Electronic device for providing recommendation message by analyzing ingested food, operation method thereof, and storage medium

Abstract

An electronic device of the present disclosure comprises: a communication interface; a memory storing instructions and including one or more storage media; and at least one processor including processing circuitry, wherein the instructions are configured to, when executed by the at least one processor individually or collectively, cause the electronic device to: receive advanced glycation end products (AGEs) data of a user from a wearable device; obtain data related to food ingested by the user; and provide a recommendation message generated by an artificial intelligence model by inputting the AGEs data and the data related to food into the artificial intelligence model, wherein the artificial intelligence model is trained to identify a correlation between the data related to food and the AGEs data, identify state information of the user on the basis of the AGEs data, and generate a recommendation message for guiding a meal of the user on the basis of the correlation and the state information.

Description

An electronic device that analyzes consumed food and provides recommendation messages, its method of operation, and a storage medium

[0001] The present disclosure relates to an electronic device that analyzes food consumed by a user and provides a recommendation message, a method of operation thereof, and a 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 of a user by coming into contact with a part of the user's body. Wearable devices can measure advanced glycation end products, and since advanced glycation end products are closely related to the user's dietary habits, this can be utilized in a method to guide the user's food intake.

[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, obtain food-related data consumed by the user, input the Advanced Glycation End Products data and the food-related data into an artificial intelligence model to provide a recommendation message generated by the artificial intelligence model, wherein the artificial intelligence model is trained to identify the correlation between the food-related data and the Advanced Glycation End Products data, identify the user's state information based on the Advanced Glycation End Products data, and generate the recommendation message that guides the user's meal based on the correlation and the state information.

[0007] A method of operation of an electronic device according to an embodiment of the present disclosure includes receiving Advanced Glycation End Products (AGEs) data of a user from a wearable device, obtaining data related to food consumed by the user, and inputting the Advanced Glycation End Products data and the food related data into an artificial intelligence model to provide a recommendation message generated by the artificial intelligence model, wherein the artificial intelligence model is trained to identify a correlation between the food related data and the Advanced Glycation End Products data, identify the user's state information based on the Advanced Glycation End Products data, and generate the recommendation message that guides the user's meal based on the correlation and the state 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 food-related data consumed by the user, inputs the Advanced Glycation End Products data and the food-related data into an artificial intelligence model to provide a recommendation message generated by the artificial intelligence model, and the artificial intelligence model is trained to identify the correlation between the food-related data and the Advanced Glycation End Products data, identify the user's state information based on the Advanced Glycation End Products data, and generate the recommendation message that guides the user's meal based on the correlation and the state 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 identifying food consumed by a user according to an embodiment of the present disclosure.

[0017] FIG. 8 illustrates an electronic device that acquires food-related data based on an artificial intelligence model according to an embodiment of the present disclosure.

[0018] FIG. 9 illustrates food-related data according to food consumed on a daily basis according to an embodiment of the present disclosure.

[0019] FIG. 10 illustrates an artificial intelligence model for obtaining a recommendation message according to an embodiment of the present disclosure.

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

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

[0022] FIG. 13 illustrates an electronic device that provides notification of an allergenic component 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" and "B" or "A or 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] FIG. 1 illustrates an electronic device and a wearable device according to an embodiment of the present disclosure.

[0035]

[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), tablet PC (personal computer), mobile phone, video phone, e-book reader, desktop PC, laptop PC, workstation, server, PDA (personal digital assistant), PMP (portable multimedia player), MP3 player, medical device, camera, virtual reality (VR) implementation device, spatial computing device, or 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) 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).

[0042] 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.

[0043] Additionally, the wearable device (100) includes an electrocardiogram (ECG) measurement sensor and can measure the user's electrocardiogram using the ECG measurement sensor.

[0044] 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.

[0045] 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. That is, the concentration of advanced glycation end products is closely related to the 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.

[0046] 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.

[0047] According to an embodiment, the wearable device (100) measures the concentration (or measured amount) of the final glycation product using a sensor and can visually provide the concentration of the final glycation product.

[0048] According to an embodiment, the wearable device (100) may measure the concentration of the user's final glycation product in real time or measure the concentration of the final glycation product at a preset time. For example, since the concentration of the final glycation product is closely related to the food consumed by the user, the wearable device (100) may measure the concentration of the final glycation product about 1 hour after the user's meal time. However, it is not limited thereto, and the wearable device (100) may measure the concentration of the final glycation product according to user input, or may measure the concentration of the final glycation product before the user goes to sleep or immediately after waking up according to a setting.

[0049] According to an embodiment, the wearable device (100) can transmit data including the concentration of the final glycation product (hereinafter, final glycation product data) to the electronic device (10), and when the final glycation product data is received, the electronic device (10) can visually provide the concentration of the final glycation product.

[0050] According to an embodiment, the electronic device (10) can calculate the average of a plurality of final glycation product concentrations measured over a certain period based on the concentration of the final glycation product measured in real time at the wearable device (100) or the concentration of the final glycation product measured at a preset time.

[0051] According to an embodiment, the electronic device (10) can obtain the average of the concentrations of a plurality of final saccharification products measured over a certain period as a representative concentration corresponding to a certain period.

[0052] For example, the electronic device (10) can calculate a representative daily concentration based on data including concentrations of multiple end-glycation products obtained by measuring the wearable device (100) over a day, and can visually provide the accumulated representative daily concentrations for the user to track, observe, or monitor. For example, the electronic device (10) can visually provide a trend of change in the representative daily concentrations.

[0053] For example, the electronic device (10) can calculate a representative concentration on a weekly basis (or, monthly, or yearly basis) based on data including concentrations of multiple final glycation products obtained by measuring the wearable device (100) over a week, and can visually provide the accumulated representative concentrations on a weekly basis.

[0054] According to an embodiment, each of the wearable device (100) and the electronic device (10) can identify which of a plurality of ranges the concentration of the final glycation product belongs to and provide a visual effect corresponding to the range in which the concentration of the final glycation product is included.

[0055] For example, multiple ranges may include a high-risk range, a risk range, a normal range, and a low range.

[0056] According to an embodiment, if the concentration of the final glycation product falls within a high-risk range, the electronic device (10) may provide (or display) a visual effect corresponding to the high-risk range using a display (11) (e.g., a visual effect including a warning text on a red background (e.g., the concentration of the final glycation product is too high) because the concentration of the final glycation product is high). According to an embodiment, the wearable device (100) may also provide (or display) a visual effect as described above using a display (e.g., the display (140) of FIG. 2).

[0057] According to an embodiment, when the electronic device (10) receives final glycation product data from the wearable device (100) using a communication circuit (not shown), it can provide (or display) a recommendation message to reduce the user's final glycation product based on the final glycation product data using a display (11).

[0058] According to an embodiment, the electronic device (10) can identify the food consumed by the user, identify the change in the concentration of the final glycation product caused by the food consumed by the user, and provide (or display) a recommendation message using the display (11) to guide the user's eating habits to lower the concentration of the final glycation product or so that the concentration of the final glycation product falls within a normal range (or a low range).

[0059] For example, if the electronic device (10) identifies that the food consumed by the user is a major cause of increasing the user's end-stage glycation product concentration, it can provide a recommendation message guiding the user to consume healthy food to lower the user's end-stage glycation product, and can provide a notification indicating that the current state (e.g., current end-stage glycation product concentration) is a dangerous state.

[0060] 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.

[0061] 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.

[0062] 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.

[0063] 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).

[0064] 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)).

[0065] 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).

[0066] 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).

[0067] 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.

[0068] The microphone (130) can acquire sound output from an external object. According to an embodiment, the number of microphones (130) may be one or more. The speaker (180) can output sound to the outside. According to an embodiment, the number of speakers (180) may be one or more.

[0069] 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.

[0070] 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).

[0071] 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 (radio frequency identification), UWB (ultra wide band), GNSS (global navigation satellite system), or RF (radio frequency) communication.

[0072] 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.

[0073] 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) ray. The emitting unit (171) may include a light-emitting element for outputting light corresponding to infrared, visible light, and ultraviolet, respectively.

[0074] 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.

[0075] 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.

[0076] 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).

[0077] 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.

[0078] For example, the processor (110) outputs light of a specific wavelength to the user's skin, and when the output light is reflected by the user's skin or blood vessels and received, the received light can be analyzed to obtain information about a specific substance (or specific component) within the body's skin or blood vessels.

[0079] 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.

[0080] 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.

[0081] For example, the processor (110) outputs light of a specific wavelength to the user's skin, and when the output light is reflected by the user's skin or blood vessels and 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.

[0082] 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.

[0083] 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.

[0084] FIG. 3 is a perspective view of the front of a wearable device according to an embodiment of the present disclosure.

[0085] Referring to FIGS. 3 and 4, a wearable device (200) according to an embodiment (e.g., the wearable device (100) of FIGS. 1 and 2) 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.

[0086] 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.

[0087] 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.

[0088] 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).

[0089] 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.

[0090] 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.

[0091] 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.

[0092] 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).

[0093] 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.

[0094] FIG. 5 is an exploded perspective view of an electronic device according to an embodiment of the present disclosure.

[0095] Referring to FIG. 5, a wearable device (300) (e.g., the wearable device (100) of FIG. 1 and 2, or the wearable device (200) of FIG. 3 and 4) comprises a side bezel structure (310) (e.g., the side bezel structure (206) of FIG. 3 and 4), 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). It may be possible. 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. The support member (360) may have a display (220) attached to one side and a printed circuit board (380) attached to the other side. A printed circuit board (380) may be equipped with a processor (e.g., processor (110) of FIG. 2), memory (e.g., memory (120) of FIG. 2), and / or an interface. The processor may include, for example, one or more of a central processing unit, a graphics processing unit (GPU), an application processor, a sensor processor, or a communication processor.

[0096] 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.

[0097] 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).

[0098] 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).

[0099] 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.

[0100] 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.

[0101] FIG. 6 illustrates a sensor according to an embodiment of the present disclosure.

[0102] Referring to FIG. 6, according to an embodiment, the sensor (170) may include a light-emitting part (171) and a light-receiving part (172).

[0103] According to an embodiment, the light-emitting unit (171) may include a first light source and a second light source.

[0104] 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 oblique angle to minimize specular reflection. For example, the first light source and the second light source may each be positioned at an angle relative to the normal to the surface of the skin, and the angle of inclination may be adjusted to reduce specular reflection and ensure light transmission efficiency to the skin. According to an embodiment, the first light source may be configured to irradiate light of a wavelength band corresponding to UV rays (e.g., a first wavelength band), and the second light source may be configured to irradiate light of a wavelength band corresponding to visible light (e.g., a second wavelength band).

[0105] 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 the wavelength band of 100 to 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.

[0106] For example, the second light source may be configured to irradiate light of a second wavelength band corresponding to visible light. Specifically, the 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.

[0107] 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.

[0108] According to the embodiment, the light receiving unit (172) can measure the intensity of the incident light.

[0109] 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.

[0110] As an example, the light receiving unit (172) may include a photodiode that converts light energy into electrical energy.

[0111] 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.

[0112] According to an embodiment, the wearable device (100) can measure the final glycation product using autofluorescence.

[0113] 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.

[0114] 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] (the B (blue) wavelength band of visible light).

[0115] According to an embodiment, the wearable device (100) uses a light-emitting part 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.

[0116] According to an embodiment, the wearable device (100) detects a fluorescent signal using a light receiving unit, and the intensity of the fluorescent signal may be proportional to the concentration of the final glycation product in the skin.

[0117] 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.

[0118] FIG. 7 illustrates an electronic device for identifying food consumed by a user according to an embodiment of the present disclosure.

[0119] The food consumed by the user and the concentration of advanced glycation end products are closely related.

[0120] For example, advanced glycation end products can be generated in large quantities during specific cooking processes, and consuming food containing advanced glycation end products can increase the concentration of advanced glycation end products in the user's body.

[0121] For example, cooking methods such as baking, frying, and grilling, which involve cooking at high temperatures, can generate large amounts of advanced glycation end products (AGEs) as sugars, proteins, and lipids react. Additionally, foods high in protein, such as meat; foods high in fat, such as dairy products; or foods containing refined carbohydrates, such as pizza, bread, and pastries, can generate large amounts of AGEs when cooked at high temperatures.

[0122] According to an embodiment, the electronic device (10) can identify the food consumed by the user to manage the user's final glycation product and acquire food-related data.

[0123] For example, the electronic device (10) can identify the food consumed by the user and obtain food-related data such as calories, serving size, protein, fat, and carbohydrates. According to an embodiment, the electronic device (10) can obtain a meal score for the food consumed by the user based on the food-related data and provide a recommendation message to guide the user's healthy eating habits. Additionally, the electronic device (10) can use a communication circuit (not shown) to provide a recommendation message to guide the user's eating habits to lower the concentration of the user's end-glycated products based on the end-glycated product data received from the wearable device (100) if the user's end-glycated product concentration is in a dangerous range (or high-risk range).

[0124] According to an embodiment, the electronic device (10) includes a camera and can identify food (1) included in an image received from the camera. For example, the electronic device (10) can input an image received from the camera into an artificial intelligence model to recognize food (1) included in the image and obtain food-related data.

[0125] For example, a user of the electronic device (10) can photograph the food (1) using a camera provided in the electronic device (10) before consuming the food (1), and the electronic device (10) can obtain an image corresponding to the food (1).

[0126] According to the embodiment, the artificial intelligence model can be implemented in various ways, such as a CNN (convolutional neural network)-based artificial intelligence model trained to recognize food (1) by analyzing the spatial pattern of an image, or a ViT (vision transformer) or GAN (generative adversarial network)-based artificial intelligence model.

[0127] According to an embodiment, when an image is input, the artificial intelligence model can identify the food (1) included in the image and output food-related data based on the food (1). A detailed description of the food-related data will be provided later with reference to FIG. 8.

[0128] However, the electronic device (10) may identify the food (1) based on an image containing a barcode corresponding to the food (1) (e.g., a barcode printed on a package packaging the food (1)), or may identify the food (1) based on user input in which the user directly inputs the food (1) consumed by the user.

[0129] FIG. 8 illustrates food-related data (B) identified by an electronic device according to an embodiment of the present disclosure.

[0130] Referring to FIG. 8, the electronic device (10) can obtain food-related data (B) based on an image containing food (1) (or an image of food (1) taken). As described above, the electronic device (10) can obtain food-related data (B) by inputting an image received from a camera (e.g., an image containing food (1)) into an artificial intelligence model (1000).

[0131] According to an embodiment, food-related data (B) may include the name of the food, cooking method (e.g., various cooking methods such as baking, frying, boiling, and smoking), nutrition information, serving size, and allergens (or ingredients).

[0132] For example, nutritional information may include values ​​(or amounts) corresponding to each of the expected intake amount, expected calories, protein, fat (including trans fat and saturated fat), carbohydrates (including sugars), vitamins, and minerals of the food (1) that the user intends to consume.

[0133] However, it is not limited thereto, and the electronic device (10) may also obtain food-related data (B) based on barcode information if the image received from the camera includes barcode information. For example, the electronic device (10) may identify a product (i.e., food (1)) corresponding to the barcode information by linking with a database. The database may include barcode information corresponding to each of the various products.

[0134] Additionally, the electronic device (10) can identify products based on RFID (radio frequency identification). For example, the electronic device (10) can identify a product (i.e., food (1)) by communicating with an RFID tag provided on a package that packages the product.

[0135] According to an embodiment, the electronic device (10) can identify food-related data (B) corresponding to the identified food (1). For example, the electronic device (10) can search for the identified food (1) in a database to obtain nutrition facts (A) and obtain food-related data (B) based on the nutrition facts (A). According to an embodiment, the database may include nutrition facts corresponding to each of the various foods.

[0136] According to the embodiment, the database may be stored on an external server or on an electronic device (10).

[0137] According to an embodiment, the electronic device (10) may include a nutritional information table (A) in an image received from a camera, and the electronic device (10) may obtain food-related data (B) based on the nutritional information table (A) included in the image.

[0138] FIG. 9 illustrates food-related data (B) according to the food consumed on a daily basis according to an embodiment of the present disclosure.

[0139] Referring to FIG. 9, the electronic device (10) can identify the food (1) consumed by the user through various methods as described in FIG. 7 and FIG. 8, and obtain food-related data (B). For example, the electronic device (10) can obtain the content of each of the total calories, protein, fat, carbohydrates, or minerals consumed by the user on a daily basis.

[0140] Additionally, the electronic device (10) can obtain the average of the concentrations (C) of multiple final glycation products measured by the wearable device (100) over a day as a representative concentration based on the final glycation product data received from the wearable device (100) using a communication circuit (not shown).

[0141] According to an embodiment, the electronic device (10) can identify the correlation between food-related data (B) and final glycation product data.

[0142] For example, the electronic device (10) inputs the final glycation product data received from the wearable device (100) and the food-related data (B) identified by the electronic device (10) into the artificial intelligence model (1000), and the artificial intelligence model (1000) can identify the correlation between the food-related data (B) and the final glycation product data.

[0143] For example, the artificial intelligence model (1000) may be a model trained to calculate a low meal score for the food consumed by the user when it is identified that the user has consumed a large amount of food (1) prepared using cooking methods such as baking, frying, or grilling, which are cooked at high temperatures, such as meat, dairy products, or refined carbohydrates containing large amounts of sugars, based on food-related data (B).

[0144] Additionally, the artificial intelligence model (1000) may be a model trained to calculate a high meal score for the food consumed by the user when it is identified that the user has consumed a large amount of anthocyanin-containing berry fruits, omega-3 fatty acids-containing nuts, vitamins-containing vegetables, and ingredients containing antioxidants and anti-inflammatory components based on food-related data (B).

[0145] According to an embodiment, the artificial intelligence model (1000) may be a model trained to identify the correlation between the food consumed by the user (or the nutritional components consumed by the user (e.g., carbohydrates, fats, proteins)) and the final glycation product data obtained by the wearable device (100) based on food-related data (B).

[0146] For example, the artificial intelligence model (1000) may be a model trained to identify a high correlation between the food consumed by the user and the concentration of the user's end-glycation product (C) when the daily meal score is low and the concentration of the user's end-glycation product (C) is identified as high depending on the food consumed by the user.

[0147] As another example, the artificial intelligence model (1000) may be a model trained to identify a low correlation when the daily meal score is a low value and the concentration (C) of the final glycation product is identified as low.

[0148] Depending on the embodiment, blood sugar control ability and insulin resistance may vary from person to person. For example, if the user has diabetes, the change in the concentration (C) of advanced glycation end products according to the food consumed by the user may be more closely related.

[0149] In addition, since antioxidant capacity, genetic factors, and lifestyle habits (e.g., activity level, stress index) vary from person to person, even if the food consumed by the user contains large amounts of fat and protein and is meat cooked at high temperatures, the change in the concentration (C) of the advanced glycation end products of the user who consumed the meat may not be significant.

[0150] According to an embodiment, the artificial intelligence model (1000) can identify the correlation between food-related data (B) reflecting the user's blood sugar control ability, insulin resistance, and genetic factors, and the final glycation product data obtained by the wearable device (100).

[0151] In addition, the artificial intelligence model (1000) can identify the user's status information based on the final glycation product data.

[0152] For example, the artificial intelligence model (1000) can identify which of the multiple ranges the user's final saccharification product belongs to based on the final saccharification product data, and identify state information corresponding to the range to which the final saccharification product belongs.

[0153] For example, the artificial intelligence model (1000) can identify the status information as low risk if the user's final glycation product falls within a first range of less than 50. Additionally, the artificial intelligence model (1000) can identify the status information as moderate risk if the user's final glycation product falls within a second range of 50 or more and less than 100. As an example, the artificial intelligence model (1000) can identify the status information as high risk if the user's final glycation product falls within a third range of 100 or more and less than 150. Additionally, the artificial intelligence model (1000) can identify the status information as very high risk if the user's final glycation product falls within a fourth range of 150 or more.

[0154] According to an embodiment, the electronic device (10) can process final glycation product data according to a predefined operation rule or an artificial intelligence model (1000) and provide a recommendation message. The predefined operation rule or artificial intelligence model (1000) is characterized by being created through learning.

[0155] Here, being created through learning means that a predefined operation rule or artificial intelligence model (1000) of the desired characteristics 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.

[0156] An artificial intelligence model (1000) 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 a convolutional neural network (CNN), a deep neural network (DNN), a recurrent neural network (RNN), a restricted Boltzmann machine (RBM), a deep belief network (DBN), a bidirectional recurrent deep neural network (BRDNN), deep Q-networks, and a Transformer, and the neural networks in this disclosure are not limited to the examples described above except where specified.

[0157] A learning algorithm is a method of training a specific target device (e.g., a robot) using a number of learning data so that the specific target device can 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. A more detailed description of the artificial intelligence model (1000) will be provided with reference to FIG. 10.

[0158] FIG. 10 illustrates an artificial intelligence model for obtaining a recommendation message according to an embodiment of the present disclosure.

[0159] Referring to FIG. 10, the electronic device (10) can input final glycation product data received from a wearable device (100) into an artificial intelligence model (1000) using food-related data and a communication circuit (not shown).

[0160] According to an embodiment, FIG. 10 assumes an embodiment in which an electronic device (10) inputs food-related data and final glycation product data into a single artificial intelligence model (1000), but it is understood that this is not limited thereto. For example, the electronic device (10) may input an image into a first artificial intelligence model to obtain food-related data, and input the food-related data into a second artificial intelligence model to obtain nutritional components such as the user's intake of calories, fat, and carbohydrates. According to an embodiment, the electronic device (10) may input the nutritional components and the user's final glycation product data into a third artificial intelligence model to obtain a recommendation message (D). However, the first to third artificial intelligence models are examples for convenience of explanation, and the electronic device (10) may use multiple artificial intelligence models for different purposes to obtain the recommendation message (D).

[0161] According to an embodiment, the artificial intelligence model (1000) can identify a correlation between food-related data based on the food consumed by the user and the concentration of the user's final glycation product as described above.

[0162] According to an embodiment, the artificial intelligence model (1000) may be a model trained to generate a recommendation message (D) that guides the user's meal based on correlations and final glycation product data.

[0163] For example, if the artificial intelligence model (1000) has a large change in the concentration of the user’s end-glycation products depending on the food consumed by the user, it can identify a high correlation and, for users with high end-glycation product concentrations, it is necessary to guide them to eat appropriate meals to lower the end-glycation product concentration, so it can generate and output a recommendation message (D) to guide the meal.

[0164] For example, if an artificial intelligence model (1000) identifies a correlation of 0.7 or higher, it can generate and output a recommendation message (D) that guides a meal to a user whose final glycation product concentration is measured in the third or fourth range among multiple ranges (e.g., please reduce meat intake and increase fruit and vegetable intake).

[0165] According to the embodiment, 0.7 is an example for convenience of explanation and is not limited thereto. For example, an artificial intelligence model (1000) can be trained to generate a recommendation message (D) that guides a user's meal when a correlation greater than a threshold value (e.g., 0.5) is identified based on the learning results.

[0166] For example, if the change in the concentration of the user's end-glycation products is small depending on the food consumed by the user, the artificial intelligence model (1000) may identify a low correlation and not generate a recommendation message (D) to guide the meal.

[0167] For example, if the artificial intelligence model (1000) identifies a correlation of less than 0.3, the change in the concentration of the final glycation product according to the food consumed by the user is small, so the artificial intelligence model (1000) can provide a notification according to which of the multiple ranges the concentration of the final glycation product falls into, rather than a recommendation message (D) that guides the meal.

[0168] For example, if an artificial intelligence model (1000) identifies a correlation of less than 0.3, it can identify which of the multiple ranges the concentration of the user's final glycation product falls into based on the final glycation product data, without considering the food consumed by the user.

[0169] According to an embodiment, if the concentration of the final glycation product falls within the third range of high risk or the fourth range of very high risk among a plurality of ranges, the artificial intelligence model (1000) may generate and output a visual effect indicating that the user's final glycation product concentration is dangerous (e.g., a visual effect including a warning text on a red background (e.g., the concentration of the final glycation product is too high) because the concentration of the final glycation product is high). For example, the artificial intelligence model (1000) generates a layout of the recommendation message (D) (e.g., font size, font color, background color of the recommendation message) along with the recommendation message (D), and the electronic device (10) may provide (or display) the recommendation message (D) using a display (11) based on the layout.

[0170] According to the embodiment, 0.3 is an example for convenience of explanation and is not limited thereto. For example, if an artificial intelligence model (1000) identifies a correlation below a threshold value based on the learning result, it can identify which of the multiple ranges the concentration of the final glycation product belongs to based on the final glycation product data.

[0171] According to an embodiment, when the electronic device (10) receives biometric data from the wearable device (100), it can input food-related data, final glycation product data, and biometric data into an artificial intelligence model (1000).

[0172] According to an embodiment, the wearable device (100) can acquire biometric data such as heart rate indicating 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), the user's body temperature, and activity level (e.g., number of steps, distance traveled, calories burned).

[0173] According to an embodiment, the wearable device (100) transmits biometric data to the electronic device (10) using a communication circuit (160), and the electronic device (10) can obtain state information based on the biometric data and the final glycation product data. Additionally, the electronic device (10) inputs the biometric data and the final glycation product data into an artificial intelligence model (1000), and the artificial intelligence model (1000) can be trained to output a recommendation message (D) that guides 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 based on the biometric data and the final glycation product data.

[0174] For example, the artificial intelligence model (1000) can acquire and output user status information based on biological data and end-glycation product data. According to an embodiment, the status information may include the user's health condition, such as high fatigue, high stress, or very poor concentration of end-glycation products.

[0175] For example, if the final glycation product is included in the first section among a plurality of sections, the electronic device (10) identifies the user's health risk due to the final glycation product as low and can provide a notification (e.g., maintain your current diet) based on the low health risk.

[0176] For example, if the electronic device (10) is included in the fourth section among the multiple sections, it can identify the user's health risk due to the final glycation product as very high risk and provide a notification (e.g., improve your diet immediately. Consume foods rich in antioxidants) according to the very high health risk.

[0177] FIG. 11 illustrates an electronic device that provides a recommendation message according to an embodiment of the present disclosure.

[0178] Referring to FIG. 11, the artificial intelligence model may generate and output a recommendation message (D) in text form, or may generate and output a recommendation message (D) in graphic form.

[0179] For example, the device (100) can visually display where the concentration (C) of the user's final glycation product corresponds to between low and high based on the final glycation product data received from the wearable device (100).

[0180] According to an embodiment, the electronic device (10) can identify the food consumed by the user in various ways, such as identifying the food consumed by the user based on objects (e.g., food, barcode) included in an image, or identifying the food consumed by the user based on the user's input.

[0181] According to an embodiment, the electronic device (10) can calculate a meal score (E) for food consumed by the user based on food-related data and can visually display the meal score (E).

[0182] For example, an electronic device (10) inputs food-related data into an artificial intelligence model, and the artificial intelligence model is trained to output a low meal score (E) when it is identified that a food containing a large amount of end-glycation products has been consumed based on the ingredients constituting the food (e.g., meat, dairy, vegetables) and the cooking method of the food (e.g., baking, frying, boiling, smoking) and the nutritional facts of the food included in the food-related data, and can be trained to output a high meal score (E) when it is identified that a food containing a small amount of end-glycation products has been consumed.

[0183] Referring to FIG. 11, the electronic device (10) can provide a food score (E) for the food consumed by the user (e.g., predicted levels of AGEs according to daily food intake in FIG. 11) along with the concentration (C) of the user's end-glycation products.

[0184] According to an embodiment, if the correlation between the final glycation product predicted based on the food consumed by the user and the user's final glycation product is greater than or equal to a threshold value, the electronic device (10) can provide a recommendation message (D) in a graphic form to lower the user's final glycation product.

[0185] For example, the artificial intelligence model generates a graphical recommendation message (D) that guides reducing the intake of sugar and fat containing high levels of advanced glycation products when heated at high temperatures, and increasing the intake of amino acids and antioxidants that lower the concentration (C) of advanced glycation products in the body, and the electronic device (10) can display the recommendation message (D) using a display (11).

[0186] According to an embodiment, the artificial intelligence model may generate a graphical recommendation message (D) that guides the cooking of food using moist heat cooking methods at low temperatures, such as steaming and boiling, because dry heat cooking methods at high temperatures, such as frying, baking, and grilling, generate a large amount of final saccharification products.

[0187] According to the embodiment, the recommendation message (D) output by the electronic device (10) can be in various forms.

[0188] FIG. 12 illustrates an electronic device that provides a recommendation message guiding a meal according to an embodiment of the present disclosure.

[0189] Referring to FIG. 12, the electronic device (10) can provide a recommendation message (D) to improve the user's health condition using an artificial intelligence model. For example, the electronic device (10) can obtain a recommendation message (D) that guides the user's diet to lower the concentration of end-glycation products based on end-glycation product data.

[0190] For example, depending on individual metabolic status, health status, and genetic characteristics, the degree to which advanced glycation end products are produced in the body may vary from person to person even when consuming the same food.

[0191] According to an embodiment, the electronic device (10) can identify the trend of change in the concentration of the final glycation product received from the wearable device (100), the degree to which the final glycation product is generated in the body based on the food consumed by the user, or the sensitivity of the change in the concentration of the final glycation product in the body based on the food consumed by the user.

[0192] For example, the electronic device (10) inputs food-related data and end-glycation product data into an artificial intelligence model, and the artificial intelligence model can identify correlations based on changes in the concentration of end-glycation products in the user's body caused by the food consumed by the user.

[0193] According to an embodiment, if the correlation is above a threshold value, the electronic device (10) can obtain a recommendation message (D) to guide the user's diet using an artificial intelligence model, as the sensitivity to changes in the concentration of advanced glycation end products in the body due to food consumed by the user is high according to the user's personal characteristics (e.g., genetic characteristics, metabolic state, health state).

[0194] For example, the electronic device (10) inputs food-related data into an artificial intelligence model, and if the artificial intelligence model identifies that refined carbohydrates, fats, and proteins have been consumed through food cooked using a cooking method that cooks ingredients at high temperatures based on the food-related data, it can provide a recommendation message (D) to guide the user's diet (e.g., reduce carbohydrate intake, reduce meat intake, fried foods are not good, mainly eat fresh foods) because the user has consumed food that generates a large amount of end-products of glycation.

[0195] According to an embodiment, the electronic device (10) may provide a recommendation message (D) in text form as shown in FIG. 12. However, this is an example for convenience of explanation and is not limited thereto. For example, the electronic device (10) may provide a recommendation message (D) in graphic form as shown in FIG. 11, or may provide a recommendation message (D) by outputting it in sound form.

[0196] FIG. 13 illustrates an electronic device that provides notification of an allergenic component according to an embodiment of the present disclosure.

[0197] Referring to FIG. 13, the electronic device (10) can obtain user profile information based on user input. For example, the electronic device (10) may store profile information including information about allergy-causing ingredients for the user. According to an embodiment, the profile information may include personal information about the user's health status, such as height, weight, presence of disease (e.g., diabetes), and medications taken, in addition to information about allergy-causing ingredients.

[0198] According to an embodiment, the electronic device (10) identifies food and can identify whether food-related data based on the identified food contains ingredients that cause allergies to the user.

[0199] For example, an electronic device (10) can identify food and identify the components (e.g., ingredients) that make up the identified food.

[0200] According to an embodiment, the electronic device (10) can identify whether the identified food contains ingredients that cause allergies to the user by comparing food-related data containing information about the ingredients constituting the food with profile information containing information about allergy-causing ingredients. For example, based on nutrition facts (A), if the ingredients constituting the food contain ingredients that cause allergies to the user, the electronic device (10) can provide a notification (F) to warn the user of consumption.

[0201] For example, the electronic device (10) identifies a pizza based on an image, and if an allergenic ingredient (e.g., cheese) included in the profile information is identified among the various ingredients (e.g., flour, cheese, tomato) that make up the pizza, it can provide a notification (F) to warn the user of consumption.

[0202] FIG. 14 is a flowchart illustrating a method of operation of an electronic device according to an embodiment of the present disclosure.

[0203] In the following embodiments, each operation may be performed sequentially, but is not necessarily performed sequentially. For example, the order of each operation may be changed, and at least two operations may be performed in parallel.

[0204] According to one embodiment, S1410 to S1430 can be understood as being performed in a processor (not shown) of an electronic device (e.g., electronic device (10) of FIG. 1).

[0205] According to an embodiment, the method of operating the electronic device receives advanced glycation end products (AGEs) data from a wearable device using a communication circuit (not shown) of the electronic device (10) (S1410).

[0206] Data related to the food consumed by the user is obtained according to the embodiment (S1420).

[0207] According to the embodiment, final glycation product data and food-related data are input into an artificial intelligence model to provide a recommendation message generated by the artificial intelligence model (S1430).

[0208] According to an embodiment, an artificial intelligence model can be trained to identify the correlation between food-related data and end-glycation product data, identify user status information based on end-glycation product data, and generate recommendation messages to guide the user's meal based on the correlation and status information.

[0209] According to the embodiment, the operation S1420 for acquiring food-related data may include the operation of acquiring food-related data by inputting an image received from a camera into an artificial intelligence model.

[0210] According to an embodiment, an artificial intelligence model is trained to recognize food included in an image and acquire food-related data, and the food-related data may include at least one of the intake amount, calories, content of carbohydrates, fat, protein, or minerals, or the cooking method of the food.

[0211] According to an embodiment, the operation S1420 for acquiring food-related data may include, if an image received from a camera includes barcode information, an operation for acquiring food-related data based on barcode information, and if the image includes nutrition facts, an operation for acquiring food-related data based on nutrition facts.

[0212] According to an embodiment, the method of operation may include identifying whether food contains allergy-causing ingredients based on food-related data and user profile information, and providing a notification when allergy-causing ingredients are identified in the food.

[0213] According to an embodiment, an artificial intelligence model can be trained to identify correlations by identifying changes in the user's end-glycation products based on the food consumed by the user, based on food-related data and end-glycation product data.

[0214] According to an embodiment, the operation S1430 for providing a recommendation message includes, when a user's biometric data is received from a wearable device using a communication circuit (not shown), inputting food-related data, end-glycation product data, and biometric data into an artificial intelligence model, wherein the artificial intelligence model is trained to acquire state information based on the biometric data and end-glycation product data, and the biometric data may include at least one of heart rate, stress index, sleep information, or activity level.

[0215] According to an embodiment, the artificial intelligence model can be trained to obtain a meal score for food consumed by the user based on food-related data, and to generate a recommendation message that guides the user's meal if the meal score is below a threshold score.

[0216] According to an embodiment, an artificial intelligence model can be trained to generate a recommendation message that guides a user's meal if the correlation is greater than or equal to a threshold value.

[0217] According to an embodiment, the artificial intelligence model can be trained to identify a range containing final saccharification product data among a plurality of ranges when the correlation is below a threshold value, generate a first recommendation message based on first state information corresponding to the first range when the final saccharification product data is included in a first range among the plurality of ranges, and generate a second recommendation message based on second state information corresponding to the second range when the final saccharification product data is included in a second range among the plurality of ranges.

[0218] According to an embodiment, an electronic device (e.g., the electronic device (10) of FIG. 1) may be configured such that, when the instructions are executed individually or collectively by the at least one processor, the electronic device receives Advanced Glycation End Products (AGEs) data from a user from a wearable device, obtains data related to food consumed by the user, inputs the Advanced Glycation End Products data and the food related data into an artificial intelligence model, and provides a recommendation message generated by the artificial intelligence model.

[0219] According to an embodiment, the artificial intelligence model may be trained to identify the correlation between the food-related data and the final glycation product data, identify the user's state information based on the final glycation product data, and generate the recommendation message that guides the user's meal based on the correlation and the state information.

[0220] According to an embodiment, the electronic device further includes a camera, and the instructions may be configured to cause the electronic device to input an image received from the camera into the artificial intelligence model to obtain the food-related data.

[0221] According to an embodiment, the artificial intelligence model can be trained to recognize food included in the image and acquire food-related data.

[0222] According to an embodiment, the food-related data may include at least one of the intake amount, calories, the content of carbohydrates, fats, proteins, or minerals, or the cooking method of the food.

[0223] According to an embodiment, the electronic device further includes a camera, and the instructions may be configured such that the electronic device obtains the food-related data based on the barcode information if the image received from the camera includes barcode information, and obtains the food-related data based on the nutrition facts if the image includes nutrition facts.

[0224] According to an embodiment, the instructions may be configured to enable the electronic device to identify whether the food contains an allergy-causing ingredient based on the food-related data and the user's profile information, and to provide a notification if the allergy-causing ingredient is identified in the food.

[0225] According to an embodiment, the artificial intelligence model can be trained to identify the correlation by identifying the change in the user's end glycation product according to the food consumed by the user, based on the food-related data and the end glycation product data.

[0226] 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 food-related data, the final glycation product data, and the biometric data into the artificial intelligence model.

[0227] According to an embodiment, the artificial intelligence model is trained to acquire state information based on the biodata and the end-glycation product data, and the biodata may include at least one of heart rate, stress index, sleep information, or activity level.

[0228] According to an embodiment, the artificial intelligence model may be trained to obtain a meal score for the food consumed by the user based on the food-related data, and to generate a recommendation message that guides the user's meal if the meal score is below a threshold score.

[0229] According to an embodiment, the artificial intelligence model can be trained to generate the recommendation message that guides the user's meal if the correlation is greater than or equal to a threshold value.

[0230] According to an embodiment, the artificial intelligence model may be trained to identify a range among a plurality of ranges in which the final saccharification product data is included when the correlation is less than the threshold value, generate a first recommendation message based on first state information corresponding to the first range when the final saccharification product data is included in a first range among the plurality of ranges, and generate a second recommendation message based on second state information corresponding to the second range when the final saccharification product data is included in a second range among the plurality of ranges.

[0231] According to an embodiment, the instructions may be configured to allow the electronic device to provide the recommendation message in either a graphic form or a text form.

[0232] According to an embodiment, the method of operation of an electronic device includes receiving Advanced Glycation End Products (AGEs) data of a user from a wearable device, acquiring data related to food consumed by the user, and inputting the Advanced Glycation End Products data and the food related data into an artificial intelligence model to provide a recommendation message generated by the artificial intelligence model, wherein the artificial intelligence model may be trained to identify the correlation between the food related data and the Advanced Glycation End Products data, identify the user's state information based on the Advanced Glycation End Products data, and generate the recommendation message that guides the user's meal based on the correlation and the state information.

[0233] According to an embodiment, the operation of acquiring food-related data includes the operation of acquiring food-related data by inputting an image received from a camera into an artificial intelligence model, and the artificial intelligence model is trained to acquire food-related data by recognizing food included in the image, and the food-related data may include at least one of the intake amount, calories, content of carbohydrates, fat, protein, or minerals, or the cooking method of the food.

[0234] According to an embodiment, the operation of acquiring the food-related data may include, if the image received from the camera includes barcode information, the operation of acquiring the food-related data based on the barcode information, and if the image includes nutrition facts, the operation of acquiring the food-related data based on the nutrition facts.

[0235] According to an embodiment, the method of operation may include identifying whether the food contains an allergy-causing component based on the food-related data and the user's profile information, and providing a notification when the allergy-causing component is identified in the food.

[0236] According to an embodiment, the artificial intelligence model can be trained to identify the correlation by identifying the change in the user's end glycation product according to the food consumed by the user, based on the food-related data and the end glycation product data.

[0237] According to an embodiment, the operation of providing the recommendation message includes, when the user's biometric data is received from the wearable device, inputting the food-related data, the final glycation product data, and the biometric data into the artificial intelligence model, wherein the artificial intelligence model is trained to acquire the state information based on the biometric data and the final glycation product data, and the biometric data may include at least one of heart rate, stress index, sleep information, or activity level.

[0238] According to an embodiment, the artificial intelligence model may be trained to obtain a meal score for the food consumed by the user based on the food-related data, and to generate a recommendation message that guides the user's meal if the meal score is below a threshold score.

[0239] According to an embodiment, the artificial intelligence model can be trained to generate the recommendation message that guides the user's meal if the correlation is greater than or equal to a threshold value.

[0240] According to an embodiment, the artificial intelligence model may be trained to identify a range among a plurality of ranges in which the final saccharification product data is included when the correlation is less than the threshold value, generate a first recommendation message based on first state information corresponding to the first range when the final saccharification product data is included in a first range among the plurality of ranges, and generate a second recommendation message based on second state information corresponding to the second range when the final saccharification product data is included in a second range among the plurality of ranges.

[0241] 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.

[0242] 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.

[0243] 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.

[0244] 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.

[0245] 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, Receiving user's Advanced Glycation End Products (AGEs) data (C) from a wearable device, Obtain food-related data (B) consumed by the above user, and The above-mentioned final glycation product data (C) and the above-mentioned food-related data (B) are input into an artificial intelligence model (1000) to provide a recommendation message (D) generated by the artificial intelligence model (1000). The above artificial intelligence model (1000) is, An electronic device trained to identify the correlation between the above food-related data (B) and the above final glycation product data (C), identify the user's state information based on the above final glycation product data (C), and generate the above recommendation message (D) that guides the user's meal based on the correlation and the above state information.

2. In Paragraph 1, Including a camera; further The above instructions cause the electronic device, The image received from the camera is input into the artificial intelligence model (1000) to obtain the food-related data (B), The above artificial intelligence model (1000) is, It is trained to recognize food included in the above image and acquire the above food-related data (B), and The above food-related data (B) is, An electronic device comprising at least one of the intake amount, calorie, carbohydrate, fat, protein, or mineral content, or the cooking method of the food.

3. In Paragraph 1 or 2, Including a camera; further The above instructions cause the electronic device, If the image received from the camera includes barcode information, the food-related data (B) is obtained based on the barcode information, and An electronic device configured to obtain food-related data (B) based on the nutrition facts (A) when the image above includes a nutrition facts table (A).

4. In Paragraph 2, The above instructions cause the electronic device, Based on the above food-related data (B) and the user's profile information, it is determined whether the food contains allergy-causing ingredients, and An electronic device configured to provide a notification when the allergenic component in the above food is identified.

5. In Paragraph 1 or 2, The above artificial intelligence model (1000) is, An electronic device trained to identify the correlation by identifying the change in the user's end glycation product according to the food consumed by the user based on the above food-related data (B) and the above end glycation product data (C).

6. In Paragraph 5, The above instructions cause the electronic device, When the user's biometric data is received from the wearable device, the food-related data (B), the final glycation product data (C), and the biometric data are configured to be input into the artificial intelligence model (1000). The above artificial intelligence model (1000) is, It is trained to acquire the state information based on the above biological data and the above final glycation product data (C), and The above biometric data is, An electronic device comprising at least one of heart rate, stress index, sleep information, or activity level.

7. In Paragraph 1 or 2, The above artificial intelligence model (1000) is, Based on the above food-related data (B), a meal score (E) for the food consumed by the user is obtained, and An electronic device trained to generate the recommendation message (D) that guides the user's meal when the above meal score (E) is below a threshold score.

8. In Paragraph 1 or 2, The above artificial intelligence model (1000) is, An electronic device trained to generate the recommendation message (D) that guides the user's meal when the above correlation is greater than or equal to a threshold value.

9. In Paragraph 8, The above artificial intelligence model (1000) is, If the above correlation is less than the above threshold value, identify the range among the plurality of ranges that includes the final saccharification product data (C), and If the above final saccharification product data (C) is included in the first range among the plurality of ranges, a first recommendation message (D) is generated based on the first state information corresponding to the first range, and An electronic device trained to generate a second recommendation message (D) based on second state information corresponding to the second range when the final saccharification product data (C) is included in the second range among the plurality of ranges.

10. In Paragraph 1 or 2, The above instructions cause the electronic device, An electronic device configured to provide the above recommendation message (D) in either a graphic form or a text form.

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; The operation of obtaining data related to food consumed by the above user; and The operation of inputting the above-mentioned final glycation product data and the above-mentioned food-related data into an artificial intelligence model and providing a recommendation message generated by the artificial intelligence model; The above artificial intelligence model is, A method of operation trained to identify the correlation between the above food-related data and the above final glycation product data, identify the user's state information based on the above final glycation product data, and generate the above recommendation message that guides the user's meal based on the correlation and the above state information.

12. In Paragraph 11, The operation of acquiring the above food-related data is, The operation of acquiring food-related data by inputting an image received from a camera into the artificial intelligence model; The above artificial intelligence model is, It is trained to recognize food included in the above image and acquire food-related data, The above food-related data is, A method of operation comprising at least one of the intake amount, the content of calories, carbohydrates, fats, proteins, or minerals, or the cooking method of the food.

13. In Paragraph 11 or 12, The operation of acquiring the above food-related data is, If the image received from the camera includes barcode information, the operation of acquiring the food-related data based on the barcode information; and A method of operation comprising: acquiring food-related data based on nutrition facts when the above image includes nutrition facts.

14. In Paragraph 12, An operation to identify whether the food contains allergy-causing ingredients based on the above food-related data and the user's profile information; and A method of operation comprising: providing a notification when the allergenic component is identified in the above food.

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, Receives the user's Advanced Glycation End Products (AGEs) data from a wearable device, and Acquire data related to the food consumed by the above user, and It is configured to input the above-mentioned final glycation product data and the above-mentioned food-related data into an artificial intelligence model to provide recommendation messages generated by the artificial intelligence model, and The above artificial intelligence model is, A storage medium trained to identify the correlation between the above food-related data and the above final glycation product data, identify the user's state information based on the above final glycation product data, and generate the above recommendation message that guides the user's meal based on the above correlation and the above state information.

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