Electronic device for providing recommendation message for skin care, operation method thereof, and non-transitory computer-readable storage medium

Abstract

An electronic device according to the present disclosure comprises: a communication interface; a memory, which stores instructions and includes one or more storage media; and at least one processor including processing circuitry, wherein, when executed individually or collectively by the at least one processor, the instructions instruct the electronic device to: receive advanced glycation end products (AGEs) data of a user from a wearable device; acquire weather data on the basis of position information of the user wearing the wearable device; input the AGEs data and the weather data into an artificial intelligence model; and allow the artificial intelligence model to provide a recommendation message generated, the artificial intelligence model being trained to: acquire state information of the user on the basis of the AGEs data, and generate the recommendation message for guiding a behavior of the user on the basis of an ultraviolet index and an air quality index included in the weather data and the state information.

Description

An electronic device providing recommendation messages for skin care, a method of operation thereof, and a non-transient computer-readable storage medium

[0001] The present disclosure relates to an electronic device for providing recommendation messages for skin care, a method of operation thereof, and a non-transient computer-readable storage medium.

[0002] With the recent advancement of electronic technology, various types of electronic devices are being developed.

[0003] In particular, wearable devices capable of contacting parts of the user's body are being developed and distributed.

[0004] Wearable devices can acquire and provide biometric information by coming into contact with a part of the user's body. Since wearable devices can measure advanced glycation end products and advanced glycation end products are closely related to the user's skin condition, there is a need for a method to utilize this for the user's skin care.

[0005] The information described above may be provided as related art for the purpose of aiding understanding of the present disclosure. No claim or determination is made as to whether any of the foregoing may be applied as prior art related to the present disclosure.

[0006] An electronic device according to an embodiment of the present disclosure comprises a communication interface, a memory and at least one processor including a processing circuit that stores instructions and includes one or more storage media, wherein when the instructions are executed individually or collectively by the at least one processor, the electronic device is configured to receive advanced glycation end products (AGEs) data of a user from a wearable device, acquire weather data based on location information of the user wearing the wearable device, input the advanced glycation end products data and the weather data into an artificial intelligence model, and provide a recommendation message generated by the artificial intelligence model, wherein the artificial intelligence model is trained to acquire status information of the user based on the advanced glycation end products data and to generate a recommendation message that guides the user's behavior based on the UV index, air quality index included in the weather data and the status information.

[0007] A method of operation of an electronic device according to an embodiment of the present disclosure includes receiving data of an advanced glycation end products (AGEs) of a user from a wearable device, acquiring weather data based on location information of the user wearing the wearable device, inputting the advanced glycation end products data and the weather data into an artificial intelligence model, and providing a recommendation message generated by the artificial intelligence model, wherein the artificial intelligence model acquires status information of the user based on the advanced glycation end products data and is trained to generate a recommendation message that guides the user's behavior based on the ultraviolet index, air quality index included in the weather data, and the status information.

[0008] In a non-transient storage medium for storing computer-readable instructions according to an embodiment of the present disclosure, the instructions are configured such that when executed by at least one processor of an electronic device, the electronic device receives advanced glycation end products (AGEs) data of a user from a wearable device, obtains weather data based on location information of the user wearing the wearable device, inputs the advanced glycation end products data and the weather data into an artificial intelligence model, and provides a recommendation message generated by the artificial intelligence model, wherein the artificial intelligence model is trained to obtain status information of the user based on the advanced glycation end products data and to generate a recommendation message that guides the user's behavior based on the UV index, air quality index included in the weather data and the status information.

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

[0010] FIG. 1 illustrates an electronic device and a wearable device according to an embodiment of the present disclosure.

[0011] FIG. 2 is a block diagram of a wearable device according to an embodiment of the present disclosure.

[0012] FIG. 3 is a perspective view of the front of a wearable device according to an embodiment of the present disclosure.

[0013] FIG. 4 is a perspective view of the rear side of a wearable device according to an embodiment of the present disclosure.

[0014] FIG. 5 is an exploded perspective view of a wearable device according to an embodiment of the present disclosure.

[0015] FIG. 6 illustrates a sensor according to an embodiment of the present disclosure.

[0016] FIG. 7 illustrates an electronic device for displaying final saccharification product data according to an embodiment of the present disclosure.

[0017] FIG. 8 illustrates an artificial intelligence model that outputs a recommendation message according to an embodiment of the present disclosure.

[0018] FIG. 9 illustrates an electronic device for acquiring final saccharification product data, weather data, and indoor environment data according to an embodiment of the present disclosure.

[0019] FIG. 10 illustrates an artificial intelligence model that outputs a recommendation message based on final saccharification product data, weather data, and indoor environment data according to an embodiment of the present disclosure.

[0020] FIG. 11 illustrates an electronic device that provides a recommendation message for improving skin condition according to an embodiment of the present disclosure.

[0021] FIG. 12 illustrates an electronic device that provides a recommendation message guiding a user's actions according to an embodiment of the present disclosure.

[0022] FIG. 13 illustrates an electronic device that controls an external electronic device according to an embodiment of the present disclosure.

[0023] FIG. 14 is a flowchart illustrating a method of operation of an electronic device according to an embodiment of the present disclosure.

[0024] It should be noted that the same reference numbers are used throughout the drawings to denote identical or similar elements, features, and structures.

[0025] The present disclosure will be described in detail below with reference to the attached drawings.

[0026] The terms used in the embodiments of this disclosure have been selected to be as widely used as possible, taking into account their functions within this disclosure; however, these terms may vary depending on the intent of those skilled in the art, case law, the emergence of new technologies, etc. Additionally, in specific cases, terms have been arbitrarily selected by the applicant, and in such cases, their meanings will be described in detail in the relevant explanatory section of this disclosure. Therefore, terms used in this disclosure should be defined not merely by their names, but based on their meanings and the overall content of this disclosure.

[0027] In this specification, expressions such as “have,” “may have,” “include,” or “may include” indicate the presence of such features (e.g., numerical values, functions, operations, or components such as parts) and do not exclude the presence of additional features.

[0028] The expression "at least one of A and / or B" should be understood as representing either "A" or "B" or "A and B".

[0029] Expressions such as "first," "second," "first," or "second" used in this specification may modify various components regardless of order and / or importance, and are used only to distinguish one component from another and do not limit said components.

[0030] Where it is stated that a component (e.g., Component 1) is "(operatively or communicatively) coupled with / to" or "connected to" another component (e.g., Component 2), it should be understood that the component may be directly connected to the other component or connected through the other component (e.g., Component 3).

[0031] Singular expressions include plural expressions unless the context clearly indicates otherwise. In this application, terms such as "comprising" or "consisting of" are intended to specify the existence of the features, numbers, steps, actions, components, parts, or combinations thereof described in the specification, and should be understood as not precluding the existence or addition of one or more other features, numbers, steps, actions, components, parts, or combinations thereof.

[0032] In the present disclosure, the term "user" may refer to a person using an electronic device or a device using an electronic device (e.g., an artificial intelligence electronic device).

[0033] Embodiments of the present disclosure will be described in more detail below with reference to the attached drawings.

[0034]

[0035] FIG. 1 illustrates an electronic device and a wearable device according to an embodiment of the present disclosure.

[0036] According to an embodiment, the electronic device (10) may be implemented as a user terminal device (e.g., a smartphone). However, it is not limited thereto, and the electronic device (10) may include at least one of a TV (television), a tablet PC (personal computer), a mobile phone, a video phone, an e-book reader, a desktop PC, a laptop PC, a workstation, a server, a PDA (personal digital assistant), a PMP (portable multimedia player), an MP3 player, a medical device, a camera, a virtual reality (VR) implementation device, a spatial computing device, or a wearable device. Here, the wearable device may include at least one of an accessory type (e.g., a watch, ring, bracelet, anklet, necklace, glasses, contact lens, or head-mounted device (HMD)), a fabric or clothing integrated type (e.g., electronic clothing), a body-attached type (e.g., a skin pad or tattoo), or a bio-implantable circuit. For convenience of explanation, the electronic device (10) will be described below as a user terminal device.

[0037] A wearable device (100) according to various embodiments of the present disclosure may include a portable device. Here, a wearable device refers to a device that includes a flexible material (e.g., silicone rubber, fiber) and can be worn by a user or can come into contact with a part of the user's body. For example, various types of devices that can be worn on the body by a person or animal, such as a watch, clothing, shoes, gloves, glasses, a hat, and jewelry (e.g., a ring), may be included in the wearable device.

[0038] In FIG. 1, for convenience of explanation, the wearable device (100) is depicted as a watch-shaped wearable device that a user can wear on their wrist, but it is not limited thereto, and the wearable device (100) may be implemented as a ring-shaped wearable device that a user can wear on their finger.

[0039] According to an embodiment, the wearable device (100) can measure the user's body information by coming into contact with a part of the user's body.

[0040] According to an embodiment, the wearable device (100) can measure the user's physical activity (e.g., number of steps, calories burned, distance traveled), body temperature, and bioelectrical impedance.

[0041] According to an embodiment, the wearable device (100) can identify sleep patterns such as deep sleep, light sleep, and REM (rapid eye movement) sleep based on heart rate, physical activity, and body temperature. According to an embodiment, the wearable device (100) measures impedance by applying a minute electrical signal to the human body and can measure the amount of water and body fat in the user's body through bioelectrical impedance analysis (BIA). Additionally, the wearable device (100) includes an electrocardiogram (ECG) measurement sensor and can measure the user's electrocardiogram using the ECG measurement sensor.

[0042] According to an embodiment, the wearable device (100) includes a photoplethysmogram (PPG) sensor (or, photoplethysmogram sensor, photoplethysmogram sensor), and can measure heart rate and blood oxygen saturation (SpO2) using the PPG sensor.

[0043] A wearable device (100) according to an embodiment of the present disclosure can measure advanced glycation end products (AGEs). For example, advanced glycation end products may include compounds formed when proteins or fats in the body bind non-enzymatically with sugars. For example, advanced glycation end products may accumulate in the body through a glycation reaction in which proteins or fats bind non-enzymatically with sugars, or through food consumed by the user. For example, the amount of advanced glycation end products in the user's body may increase due to the consumption of high-fat, high-protein foods.

[0044] According to the embodiments, advanced glycation end products can be used as a health indicator representing the biological aging (e.g., age) of the user. For example, advanced glycation end products can reduce the elasticity of blood vessels, thereby causing cardiovascular diseases such as arteriosclerosis, and can accelerate skin aging.

[0045] According to an embodiment, the wearable device (100) can measure the final glycation product using a sensor and visually provide the final glycation product data.

[0046] According to an embodiment, the wearable device (100) transmits the final glycation product data to the electronic device (10), and the electronic device (10) can visually provide the final glycation product data.

[0047] According to an embodiment, the electronic device (10) provides a graph corresponding to the final glycation product data (1) received from the wearable device (100), and can also visually provide the UV index (2) and air quality index (3) that affect the user's skin condition, health condition, or outdoor activities.

[0048] According to an embodiment, when the electronic device (10) receives final glycation product data from the wearable device (100), it can analyze the user's skin condition (or degree of aging) based on the final glycation product data and provide a recommendation message to improve (or maintain) the skin condition.

[0049]

[0050] FIG. 2 is a block diagram of a wearable device according to an embodiment of the present disclosure. FIG. 2 is a block diagram of an exemplary wearable device capable of performing the operations described according to an embodiment of the present disclosure.

[0051] Referring to FIG. 2, the wearable device (100) may be implemented in various forms that can be worn by a user, such as a smart watch, a smart band, a smart ring, wireless earphones, or smart glasses. The components, their relationships, and their functions illustrated in FIG. 2 are illustrative only and are not intended to limit the implementations described or claimed herein. The wearable device (100) may be referred to as a mobile device, a user device, a multifunctional device, a portable device, or a server.

[0052] A wearable device (100) may include components comprising at least one processor (110) (hereinafter referred to as processor (110)), at least one memory (120) (hereinafter referred to as memory (120)), at least one display (140) (hereinafter referred to as display (140)), at least one image sensor (150) (hereinafter referred to as image sensor (150)), at least one communication circuit (160) (hereinafter referred to as communication circuit (160)), and / or at least one sensor (170) (hereinafter referred to as sensor (170)). The components are merely exemplary. For example, the wearable device (100) may include other components (e.g., power management integrated circuitry (PMIC), audio processing circuit, antenna, rechargeable battery, or input / output interface). For example, some components may be omitted from the wearable device (100). For example, some components can be integrated into a single component.

[0053] The processor (110) may be implemented as one or more IC (integrated circuit (or circuitry)) chips and may perform various data processing operations. The processor (110) may include at least one electrical circuit and may process instructions (or programs, data) stored in memory (120) individually or collectively in a distributed manner. The processor (110) may include a processor assembly comprising one or more processing circuits. The processor (110) may include any processing circuit that is operative to control the performance and operation of one or more components of the wearable device (100) (e.g., memory (120), microphone (130), display (140), image sensor (150), communication circuit (160), sensor (170) and / or speaker (180)). For example, the processor (110) (e.g., application processor (AP)) may be implemented as a system on chip (SoC) (e.g., a single chip or a chipset). For example, the processor (110) may be implemented as a plurality of cores (or at least one core circuit), a plurality of chips, or a plurality of chipsets. For example, the processor (110) may include one or more processing circuits. For example, the processor (110) may include one or more processing circuits configured to perform the various functions of the present disclosure individually and / or collectively. As an example without limitation, at least a portion of the processor (110) may be included in a first chip of the wearable device (100), and at least another portion of the processor (110) may be included in a second chip of the wearable device (100) different from the first chip of the wearable device (100).

[0054] For example, the processor (110) may include a central processing unit (111), a graphics processing unit (112), a neural processing unit (113), an image signal processor (114), a display controller (115), a memory controller (116), a storage controller (117), a communication processor (118), and / or a sensor interface (119). These components of the processor (110) are merely exemplary. For example, the processor (110) may include other components. For example, some components of the processor (110) may be omitted from the processor (110). For example, some components of the processor (110) may be included as separate components of the wearable device (100) outside of the processor (110). For example, some components of the processor (110) (e.g., memory controller (116)) may be included in other components (e.g., at least part of memory (120), an interface (e.g. available for connection to at least one component of the wearable device (100)), a display (140) and / or an image sensor (150)).

[0055] The processor (110) can cause other components of the wearable device (100) to perform various operations by executing instructions stored in memory (120). The CPU (111) (or central processing circuit) may be configured to control the components of the processor (110) based on the execution of instructions stored in memory (120) (e.g., volatile memory (121) and / or non-volatile memory (122)). The GPU (112) (or graphics processing circuit) may be configured to execute parallel operations (e.g., rendering). The NPU (113) (or neural processing circuit, or AI (artificial intelligence) chip) may be configured to execute operations for an artificial intelligence model (e.g., convolution computation). An ISP (114) (or image signal processing circuit) may be configured to process a raw image acquired through an image sensor (150) into a format suitable for a component within the wearable device (100) or a component of the processor (110). A display controller (115) (or display control circuit, or DPU (display processing unit)) may be configured to process an image acquired from a CPU (111), GPU (112), ISP (114), or memory (120) (e.g., volatile memory (121)) into a format suitable for a display (140). A memory controller (116) (or memory control circuit) may be configured to control reading data from the volatile memory (121) and writing data to the volatile memory (121). A storage controller (117) (or storage control circuit) may be configured to control reading data from the non-volatile memory (122) and writing data to the non-volatile memory (122).The CP (118) (communication processing circuit) may be configured to process data obtained from a component of the processor (110) into a format suitable for transmitting to another electronic device via the communication circuit (160), or to process data obtained from another electronic device via the communication circuit (160) into a format suitable for processing by the component of the processor (110). For example, the communication circuit (160) may include one or more communication circuits. The sensor interface (119) (or sensing data processing circuit, sensor hub) may be configured to process data regarding the state of the wearable device (100) and / or the state around the wearable device (100), obtained through the sensor (170), into a format suitable for the component of the processor (110).

[0056] Memory (120) may include one or more storage media (or one or more storage devices). For example, memory (120) may include a memory assembly comprising one or more storage media. For example, the one or more storage media may include a hard drive, a flash memory, a permanent memory such as ROM (read-only memory) (e.g., non-volatile memory (122)), a semi-permanent memory such as RAM (random access memory) (e.g., volatile memory (121)), any other suitable type of storage (or storage assembly), or any combination thereof. Memory (120) may include a cache memory, which is one or more different types of memory used to temporarily store data for a function or feature of the wearable device (100). As an example, but not limited to, the cache memory may be included within the processor (110). The memory (120) may be fixedly embedded in the wearable device (100) or incorporated into one or more suitable types of components (e.g., a SIM (subscriber identity module) card and / or an SD (secure digital) card) that can be repeatedly inserted into and removed from the wearable device (100).

[0057] For example, memory (120) may store one or more software applications, such as operating system (or system) software applications, firmware software applications, driver software applications, plugin (e.g., add-in, add-on, and / or applet) software applications, and / or any other suitable software applications. For example, the one or more software applications may include instructions executable by the processor (110). For example, memory (120) may store instructions that can be called by an application programming interface (API). For example, memory (120) may store instructions within a library.

[0058] The microphone (130) can acquire sound output from an external object. Depending on the embodiment, the number of microphones (130) may be one or more. The speaker (180) can output sound to the outside. Depending on the embodiment, the number of speakers (180) may be one or more.

[0059] The display (140) is controlled by the processor (110) and can output visualized information to the user. The visualized information may include visual objects displayed on the display (140). For example, the visual objects may include screens, images, icons, GUI (graphic user interface), and UI (user interface) elements. For example, the display (140) may be implemented as a flat panel display (FPD), a curved display, or a flexible display. For example, the display (140) may be implemented as various types of displays such as a liquid crystal display (LCD), an active matrix organic light emitting diodes (AMOLED) display, an LED (light emitting diodes), a micro LED, or a Mini LED.

[0060] The display (140) may include a touch detection circuit configured to detect touch. The touch detection circuit may acquire user input for the display (140). For example, the touch detection circuit may detect an input (e.g., touch input or hovering input) at a specific location by measuring a change in a signal (e.g., voltage, light intensity, resistance, or charge) at a specific location of the display (140), and provide information regarding the detected input to the processor (110).

[0061] The communication circuit (160) can perform data communication with other electronic devices under the control of the processor (110). For example, the communication circuit (160) can transmit and receive control commands or data with other electronic devices. For example, the communication circuit (160) can support the transmission and / or reception of electrical signals based on various types of protocols such as Ethernet, LAN (local area network), WAN (wide area network), WiFi (wireless fidelity), Bluetooth, BLE (bluetooth low energy), ZigBee, NFC (near field communication), ANT+, Cellular (LTE, 5G, 6G, NB-IoT), RFID, UWB (ultra wide band), GNSS (global navigation satellite system), or RF communication.

[0062] The sensor (170) can generate electrical information that can be processed by the processor (110) and / or memory (120) from non-electronic information related to the wearable device (100). The information may be referred to as sensing data. The sensor (170) can detect the operating state of the wearable device (100) (e.g., power or temperature) or the external environmental state (e.g., the state of the user) and generate electrical information corresponding to the detected state.

[0063] According to an embodiment, the sensor (170) may include an emitting unit (171) and a receiving unit (or detector) (172). The sensor (170) may output light to the outside through the emitting unit (171) under the control of the processor (110). The output of light (or light) may be replaced with expressions such as emission, diffusion, or irradiation of light, for example. The emitting unit (171) may include a plurality of light-emitting elements. For example, the light-emitting elements may be implemented as an LED (light emitting diode), a laser diode, or a VCSEL (vertical cavity surface emitting laser). The light output by the emitting unit (171) may include at least one of infrared (IR, infrared ray), visible light, or ultraviolet (UV, ultraviolet ray). The emitting unit (171) may include a light-emitting element for outputting light corresponding to infrared, visible light, and ultraviolet, respectively.

[0064] The light emitted from the light-emitting unit (171) can be irradiated onto the user's skin. The user's skin may include various body parts where the sensor (170) comes into contact. For example, body parts may include the palms or soles where the skin epidermal layer is thick, areas where venous blood or capillary blood is located, and other areas within the body with high blood vessel density such as fingers, toes, or earlobes. Additionally, body parts may include the wrists, fingers, and the inside of the ears where the sensor (170) can come into contact when the wearable device (100) is worn.

[0065] At least a portion of the light irradiated from the light-emitting unit (171) may be scattered or reflected by the user's body (e.g., skin, skin tissue, fat layer, vein, artery, or capillary). The light-receiving unit (172) may receive the scattered or reflected light and convert the received light into an electrical signal. For example, the light-receiving unit (172) may be composed of at least one photodiode (PD) and phototransistor. However, it is not limited thereto and may be implemented as a CMOS (complementary metal-oxide semiconductor) image sensor or a CCD (charge-coupled device) image sensor. As a non-limiting example, the sensor (170) may include an amplifier for amplifying the electrical signal and an analog-to-digital converter (ADC) for converting the electrical signal into a digital signal.

[0066] According to an embodiment, the sensor (170) can measure the user's biometric information based on received light under the control of the processor (110).

[0067] For example, the processor (110) outputs visible light (e.g., green light, red light, or blue light) or infrared light to the user's skin, and when the output light is reflected by a blood vessel and received, it measures the amount of light reflected or absorbed based on the received light to obtain a PPG signal, and can obtain heart rate (HR), oxygen saturation (SpO2), blood pressure, blood volume, and stress index using the PPG signal.

[0068] For example, the processor (110) outputs light of a specific wavelength to the user's skin, and when the output light is transmitted or reflected by the user's skin or blood vessels and at least partially received, the received light can be analyzed to obtain information regarding a specific substance (or specific component) within the body's skin or blood vessels.

[0069] For example, the processor (110) can estimate the concentration of a specific substance using data obtained by the sensor (170). The substance may include, for example, an antioxidant substance including a carotenoid, glucose, urea, lactate, triglyceride, total protein, cholesterol, or ethanol.

[0070] For example, the processor (110) can measure the final saccharification product using data obtained by the sensor (170). A detailed explanation of this is to be made with reference to FIG. 6.

[0071] For example, the processor (110) outputs light of a specific wavelength to the user's skin, and when the output light is transmitted or reflected by the user's skin or blood vessels and at least partially received, it analyzes the received light to measure the pulse wave and can identify the heart rate based on the blood flow amount that changes according to the pulse wave.

[0072] According to an embodiment, the sensor (170) may include an illuminance sensor, an acceleration sensor, a gyroscope sensor, a geomagnetic sensor, a barometer, or a temperature sensor.

[0073] An illuminance sensor can detect the brightness of external light. For example, the processor (110) can control the brightness of the display (140) using sensor data detected by the illuminance sensor. An accelerometer can detect acceleration or impact caused by the movement of the wearable device (100) or the movement of a user carrying the wearable device (100). A gyroscope can detect the direction or angle of rotation of the wearable device (100) caused by the movement of the wearable device (100) or the movement of a user carrying the wearable device (100). A geomagnetic sensor can detect the direction of the geomagnetic field. For example, the processor (110) can identify the user's actions (or movements) using sensor data detected by the accelerometer, gyroscope, or geomagnetic sensor. A barometric pressure sensor can detect atmospheric pressure. For example, the processor (110) can obtain altitude information of the wearable device (100) using sensor data detected by the barometric pressure sensor. The temperature sensor can measure the body temperature in a contact or non-contact manner. For example, the processor (110) can obtain body temperature information of the user using sensor data detected by the temperature sensor.

[0074]

[0075] FIG. 3 is a perspective view of the front of a wearable device according to an embodiment of the present disclosure.

[0076] Referring to FIGS. 3 and 4, a wearable device (200) according to an embodiment (e.g., the wearable device (200) of FIG. 1) may include a housing (210) comprising a first surface (or front) (210A), a second surface (or rear) (210B), and a side (210C) surrounding the space between the first surface (210A) and the second surface (210B), and a fastening member (250, 260) connected to at least a part of the housing (210) and configured to detachably fasten the wearable device (200) to a part of a user's body (e.g., wrist or ankle). In another embodiment, the housing may refer to a structure forming some of the first surface (210A), the second surface (210B), and the side (210C) of FIGS. 3 and 4. According to an embodiment, the first surface (210A) may be formed by a front plate (201) in which at least a portion is substantially transparent (e.g., a glass plate containing various coating layers, or a polymer plate). The second surface (210B) may be formed by a rear plate (207) in which it is substantially opaque. The rear plate (207) may be formed by, for example, coated or colored glass, ceramic, polymer, metal (e.g., aluminum, stainless steel (STS), or magnesium), or a combination of at least two of the above materials. The side surface (210C) may be formed by a side bezel structure (or "side member") (206) comprising metal and / or polymer, which is combined with the front plate (201) and the rear plate (207). In some embodiments, the rear plate (207) and the side bezel structure (206) may be formed integrally and may comprise the same material (e.g., a metallic material such as aluminum). The above-mentioned connecting members (250, 260) can be formed in various materials and shapes. They can be formed such that an integral and a plurality of unit links are movable with each other by means of woven fabric, leather, rubber, urethane, metal, ceramic, or a combination of at least two of the above materials.

[0077] According to an embodiment, the wearable device (200) may include at least one of a display (220, see FIG. 5), an audio module (205, 208), a sensor module (211), a key input device (202, 203, 204), and a connector hole (209). In some embodiments, the wearable device (200) may omit at least one of the components (e.g., a key input device (202, 203, 204), a connector hole (209), or a sensor module (211)) or additionally include other components.

[0078] The display (220) may be visually exposed, for example, through a significant portion of the front plate (201). The shape of the display (220) may correspond to the shape of the front plate (201) and may be various shapes such as circular, elliptical, or polygonal. The display (220) may be combined with or placed adjacent to a touch detection circuit, a pressure sensor capable of measuring the intensity (pressure) of the touch, and / or a fingerprint sensor.

[0079] The audio module (205, 208) may include a microphone hole (205) and a speaker hole (208). A microphone (e.g., the microphone (130) of FIG. 2) for acquiring external sound may be placed inside the microphone hole (205), and in some embodiments, a plurality of microphones may be placed to detect the direction of sound. The speaker hole (208) may be used as an external speaker and a receiver for calls. In some embodiments, the speaker hole (208) and the microphone hole (205) may be implemented as a single hole, or a speaker (e.g., the speaker (180) of FIG. 2) may be included without the speaker hole (208) (e.g., a piezo speaker).

[0080] A sensor (211) (e.g., sensor (170) of FIG. 2) can generate an electrical signal or data value corresponding to an internal operating state of the wearable device (200) or an external environmental state. The sensor (211) may include, for example, a biosensor (211) (e.g., a heart rate monitor (HRM) sensor) disposed on a second surface (210B) of the housing (210). The wearable device (200) may further include at least one of a sensor not illustrated, for example, a gesture sensor, a gyroscope sensor, a barometric pressure sensor, a magnetic sensor, an accelerometer sensor, a grip sensor, a color sensor, an IR (infrared) sensor, a biosensor, a temperature sensor, a humidity sensor, or an illuminance sensor.

[0081] The sensor (211) may include electrode regions (213, 214) forming part of the surface of the wearable device (200) and a biosignal detection circuit electrically connected to the electrode regions (213, 214). For example, the electrode regions (213, 214) may include a first electrode region (213) and a second electrode region (214) disposed on a second surface (210B) of the housing (210). The sensor (211) may be configured such that the electrode regions (213, 214) acquire an electrical signal from a part of the user's body, and the biosignal detection circuit detects the user's biosignal information based on the electrical signal.

[0082] The key input devices (202, 203, 204) may include a wheel key (202) disposed on a first surface (210A) of the housing (210) and rotatable in at least one direction, and / or a side key button (203, 204) disposed on a side (210C) of the housing (210). The wheel key may be in a shape corresponding to the shape of the front plate (201). In another embodiment, the wearable device (200) may not include some or all of the aforementioned key input devices (202, 203, 204), and the key input devices (202, 203, 204) that are not included may be implemented in other forms, such as soft keys, on the display (220). The connector hole (209) may include another connector hole (not shown) that can accommodate a connector (e.g., a USB (universal serial bus) connector) for transmitting and receiving power and / or data with an external electronic device and a connector for transmitting and receiving audio signals with an external electronic device. The wearable device (200) may further include a connector cover (not shown) that covers at least a portion of the connector hole (209) and blocks the entry of external foreign matter into the connector hole.

[0083] The fastening member (250, 260) can be detachably fastened to at least a portion of the housing (210) using a locking member (251, 261). The fastening member (250, 260) may include one or more of a fixing member (252), a fixing member fastening hole (253), a band guide member (254), and a band fixing ring (255).

[0084] The fixing member (252) may be configured to fix the housing (210) and the fastening member (250, 260) to a part of the user's body (e.g., wrist or ankle). The fixing member fastening hole (253) may fix the housing (210) and the fastening member (250, 260) to a part of the user's body in correspondence with the fixing member (252). The band guide member (254) may be configured to limit the range of movement of the fixing member (252) when the fixing member (252) is fastened to the fixing member fastening hole (253), thereby allowing the fastening member (250, 260) to be fastened in close contact with a part of the user's body. The band fixing ring (255) may limit the range of movement of the fastening member (250, 260) when the fixing member (252) and the fixing member fastening hole (253) are fastened.

[0085]

[0086] FIG. 5 is an exploded perspective view of an electronic device according to an embodiment of the present disclosure.

[0087] Referring to FIG. 5, a wearable device (300) (e.g., the wearable device (100) of FIG. 1, or the wearable device (200) of FIG. 3 and 4) may include a side bezel structure (310), a wheel key (320) (e.g., the wheel key (202) of FIG. 3 and 4), a front plate (201), a display (220), a first antenna (350), a second antenna (355), a support member (360) (e.g., a bracket), a battery (370), a printed circuit board (380), a sealing member (390), a rear plate (393) (e.g., the rear plate (207) of FIG. 3 and 4), and a fastening member (395, 397) (e.g., the fastening member (250, 260) of FIG. 3 and 4). At least one of the components of the wearable device (300) may be identical or similar to at least one of the components of the wearable device (100) of FIG. 1 or the wearable device (200) of FIG. 3 and 4, and redundant descriptions are omitted below. The support member (360) may be disposed inside the wearable device (300) and connected to the side bezel structure (310), or may be formed integrally with the side bezel structure (310). The support member (360) may be formed, for example, from a metal material and / or a non-metal (e.g., polymer) material. A display (220) may be attached to one side of the support member (360), and a printed circuit board (380) may be attached to the other side. A processor (e.g., the processor (110) of FIG. 2), a memory (e.g., the memory (120) of FIG. 2), and / or an interface may be mounted on the printed circuit board (380). The processor may include, for example, one or more of a central processing unit, a GPU (graphic processing unit), an application processor, a sensor processor, or a communication processor.

[0088] The memory may include, for example, volatile memory (e.g., volatile memory (121) of FIG. 2) or non-volatile memory (e.g., non-volatile memory (122) of FIG. 2). The interface may include, for example, a high definition multimedia interface (HDMI), a universal serial bus (USB) interface, an SD card interface, and / or an audio interface. The interface may electrically or physically connect the wearable device (300) to an external electronic device and may include a USB connector, an SD card / multimedia card (MMC) connector, or an audio connector.

[0089] The battery (370) is a device for supplying power to at least one component of the wearable device (300) and may include, for example, a non-rechargeable primary battery, a rechargeable secondary battery, or a fuel cell. At least a portion of the battery (370) may be disposed substantially coplanar with, for example, a printed circuit board (380). The battery (370) may be disposed integrally inside the wearable device (300) or may be disposed detachably from the wearable device (300).

[0090] The first antenna (350) may be positioned between the display (220) and the support member (360). The first antenna (350) may include, for example, a near field communication (NFC) antenna, a wireless charging antenna, and / or a magnetic secure transmission (MST) antenna. The first antenna (350) may, for example, communicate near field with an external device, wirelessly transmit and receive power required for charging, and transmit a magnetic-based signal including a near field communication signal or payment data. In another embodiment, the antenna structure may be formed by a part of the side bezel structure (310) and / or a combination thereof of the support member (360).

[0091] A second antenna (355) may be positioned between the printed circuit board (380) and the back plate (393). The second antenna (355) may include, for example, a near field communication (NFC) antenna, a wireless charging antenna, and / or a magnetic secure transmission (MST) antenna. The second antenna (355) may, for example, communicate near field with an external device, wirelessly transmit and receive power required for charging, and transmit a magnetic-based signal including a near field communication signal or payment data. In another embodiment, the antenna structure may be formed by a part of the side bezel structure (310) and / or the back plate (393) or a combination thereof.

[0092] The sealing member (390) may be positioned between the side bezel structure (310) and the rear plate (393). The sealing member (390) may be configured to block moisture and foreign matter from entering the space enclosed by the side bezel structure (310) and the rear plate (393) from the outside.

[0093]

[0094] FIG. 6 illustrates a sensor according to an embodiment of the present disclosure.

[0095] Referring to FIG. 6, according to an embodiment, the sensor (170) may include a light-emitting part (171) and a light-receiving part (172).

[0096] According to an embodiment, the light-emitting unit (171) may include a first light source and a second light source.

[0097] According to an embodiment, the first light source and the second light source may each be configured to irradiate light of different wavelength bands onto the skin. According to an embodiment, the first light source and the second light source may each be positioned to irradiate light toward the skin and may be positioned at an angle to minimize specular reflection.

[0098] According to an embodiment, the first light source may be set to irradiate light in a wavelength band corresponding to UV light (e.g., a first wavelength band), and the second light source may be set to irradiate light in a wavelength band corresponding to visible light (e.g., a second wavelength band).

[0099] For example, the first light source may be configured to irradiate light of a first wavelength band corresponding to UV rays. Specifically, the UV rays may be a short wavelength band of about 100 to about 400 [nm], and the first wavelength band may include all or part of a wavelength band of about 100 to about 400 [nm] corresponding to UV rays. In an example, the first light source may irradiate light of a first wavelength band of about 315 to about 400 [nm] corresponding to UV-A rays, and in particular, may irradiate light of a specific wavelength region within the wavelength band of UV-A rays. As described below, skin that has absorbed light of the first wavelength band may emit light of a wavelength band different from the first wavelength band through autofluorescence.

[0100] For example, the second light source may be configured to irradiate light of a second wavelength band corresponding to visible light. Specifically, visible light may be a wavelength band of about 380 to about 780 [nm], and the second wavelength band may include the entire wavelength band of visible light or include a portion of the wavelength band of visible light having a specific color. In an example, the second light source may irradiate light that is evenly distributed across the second wavelength band corresponding to visible light. In another example, the second light source may irradiate light of a second wavelength band corresponding to a portion of the wavelength band of visible light.

[0101] According to an embodiment, each of the first light source and the second light source may include a light-emitting diode (LED) that emits light using electrical energy.

[0102] According to the embodiment, the light receiving unit (172) can measure the intensity of the incident light.

[0103] According to an embodiment, the light receiving unit (172) can detect light signals of visible light, ultraviolet light, and / or infrared light that can be classified into an optical region among electromagnetic waves and convert them into electrical signals.

[0104] As an example, the light receiving unit (172) may include a photodiode that converts light energy into electrical energy.

[0105] According to an embodiment, the photodiodes may be provided in multiple units that sense light intensity in the R (red), G (green), and B (blue) bands, respectively, so as to measure light intensity across the entire visible light wavelength band. For example, the photodiodes may be provided in multiple units that sense light intensity in the R, G, B, and W (white) bands, respectively.

[0106] According to an embodiment, the wearable device (100) can measure the final glycation product using autofluorescence.

[0107] According to an embodiment, autofluorescence is a phenomenon in which, when a molecular structure absorbs UV light or visible light, the energy state changes from a ground state to an excited state, and as the energy state transitions from the excited state to another state (e.g., a state with a relatively lower energy level than the excited state), energy is emitted, thereby emitting light with a wavelength band relatively longer than the irradiated light.

[0108] Various molecules contained in organisms have different autofluorescence characteristics, and in the case of advanced glycation products (AGEs), when they absorb light in the wavelength band with the highest light intensity at about 370 [nm] (corresponding to UV-A light), they have the characteristic of emitting light in the wavelength band with the highest light intensity at about 450 [nm] (blue wavelength band of visible light).

[0109] According to an embodiment, the wearable device (100) uses a light-emitting part (171) to irradiate the skin with ultraviolet light (e.g., about 370 [nm]) to excite the final glycation product, and the final glycation product can absorb the ultraviolet light and emit a fluorescent signal.

[0110] According to an embodiment, the wearable device (100) detects a fluorescent signal using a light receiving unit (172), and the intensity of the fluorescent signal may be proportional to the concentration of the final glycation product in the skin.

[0111] According to an embodiment, the wearable device (100) can measure the final glycation product based on light irradiated onto the skin and a fluorescent signal reflected from the skin in a non-invasive manner.

[0112]

[0113] FIG. 7 illustrates an electronic device for displaying final saccharification product data according to an embodiment of the present disclosure.

[0114] Referring to FIG. 7, the wearable device (100) can visually display the final glycation product data and transmit the final glycation product data to an electronic device (e.g., the electronic device (10) of FIG. 1).

[0115] According to an embodiment, the electronic device (10) can visually display the final saccharification product data (1). For example, as shown in FIG. 7, the electronic device (10) can provide a graph of the daily change trend of the user's final saccharification product based on the final saccharification product data (1).

[0116] According to the embodiment, since the final glycation product is closely related to the user's dietary habits and lifestyle habits and indicates the degree of skin aging, the wearable device (100) and electronic device (10) can visually display the final glycation product data (1).

[0117] For example, advanced glycation end products can be correlated with glycated hemoglobin (HbA1c).

[0118] For example, advanced glycation end products increase in concentration under hyperglycemic conditions and accumulate in the body over a long period, so they are associated with chronic diseases and aging.

[0119] For example, glycated hemoglobin includes the form in which hemoglobin is bound to glucose and can represent the average blood glucose level over a period of 2 to 3 months. Since both the final glycation end product and glycated hemoglobin increase under hyperglycemic conditions, the final glycation end product and glycated hemoglobin may be correlated.

[0120] According to an embodiment, the electronic device (10) can monitor the user's final glycation product and provide recommendation messages to improve the user's skin condition and to improve the user's dietary habits and lifestyle habits based on the final glycation product.

[0121] For example, if the final glycation product is included in the first-1 section among a plurality of first sections, the electronic device (10) may identify the degree of aging according to the final glycation product as low or identify the skin condition as good, and provide a recommendation message corresponding to low among a plurality of recommendation messages (e.g., maintain your current diet). For example, the plurality of first sections may include the first-1 section (e.g., less than 50), the first-2 section (e.g., 50 or more to less than 100), the first-3 section (e.g., 100 or more to less than 150), and the first-4 section (e.g., 150 or more).

[0122] For example, if the electronic device (10) is included in the first-fourth of the plurality of first sections, it may identify the degree of aging due to the final glycation product as very high or identify the skin condition as very poor, and provide a recommendation message corresponding to very high among the plurality of recommendation messages (e.g., improve your diet immediately. Eat foods rich in antioxidants).

[0123] A detailed explanation of this will be provided with reference to Fig. 8.

[0124]

[0125] FIG. 8 illustrates an artificial intelligence model that outputs a recommendation message according to an embodiment of the present disclosure.

[0126] In the example described above, it is assumed that the electronic device (10) provides one of a plurality of recommendation messages based on which of the plurality of first sections the final saccharification product is included in, but is not limited thereto.

[0127] According to an embodiment, the electronic device (10) inputs the final saccharification product data (1) into an artificial intelligence model (1000) and can provide a status message (A) generated by the artificial intelligence model (1000). According to an embodiment, the status message (A) may be called a recommendation message, and for convenience of explanation, it will be referred to as a recommendation message.

[0128] For example, the artificial intelligence model (1000) may be a model trained to analyze the user's skin condition based on the final glycation product data (1) and generate a recommendation message to improve the skin condition when the final glycation product data (1) is input.

[0129] For example, an artificial intelligence model can be trained to predict the degree of aging based on multiple advanced glycation end product data, and to output recommendation messages to improve skin condition and guide the food consumed by the user based on dietary habits (e.g., nutritional components, foods) and lifestyle habits (e.g., sleep patterns, activity levels, stress levels) that can reduce the degree of aging.

[0130] According to an embodiment, the electronic device (10) can process final glycation product data according to a predefined operation rule or artificial intelligence model and provide a recommendation message. The predefined operation rule or artificial intelligence model is characterized by being created through learning.

[0131] Here, being created through learning means that a predefined operation rule or artificial intelligence model of a desired characteristic is created by applying a learning algorithm to a number of learning data. This learning may be performed on the electronic device (10) itself where the artificial intelligence according to the present disclosure is performed, or it may be performed through a separate server / system.

[0132] An artificial intelligence model may be composed of multiple neural network layers. At least one layer has at least one weight value and performs the layer's operation through the result of the operation of the previous layer and at least one defined operation. Examples of neural networks include CNN (convolutional neural network), DNN (deep neural network), RNN (recurrent neural network), RBM (restricted Boltzmann machine), DBN (deep belief network), BRDNN (bidirectional recurrent deep neural network), deep Q-networks, and Transformer, and the neural networks in this disclosure are not limited to the aforementioned examples except where specified.

[0133] A learning algorithm is a method of training a specific target device (e.g., a robot) using a number of learning data to enable the target device to make decisions or predictions on its own. Examples of learning algorithms include supervised learning, unsupervised learning, semi-supervised learning, or reinforcement learning, and the learning algorithms in this disclosure are not limited to the aforementioned examples except where specified.

[0134] Referring to FIG. 8, the electronic device (10) can obtain weather data based on the location information of a user wearing a wearable device (100).

[0135] For example, the electronic device (10) can request real-time weather data (or weather data for a specific time (e.g., time of going out)) from a weather API (application programming interface) based on location information set in the electronic device (10) or location information based on the GPS of the wearable device (100), and obtain weather data.

[0136] According to the embodiment, the weather data may include at least one of the UV index (ultraviolet index) (2), air quality index (3), temperature, or humidity.

[0137] For example, the UV index (2) is divided into multiple second sections, and among the multiple second sections, the second-1 section may be less than 3 (low risk), the second-2 section may be 3 or more and less than 6 (medium risk), the second-3 section may be 6 or more and less than 8 (high risk), and the second-4 section may be 8 or more (very high risk). The above-described example is an assumption for convenience of explanation and is not limited thereto.

[0138] For example, the air quality index (3) (AQI) is calculated by combining the concentrations of various air pollutants such as PM 2.5 (particulate matter 2.5) and PM 10, which are classified according to the size of fine dust, as well as nitrogen dioxide (NO2), ozone (O3), carbon monoxide (CO), and sulfur dioxide (SO2), and can be divided into multiple third sections.

[0139] For example, among multiple third sections, the third-1 section may include 0 to 50 and indicate good, the third-2 section may include 51 to 100 and indicate average, the third-3 section may include 101 to 250 and indicate poor, and the third-4 section may include 251 to 500 and indicate very poor.

[0140] According to an embodiment, the electronic device (10) can input final saccharification product data (1) and weather data into an artificial intelligence model (1000). The artificial intelligence model (1000) can output a recommendation message that guides the user's behavior based on the final saccharification product data (1) and weather data.

[0141] For example, the electronic device (10) can obtain and provide recommendation messages based on predefined operation rules created through learning. For example, predefined operation rules can be represented as shown in Table 1 below.

[0142] Status Threshold Output AGEs UV Index Air Quality (μg / m³) Recommendation Message PM2.5 PM10 Low Risk < 50 < 3 < 12 < 20 Skin condition is good. Use SPF 30 sunscreen and drink plenty of water. Moderate Risk 50 ≤Index < 100 < 3 < 12 < 20 Be mindful of signs of aging. Consume antioxidants and use moisturizer. High UV Exposure < 50 ≥ 6 < 35 < 50 High UV exposure detected! When going outdoors, use SPF 50 sunscreen and wear a hat and sunglasses. Elevated AGEs Levels ≥ 100 < 3 < 35 < 50 Skin condition is poor. Be mindful of fried and high-fat foods. Consume antioxidants and fruits. High Risk 100 ≤Index < 150 6 ≤Index < 835 ≤PM < 5550 ≤PM < 100 Skin condition is poor. High UV exposure detected! Be careful with fried and high-fat foods. Avoid going outside. < 100 ≥8 ≥55 ≥100 Very High Risk ≥150 ≥8 ≥55 ≥100 Degree of aging is very high. Consult a specialist. Avoid outdoor activities. Use SPF 50 sunscreen and wear a hat and sunglasses. Moderate to High Risk Other combinations Be careful with the degree of aging. Indoor activities are recommended. Consumption of fresh foods is recommended.

[0143] In Table 1 described above, the threshold value is an example for convenience of explanation, and the recommendation message is also an example for convenience of explanation and is not limited thereto. Referring to FIG. 8, the electronic device (10) can input user data into the artificial intelligence model (1000). According to an embodiment, the user data may include various biometric data that the wearable device (100) can acquire, in addition to the final glycation product data (1) described above.

[0144] For example, an electronic device (10) may receive user data from a wearable device (100), including biometric data such as heart rate (HR) indicating the user's heart health status or exercise intensity, heart rate variability (HRV) indicating stress level or fatigue, blood oxygen level (SpO2) indicating breathing status based on oxygen concentration in the blood, sleep analysis indicating the user's sleep quality (deep sleep, light sleep, REM sleep), the user's body temperature, and activity level (e.g., number of steps, distance traveled, calories burned).

[0145] According to an embodiment, the electronic device (10) inputs user data and weather data into an artificial intelligence model, and the artificial intelligence model (1000) obtains status information based on the user data and weather data, and can generate a recommendation message based on the status information (e.g., activity level is too low. A walk is recommended, and when going out, the UV index (2) is somewhat high, so please use sunscreen).

[0146] FIG. 9 illustrates an electronic device for acquiring final saccharification product data, weather data, and indoor environment data according to an embodiment of the present disclosure.

[0147] Referring to FIG. 9, the electronic device (10) can receive final glycation product data (1) and user biometric data from a wearable device (100). According to an embodiment, the electronic device (10) can visually provide the final glycation product data (1) and can visually provide the UV index (2) and air quality index (3) received from a weather app API, respectively.

[0148] According to an embodiment, the electronic device (10) requests the transmission of final saccharification product data (1) at preset time intervals, and can identify the change trend by accumulating the final saccharification product data (1) received from the wearable device (100).

[0149] For example, the electronic device (10) requests the transmission of final glycation product data (1) from the wearable device (100) once a day (e.g., at 22:00), and when new final glycation product data (1) is received, it can identify a daily change trend based on the final glycation product data (1) previously received from the wearable device (100) (e.g., final glycation product data received yesterday) and the new glycation product data (1) (e.g., final glycation product data received today).

[0150] For example, the electronic device (10) can identify the trend of change in the final glycation product by accumulating the final glycation product data (1) received from the wearable device (100) for 7 days.

[0151] For example, the electronic device (10) identifies the change trend as decreasing, maintaining, or increasing, and if the change trend is identified as increasing, it may provide a recommendation message including a warning phrase (e.g., maintain a healthy diet for your skin).

[0152] For example, if the electronic device (10) identifies a change trend as decreasing, it may provide a recommendation message including a compliment phrase (e.g., "Please keep it as it is now").

[0153]

[0154] Referring to FIG. 9, the electronic device (10) can acquire indoor environment data in addition to user data and weather data, and acquire recommendation messages based on the indoor environment data.

[0155] For example, the electronic device (10) communicates with an external electronic device (200) and can receive indoor environment data from the external electronic device (200).

[0156] For example, the external electronic device (200) includes an IoT (Internet of Things) device, and the external electronic device (200) may include various devices capable of controlling indoor temperature, humidity, and air quality, such as a humidifier, a dehumidifier, an air purifier, and an air conditioner.

[0157] According to an embodiment, the electronic device (10) receives indoor environment data from an external electronic device (200), and the indoor environment data may include at least one of indoor temperature information, indoor humidity information, or indoor air quality index.

[0158] According to an embodiment, the electronic device (10) may provide a recommendation message that guides the user's actions to improve (or maintain) the user's skin based on at least one of indoor temperature information, indoor humidity information, or indoor air quality index included in indoor environment data.

[0159] For example, the electronic device (10) can provide a recommendation message that guides the user's actions based on indoor temperature information, such as, "The indoor temperature is too high. Please turn on the air conditioner to lower the indoor temperature."

[0160] For example, the electronic device (10) can provide recommendation messages that guide the user's actions based on indoor humidity information, such as, "The indoor humidity is too low and it is dry. Please turn on the humidifier," or "The indoor humidity is too low and it is dry. Please drink plenty of water."

[0161] For example, the electronic device (10) can provide a recommendation message that guides the user's actions based on the indoor air quality index, such as, "The indoor air quality is very bad, please turn on the air purifier."

[0162]

[0163] FIG. 10 illustrates an artificial intelligence model that outputs a recommendation message based on final saccharification product data, weather data, and indoor environment data according to an embodiment of the present disclosure.

[0164] Referring to FIG. 10, the electronic device (10) inputs at least one of user data, weather data, or indoor environment data into an artificial intelligence model (1000), and the artificial intelligence model (1000) can provide a recommendation message (A) to guide the user's behavior.

[0165] For example, the artificial intelligence model (1000) can process user data, weather data, and indoor environment data. For example, the artificial intelligence model (1000) can clean the data (user data, weather data, and indoor environment data) by adding missing data or removing outliers to improve reliability and accuracy. For example, the artificial intelligence model (1000) can add missing data as average data (e.g., if indoor temperature data is missing, add average indoor temperature data).

[0166] According to an embodiment, the artificial intelligence model (1000) can normalize data of different units, such as final saccharification product data (1), weather data (e.g., UV index (2), air quality index (3)), and indoor environment data, to compare them on the same standard.

[0167] According to an embodiment, the artificial intelligence model (1000) can identify the trend of change in the user's skin condition by collecting user data, weather data, and indoor environment data on a daily, weekly, and monthly basis. According to an embodiment, the artificial intelligence model (1000) can provide a recommendation message to improve the user's skin condition based on the trend of change.

[0168] According to an embodiment, the artificial intelligence model (1000) can identify the correlation between user data and weather data using the Pearson correlation coefficient. For example, the artificial intelligence model (1000) can identify the strength and direction of the linear relationship between two variables using the Pearson correlation coefficient.

[0169] For example, an artificial intelligence model (1000) can identify a correlation between the end-stage glycation product and the UV index (or air quality index) based on the Pearson correlation coefficient. For example, if the correlation is high, the artificial intelligence model (1000) can identify that the higher the UV index (or the more exposure to UV), the higher the concentration of the end-stage glycation product, thereby accelerating the degree of aging.

[0170] For example, if the Pearson correlation coefficient is 0.8, there is a strong correlation between the final glycation product and the UV index (or air quality index), and as the UV index increases (or as exposure to UV increases), the concentration of the final glycation product increases and accelerates the degree of aging, so the artificial intelligence model (1000) can identify state information by considering the UV index with a higher weight. For example, in the following mathematical formula 1, the artificial intelligence model (1000) can set w1, w2, and w3 respectively by considering the Pearson correlation coefficient.

[0171] [Mathematical Formula 1]

[0172] S = w1 * AGEs + w2 * UV index + w3 * air quality index

[0173] The above mathematical formula 1 is merely an example to aid understanding and is not limited thereto; it can be modified, applied, or extended in various ways.

[0174] Here, S can represent quantitatively identified state information. Depending on the value of S, the artificial intelligence model (1000) can quantitatively identify state information as low, normal, high, or very high. w1, w2, and w3 can each represent a first weight for the final saccharification product according to the correlation coefficient, a second weight for the UV index, and a third weight for the air quality index.

[0175] According to an embodiment, the artificial intelligence model (1000) can identify linear relationships between variables to quantitatively identify state information (e.g., user's skin condition). For example, the artificial intelligence model (1000) can identify threshold values ​​related to final glycation products (e.g., multiple first intervals), threshold values ​​related to UV index (e.g., multiple second intervals), and threshold values ​​related to air quality index (e.g., multiple third intervals).

[0176] According to an embodiment, the artificial intelligence model (1000) can provide recommendation messages to improve or maintain the user's skin condition based on user data, weather data, and indoor environment data (e.g., recommendation messages that guide actions to lower the concentration of advanced glycation end products and minimize exposure to UV and fine dust).

[0177] Meanwhile, the artificial intelligence model (1000) may generate a control signal to control an external electronic device (200). For example, the artificial intelligence model (1000) may generate a control signal to improve (or maintain) the user's skin based on at least one of indoor temperature information, indoor humidity information, or indoor air quality index included in indoor environment data, and the electronic device (10) may transmit the control signal to the external electronic device (200) to control the external electronic device (200).

[0178] For example, an artificial intelligence model (1000) can generate a first control signal to lower the indoor temperature based on indoor temperature information. The electronic device (10) transmits the first control signal to an external electronic device (200) (e.g., an air conditioner), and the external electronic device (200) can be controlled according to the first control signal to perform an action to lower the indoor temperature.

[0179] For example, an artificial intelligence model (1000) can generate a second control signal to increase indoor humidity based on indoor humidity information. The electronic device (10) transmits the second control signal to an external electronic device (200) (e.g., a humidifier), and the external electronic device (200) can be controlled according to the second control signal to perform an operation to increase indoor humidity.

[0180] For example, an artificial intelligence model (1000) can generate a third control signal to improve indoor air quality based on an indoor air quality index (e.g., to remove harmful substances (e.g., fine dust) from indoor air). The electronic device (10) transmits the third control signal to an external electronic device (200) (e.g., an air purifier), and the external electronic device (200) can be controlled according to the third control signal to perform an operation to improve indoor air quality.

[0181] For example, the electronic device (10) inputs indoor environment data received from an external electronic device (200) into an artificial intelligence model (1000), and the artificial intelligence model (1000) can generate a control signal to improve the user's skin condition or a control signal to mitigate environmental conditions (e.g., air quality, temperature, humidity) that have an adverse effect on the user's skin condition based on the indoor environment data.

[0182] For example, if the indoor humidity is low and dry, the artificial intelligence model (1000) can operate an external electronic device (200), such as a humidifier, to generate a control signal to increase the indoor humidity, and the electronic device (10) can transmit the control signal generated by the artificial intelligence model (1000) to the external electronic device (200).

[0183] For example, if the indoor air quality index is very bad, the artificial intelligence model (1000) can operate an external electronic device (200), such as an air purifier, to generate a control signal to improve the indoor air quality (e.g., to lower the concentration of fine dust), and the electronic device (10) can transmit the control signal generated by the artificial intelligence model (1000) to the external electronic device (200).

[0184]

[0185]

[0186] FIGS. 11 to 13 illustrate examples of recommendation messages provided by an electronic device (10).

[0187] FIG. 11 illustrates an electronic device that provides a recommendation message (A) for improving skin condition according to an embodiment of the present disclosure.

[0188] Referring to FIG. 11, the electronic device (10) can provide a recommendation message (A) to improve the user's skin condition using an artificial intelligence model.

[0189] For example, if the electronic device (10) identifies the final glycation product as high based on the final glycation product data and weather data (e.g., the final glycation product data is included in the first-3 interval among a plurality of first intervals) and identifies the UV index as high (e.g., the UV index is included in the second-3 interval among a plurality of second intervals), it can provide a recommendation message (A) (e.g., please apply sunscreen when going out. For your skin, regular sleep is recommended).

[0190] FIG. 12 illustrates an electronic device that provides a recommendation message (A) guiding a user's actions according to an embodiment of the present disclosure.

[0191] Referring to FIG. 12, the electronic device (10) can guide the user's behavior by providing a recommendation message (A) (e.g., recommending wearing a mask when going out) when the final saccharification product is identified as low based on the final saccharification product data (e.g., the final saccharification product data is included in the 1-1 section among a plurality of first sections) and when the air quality index is identified as bad based on the weather data (e.g., the air quality index is included in the 3-3 section among a plurality of third sections).

[0192] FIG. 13 illustrates an electronic device that controls an external electronic device according to an embodiment of the present disclosure.

[0193] Referring to FIG. 13, if the electronic device (10) identifies that the indoor humidity is low based on indoor environment data, it can provide a recommendation message (A) to increase the indoor humidity (e.g., "The room is dry. Please turn on the humidifier."). Additionally, the electronic device (10) can generate a control signal to control an external electronic device (200) and transmit the control signal to the external electronic device (200).

[0194]

[0195] FIG. 14 is a flowchart illustrating a method of operation of an electronic device according to an embodiment of the present disclosure.

[0196] According to an embodiment, the method of operation of an electronic device can receive advanced glycation end products (AGEs) data from a wearable device (S1410).

[0197] According to an embodiment, weather data can be obtained based on the location information of a user wearing a wearable device (S1420).

[0198] According to the embodiment, final saccharification product data and weather data can be input into an artificial intelligence model (S1430).

[0199] According to the embodiment, a recommendation message generated by an artificial intelligence model can be provided (S1440).

[0200] According to an embodiment, the artificial intelligence model can be trained to acquire user status information based on final glycation product data and to generate recommendation messages that guide user behavior based on UV index, air quality index, and status information included in weather data.

[0201] According to an embodiment, the artificial intelligence model can be trained to identify the user's skin condition based on the section containing the final glycation product data among a plurality of first sections, guide the user to consume food to improve the skin condition, and generate recommendation messages that guide the user's behavior.

[0202] According to an embodiment, the operation S1440 for providing recommendation messages may include, if the final glycation product data among the plurality of first sections is included in the 1-1 section, identifying the user's skin condition as good and providing a first recommendation message among the plurality of recommendation messages that guides food and behaviors to maintain the skin condition, and if the final glycation product data among the plurality of first sections is included in the 1-2 section, identifying the skin condition as dangerous and providing a second recommendation message among the plurality of recommendation messages that guides food and behaviors to improve the skin condition.

[0203] According to an embodiment, an artificial intelligence model is trained to generate a recommendation message that guides a user's behavior based on a plurality of second sections that include a UV index and a plurality of third sections that include an air quality index; if the UV index among the plurality of second sections is included in section 2-1, the UV index is identified as low; if the UV index among the plurality of second sections is included in section 2-2, the UV index is identified as high; if the air quality index among the plurality of third sections is included in section 3-1, the air quality index is identified as good; if the air quality index among the plurality of third sections is included in section 3-2, the air quality index is identified as bad; and if the UV index is identified as high or the air quality index is identified as bad, a recommendation message is generated to guide the user to refrain from going out or to wear a mask. It can be trained to identify, and if the air quality index is included in section 3-1 among multiple third sections, the air quality index is identified as good; if the air quality index is included in section 3-2 among multiple third sections, the air quality index is identified as bad; and if the UV index is identified as high or the air quality index is identified as bad, it can be trained to generate a recommendation message guiding the user to refrain from going outside or to wear a mask.

[0204] According to the embodiment, the operation S1430 of inputting to the artificial intelligence model may include the operation of inputting the final glycation product data and the biometric data into the artificial intelligence model when the user's biometric data is received from the wearable device.

[0205] According to an embodiment, an artificial intelligence model can be trained to generate an ecological message that guides the user's behavior based on biometric data.

[0206] According to an embodiment, the biometric data may include at least one of heart rate, stress index, sleep information, or activity level.

[0207] According to the embodiment, the operation S1430 for inputting to the artificial intelligence model may include the operation of inputting the final saccharification product data and the indoor environment data into the artificial intelligence model when the user's indoor environment data is received from an external electronic device.

[0208] According to an embodiment, indoor environment data may include at least one of indoor temperature information, indoor humidity information, or indoor air quality index.

[0209] According to an embodiment, the artificial intelligence model may be trained to acquire at least one of a first control signal for adjusting the indoor temperature based on indoor temperature information, a second control signal for adjusting the indoor humidity based on indoor humidity information, or a third control signal for adjusting the indoor air quality based on an indoor air quality index.

[0210] According to an embodiment, the operation of acquiring at least one of a first control signal, a second control signal, or a third control signal through an artificial intelligence model and transmitting it to an external electronic device may be further included.

[0211] According to the embodiment, the operation of requesting the transmission of final glycation product data to a wearable device at preset time intervals may be further included.

[0212] According to an embodiment, when new final glycation product data is received from a wearable device, the method may further include an operation to obtain a change trend based on the final glycation product data received from the wearable device prior to a preset time and the new final glycation product data, and an operation to identify the change trend as one of decreasing, maintaining, or increasing.

[0213] According to an embodiment, the operation S1440 for providing a recommendation message may include providing a recommendation message containing a warning phrase when the change trend is identified as increasing.

[0214]

[0215] According to an embodiment, an electronic device (e.g., the electronic device (10) of FIG. 1) may be configured to include a communication interface, a memory and at least one processor including a processing circuit that stores instructions and includes one or more storage media, and when the instructions are executed individually or collectively by the at least one processor, the electronic device may be configured to receive advanced glycation end products (AGEs) data from a user from a wearable device, obtain weather data based on location information of the user wearing the wearable device, input the advanced glycation end products data and the weather data into an artificial intelligence model, and provide a recommendation message generated by the artificial intelligence model.

[0216] According to an embodiment, the artificial intelligence model can be trained to acquire the user's status information based on the final saccharification product data, and to generate a recommendation message that guides the user's behavior based on the UV index, air quality index included in the weather data, and the status information.

[0217] According to an embodiment, the artificial intelligence model may be trained to identify the user's skin condition based on the section containing the final glycation product data among a plurality of first sections, guide the food consumed by the user to improve the skin condition, and generate the recommendation message that guides the user's behavior.

[0218] According to an embodiment, the instructions may be configured such that, if the final glycation product data among a plurality of first sections is included in the 1-1 section, the electronic device identifies the skin condition as good and provides a first recommendation message among a plurality of recommendation messages that guides food and behaviors to maintain the skin condition, and if the final glycation product data among the plurality of first sections is included in the 1-2 section, the device identifies the skin condition as dangerous and provides a second recommendation message among a plurality of recommendation messages that guides food and behaviors to improve the skin condition.

[0219] According to an embodiment, the artificial intelligence model is trained to generate a recommendation message that guides the user's behavior based on a plurality of second sections that include the UV index and a plurality of third sections that include the air quality index. If the UV index is included in section 2-1 among the plurality of second sections, the UV index is identified as low; if the UV index is included in section 2-2 among the plurality of second sections, the UV index is identified as high; if the air quality index is included in section 3-1 among the plurality of third sections, the air quality index is identified as good; if the air quality index is included in section 3-2 among the plurality of third sections, the air quality index is identified as bad; and if the UV index is identified as high or the air quality index is identified as bad, the model is trained to generate a recommendation message that guides the user to refrain from going out or to wear a mask.

[0220] According to an embodiment, the instructions may be configured such that when the user's biometric data is received from the wearable device, the electronic device inputs the final glycation product data and the biometric data into the artificial intelligence model.

[0221] According to an embodiment, the artificial intelligence model can be trained to generate the ecological message that guides the user's behavior based on the biometric data.

[0222] According to an embodiment, the biometric data may include at least one of heart rate, stress index, sleep information, or activity level.

[0223] According to an embodiment, the electronic device may further include a communication circuit that communicates with an external electronic device.

[0224] According to an embodiment, the instructions may be configured such that when the user's indoor environment data is received from the external electronic device, the electronic device inputs the final saccharification product data and the indoor environment data into the artificial intelligence model.

[0225] According to an embodiment, the indoor environment data may include at least one of indoor temperature information, indoor humidity information, or indoor air quality index.

[0226] According to an embodiment, the artificial intelligence model may be trained to acquire at least one of a first control signal for adjusting the indoor temperature based on the indoor temperature information, a second control signal for adjusting the indoor humidity based on the indoor humidity information, or a third control signal for adjusting the indoor air quality based on the indoor air quality index.

[0227] According to an embodiment, the instructions may be configured such that the electronic device acquires at least one of the first control signal, the second control signal, or the third control signal through the artificial intelligence model and transmits it to the external electronic device.

[0228] According to an embodiment, the instructions may be configured to cause the electronic device to request the transmission of the final glycation product data to the wearable device at preset time intervals.

[0229] According to an embodiment, the instructions may be configured such that when new final glycation product data is received from the wearable device, the electronic device obtains a change trend based on the final glycation product data received from the wearable device prior to the preset time and the new final glycation product data, identifies the change trend as one of decrease, maintenance, or increase, and if the change trend is identified as increase, provides the recommendation message including a warning phrase.

[0230] According to an embodiment, the final saccharification product data may include autofluorescence data.

[0231] According to an embodiment, the artificial intelligence model can obtain the user's state information based on the correlation coefficient between the final glycation product data, including the autofluorescence data, and the degree of aging (ages).

[0232]

[0233] According to an embodiment, a method of operation of an electronic device (e.g., the electronic device (10) of FIG. 1) may include receiving data of an advanced glycation end products (AGEs) from a wearable device, obtaining weather data based on location information of the user wearing the wearable device, inputting the data of the advanced glycation end products and the weather data into an artificial intelligence model, and providing a recommendation message generated by the artificial intelligence model.

[0234] According to an embodiment, the artificial intelligence model can be trained to acquire the user's status information based on the final saccharification product data, and to generate a recommendation message that guides the user's behavior based on the UV index, air quality index included in the weather data, and the status information.

[0235] According to an embodiment, the artificial intelligence model may be trained to identify the user's skin condition based on the section containing the final glycation product data among a plurality of first sections, guide the food consumed by the user to improve the skin condition, and generate the recommendation message that guides the user's behavior.

[0236] According to an embodiment, the operation of providing the recommendation message may include, if the final glycation product data among the plurality of first sections is included in the 1-1 section, identifying the skin condition as good and providing a first recommendation message among the plurality of recommendation messages that guides food and behaviors to maintain the skin condition, and if the final glycation product data among the plurality of first sections is included in the 1-2 section, identifying the skin condition as dangerous and providing a second recommendation message among the plurality of recommendation messages that guides food and behaviors to improve the skin condition.

[0237] According to an embodiment, the artificial intelligence model is trained to generate the recommendation message that guides the user's behavior based on a plurality of second sections that include the UV index and a plurality of third sections that include the air quality index. If the UV index is included in section 2-1 among the plurality of second sections, the model identifies the UV index as low; if the UV index is included in section 2-2 among the plurality of second sections, the model identifies the UV index as high; if the air quality index is included in section 3-1 among the plurality of third sections, the model identifies the air quality index as good; if the air quality index is included in section 3-2 among the plurality of third sections, the model identifies the air quality index as bad; and if the UV index is identified as high or the air quality index is identified as bad, the model is trained to generate the recommendation message that guides the user to refrain from going out or to wear a mask. According to an embodiment, the action input to the artificial intelligence model is the user's bio from the wearable device When data is received, the operation may include inputting the final glycation product data and the biological data into the artificial intelligence model.

[0238] According to an embodiment, the artificial intelligence model can be trained to generate the ecological message that guides the user's behavior based on the biometric data.

[0239] According to an embodiment, the biometric data may include at least one of heart rate, stress index, sleep information, or activity level.

[0240] According to an embodiment, the operation of inputting to the artificial intelligence model may include the operation of inputting the final saccharification product data and the indoor environment data to the artificial intelligence model when the user's indoor environment data is received from an external electronic device.

[0241] According to an embodiment, the indoor environment data may include at least one of indoor temperature information, indoor humidity information, or indoor air quality index.

[0242] According to an embodiment, the artificial intelligence model may be trained to acquire at least one of a first control signal for adjusting the indoor temperature based on the indoor temperature information, a second control signal for adjusting the indoor humidity based on the indoor humidity information, or a third control signal for adjusting the indoor air quality based on the indoor air quality index.

[0243] According to an embodiment, the operation of acquiring at least one of the first control signal, the second control signal, or the third control signal through the artificial intelligence model and transmitting it to the external electronic device may be further included.

[0244] According to the embodiment, the operation of requesting the transmission of the final glycation product data to the wearable device at preset time intervals may be further included.

[0245] According to an embodiment, when new final glycation product data is received from the wearable device, the method may further include the operation of obtaining a change trend based on the final glycation product data received from the wearable device prior to the preset time and the new final glycation product data, and the operation of identifying the change trend as one of decreasing, maintaining, or increasing.

[0246] According to an embodiment, the operation of providing the recommendation message may include providing the recommendation message including a warning phrase when the change trend is identified as increasing.

[0247]

[0248] According to an embodiment, in a storage medium storing computer-readable instructions, the instructions may be configured such that when executed by at least one processor of an electronic device (e.g., electronic device (10) of FIG. 1), the electronic device inputs the advanced glycation end products (AGEs) data of a user into an artificial intelligence model and provides a recommendation message generated by the artificial intelligence model when the user's advanced glycation end products data is received from a wearable device.

[0249] According to an embodiment, the artificial intelligence model can be trained to acquire state information of the user based on the final glycation product data and to generate a recommendation message that guides the user's behavior based on the state information.

[0250]

[0251] Meanwhile, the methods according to the various embodiments of the present disclosure described above may be implemented in the form of an application installable on an existing electronic device. Alternatively, the methods according to the various embodiments of the present disclosure described above may be performed using a deep learning-based learned neural network (or deep learned neural network), that is, a learning network model. Furthermore, the methods according to the various embodiments of the present disclosure described above may be implemented solely through a software upgrade or a hardware upgrade of an existing display device. Additionally, the various embodiments of the present disclosure described above may also be performed through an embedded server equipped in the display device or an external server of the display device.

[0252] Meanwhile, according to the exemplary embodiments of the present disclosure, the various embodiments described above may be implemented as software comprising instructions stored on a machine-readable storage medium (e.g., a computer). The machine may include a display device (e.g., a display device (A)) according to the disclosed embodiments, which is a device capable of calling instructions stored from the storage medium and operating according to the called instructions. When instructions are executed by a processor, the processor may perform a function corresponding to the instructions directly or by using other components under the control of the processor. Instructions may include code provided or executed by a compiler or an interpreter. The machine-readable storage medium may be provided in the form of a non-transitory storage medium. Here, "non-transitory" means only that the storage medium does not contain a signal and is tangible, and does not distinguish whether data is stored semi-permanently or temporarily in the storage medium.

[0253] Additionally, according to one embodiment, the method according to the various embodiments described above may be provided by being included in a computer program product. The computer program product may be traded between a seller and a buyer as a product. The computer program product may be distributed online in the form of a device-readable storage medium (e.g., compact disc read-only memory (CD-ROM)) or through an application store (e.g., Play Store™). In the case of online distribution, at least a portion of the computer program product may be temporarily stored or provided on a storage medium, such as the memory of a manufacturer's server, an application store's server, or a relay server.

[0254] Additionally, each component (e.g., module or program) according to the various embodiments described above may be composed of a singular or multiple entities, and some of the aforementioned sub-components may be omitted, or other sub-components may be further included in the various embodiments. Generally or additionally, some components (e.g., module or program) may be integrated into a single entity to perform the functions performed by each of the respective components prior to integration in the same or similar manner. The operations performed by the module, program, or other components according to the various embodiments may be executed sequentially, in parallel, iteratively, or heuristically, or at least some operations may be executed in a different order, omitted, or other operations added.

[0255] Although preferred embodiments of the present disclosure have been illustrated and described above, the present disclosure is not limited to the specific embodiments described above. It is understood that various modifications can be made by those skilled in the art without departing from the essence of the present disclosure as claimed in the claims, and such modifications should not be understood individually from the technical spirit or perspective of the present disclosure.

Claims

1. In an electronic device, Communication interface; Memory that stores instructions and includes one or more storage media; and at least one processor including a processing circuit; and When the above instructions are executed individually or collectively by the at least one processor, the electronic device, Advanced glycation end products (AGEs) data of a user is received from a wearable device, and Weather data is obtained based on the location information of the user wearing the above-mentioned wearable device, and The above final saccharification product data and the above weather data are input into an artificial intelligence model, and It is configured to provide recommendation messages generated by the above artificial intelligence model, and The above artificial intelligence model is, An electronic device trained to acquire status information of the user based on the above-mentioned final glycation product data, and to generate a recommendation message that guides the user's behavior based on the UV index, air quality index included in the weather data, and the status information.

2. In Paragraph 1, The above artificial intelligence model is, Based on the section containing the final glycation product data among a plurality of first sections, the skin condition of the user is identified, and An electronic device trained to generate recommendation messages that guide the food consumed by the user to improve the above skin condition and guide the user's behavior.

3. In Paragraph 2, The above instructions cause the electronic device, If the final glycation product data among the plurality of first sections is included in the 1-1 section, the skin condition is identified as good, and among the plurality of recommendation messages, a first recommendation message is provided that guides food and behaviors to maintain the skin condition. An electronic device configured to identify the skin condition as a risk and provide a second recommendation message among the plurality of recommendation messages that guides food and behaviors to improve the skin condition when the final glycation product data is included in the first-2 section among the plurality of first sections.

4. In Paragraph 1 or 2, The above artificial intelligence model is, It is trained to generate the recommendation message that guides the user's behavior based on the portion of the plurality of second sections that includes the UV index and the portion of the plurality of third sections that includes the air quality index. If the UV index is included in the 2-1 section among the plurality of 2 sections above, the UV index is identified as low, and If the UV index is included in the 2-2 section among the plurality of 2 sections above, the UV index is identified as high, and If the air quality index is included in section 3-1 among the plurality of third sections above, the air quality index is identified as good, and If the air quality index is included in section 3-2 among the plurality of third sections mentioned above, the air quality index is identified as poor, and An electronic device trained to generate a recommendation message that guides the user to refrain from going outside or to wear a mask when the UV index is identified as high or the air quality index is identified as poor.

5. In Paragraph 1 or 2, The above instructions cause the electronic device, When the user's biometric data is received from the wearable device, the system is configured to input the final glycation product data and the biometric data into the artificial intelligence model. The above artificial intelligence model is, It is trained to generate the ecological message that guides the user's behavior based on the above biometric data, and The above biometric data is, An electronic device comprising at least one of heart rate, stress index, sleep information, or activity level.

6. In Paragraph 1 or 2, It further includes a communication circuit that communicates with an external electronic device, The above instructions cause the electronic device, When the user's indoor environment data is received from the external electronic device, the system is configured to input the final saccharification product data and the indoor environment data into the artificial intelligence model. The above indoor environment data is, It includes at least one of indoor temperature information, indoor humidity information, or indoor air quality index, and The above artificial intelligence model is, An electronic device learned to acquire at least one of a first control signal for adjusting an indoor temperature based on the indoor temperature information, a second control signal for adjusting an indoor humidity based on the indoor humidity information, or a third control signal for adjusting the indoor air quality based on the indoor air quality index.

7. In Paragraph 6, The above instructions cause the electronic device, An electronic device configured to acquire at least one of the first control signal, the second control signal, or the third control signal through the artificial intelligence model and transmit it to the external electronic device.

8. In Paragraph 1 or 2, The above instructions cause the electronic device, An electronic device configured to request the transmission of the final glycation product data to the wearable device at preset time intervals.

9. In Paragraph 8, The above instructions cause the electronic device, When new final glycation product data is received from the wearable device, a change trend is obtained based on the final glycation product data received from the wearable device prior to the preset time and the new final glycation product data, and Identifying the above change trend as a decrease, maintenance, or increase, An electronic device configured to provide the recommendation message including a warning phrase when the above change trend is identified as increasing.

10. In Paragraph 1 or 2, The above final saccharification product data is, Includes autofluorescence data, The above artificial intelligence model is, An electronic device that acquires user status information based on the correlation coefficient between the final glycation product data including the autofluorescence data and the degree of aging (ages).

11. In a method of operating an electronic device, The above method is, The operation of receiving a user's advanced glycation end products (AGEs) data from a wearable device; An operation to acquire weather data based on location information of the user wearing the above-mentioned wearable device; The operation of inputting the above-mentioned final saccharification product data and the above-mentioned weather data into an artificial intelligence model; and The operation of providing a recommendation message generated by the above artificial intelligence model; is included, The above artificial intelligence model is, A method of operation trained to acquire status information of the user based on the above-mentioned final glycation product data, and to generate a recommendation message that guides the user's behavior based on the UV index, air quality index included in the above-mentioned weather data and the above-mentioned status information.

12. In Paragraph 11, The above artificial intelligence model is, Based on the section containing the final glycation product data among a plurality of first sections, the skin condition of the user is identified, and A method of operation trained to generate recommendation messages that guide the food consumed by the user to improve the above skin condition and guide the user's behavior.

13. In Paragraph 12, The operation of providing the above recommendation message is, If the final glycation product data among a plurality of first sections is included in the 1-1 section, the operation of identifying the skin condition as good and providing a first recommendation message among a plurality of recommendation messages that guides food and behaviors to maintain the skin condition; and A method of operation comprising: identifying the skin condition as a risk when the final glycation product data among the plurality of first sections is included in the first-2 section, and providing a second recommendation message among the plurality of recommendation messages that guides food and behaviors to improve the skin condition.

14. In Paragraph 11 or 12, The above artificial intelligence model is, It is trained to generate the recommendation message that guides the user's behavior based on the portion of the plurality of second sections that includes the UV index and the portion of the plurality of third sections that includes the air quality index. If the UV index is included in the 2-1 section among the plurality of 2 sections above, the UV index is identified as low, and If the UV index is included in the 2-2 section among the plurality of 2 sections above, the UV index is identified as high, and If the air quality index is included in section 3-1 among the plurality of third sections above, the air quality index is identified as good, and If the air quality index is included in section 3-2 among the plurality of third sections mentioned above, the air quality index is identified as poor, and A method of operation learned to generate a recommendation message that guides the user to refrain from going out or to wear a mask when the UV index is identified as high or the air quality index is identified as poor.

15. In a non-transient storage medium storing computer-readable instructions, said instructions, when executed by at least one processor of an electronic device, cause said electronic device, When a user's advanced glycation end products (AGEs) data is received from a wearable device, the said advanced glycation end products data is input into an artificial intelligence model, and It is configured to provide recommendation messages generated by the above artificial intelligence model, and The above artificial intelligence model is, A storage medium trained to acquire state information of the user based on the above-mentioned final glycation product data, and to generate a recommendation message that guides the user's behavior based on the state information.

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