4G network, such as NR access technology (new radio access technology). The NR access technology can support high-speed transmission of high-capacity data (eMBB (enhanced mobile broadband)), minimization of terminal power and connection of multiple terminals (mMTC (massive machine type communications)), or high reliability and low latency (URLLC (ultra-reliable and low-latency communications)). The wireless communication module (192) can support, for example, a high-frequency band (e.g., mmWave band) to achieve a high data transmission rate. The wireless communication module (192) can support various technologies for securing performance in a high-frequency band, such as beamforming, massive multiple-input and multiple-output (MIMO), full dimensional MIMO (FD-MIMO), array antenna, analog beam-forming, or large scale antenna. The wireless communication module (192) can support various requirements specified in the electronic device (101), an external electronic device (e.g., the electronic device (104)), or a network system (e.g., the second network (199)). According to one embodiment, the wireless communication module (192) can support a peak data rate (e.g., 20 Gbps or more) for realizing eMBB, a loss coverage (e.g., 664 dB or less) for realizing mMTC, or a U-plane latency (e.g., 0.5 ms or less for downlink (DL) and uplink (UL), or 6 ms or less for round trip) for realizing URLLC.

[0049] The antenna module (197) can transmit or receive signals or power to or from an external device (e.g., an external electronic device). In one embodiment, the antenna module (197) may include an antenna including a radiator formed of a conductor or a conductive pattern formed on a substrate (e.g., a PCB). In one embodiment, the antenna module (197) may include a plurality of antennas (e.g., an array antenna). In this case, at least one antenna suitable for a communication method used in a communication network, such as the first network (198) or the second network (199), may be selected from the plurality of antennas, for example, by the communication module (190). A signal or power may be transmitted or received between the communication module (190) and an external electronic device via the at least one selected antenna. In some embodiments, in addition to the radiator, another component (e.g., a radio frequency integrated circuit (RFIC)) may be additionally formed as a part of the antenna module (197).

[0050] According to various embodiments, the antenna module (197) may form a mmWave antenna module. In one embodiment, the mmWave antenna module may include a printed circuit board, an RFIC disposed on or adjacent a first side (e.g., a bottom side) of the printed circuit board and capable of supporting a designated high-frequency band (e.g., a mmWave band), and a plurality of antennas (e.g., an array antenna) disposed on or adjacent a second side (e.g., a top side or a side side) of the printed circuit board and capable of transmitting or receiving signals in the designated high-frequency band.

[0051] At least some of the above components can be interconnected and exchange signals (e.g., commands or data) with each other via a communication method between peripheral devices (e.g., a bus, GPIO (general purpose input and output), SPI (serial peripheral interface), or MIPI (mobile industry processor interface)).

[0052] According to one embodiment, commands or data may be transmitted or received between the electronic device (101) and an external electronic device (104) via a server (108) connected to a second network (199). Each of the external electronic devices (102 or 104) may be the same or a different type of device as the electronic device (101). According to one embodiment, all or part of the operations executed in the electronic device (101) may be executed in one or more of the external electronic devices (102, 104, or 108). For example, when the electronic device (101) is to perform a certain function or service automatically or in response to a request from a user or another device, the electronic device (101) may, instead of or in addition to executing the function or service itself, request one or more external electronic devices to perform the function or at least a part of the service. One or more external electronic devices that receive the request may execute at least a portion of the requested function or service, or an additional function or service related to the request, and transmit the result of the execution to the electronic device (101). The electronic device (101) may process the result as is or additionally and provide it as at least a portion of a response to the request. For this purpose, cloud computing, distributed computing, mobile edge computing (MEC), or client-server computing technology may be used, for example. The electronic device (101) may provide an ultra-low latency service by using distributed computing or mobile edge computing, for example. In another embodiment, the external electronic device (104) may include an Internet of Things (IoT) device. The server (108) may be an intelligent server utilizing machine learning and / or a neural network. According to one embodiment, the external electronic device (104) or the server (108) may be included in the second network (199).The electronic device (101) can be applied to intelligent services (e.g., smart home, smart city, smart car, or healthcare) based on 5G communication technology and IoT-related technology.

[0053] FIG. 2A is a front perspective view of a wearable device according to one embodiment. FIG. 2B is a rear perspective view of a wearable device according to one embodiment. FIG. 2C is a drawing illustrating a glass cover on the rear of a wearable device according to one embodiment.

[0054] According to one embodiment, an electronic device (e.g., electronic device (101)) may include a wearable device (200) that is worn on a part of a user's body. For example, the wearable device (200) may be referred to as a wrist-wearable electronic device or a smart watch.

[0055] Referring to FIG. 2A, a wearable device (200) according to one embodiment may include a display (201) and a housing assembly (205). The display (201) and the housing assembly (205) may define the exterior of the wearable device (200).

[0056] According to one embodiment, the display (201) may define at least a portion of the front surface (200A) of the wearable device (200) (e.g., the surface of the wearable device (200) facing the -z direction). The display (201) may be configured to display visual information. For example, if the wearable device (200) is a wrist-worn electronic device (e.g., a smart watch), the display (201) may be configured to display not only time information or date information, but also activity information of a user wearing the wearable device (200), health information, information related to the state of charge (SOC) of a battery, weather information, and / or notification information related to an event of an electronic device (e.g., a smart phone) wirelessly connected to the wearable device (200). The display (201) may include a display panel and a substantially transparent window disposed on the display panel. The display (201) may include, or be adjacent to, a touch detection circuit, a pressure sensor for measuring the intensity (pressure) of a touch, and / or a fingerprint sensor.

[0057] According to one embodiment, at least a portion of the housing assembly (205) may be formed of a conductive material. For example, the housing assembly (205) may be formed of a metal material. Since metal materials provide high durability, ease of manufacturing, or superior appearance quality, the housing assembly (205) may be formed of a conductive material. The housing assembly (205) may define a portion of the front surface (200A) of the wearable device (200). For example, the housing assembly (205) may form the front surface (200A) of the wearable device (200) together with the display (201). The housing assembly (205) may define at least a portion of the side surface (200C) and at least a portion of the back surface (200B) of the wearable device (200).

[0058] Referring to FIGS. 2A and 2B, according to one embodiment, a housing assembly (205) may include a bezel (210), a conductive front housing (220), a bracket (230), a conductive rear housing (240), and a glass cover (250). The bezel (210), the bracket (230), the conductive front housing (220), the conductive rear housing (240), and the glass cover (250) may be coupled to each other to form a portion of the exterior of the wearable device (200).

[0059] According to one embodiment, the bezel (210) may define a portion of the front surface (200A) of the wearable device (200). The bezel (210) may fix and protect the display (201) by covering an edge portion of the front surface (200A) of the display (201). The bezel (210) may be referred to as a front decoration in that it forms a portion of the front surface (200A) of the wearable device (200). The bezel (210) may be formed of a conductive material, for example, a conductive metal, but is not limited thereto. For example, the bezel (210) may be formed of a non-conductive material, such as plastic or ceramic. The bezel (210) may be mounted on the conductive front housing (220). Alternatively or optionally, the bezel (210) may be formed integrally with the conductive front housing (220).

[0060] In one embodiment, a conductive front housing (220) supporting a bezel (210) may be mounted on a bracket (230). The conductive front housing (220) may be formed of a conductive material, for example, an electrically conductive metal. For example, but not limited to, the conductive front housing (220) may form a portion of the front surface (200A) of the wearable device (200). For example, the conductive front housing (220) may form a portion of the front surface (200A) corresponding to a peripheral area of ​​the bezel (210). The conductive front housing (220) may be referred to as a front metal.

[0061] According to one embodiment, the bracket (230) may define at least a portion of a side surface (200C) of the wearable device (200) between the front surface (200A) of the wearable device (200) and the back surface (200B) of the wearable device (200). The bracket (230) may be referred to as a side surface member, a side wall portion, a side frame, or a side structure in that it forms at least a portion of the side surface (200C) of the wearable device (200).

[0062] According to one embodiment, the wearable device (200) may include a key input device for user input. For example, the key input device may include side key buttons (206) and / or a crown (207) arranged on a side surface (200C) of the bracket (230). The side key buttons (206) may be configured to receive user input by being pressed by a user. The crown (207) may be configured to receive user input by being pressed or rotated by a user.

[0063] According to one embodiment, the wearable device (200) may include straps (208) for fixing the wearable device (200) to a part of the user's body. The straps (208) may be coupled to a bracket (230). For example, the straps (208) may be respectively coupled to both sides of the bracket (230). The straps (208) may be formed as an integral or multiple unit links that are movable to each other, for example, by using a woven material, leather, rubber, urethane, metal, ceramic, or a combination of at least two of the above materials. The straps (208) may be fastened to each other through a fixing member (209-1), thereby fixing the wearable device (200) to a part of the user's body (e.g., a wrist). In FIGS. 2A and 2B, the fixing member (209-1) is illustrated as being positioned at the end of one of the straps (208) and inserted into a fastening hole (209-2) included in another one of the straps (208), but is not limited thereto.

[0064] Referring to FIG. 2B, the conductive rear housing (240) and the glass cover (250) may define at least a portion of the rear surface (200B) of the wearable device (200). The conductive rear housing (240) may be mounted on the bracket (230). The glass cover (250) may be mounted on the conductive rear housing (240). The glass cover (250) may be positioned at the center of the conductive rear housing (240). When the wearable device (200) is worn on a part of the user's body (e.g., wrist), the glass cover (250) may come into contact with the part of the user's body. When the wearable device (200) is worn on a part of the user's body (e.g., wrist), the conductive rear housing (240) and the glass cover (250) may come into contact with the part of the user's body.

[0065] In one embodiment, some of the sensors (e.g., optical sensors (251 to 259), electrode sensors (271, 275, 206), and / or motion sensor (280)) included in the sensor module (e.g., 176 of FIG. 1) may be visible from the outside through the glass cover (250). Depending on the embodiment, the sensor module (e.g., 176 of FIG. 1) may further include a temperature sensor, a magnetic sensor, a barometric pressure sensor, an illuminance sensor, and / or a position sensor.

[0066] In one embodiment, the optical sensors (251 to 259) may include a light emitter for emitting light of a specified frequency and a light receiver for receiving light of a specified frequency. In one embodiment, the specified frequency may be a frequency for visible light, infrared, and / or ultraviolet light. In one embodiment, the specified frequency may be a frequency for detecting specific biometric information and / or specific substances within the body.

[0067] In one embodiment, specific biometric information within the body may include heart rate, heart rate variation (HRV), saturation of percutaneous oxygen (SpO2), arterial stiffness, blood pressure, and / or arterial age. Here, heart rate may represent the number of heartbeats per unit time. Oxygen saturation may represent the ratio of the amount of hemoglobin bound to oxygen in the blood to the total amount of hemoglobin. Arterial stiffness may represent the degree of hardening of blood vessels. Blood pressure may represent the pressure exerted on the blood vessel walls when blood sent from the heart flows through the blood vessels. Vascular age is a physiological age representing the degree of aging of blood vessels and may be related to arterial stiffness. In one embodiment, specific biometric information within the body may include information on stress, blood sugar level, and / or irregular heart rhythm notification (IHRN).

[0068] In one embodiment, a specific substance within the body may be a cell, blood vessel, protein, deoxyribonucleic acid (DNA), ribonucleic acid (RNA), blood sugar, and / or a metabolite within the body. For example, the substance may be transported from the blood vessel to the epidermis of the skin by diffusion. Accordingly, the epidermis may contain not only cells and body fluids but also blood sugar. In one embodiment, a specific optical sensor (e.g., optical sensor (258)) among the optical sensors (251 to 259) may obtain an optical signal for measuring the concentration of blood sugar in the epidermis through light of a specified frequency.

[0069] In one embodiment, the light emitting unit of the optical sensors (251 to 259) may be a light emitting diode (LED), a laser diode, and / or a vertical cavity surface emitting laser (VCSEL) diode. In one embodiment, the light receiving unit of the optical sensors (251 to 259) may be a photodiode and / or a complementary metal-oxide semiconductor (CMOS) sensor.

[0070] In one embodiment, the electrode sensors (271, 275, 206) may be in contact with the user's body. In one embodiment, the electrode sensors (271, 275, 206) may measure electrical characteristics (e.g., voltage, current, and / or impedance) of the user's body. In one embodiment, the electrical characteristics may represent the user's electrocardiogram (ECG), electromyogram (EMG), electroencephalogram (EEG), body composition, and / or galvanic skin response. For example, when some of the electrode sensors (271, 275, 206) are in contact with one part of the user (e.g., a part of the left arm) and other some of the electrode sensors (206) are in contact with the other part of the user (e.g., a part of the right arm (e.g., a right finger)), the electrical signals of the body can be measured through the electrode sensors (271, 275, 206). In one embodiment, the wearable device (200) can identify an electrocardiogram (ECG), an electromyogram (EMG), an electroencephalogram (EEG), and / or body composition and / or galvanic skin response by analyzing the electrical signals of the body. In one embodiment, the body composition can include body fat, muscle mass, bone mass, and / or body water. In one embodiment, the skin electrodermal response may represent the electrodermal activity (EDA) of the skin (e.g., skin conductance, galvanic skin response (GSR), electrodermal response (EDR), and / or psychogalvanic reflex (PRG)).

[0071] In one embodiment, the motion sensor (280) can detect the state (or posture, direction), movement, and inertia of the wearable device (200). In one embodiment, the motion sensor (280) can include a 6-axis sensor (e.g., an acceleration sensor and a gyro sensor).

[0072] For example, the motion sensor (280) can detect the position, speed, acceleration, rotational state, angular velocity, and / or angular acceleration due to the movement of the wearable device (200). The motion sensor (280) can generate a signal regarding the movement of the wearable device (200). In one embodiment, the signal regarding the movement can indicate the position, speed, acceleration, rotational state, angular velocity, and / or angular acceleration due to the movement of the wearable device (200).

[0073] In one embodiment, the temperature sensor may generate a signal for detecting the temperature of the body and / or the temperature inside the wearable device (200). In one embodiment, a temperature sensor for measuring the temperature of the body (e.g., an infrared temperature sensor, a non-contact temperature sensor) may generate a signal representing thermal infrared of the body. In one embodiment, the magnetic sensor may generate a signal for detecting the Earth's magnetic field. In one embodiment, the barometric pressure sensor may generate a signal for detecting the barometric pressure outside the wearable device (200). In one embodiment, the light sensor may generate a signal for detecting the brightness of external light of the wearable device (200). In one embodiment, the location sensor may generate a signal for detecting the current location of the wearable device (200) based on a global navigation satellite system (GNSS) communication module (e.g., a global positioning system (GPS) module).

[0074] FIG. 3 is a block diagram of a wearable device according to an embodiment. FIG. 4a is graphs of sensing values ​​measured by a wearable device according to an embodiment. FIG. 4b is graphs of sensing values ​​measured by a wearable device according to an embodiment. FIG. 5 is a diagram showing time intervals divided based on sensing values ​​measured by a wearable device according to an embodiment. FIG. 6a is graphs comparing a general blood sugar level pattern and a personalized blood sugar level pattern according to an embodiment. FIG. 6b is graphs comparing a general blood sugar level pattern and a personalized blood sugar level pattern according to an embodiment. FIG. 7a is a diagram illustrating a graph in which information is tagged to a blood sugar level pattern analyzed by a wearable device according to an embodiment. FIG. 7b is a diagram illustrating information tagged to a blood sugar level pattern analyzed by a wearable device according to an embodiment. FIG. 7C is a diagram illustrating information tagged to a blood sugar level pattern analyzed by a wearable device according to an embodiment. FIG. 7D is a diagram illustrating information tagged to a blood sugar level pattern analyzed by a wearable device according to an embodiment.

[0075] The wearable device (200) of FIG. 3 may correspond to the wearable device (200) described with reference to FIGS. 2a to 2c.

[0076] Referring to FIG. 3, the wearable device (200) may include a display (310), a processor (320), a sensor module (330), a continuous blood glucose measurement sensor (340), a memory (350), and / or a communication circuit (370). In one embodiment, the memory (350) may include a status analysis module (361), a blood glucose analysis module (363), an artificial intelligence (AI) module (365), a general blood glucose pattern profile (367), and a personalized blood glucose pattern profile (369).

[0077] In one embodiment, the display (310) may correspond to the display module (160) of FIG. 1 and / or the display (201) of FIG. 2A. In one embodiment, the processor (320) may correspond to the processor (120) of FIG. 1. In one embodiment, the sensor module (330) may correspond to the sensor module (176) of FIG. 1. In one embodiment, the sensor module (330) may include optical sensors (251 to 257, and 259), electrode sensors (271, 275, 206), and / or motion sensors (280) of FIG. 2A and / or FIG. 2C. In one embodiment, the sensor module (330) may further include a temperature sensor, a magnetic sensor, a barometric pressure sensor, an illuminance sensor, and / or a position sensor. In one embodiment, the continuous blood glucose measurement sensor (340) may be included in the sensor module (176) of FIG. 1. In one embodiment, the continuous blood glucose measurement sensor (340) may correspond to the optical sensor (258) of FIG. 2C. In one embodiment, the memory (350) may correspond to the memory (130) of FIG. 1. In one embodiment, the communication circuit (370) may correspond to the communication module (190) of FIG. 1.

[0078] In one embodiment, the processor (320) may identify a user's status based on sensing data received from a sensor module (330) (e.g., optical sensors (251 to 259), electrode sensors (271, 275, 206), and / or motion sensor (280)). In one embodiment, the processor (320) may identify the user's status through a status analysis module (361). In one embodiment, the user's status may include an activity status and / or a vital status. For example, referring to FIG. 4A, the sensing data may include data representing a vital status (e.g., temperature data (411), blood pressure data (413), stress data (415), heart rate data (417), and / or heart rate variability data (419)). However, the present invention is not limited thereto. For example, sensing data may include data indicating an activity state (e.g., position, speed, acceleration, rotational state, angular velocity, and / or angular acceleration due to movement of the wearable device (200).

[0079] In one embodiment, the processor (320) may identify information related to the user's exercise (hereinafter, exercise information) based on the sensing data. In one embodiment, the exercise information may be a part of the sensing data for identifying an activity state. For example, the exercise information may include position, speed, acceleration, rotational state, angular velocity, and / or angular acceleration due to the movement of the wearable device (200). For example, the exercise information may include temperature and / or the current position of the wearable device (200). For example, the exercise information may include heart rate, heart rate variability, oxygen saturation, blood pressure, electrocardiogram, and / or body composition. However, the present invention is not limited thereto. For example, the exercise information may be a sensing value of each of the axes (e.g., 3 axes, 6 axes, or 9 axes), and a characteristic point of the sensing value (e.g., peak position (peak time), slope). For example, the motion information may include motion-related information (or information processed from sensing values), such as rotation degree (gyro sensing value), movement speed, or state (e.g., walking, running).

[0080] In one embodiment, the processor (320) may identify the user's activity state (e.g., sleeping, normal activity (e.g., static activity (e.g., resting, reading a book), riding public transportation), exercise (or type of exercise)) based on the user's exercise information.

[0081] In one embodiment, the processor (320) may determine a movement pattern of the user based on a pattern of acceleration of movement of the wearable device (200), heart rate, heart rate variability, and / or changes in the current location of the wearable device (200). For example, the processor (320) may identify a user's activity state (e.g., sleeping, normal activity (e.g., stationary activity (e.g., resting, reading a book), riding public transportation), or exercising) based on the user's movement pattern.

[0082] In one embodiment, the processor (320) may identify the user's activity state as a sleep state if the heart rate decreases and the heart rate variability is low (i.e., the heart rate is stabilized). In one embodiment, the processor (320) may identify the user's activity state as an exercise state (or a state of performing a specific exercise) if the user's movement pattern exhibits a movement pattern associated with a specific exercise (e.g., running, walking, swimming). In one embodiment, the processor (320) may identify the user's activity state as a normal activity if the user is neither in a sleep state nor in an exercise state.

[0083] In one embodiment, the processor (320) may identify information related to the user's body (hereinafter, "biometric information") based on the sensing data. In one embodiment, the biometric information may be a part of the sensing data for identifying a biological state. For example, the biometric information may include heart rate, heart rate variability, oxygen saturation, vascular stiffness, blood pressure, vascular age, stress, IHRN, electrocardiogram, electromyogram, electroencephalogram, body composition, and / or galvanic skin response. For example, the biometric information may include temperature, but is not limited thereto. The biometric information may be information previously input by the user. For example, the biometric information may include age, height, weight, gender, body fat mass, muscle mass, and / or bone mass. For example, the biometric information may include health information previously input by the user. For example, the health information may be a personal health record (PHR) registered in an external server (e.g., server (108) of FIG. 1). For example, health information may indicate pregnancy and / or a medical condition.

[0084] In one embodiment, states may be categorized into transient states and constant states. In one embodiment, a transient state may relate to an activity state and / or a physiological state. In one embodiment, a constant state may relate to some physiological state. For example, a constant state may be a state that persists for a specified period of time (e.g., one day). For example, a constant state may include age, height, weight, gender, body fat mass, muscle mass, and / or bone mass. For example, a constant state may indicate pregnancy and / or a disease.

[0085] In one embodiment, the processor (320) can identify the user's biological status (e.g., health status (e.g., sleep apnea, pregnancy, hormonal cycle)) based on the user's biological information through the status analysis module (361).

[0086] In one embodiment, the processor (320) may receive blood glucose data from a continuous blood glucose measurement sensor (340). In one embodiment, the continuous blood glucose measurement sensor (340) may measure blood glucose data representing the user's blood glucose according to a specified cycle. In one embodiment, the continuous blood glucose measurement sensor (340) may transmit blood glucose data representing blood glucose continuously measured according to a specified cycle to the processor (320). For example, referring to FIG. 4B, the blood glucose data may represent a change (420) in a blood glucose level measured according to a specified measurement cycle. For example, referring to FIG. 4B, the blood glucose data may be displayed through a graph consisting of a time axis (e.g., hour) and a blood glucose axis (e.g., milligrams per deciliter (mg / dL)).

[0087] In one embodiment, the processor (320) may identify information associated with blood glucose based on blood glucose data. In one embodiment, the information associated with blood glucose may indicate a blood glucose concentration and / or a blood glucose level. In one embodiment, the information associated with blood glucose may include a blood glucose change pattern, a blood glucose change amount, and / or a slope of a blood glucose change amount. For example, the blood glucose change pattern may be a combination of at least three or more blood glucose changes. For example, the at least three or more blood glucose changes may include a blood glucose increase, a blood glucose maintenance, and a blood glucose decrease. For example, if the blood glucose change amount (or slope) is within a specified range, the blood glucose change may be determined as blood glucose maintenance. For example, if the blood glucose change amount (or slope) is equal to or greater than an upper limit of a specified range, the blood glucose change may be determined as a blood glucose increase. For example, if the blood glucose change amount (or slope) is equal to or less than a lower limit of a specified range, the blood glucose change may be determined as a blood glucose decrease. For example, in the case of at least four blood glucose changes, the blood glucose rise and / or fall can be further subdivided. For example, to classify the blood glucose changes into four or more, two or more ranges for classifying the blood glucose changes may be set. In one embodiment, the two or more ranges may not overlap each other. For example, in the case of five blood glucose changes, three ranges for classifying the blood glucose changes may be set. For example, the three ranges may not overlap each other.

[0088] For example, if the blood glucose change (or slope) is within a specified first range, the blood glucose change may be determined as blood glucose maintenance. For example, if the blood glucose change (or slope) is within a specified second range, the blood glucose change may be determined as blood glucose elevation. For example, if the blood glucose change (or slope) is within a specified third range, the blood glucose change may be determined as blood glucose reduction. For example, if the blood glucose change (or slope) is equal to or greater than the upper limit of the specified second range, the blood glucose change may be determined as blood glucose spike. For example, if the blood glucose change (or slope) is equal to or less than the lower limit of the specified third range, the blood glucose change may be determined as blood glucose drop. In one embodiment, the first range may include a change amount of 0. In one embodiment, the upper limit of the first range may have a first change amount greater than 0. In one embodiment, the lower limit of the first range may have a second change amount less than 0. In one embodiment, the second range may be comprised of variations greater than 0. For example, the upper limit of the first range may be less than the lower limit of the second range. In one embodiment, the second range may be comprised of variations less than 0. For example, the lower limit of the first range may be greater than the upper limit of the third range.

[0089] In one embodiment, the processor (320) may identify whether a blood glucose variation pattern corresponds to a normal blood glucose pattern based on a normal blood glucose pattern profile (367). For example, the processor (320) may identify whether blood glucose variation patterns of time intervals (431, 433, 435, 437, 439, 441, 443, 445) correspond to a normal blood glucose pattern.

[0090] In one embodiment, the processor (320) can identify whether a blood glucose variation pattern identified based on blood glucose data corresponds to a normal blood glucose pattern without referring to the user's state (e.g., activity state and / or vital state). In one embodiment, the blood glucose variation pattern corresponding to the normal blood glucose pattern may include that a sequence of blood glucose variations included in the blood glucose variation pattern corresponds to a sequence of blood glucose variations of the normal blood glucose pattern. In one embodiment, the blood glucose variation pattern corresponding to the normal blood glucose pattern may include that a blood glucose variation amount (or slope) of blood glucose variations included in the blood glucose variation pattern corresponds to a blood glucose variation amount (or slope) of blood glucose variations of the normal blood glucose pattern.

[0091] In one embodiment, a typical blood sugar pattern may be a blood sugar pattern exhibited by a normal person. For example, a typical blood sugar pattern may be a blood sugar pattern identified in situations where there are no factors (or health problems) that affect blood sugar levels. For example, factors that affect blood sugar levels may include being overweight, a medical condition (e.g., diabetes), pregnancy, or sleep disorders. In one embodiment, a typical blood sugar pattern may be a blood sugar pattern exhibited by a normal person during daily activities (e.g., eating, exercising, walking, sleeping).

[0092] For example, the processor (320) can identify that the blood sugar change patterns of the time intervals (431, 435, 439) correspond to a general blood sugar pattern due to a meal.

[0093] In one embodiment, the processor (320) may identify a difference value between the blood glucose level of the blood glucose variation pattern and the blood glucose level of the general blood glucose pattern based on the blood glucose variation pattern being identified as a general blood glucose pattern. In one embodiment, the processor (320) may determine whether to register the blood glucose variation pattern as a personalized blood glucose pattern based on the difference value. In one embodiment, the processor (320) may determine whether to register the blood glucose variation pattern as a personalized blood glucose pattern based on the difference value being greater than or equal to a reference threshold value. In one embodiment, the difference value may include a difference value between the highest blood glucose level of the blood glucose variation pattern and the highest blood glucose level of the general blood glucose pattern. In one embodiment, the difference value may include a difference value between the lowest blood glucose level of the blood glucose variation pattern and the lowest blood glucose level of the general blood glucose pattern.

[0094] In one embodiment, the processor (320) may determine whether a factor affecting blood sugar levels exists based on a difference value exceeding a reference threshold. For example, the factor affecting blood sugar levels may be related to the user's constant state. For example, the user's constant state may be the user's long-term health status. For example, the user's constant state may be the user's long-term biometric information (e.g., weight, pregnancy status, and / or hormonal cycle). For example, referring to FIG. 6A, the normal blood sugar pattern (610) during a meal (641, 643, 645) of a normal person may have a pattern in which blood sugar continuously rises below a threshold value during a meal (631, 633, 635) of a normal person and then continuously falls after the meal, whereas the blood sugar pattern (615) during a meal (641, 643, 645) of a pregnant user may have a pattern in which blood sugar rises above a threshold value during a meal (631, 633, 635) and then quickly falls. In addition, the normal blood sugar pattern (610) during a meal (641, 643, 645) of a normal person may have a pattern in which blood sugar rises relatively late after starting a meal (641, 643, 645) and high blood sugar is maintained for a relatively long time, compared to the blood sugar pattern (615) during a meal (641, 643, 645) of a pregnant user. Accordingly, the processor (320) can determine whether there is a factor (e.g., pregnancy) affecting the user's blood sugar level based on the difference value between the rising and falling blood sugar pattern (615) and the general blood sugar pattern (610) being greater than or equal to a reference threshold value. Based on the determination that there is a factor (e.g., pregnancy) affecting the user's blood sugar level, the processor (320) can classify the rising and falling blood sugar pattern (615) as a personalized blood sugar pattern (e.g., blood sugar pattern according to pregnancy) rather than a general blood sugar pattern.

[0095] In one embodiment, the processor (320) can identify whether a blood glucose variation pattern corresponds to a personalized blood glucose pattern based on the personalized blood glucose pattern profile (369). In one embodiment, the processor (320) can identify whether a blood glucose variation pattern not identified as a general blood glucose pattern corresponds to a personalized blood glucose pattern based on the personalized blood glucose pattern profile (369).

[0096] For example, the processor (320) can identify whether blood sugar change patterns of time intervals (433, 437, 441, 443, 445) that are not identified as general blood sugar patterns among time intervals (431, 433, 435, 437, 439, 441, 443, 445) correspond to personalized blood sugar patterns.

[0097] In one embodiment, the processor (320) may identify whether a blood sugar change pattern corresponds to a personalized blood sugar pattern by referring to a user's state (510) (e.g., activity state and / or vital state). For example, referring to FIG. 5 , the processor (320) may identify whether a blood sugar change pattern corresponds to a personalized blood sugar pattern based on specific states (511, 513, 515, 517, 519, 521, 523, 525) in time intervals (431, 433, 435, 437, 439, 441, 443, 445). For example, each of the specific states (511, 513, 515, 517, 519, 521, 523, 525) may be eating, normal activity, eating, normal activity, eating, exercise, normal activity, and sleeping (or sleep apnea).

[0098] In one embodiment, the processor (320) may identify the blood glucose variation pattern as a personalized blood glucose pattern if the user's state (510) (e.g., activity state and / or vital state) during the time period in which the blood glucose variation pattern is identified is substantially the same as the user's state associated with the personalized blood glucose pattern, and the blood glucose variation pattern corresponds to the personalized blood glucose pattern. In one embodiment, the blood glucose variation pattern corresponding to the personalized blood glucose pattern may include that a sequence of blood glucose changes included in the blood glucose variation pattern corresponds to a sequence of blood glucose changes of the personalized blood glucose pattern. In one embodiment, the blood glucose variation pattern corresponding to the personalized blood glucose pattern may include that the amount of blood glucose change (or slope) of blood glucose changes included in the blood glucose variation pattern corresponds to the amount of blood glucose change (or slope) of blood glucose changes of the personalized blood glucose pattern.

[0099] In one embodiment, the personalized blood sugar pattern may be a blood sugar pattern different from the blood sugar pattern exhibited by a normal person. For example, the personalized blood sugar pattern may be a blood sugar pattern identified in a situation where factors affecting blood sugar are present. In one embodiment, the general blood sugar pattern may be a blood sugar pattern exhibited during daily life (e.g., eating, exercising, walking, sleeping) in a situation where factors affecting blood sugar are present. For example, the personalized blood sugar pattern may be a blood sugar pattern in a situation where factors affecting blood sugar are identified based on sensing data. For example, referring to FIG. 6B, the general blood sugar pattern (650) during sleep may have a pattern in which blood sugar continuously drops during sleep for a normal person, whereas the personalized blood sugar pattern (655) during sleep for a user with sleep apnea may have a pattern in which blood sugar continuously rises. Accordingly, the processor (320) can classify a blood sugar pattern in which blood sugar continuously rises as a personalized blood sugar pattern (e.g., a blood sugar pattern according to sleep apnea) rather than a general blood sugar pattern, by referring to the user's state (525) of the time section (445) (e.g., the activity state is sleep and the vital state indicates sleep apnea).

[0100] In one embodiment, the processor (320) may identify a blood glucose change pattern that is not classified based on the general blood glucose pattern profile (367) and the personalized blood glucose pattern profile (369) as an unclassified blood glucose pattern. For example, referring to FIG. 5, the processor (320) may identify blood glucose change patterns of time intervals (433, 437, 441, 443) that are not identified as a general blood glucose pattern and a personalized blood glucose pattern among time intervals (431, 433, 435, 437, 439, 441, 443, 445) as an unclassified blood glucose pattern.

[0101] In one embodiment, the processor (320) may perform an operation to analyze an unclassified blood glucose pattern based on the identification of a blood glucose change pattern classified as an unclassified blood glucose pattern.

[0102] In one embodiment, the processor (320) may adjust (or reduce) the measurement cycle of the sensor module (330) and / or the continuous blood glucose measurement sensor (340) based on the identification of a blood glucose variation pattern classified as an unclassified blood glucose pattern. For example, the processor (320) may adjust the cycle for measuring the blood glucose of the user of the continuous blood glucose measurement sensor (340) based on the identification of a blood glucose variation pattern classified as an unclassified blood glucose pattern. For example, the processor (320) may reduce the cycle for measuring the blood glucose of the user of the continuous blood glucose measurement sensor (340). In one embodiment, the processor (320) may analyze the unclassified blood glucose pattern based on the sensing data and / or blood glucose data measured after adjusting the measurement cycle of the sensor module (330) and / or the continuous blood glucose measurement sensor (340).

[0103] In one embodiment, the processor (320) may adjust (or increase) the resolution of the sensor module (330) and / or the continuous blood glucose measurement sensor (340) based on the identification of a blood glucose variation pattern classified as an unclassified blood glucose pattern. For example, the processor (320) may adjust the resolution for measuring the blood glucose of the user of the continuous blood glucose measurement sensor (340) based on the identification of a blood glucose variation pattern classified as an unclassified blood glucose pattern. For example, the processor (320) may increase the resolution for measuring the blood glucose of the user of the continuous blood glucose measurement sensor (340). In one embodiment, the processor (320) may analyze the unclassified blood glucose pattern based on the sensing data and / or blood glucose data measured after adjusting the resolution cycle of the sensor module (330) and / or the continuous blood glucose measurement sensor (340).

[0104] In one embodiment, the processor (320) may change a sensor (e.g., a temperature sensor) included in the sensor module (330) from a disabled (or turned off) state to an enabled (or turned on) state based on the identification of a blood glucose change pattern classified as an unclassified blood glucose pattern. In one embodiment, the processor (320) may analyze the unclassified blood glucose pattern based on sensing data of an enabled sensor measured after enabling the sensor.

[0105] In one embodiment, the processor (320) may register an unclassified blood sugar pattern as a personalized blood sugar pattern in a personalized blood sugar pattern profile (369) based on the user's status identified based on new sensing data. For example, if stress data is newly identified during a time period (437) in which a blood sugar change pattern is identified and the stress data represents a user's stress state, the processor (320) may register the blood sugar change pattern of the time period (437) as a personalized blood sugar pattern according to stress in the personalized blood sugar pattern profile (369). For example, if exercise data is newly identified during a time period (441) in which a blood sugar change pattern is identified and the exercise data represents a user's exercise state, the processor (320) may register the blood sugar change pattern of the time period (441) as a personalized blood sugar pattern according to exercise in the personalized blood sugar pattern profile (369).

[0106] In one embodiment, the processor (320) may analyze an unclassified blood glucose pattern based on the user's status identified based on new sensing data. For example, the processor (320) may analyze the unclassified blood glucose pattern through the AI ​​module (365). For example, the processor (320) may analyze, through the AI ​​module (365), whether the unclassified blood glucose pattern is a blood glucose pattern generated by a temporary state or a constant state.

[0107] In one embodiment, the processor (320) may register the unclassified blood sugar pattern as a personalized blood sugar pattern in a personalized blood sugar pattern profile (369) according to the constant state based on the determination that the unclassified blood sugar pattern is a blood sugar pattern generated by the constant state.

[0108] In one embodiment, the AI ​​module (365) may include an AI model including multiple parameters related to a neural network having a structure based on an encoder and a decoder, such as a transformer. In one embodiment, the AI ​​module (365) may include a bi-directional model based on learning for an encoder (e.g., bidirectional encoder representations from transformers (BERT)) or an auto-encoding model (e.g., a diffusion model). In one embodiment, the AI ​​module (365) may include an auto-regressor model based on learning for a decoder (e.g., a generative pre-trained transformer (GPT)). In one embodiment, the AI ​​module (365) may include a sequence-to-sequence model (e.g., stable diffusion, DALL-E 2) based on learning for an encoder and a decoder. In one embodiment, the AI ​​module (365) may be an AI model trained to classify blood sugar change patterns according to a constant state and a constant state.

[0109] In one embodiment, the processor (320) may display tag information together with changes in blood sugar levels (420) through the display (310). In one embodiment, the processor (320) may display tag information together with changes in blood sugar levels (420) for a specified period (e.g., daily) through the display (310). For example, referring to FIG. 7A, the tag information may include tags (711, 713, 715, 717, 719, 721) (or visual objects) representing general blood sugar patterns and / or personalized blood sugar patterns identified according to blood sugar change patterns. In one embodiment, the tags (711, 713, 717) (or visual objects) may represent types of general blood sugar patterns (e.g., breakfast, lunch, dinner, and / or dessert). In one embodiment, tags (715, 719, 721) (or visual objects) may indicate a type of personalized blood glucose pattern (e.g., stress, exercise, and / or sleep apnea). In one embodiment, tags (711, 713, 717) (or visual objects) may indicate a user's status (e.g., taking a walk after a meal, concentrating on work) along with a type of general or personalized blood glucose pattern.

[0110] In one embodiment, the processor (320) may display information for blood sugar control through the display (310).

[0111] For example, referring to FIG. 7B, the processor (320) may display, through the display (310), information (731, 735) that guides actions for blood sugar control after a meal. For example, the processor (320) may display, through the display (310), information (731) indicating a blood sugar level that can be controlled through actions for blood sugar control after a meal and a description (735) that guides actions for blood sugar control after a meal. In one embodiment, the information (731) indicating a blood sugar level that can be controlled through actions for blood sugar control after a meal may represent a portion of a graph comparing a blood sugar level when there is no walk and a blood sugar level when there is a walk.

[0112] For example, referring to FIG. 7C, the processor (320) may display, through the display (310), information (741, 745) that guides actions for blood sugar control after a meal. For example, the processor (320) may display, through the display (310), information (741) indicating a blood sugar level that is controlled when dessert is not eaten after a meal and a description (745) that guides actions for blood sugar control after a meal. In one embodiment, the information (741) indicating a blood sugar level that is controlled when dessert is not eaten after a meal may represent a portion of a graph comparing a blood sugar level when there is no dessert and a blood sugar level when dessert is eaten. However, the present invention is not limited thereto. For example, the processor (320) may display, through the display (310), information indicating a cause of a blood sugar spike. For example, the processor (320) may display, through the display (310), information indicating a blood sugar level that may increase due to a subsequent eating behavior (e.g., dessert) after a meal and an explanation guiding the user to refrain from eating dessert after a meal.

[0113] In one embodiment, the processor (320) may display a screen linking a page for blood sugar control through the display (310). For example, referring to FIG. 7D , the processor (320) may display information (751) guiding sleep apnea occurring during sleep through the display (310). For example, the processor (320) may display a visual object (755) linked to a page for confirming specific information on sleep apnea occurring during sleep through the display (310). In one embodiment, the processor (320) may display a page for confirming specific information on sleep apnea through the display (310) in response to a user input selecting the visual object (755).

[0114] The wearable device (200) described above can display blood sugar levels continuously measured at a specified interval along with blood sugar-related events (e.g., meals, exercise). In addition, the wearable device (200) described above can display blood sugar levels continuously measured at a specified interval along with events related to the user's condition (e.g., sleep apnea, stress) based on the measured sensing data. Accordingly, the wearable device (200) described above can provide the user with information indicating that a personalized blood sugar pattern reflecting the individual's characteristics (e.g., temporary condition and / or constant condition) is identified in addition to a general blood sugar pattern.

[0115] In addition, the wearable device (200) described above can induce the user's blood sugar management behavior by displaying recommendations for controlling blood sugar levels (e.g., encouraging the user to take a walk, encouraging the user to avoid desserts). Furthermore, the wearable device (200) described above can induce the user's blood sugar management behavior by displaying information for treating and / or coping with personal characteristics (e.g., sleep apnea) that affect blood sugar levels (e.g., information about symptoms, recommendations for medical consultation).

[0116] FIG. 8A is a block diagram of a wearable device according to one embodiment.

[0117] The wearable device (200) of FIG. 8a may not include a continuous blood glucose measurement sensor (340), compared to the wearable device (200) of FIG. 3.

[0118] Referring to FIG. 8a, the wearable device (200) can receive blood sugar data from a continuous blood sugar measurement sensor (801) connected wired and / or wirelessly to the wearable device (200) through a communication circuit (370).

[0119] In one embodiment, the processor (320) can identify information related to blood sugar (or blood sugar change pattern) based on blood sugar data received from the continuous blood sugar measurement sensor (801).

[0120] In one embodiment, the processor (320) can analyze blood glucose change patterns based on a general blood glucose pattern profile (367) and / or a personalized blood glucose pattern profile (369).

[0121] FIG. 8b is a block diagram of a wearable device according to one embodiment.

[0122] The wearable device (200) of FIG. 8b may not include a state analysis module (361), a blood sugar analysis module (363), an AI module (365), a general blood sugar pattern profile (367), and a personalized blood sugar pattern profile (369), compared to the wearable device (200) of FIG. 3. However, the present invention is not limited thereto. According to an embodiment, the wearable device (200) of FIG. 8b may include a state analysis module (361) or a program (or module) for providing at least some of the functions provided by the state analysis module (361). For example, in this case, the wearable device (200) may process data (or raw data) measured by the sensor module (330) and / or the continuous blood glucose measurement sensor (340) (e.g., extract identifiable information (e.g., biometric information, exercise information) from the wearable device (200)) and then transmit the processed data (or extracted information) to the electronic device (805).

[0123] In one embodiment, the electronic device (805) may include a display (810), a processor (820), a memory (850), and / or a communication circuit (870). In one embodiment, the memory (850) may include a status analysis module (861), a blood glucose analysis module (863), an AI module (865), a general blood glucose pattern profile (867), and a personalized blood glucose pattern profile (869).

[0124] In one embodiment, the electronic device (805) can receive sensing data and blood glucose data from a wearable device (200) connected wired and / or wirelessly via a communication circuit (870).

[0125] In one embodiment, the processor (820) may identify the user's movement information and / or biometric information based on sensing data received from the wearable device (200). In one embodiment, the processor (820) may identify the user's state (e.g., activity state and / or biometric state) based on the user's movement information and / or biometric information.

[0126] In one embodiment, the processor (820) can identify information related to blood sugar (or blood sugar change pattern) based on blood sugar data received from the wearable device (200).

[0127] In one embodiment, the processor (820) can analyze blood glucose variation patterns based on a general blood glucose pattern profile (867) and / or a personalized blood glucose pattern profile (869).

[0128] FIG. 8c is a block diagram of a wearable device according to one embodiment.

[0129] The wearable device (200) of FIG. 8c may not include a continuous blood glucose measurement sensor (340) compared to the wearable device (200) of FIG. 3. The wearable device (200) of FIG. 8c may not include a state analysis module (361), a blood glucose analysis module (363), an AI module (365), a general blood glucose pattern profile (367), and a personalized blood glucose pattern profile (369) compared to the wearable device (200) of FIG. 3. However, the present invention is not limited thereto. According to an embodiment, the wearable device (200) of FIG. 8c may include a state analysis module (361) or a program (or module) for providing at least some of the functions provided by the state analysis module (361). For example, in this case, the wearable device (200) may process data (or raw data) measured by the sensor module (330) and / or the continuous blood glucose measurement sensor (340) (e.g., extract identifiable information (e.g., biometric information, exercise information) from the wearable device (200)) and then transmit the processed data (or extracted information) to the electronic device (805).

[0130] In one embodiment, the electronic device (805) may include a display (810), a processor (820), a memory (850), and / or a communication circuit (870). In one embodiment, the memory (850) may include a status analysis module (861), a blood glucose analysis module (863), an AI module (865), a general blood glucose pattern profile (867), and a personalized blood glucose pattern profile (869).

[0131] In one embodiment, the electronic device (805) can receive sensing data from a wearable device (200) connected wired and / or wirelessly via a communication circuit (870).

[0132] In one embodiment, the electronic device (805) can receive blood glucose data from a continuous blood glucose measurement sensor (801) connected wired and / or wirelessly via a communication circuit (870).

[0133] In one embodiment, the processor (820) may identify the user's movement information and / or biometric information based on sensing data received from the wearable device (200). In one embodiment, the processor (820) may identify the user's state (e.g., activity state and / or biometric state) based on the user's movement information and / or biometric information.

[0134] In one embodiment, the processor (820) can identify information related to blood sugar (or blood sugar change pattern) based on blood sugar data received from the continuous blood sugar measurement sensor (801).

[0135] In one embodiment, the processor (820) can analyze blood glucose variation patterns based on a general blood glucose pattern profile (867) and / or a personalized blood glucose pattern profile (869).

[0136] Figure 9 is a flowchart showing the operation of a wearable device according to one embodiment.

[0137] Fig. 9 can be explained with reference to Figs. 2a to 7d.

[0138] In the following examples, the operations may be performed sequentially, but are not necessarily sequential. For example, the order of the operations may be changed, and at least two operations may be performed in parallel.

[0139] According to one embodiment, operations 910 to 985 may be understood to be performed by a processor (320) of a wearable device (200).

[0140] Referring to FIG. 9, in one embodiment, at operation 910, the wearable device (200) may identify a blood sugar pattern. The wearable device (200) may identify the blood sugar pattern based on blood sugar data. For example, the blood sugar pattern may be a combination of at least three or more blood sugar changes. For example, the at least three or more blood sugar changes may include blood sugar elevation, blood sugar maintenance, and blood sugar reduction. For example, if the blood sugar change (or slope) is within a specified range, the blood sugar change may be determined as blood sugar maintenance. For example, if the blood sugar change (or slope) is equal to or greater than the upper limit of the specified range, the blood sugar change may be determined as blood sugar elevation. For example, if the blood sugar change (or slope) is equal to or less than the lower limit of the specified range, the blood sugar change may be determined as blood sugar reduction.

[0141] In one embodiment, at operation 920, the wearable device (200) can determine whether the blood sugar pattern is personalized. In one embodiment, the wearable device (200) can determine whether the identified blood sugar pattern corresponds to the personalized blood sugar pattern based on the personalized blood sugar pattern profile (369). In one embodiment, the blood sugar pattern corresponding to the personalized blood sugar pattern may include that the blood sugar change amount (or slope) of the blood sugar changes included in the blood sugar pattern corresponds to the blood sugar change amount (or slope) of the blood sugar changes of the personalized blood sugar pattern.

[0142] In one embodiment, at operation 920, based on determining that the identified blood sugar pattern is a personalized blood sugar pattern, the wearable device (200) may perform operation 975. In one embodiment, at operation 920, based on determining that the identified blood sugar pattern is not a personalized blood sugar pattern, the wearable device (200) may perform operation 930.

[0143] In one embodiment, at operation 930, the wearable device (200) can determine whether it is a normal blood sugar pattern. In one embodiment, the wearable device (200) can determine whether the identified blood sugar pattern corresponds to a normal blood sugar pattern based on a normal blood sugar pattern profile (367). In one embodiment, the blood sugar pattern corresponding to a normal blood sugar pattern may include that the blood sugar change amount (or slope) of blood sugar changes included in the blood sugar pattern corresponds to the blood sugar change amount (or slope) of blood sugar changes of the normal blood sugar pattern.

[0144] In one embodiment, at operation 930, based on the determination that the identified blood sugar pattern is a normal blood sugar pattern, the wearable device (200) may perform operation 940. In one embodiment, at operation 930, based on the determination that the identified blood sugar pattern is not a normal blood sugar pattern, the wearable device (200) may perform operation 980.

[0145] In one embodiment, at operation 940, the wearable device (200) may determine whether the identified blood glucose pattern has a blood glucose outlier. In one embodiment, the wearable device (200) may determine that the identified blood glucose pattern has a blood glucose outlier based on a difference value between the blood glucose level of the identified blood glucose pattern and the blood glucose level of the normal blood glucose pattern being greater than or equal to a reference threshold value. In one embodiment, the difference value may include a difference value between the highest blood glucose level of the blood glucose pattern and the highest blood glucose level of the normal blood glucose pattern. In one embodiment, the difference value may include a difference value between the lowest blood glucose level of the blood glucose pattern and the lowest blood glucose level of the normal blood glucose pattern. However, the present invention is not limited thereto. For example, the wearable device (200) may determine that the identified blood glucose pattern has a blood glucose outlier based on a slope of the blood glucose level of the identified blood glucose pattern being higher or lower than a slope of the blood glucose level of the normal blood glucose pattern by a reference slope value.

[0146] In one embodiment, at operation 940, based on determining that there is a blood sugar outlier in the identified blood sugar pattern, the wearable device (200) may perform operation 950. In one embodiment, at operation 940, based on determining that there is no blood sugar outlier in the identified blood sugar pattern, the wearable device (200) may perform operation 970.

[0147] In one embodiment, at operation 950, the wearable device (200) may identify a relevant factor through personal information. In one embodiment, the personal information may be related to the user's constant state. For example, the user's constant state may be the user's long-term health status. For example, the user's constant state may be the user's long-term biometric information (e.g., weight, pregnancy status, and / or hormonal cycle).

[0148] In one embodiment, at operation 960, the wearable device (200) may determine whether an additional factor exists. In one embodiment, the wearable device (200) may identify an additional factor that influences the identified blood sugar pattern among at least one associated factor identified through personal information. For example, the additional factor influencing the identified blood sugar pattern may be an associated factor that constantly influences the user's blood sugar (e.g., pregnancy).

[0149] In one embodiment, at operation 960, based on determining that there is an additional factor, the wearable device (200) may perform operation 985. At operation 960, based on determining that there is no additional factor, the wearable device (200) may perform operation 970.

[0150] In one embodiment, at operation 970, the wearable device (200) may tag a visual object representing a typical blood sugar pattern. For example, a visual object representing a typical blood sugar pattern may represent a type of typical blood sugar pattern (e.g., breakfast, lunch, dinner, and / or dessert).

[0151] In one embodiment, at operation 975, the wearable device (200) may tag a visual object representing a personalized blood sugar pattern. For example, the visual object representing the personalized blood sugar pattern may represent a type of personalized blood sugar pattern (e.g., breakfast stress, exercise, and / or sleep apnea).

[0152] In one embodiment, at operation 980, the wearable device (200) may perform an operation to analyze an unclassified blood sugar pattern. Hereinafter, operation 980 may be described in detail with reference to FIG. 10.

[0153] In one embodiment, at operation 985, the wearable device (200) may register the identified blood glucose pattern as a personalized blood glucose pattern and tag a visual object. For example, the wearable device (200) may register the identified blood glucose pattern as a personalized blood glucose pattern profile (369) according to additional factors. For example, the wearable device (200) may tag a visual object representing additional factors related to the identified blood glucose pattern at the location where the blood glucose pattern was identified.

[0154] In one embodiment, the wearable device (200) can register the identified blood sugar pattern as a personalized blood sugar pattern according to additional factors through the AI ​​module (365).

[0155] Figure 10 is a flowchart showing the operation of a wearable device according to one embodiment.

[0156] Fig. 10 can be explained with reference to Figs. 2a to 7d.

[0157] In the following examples, the operations may be performed sequentially, but are not necessarily sequential. For example, the order of the operations may be changed, and at least two operations may be performed in parallel.

[0158] According to one embodiment, operations 1010 to 1070 may be understood to be performed in the processor (320) of the wearable device (200).

[0159] In one embodiment, in operation 1010, the wearable device (200) may identify a relevant factor through personal information. In one embodiment, the personal information may be related to the user's constant state. For example, the user's constant state may be the user's long-term health status. For example, the user's constant state may be the user's long-term biometric information (e.g., weight, pregnancy status, and / or hormonal cycle).

[0160] In one embodiment, at operation 1020, the wearable device (200) may determine whether an additional factor exists. In one embodiment, the wearable device (200) may identify an additional factor that influences the identified blood sugar pattern among at least one associated factor identified through personal information. For example, the additional factor influencing the identified blood sugar pattern may be an associated factor that constantly influences the user's blood sugar (e.g., pregnancy).

[0161] In one embodiment, based on determining that there is an additional factor in operation 1020, the wearable device (200) may perform operation 1030. Based on determining that there is no additional factor in operation 1020, the wearable device (200) may perform operation 1070.

[0162] In one embodiment, at operation 1030, the wearable device (200) may register the identified blood sugar pattern as a temporary blood sugar pattern. In one embodiment, the temporary blood sugar pattern may be a blood sugar pattern that has not been registered in the personalized blood sugar pattern profile (369). In one embodiment, the temporary blood sugar pattern may be a blood sugar pattern prior to being registered in the personalized blood sugar pattern profile (369).

[0163] In one embodiment, at operation 1040, the wearable device (200) may determine whether a temporary blood sugar pattern is identified. In one embodiment, the wearable device (200) may determine whether a temporary blood sugar pattern is identified among blood sugar patterns identified later. In one embodiment, the wearable device (200) may determine whether a temporary blood sugar pattern is identified among blood sugar patterns determined to be other than a normal blood sugar pattern in operation 930 of FIG. 9, which is performed later.

[0164] In one embodiment, at operation 1040, based on determining that a temporary blood sugar pattern is identified, the wearable device (200) may perform operation 1050. At operation 1040, based on determining that a temporary blood sugar pattern is not identified, the wearable device (200) may perform operation 1060.

[0165] In one embodiment, at operation 1050, the wearable device (200) may register the identified blood glucose pattern as a personalized blood glucose pattern and tag a visual object. For example, the wearable device (200) may register the identified blood glucose pattern as a personalized blood glucose pattern profile (369) as a blood glucose pattern according to additional factors. For example, the wearable device (200) may tag a visual object representing an additional factor related to the identified blood glucose pattern at the location where the blood glucose pattern was identified.

[0166] In one embodiment, at operation 1060, the wearable device (200) may deregister the temporary blood sugar pattern. For example, the wearable device (200) may determine that there is no relationship between the additional factor and the identified blood sugar pattern based on the determination that the temporary blood sugar pattern is not identified. Accordingly, the wearable device (200) may deregister the temporary blood sugar pattern.

[0167] In one embodiment, at operation 1070, the wearable device (200) may activate a sensor adjustment or additional sensor.

[0168] In one embodiment, the wearable device (200) can adjust (or decrease) the measurement cycle of the sensor module (330) and / or the continuous blood glucose measurement sensor (340). In one embodiment, the wearable device (200) can adjust (or increase) the resolution of the sensor module (330) and / or the continuous blood glucose measurement sensor (340). In one embodiment, the wearable device (200) can change a sensor (e.g., a temperature sensor) included in the sensor module (330) from being disabled (or turned off) to being enabled (or turned on). However, the present invention is not limited thereto. The wearable device (200) can display a UI that prompts the user to input other information (or queries information related to a constant state other than data detected through the sensor module (330) and / or the continuous blood glucose measurement sensor (340)), and can obtain other information (e.g., the user's personal information (e.g., pregnancy, childbirth)) input through the displayed UI.

[0169] The technical problems to be achieved in the present disclosure are not limited to the technical problems mentioned above, and other technical problems not mentioned will be clearly understood by a person having ordinary knowledge in the technical field to which the present disclosure pertains.

[0170] As described above, the electronic device (200, 805) may include a display (310, 810), at least one processor (320, 820) including a processing circuit, and a memory (350, 850) storing instructions and including one or more storage media. The instructions, when individually or collectively executed by the at least one processor (320, 820), may cause the electronic device (200, 805) to receive continuous blood glucose data for a user. The instructions, when individually or collectively executed by the at least one processor (320, 820), may cause the electronic device (200, 805) to receive sensing data for identifying a state of the user. The state of the user may include an activity state of the user and the physiological state of the user. The sensing data may include exercise information related to the activity state and biometric information related to the biological state. The instructions, when individually or collectively executed by the at least one processor (320, 820), may cause the electronic device (200, 805) to identify blood glucose patterns in time intervals in which the activity state and / or the biological state are identified based on the continuous blood glucose data and the sensing data. The instructions, when individually or collectively executed by the at least one processor (320, 820), may cause the electronic device (200, 805) to identify a personalized blood glucose pattern among the blood glucose patterns in the time intervals. The personalized blood glucose pattern may include a pattern in which the blood glucose fluctuates depending on the state.The instructions, when individually or collectively executed by the at least one processor (320, 820), may cause the electronic device (200, 805) to display, via the display (310, 810), a visual object indicating that the personalized blood glucose pattern is identified by the state within a graph representing the continuous blood glucose data.

[0171] The instructions, when individually or collectively executed by the at least one processor (320, 820), may cause the electronic device (200, 805) to identify a normal blood sugar pattern from the blood sugar patterns in the time intervals. The normal blood sugar pattern may be a blood sugar pattern of an ordinary person. The instructions, when individually or collectively executed by the at least one processor (320, 820), may cause the electronic device (200, 805) to identify the personalized blood sugar pattern from blood sugar patterns other than the normal blood sugar pattern among the blood sugar patterns in the time intervals.

[0172] The above instructions, when individually or collectively executed by the at least one processor (320, 820), may cause the electronic device (200, 805) to display, via the display (310, 810), another visual object indicating that the normal blood glucose pattern is identified within the graph.

[0173] The instructions, when individually or collectively executed by the at least one processor (320, 820), may cause the electronic device (200, 805) to identify a blood glucose pattern that falls outside a reference blood glucose range among blood glucose patterns identified as the normal blood glucose pattern. The instructions, when individually or collectively executed by the at least one processor (320, 820), may cause the electronic device (200, 805) to identify a factor related to a health condition of the user that causes the blood glucose pattern to fall outside the reference blood glucose range. The instructions, when individually or collectively executed by the at least one processor (320, 820), may cause the electronic device (200, 805) to display, through the display (310, 810), a visual object representing the factor related to the health condition of the user within the graph.

[0174] The instructions, when individually or collectively executed by the at least one processor (320, 820), may cause the electronic device (200, 805) to identify an unclassified blood glucose pattern that is not identified as the general blood glucose pattern and the personalized blood glucose pattern from the blood glucose patterns in the time intervals. The instructions, when individually or collectively executed by the at least one processor (320, 820), may cause the electronic device (200, 805) to adjust a cycle for measuring the blood glucose of the user associated with the continuous blood glucose data based on the identification of the unclassified blood glucose pattern.

[0175] The instructions, when individually or collectively executed by the at least one processor (320, 820), may cause the electronic device (200, 805) to identify an unclassified blood glucose pattern that is not identified as the general blood glucose pattern and the personalized blood glucose pattern from the blood glucose patterns in the time intervals. The instructions, when individually or collectively executed by the at least one processor (320, 820), may cause the electronic device (200, 805) to register, based on a state of the user in the time interval in which the unclassified blood glucose pattern was identified, the unclassified blood glucose pattern as the personalized blood glucose pattern corresponding to a state of the user in the time interval in which the unclassified blood glucose pattern was identified.

[0176] The instructions, when individually or collectively executed by the at least one processor (320, 820), may cause the electronic device (200, 805) to identify, based on the sensing data, a health state of the user corresponding to the activity state and / or the vital state in the time intervals. The instructions, when individually or collectively executed by the at least one processor (320, 820), may cause the electronic device (200, 805) to display, through the display (310, 810), the visual object representing the health state within the graph in the time interval in which the personalized blood glucose pattern is identified.

[0177] As described above, the electronic device (200, 805) may include a sensor (330) for acquiring the sensing data, and a blood glucose measurement sensor (340) for acquiring the continuous blood glucose data. The instructions, when individually or collectively executed by the at least one processor (320, 820), may cause the electronic device (200, 805) to receive the sensing data through the sensor (330). The instructions, when individually or collectively executed by the at least one processor (320, 820), may cause the electronic device (200, 805) to receive the continuous blood glucose data through the blood glucose measurement sensor (340).

[0178] As described above, the electronic device (200, 805) may include a sensor (330) for obtaining the sensing data, and a communication circuit (370, 870). The instructions, when individually or collectively executed by the at least one processor (320, 820), may cause the electronic device (200, 805) to receive the sensing data through the sensor (330). The instructions, when individually or collectively executed by the at least one processor (320, 820), may cause the electronic device (200, 805) to receive the continuous blood glucose data from an external electronic device (200, 805) through the communication circuit (370, 870).

[0179] The above electronic device (200) may be a wearable device worn on a part of the user's body.

[0180] As described above, the method may be performed by an electronic device (200, 805) including a display (310, 810). The method may include an operation of receiving continuous blood glucose data for a user. The method may include an operation of receiving sensing data for identifying a state of the user. The state of the user may include an activity state of the user and the physiological state of the user. The sensing data may include exercise information related to the activity state and physiological information related to the physiological state. The method may include an operation of identifying blood glucose patterns in time intervals in which the activity state and / or the physiological state are identified based on the continuous blood glucose data and the sensing data. The method may include an operation of identifying a personalized blood glucose pattern among the blood glucose patterns in the time intervals. The personalized blood glucose pattern may include a pattern in which the blood glucose fluctuates depending on the state. The method may include an action of displaying a visual object indicating that the personalized blood glucose pattern is identified by the state within a graph representing the continuous blood glucose data via the display (310, 810).

[0181] The method may include an operation of identifying a typical blood sugar pattern from the blood sugar patterns in the time intervals. The typical blood sugar pattern may be a blood sugar pattern of an ordinary person. The method may include an operation of identifying the personalized blood sugar pattern from blood sugar patterns other than the typical blood sugar pattern among the blood sugar patterns in the time intervals.

[0182] The method may include an action of displaying another visual object, via the display (310, 810), indicating that the normal blood glucose pattern is identified within the graph.

[0183] The method may include an operation of identifying a blood sugar pattern that falls outside the reference blood sugar range among blood sugar patterns identified as the general blood sugar pattern. The method may include an operation of identifying a factor related to the user's health status that causes the blood sugar pattern to fall outside the reference blood sugar range. The method may include an operation of displaying a visual object representing the factor related to the user's health status within the graph via the display (310, 810).

[0184] The method may include an operation of identifying an unclassified blood glucose pattern that is not identified as the general blood glucose pattern or the personalized blood glucose pattern among the blood glucose patterns in the time intervals. The method may include an operation of adjusting a cycle for measuring the user's blood glucose related to the continuous blood glucose data based on the identification of the unclassified blood glucose pattern.

[0185] The method may include an operation of identifying an unclassified blood sugar pattern that is not identified as the general blood sugar pattern or the personalized blood sugar pattern from the blood sugar patterns in the time intervals. The method may include an operation of registering the unclassified blood sugar pattern as the personalized blood sugar pattern corresponding to the user's state in the time interval in which the unclassified blood sugar pattern was identified, based on the user's state in the time interval in which the unclassified blood sugar pattern was identified.

[0186] The method may include an operation of identifying the health status of the user corresponding to the activity state and / or the physiological state in the time intervals based on the sensing data. The method may include an operation of displaying, through the display (310, 810), the visual object representing the health status within the graph within the time intervals in which the personalized blood sugar pattern is identified.

[0187] The method may include an operation of receiving the sensing data through a sensor (330) of the electronic device (200, 805) that obtains the sensing data. The method may include an operation of receiving the continuous blood glucose data through a blood glucose measurement sensor (340) of the electronic device (200, 805) that obtains the continuous blood glucose data.

[0188] The method may include an operation of receiving the sensing data through a sensor (330) of the electronic device (200, 805) that acquires the sensing data. The method may include an operation of receiving the continuous blood glucose data from an external electronic device (200, 805) through a communication circuit (370) of the electronic device (200, 805).

[0189] As described above, a non-transitory computer-readable recording medium can store a program including instructions. The instructions, when individually or collectively executed by at least one processor (320, 820) of an electronic device (200, 805) including a display (310, 810), can cause the electronic device (200, 805) to receive continuous blood glucose data for a user. The instructions, when individually or collectively executed by the at least one processor (320, 820), can cause the electronic device (200, 805) to receive sensing data for identifying a state of the user. The state of the user can include an activity state of the user and the physiological state of the user. The sensing data can include exercise information related to the activity state and physiological information related to the physiological state. The instructions, when individually or collectively executed by the at least one processor (320, 820), may cause the electronic device (200, 805) to identify blood glucose patterns in time intervals in which the activity state and / or the physiological state are identified based on the continuous blood glucose data and the sensing data. The instructions, when individually or collectively executed by the at least one processor (320, 820), may cause the electronic device (200, 805) to identify a personalized blood glucose pattern among the blood glucose patterns in the time intervals. The personalized blood glucose pattern may include a pattern in which the blood glucose fluctuates depending on the state.The instructions, when individually or collectively executed by the at least one processor (320, 820), may cause the electronic device (200, 805) to display, via the display (310, 810), a visual object indicating that the personalized blood glucose pattern is identified by the state within a graph representing the continuous blood glucose data.

[0190] The effects that can be obtained from the present disclosure are not limited to the effects mentioned above, and other effects that are not mentioned will be clearly understood by a person having ordinary skill in the art to which the present disclosure pertains.

[0191] Electronic devices according to the various embodiments disclosed in this document may take various forms. Electronic devices may include, for example, portable communication devices (e.g., smartphones), computer devices, portable multimedia devices, portable medical devices, cameras, wearable devices, or home appliances. Electronic devices according to the embodiments of this document are not limited to the aforementioned devices.

[0192] The various embodiments of this document and the terminology used therein are not intended to limit the technical features described in this document to specific embodiments, but should be understood to include various modifications, equivalents, or substitutes of the embodiments. In connection with the description of the drawings, similar reference numerals may be used for similar or related components. The singular form of a noun corresponding to an item may include one or more of the items, unless the context clearly indicates otherwise. In this document, each of the phrases "A or B", "at least one of A and B", "at least one of A or B", "A, B, or C", "at least one of A, B, and C", and "at least one of A, B, or C" can include any one of the items listed together in the corresponding phrase among those phrases, or all possible combinations thereof. Terms such as "first," "second," or "first" or "second" may be used merely to distinguish one component from another, and do not limit the components in any other respect (e.g., importance or order). When a component (e.g., a first component) is referred to as "coupled" or "connected" to another component (e.g., a second component), with or without the terms "functionally" or "communicatively," it means that the component can be connected to the other component directly (e.g., wired), wirelessly, or through a third component.

[0193] The term "module" used in various embodiments of this document may include a unit implemented in hardware, software, or firmware, and may be used interchangeably with terms such as logic, logic block, component, or circuit. A module may be an integral component, or a minimum unit or part of such a component that performs one or more functions. For example, according to one embodiment, a module may be implemented in the form of an application-specific integrated circuit (ASIC).

[0194] Various embodiments of the present document may be implemented as software (e.g., a program (140)) including one or more instructions stored in a storage medium (e.g., an internal memory (136) or an external memory (138)) readable by a machine (e.g., an electronic device (101)). For example, a processor (e.g., a processor (120)) of the machine (e.g., an electronic device (101)) may call at least one instruction among the one or more instructions stored from the storage medium and execute it. This enables the machine to operate to perform at least one function according to the at least one called instruction. The one or more instructions may include code generated by a compiler or code executable by an interpreter. The machine-readable storage medium may be provided in the form of a non-transitory storage medium. Here, 'non-transitory' simply means that the storage medium is a tangible device and does not contain signals (e.g., electromagnetic waves), and the term does not distinguish between cases where data is stored semi-permanently or temporarily on the storage medium.

[0195] According to one embodiment, the method according to various embodiments disclosed in this document may be provided as a computer program product. The computer program product may be traded between sellers and buyers as a product. The computer program product may be distributed in the form of a device-readable storage medium (e.g., a compact disc read-only memory (CD-ROM)) or an application store (e.g., Play Store). TM ) or directly between two user devices (e.g., smart phones), online distribution (e.g., downloading or uploading). In the case of online distribution, at least a portion of the computer program product may be at least temporarily stored or temporarily created in a machine-readable storage medium, such as the memory of a manufacturer's server, an application store's server, or an intermediary server.

[0196] According to various embodiments, each component (e.g., a module or a program) of the above-described components may include one or more entities, and some of the entities may be separately arranged in other components. According to various embodiments, one or more components or operations of the aforementioned components may be omitted, or one or more other components or operations may be added. Alternatively or additionally, a plurality of components (e.g., a module or a program) may be integrated into a single component. In such a case, the integrated component may perform one or more functions of each of the plurality of components identically or similarly to those performed by the corresponding component among the plurality of components prior to the integration. According to various embodiments, the operations performed by a module, program, or other component may be executed sequentially, in parallel, iteratively, or heuristically, or one or more of the operations may be executed in a different order, omitted, or one or more other operations may be added.

Claims

1. In electronic devices (200, 805), Display (310, 810), At least one processor (320, 820) including a processing circuit, and A memory (350, 850) storing instructions and including one or more storage media, wherein the instructions, when individually or collectively executed by the at least one processor (320, 820), cause the electronic device (200, 805) to: Receive continuous blood glucose data for the user, Receive sensing data for identifying a user's status, wherein the user's status includes an activity status of the user and a biological status of the user, and the sensing data includes movement information related to the activity status and biological information related to the biological status. Identifying blood sugar patterns in time intervals where the activity state and / or the biological state are identified based on the continuous blood sugar data and the sensing data, Identifying a personalized blood sugar pattern among the blood sugar patterns in the above time intervals, wherein the personalized blood sugar pattern includes a pattern in which the blood sugar fluctuates according to the condition, Causing the display (310, 810) to display a visual object indicating that the personalized blood sugar pattern is identified by the state within a graph representing the continuous blood sugar data. Electronic devices.

2. In claim 1, The above instructions, when individually or collectively executed by the at least one processor (320, 820), cause the electronic device (200, 805) to: Identifying a normal blood sugar pattern from the blood sugar patterns in the above time intervals, wherein the normal blood sugar pattern is a blood sugar pattern of a normal person, Causing the personalized blood sugar pattern to be identified from blood sugar patterns other than the general blood sugar pattern among the blood sugar patterns in the above time intervals, Electronic devices.

3. In claim 2, The above instructions, when individually or collectively executed by the at least one processor (320, 820), cause the electronic device (200, 805) to: Causing the display (310, 810) to display another visual object indicating that the normal blood sugar pattern is identified within the graph. Electronic devices.

4. In claim 2 or claim 3, The above instructions, when individually or collectively executed by the at least one processor (320, 820), cause the electronic device (200, 805) to: Identifying blood sugar patterns that fall outside the standard blood sugar range among the blood sugar patterns identified by the above general blood sugar patterns, Identifying factors related to the user's health that cause the blood sugar pattern to deviate from the reference blood sugar range; Causing a visual object representing the factor related to the health status of the user to be displayed within the graph through the display (310, 810). Electronic devices.

5. In any one of claims 2 to 4, The above instructions, when individually or collectively executed by the at least one processor (320, 820), cause the electronic device (200, 805) to: Identifying an unclassified blood sugar pattern that is not identified as the general blood sugar pattern and the personalized blood sugar pattern in the blood sugar patterns in the time intervals, Based on the identification of the above unclassified blood sugar pattern, causing the cycle for measuring the blood sugar of the user related to the continuous blood sugar data to be adjusted. Electronic devices.

6. In any one of claims 2 to 5, The above instructions, when individually or collectively executed by the at least one processor (320, 820), cause the electronic device (200, 805) to: Identifying an unclassified blood sugar pattern that is not identified as the general blood sugar pattern and the personalized blood sugar pattern in the blood sugar patterns in the time intervals, Based on the user's status in the time interval in which the unclassified blood sugar pattern is identified, causing the unclassified blood sugar pattern to be registered as the personalized blood sugar pattern corresponding to the user's status in the time interval in which the unclassified blood sugar pattern is identified. Electronic devices.

7. In any one of claims 1 to 6, The above instructions, when individually or collectively executed by the at least one processor (320, 820), cause the electronic device (200, 805) to: Based on the sensing data, the health status of the user corresponding to the activity status and / or the vital status is identified in the time intervals, Causing the visual object representing the health status to be displayed within the graph through the display (310, 810) within the time period in which the personalized blood sugar pattern is identified. Electronic devices.

8. In any one of claims 1 to 7, A sensor (330) that acquires the above sensing data, and Includes a blood sugar measurement sensor (340) that acquires the above continuous blood sugar data, The above instructions, when individually or collectively executed by the at least one processor (320, 820), cause the electronic device (200, 805) to: Through the above sensor (330), the sensing data is received, Causing the continuous blood sugar data to be received through the blood sugar measurement sensor (340). Electronic devices.

9. In any one of claims 1 to 8, A sensor (330) that acquires the above sensing data, and Contains a communication circuit (370, 870), The above instructions, when individually or collectively executed by the at least one processor (320, 820), cause the electronic device (200, 805) to: Through the above sensor (330), the sensing data is received, Causing the continuous blood sugar data to be received from an external electronic device (200, 805) through the above communication circuit (370, 870). Electronic devices.

10. In any one of claims 1 to 9, The above electronic device (200) is a wearable device worn on a part of the user's body. Electronic devices.

11. In a method of an electronic device (200, 805) including a display (310, 810), An action to receive continuous blood glucose data for a user; An operation of receiving sensing data for identifying a user's state, wherein the state of the user includes an activity state of the user and a biological state of the user, and the sensing data includes movement information related to the activity state and biological information related to the biological state. An operation of identifying blood sugar patterns in time intervals in which the activity state and / or the biological state are identified based on the continuous blood sugar data and the sensing data; An operation of identifying a personalized blood sugar pattern among the blood sugar patterns in the above time intervals, wherein the personalized blood sugar pattern includes a pattern in which the blood sugar fluctuates according to the condition, and An operation of displaying a visual object indicating that the personalized blood sugar pattern is identified by the state within a graph representing the continuous blood sugar data through the display (310, 810) method.

12. In claim 11, An operation of identifying a normal blood sugar pattern from the blood sugar patterns in the above time intervals, wherein the normal blood sugar pattern is a blood sugar pattern of a normal person, and An operation of identifying the personalized blood sugar pattern from blood sugar patterns other than the general blood sugar pattern among the blood sugar patterns in the time intervals method.

13. In claim 12, An action including displaying another visual object indicating that the normal blood sugar pattern is identified within the graph through the display (310, 810). method.

14. In claim 12 or claim 13, An operation of identifying a blood sugar pattern that falls outside the standard blood sugar range among blood sugar patterns identified by the above general blood sugar pattern; An action to identify a factor related to the health status of the user that causes the blood sugar pattern to deviate from the reference blood sugar range, and An operation of displaying a visual object representing the factor related to the health status of the user within the graph through the display (310, 810) method.

15. In a non-transitory computer-readable recording medium, Store one or more programs containing instructions, The above instructions, when executed individually or collectively by at least one processor (320, 820) of an electronic device (200, 805) including a display (310, 810), cause the electronic device (200, 805) to: Receive continuous blood glucose data for the user, Receive sensing data for identifying a user's status, wherein the user's status includes an activity status of the user and a biological status of the user, and the sensing data includes movement information related to the activity status and biological information related to the biological status. Identifying blood sugar patterns in time intervals where the activity state and / or the biological state are identified based on the continuous blood sugar data and the sensing data, Identifying a personalized blood sugar pattern among the blood sugar patterns in the above time intervals, wherein the personalized blood sugar pattern includes a pattern in which the blood sugar fluctuates according to the condition, Causing the display (310, 810) to display a visual object indicating that the personalized blood sugar pattern is identified by the state within a graph representing the continuous blood sugar data. Non-transitory computer-readable recording medium.

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