Wearable electronic device and method for generating user's food intake information by using same

Abstract

According to an embodiment of the present disclosure, a wearable electronic device may comprise: a plurality of sensors for acquiring user activity data; a communication module including at least one communication circuit; a memory for storing instructions; and at least one processor operatively connected to the communication module and the memory. According to an embodiment of the present disclosure, the instructions may cause, when executed by the at least one processor, the wearable electronic device to: acquire user activity data from at least one of the plurality of sensors; analyze the user activity data for a predetermined time; when it is analyzed that at least one pattern is included in the user activity data, determine a time point at which the user activity data including the at least one pattern is acquired, as a start time point of a user's food intake activity; store the user activity data as food intake data; and generate at least one piece of food intake information of the user on the basis of the food intake data.

Description

Wearable electronic device and method for generating user eating information using the same

[0001] The embodiments of the present disclosure relate to a wearable electronic device and a method for generating user eating information using the same.

[0002] Various electronic devices such as smartphones, tablet PCs, PMPs (portable multimedia players), PDAs (personal digital assistants), laptop personal computers, and / or wearable electronic devices are becoming widespread.

[0003] For example, wearable electronic devices may include a glass type that can be worn on a user's face, a watch type that can be worn on a user's wrist, or a ring type that can be worn on a user's finger.

[0004] A wearable electronic device may be equipped with various sensors (e.g., motion sensors, biosensors) that are worn on a user's body to measure biometric information and / or fitness data, and can determine information about the user's biometric status, whether exercise is in progress, and / or eating activity through data obtained from the sensors.

[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] Tracking and recording daily dietary habits is crucial for managing a user's health. Traditionally, users recorded their eating habits by manually inputting information such as the food consumed or the duration of meals. However, individually recording every food item and tracking eating patterns based on this data is quite cumbersome and requires significant effort, leading to a frequent trend of inaccurate or omitted dietary information. Furthermore, conventional recording methods have the limitation of being unable to provide real-time advice regarding meals to the user during the eating process.

[0007] A wearable electronic device according to various embodiments of the present disclosure aims to provide a method and device capable of determining the start time of a user eating activity through user activity data obtained through a plurality of sensors and generating user eating information.

[0008] According to one embodiment of the present disclosure, a wearable electronic device may include a plurality of sensors for acquiring user activity data.

[0009] According to one embodiment of the present disclosure, a wearable electronic device may include a communication module comprising at least one communication circuit.

[0010] According to one embodiment of the present disclosure, a wearable electronic device may include a memory for storing instructions.

[0011] According to one embodiment of the present disclosure, a wearable electronic device may include at least one processor operatively connected to the communication module and the memory.

[0012] According to one embodiment of the present disclosure, when the instructions are executed by the at least one processor, the wearable electronic device may acquire user activity data from at least one of the plurality of sensors.

[0013] According to one embodiment of the present disclosure, when the instructions are executed by the at least one processor, the wearable electronic device may analyze the user activity data for a predetermined period of time.

[0014] According to one embodiment of the present disclosure, when the instructions are executed by the at least one processor, the wearable electronic device may determine the time of acquiring user activity data containing at least one pattern as the time of starting user eating activity if it is analyzed that the user activity data contains at least one pattern.

[0015] According to one embodiment of the present disclosure, when the instructions are executed by the at least one processor, the wearable electronic device may store the user activity data as eating data and generate at least one user eating information based on the eating data.

[0016] According to one embodiment of the present disclosure, when the instructions are executed by the at least one processor, if it is analyzed that the at least one pattern is not included in the user activity data, the time of acquiring the user activity data that does not include the at least one pattern can be determined as the time of end of the user eating activity.

[0017] According to one embodiment of the present disclosure, when the instructions are executed by the at least one processor, the wearable electronic device can calculate the number of chewings or the chewing speed based on the feeding data stored from the start of the user's feeding activity.

[0018] According to one embodiment of the present disclosure, when the instructions are executed by the at least one processor, the number of works or the speed of works is compared with a reference number of works or a reference speed of works, and if the comparison value is higher than a predetermined ratio, a notification can be provided to the user through an output module.

[0019] According to one embodiment of the present disclosure, a method for generating user eating information may include the operation of obtaining user activity data from at least one of a plurality of sensors.

[0020] According to one embodiment of the present disclosure, a method for generating user eating information may include an operation of analyzing the user activity data for a predetermined period of time.

[0021] According to one embodiment of the present disclosure, a method for generating user eating information may include an operation of determining the time of acquiring user activity data containing at least one pattern as the time of starting user eating activity when it is analyzed that at least one pattern is included in the user activity data.

[0022] According to one embodiment of the present disclosure, a method for generating user eating information may include the operation of storing the user activity data as eating data.

[0023] According to one embodiment of the present disclosure, a method for generating user eating information may include an operation of generating at least one user eating information based on the eating data.

[0024] According to one embodiment of the present disclosure, a method for generating user eating information may further include, when it is determined that the user activity data does not contain the at least one pattern, determining the time of acquiring user activity data that does not contain the at least one pattern as the time of ending the user eating activity.

[0025] According to one embodiment of the present disclosure, a method for generating user eating information may further include an operation of calculating the number of times or the speed of eating based on eating data stored from the start of the user eating activity.

[0026] According to one embodiment of the present disclosure, a method for generating user eating information may further include an operation of comparing the number of times or the speed of times to a reference number of times or the speed of times to a reference speed of times to a reference speed of times to a user, and an operation of providing a notification to the user through an output module when the comparison value is higher than a predetermined ratio.

[0027] A wearable electronic device according to one embodiment of the present disclosure can automatically determine whether a user is engaging in eating activity using user activity data obtained through a plurality of sensors, and by generating eating information of the user, reduce the inconvenience of the user having to manually record meal information and eating habits.

[0028] A wearable electronic device according to one embodiment of the present disclosure can be worn on a user's body and can acquire user activity data precisely and accurately through a plurality of sensors.

[0029] A wearable electronic device according to one embodiment of the present disclosure can contribute to the creation of a user's healthy eating habits by providing a method for automatically generating user eating information with high accuracy.

[0030] The effects obtainable from the present disclosure are not limited to those mentioned above, and other unmentioned effects will be clearly understood by those skilled in the art to which the present disclosure belongs from the description below.

[0031] FIG. 1 is a block diagram of an electronic device in a network environment according to one embodiment of the present disclosure.

[0032] FIG. 2 is a cross-sectional view of a wearable electronic device viewed from the top surface according to one embodiment of the present disclosure.

[0033] FIG. 3 is a cross-sectional view of a wearable electronic device viewed from the front and a user wearing the wearable electronic device according to one embodiment of the present disclosure.

[0034] FIG. 4 is a block diagram illustrating a wearable electronic device and an external electronic device according to one embodiment of the present disclosure.

[0035] FIG. 5 is a flowchart illustrating a method for generating user eating information using user activity data according to one embodiment of the present disclosure.

[0036] FIG. 6 is a graph showing that at least one pattern is included in user activity data according to one embodiment of the present disclosure.

[0037] FIG. 7 is a flowchart illustrating a method for determining the start and end of a user's eating activity according to one embodiment of the present disclosure.

[0038] FIG. 8 is a flowchart illustrating a method for generating user eating information and providing notifications to the user using user activity data according to one embodiment of the present disclosure.

[0039] FIG. 9 is a diagram illustrating the flow of feeding data and user feeding information being transmitted from a wearable electronic device to an external electronic device.

[0040] FIG. 10 is a diagram illustrating the flow in which feeding data is transmitted from a wearable electronic device to an external electronic device, and user feeding information generated from the external electronic device is transmitted to the wearable electronic device.

[0041] FIG. 11 is a flowchart illustrating a method for an external electronic device to generate user feeding information according to one embodiment of the present disclosure.

[0042] FIG. 12 is a drawing illustrating a first UI provided to a user by an external electronic device based on feeding data and user feeding information, according to one embodiment of the present disclosure.

[0043] Hereinafter, embodiments of the present disclosure are described in detail with reference to the drawings so that those skilled in the art can easily practice them. However, the present disclosure may be embodied in various different forms and is not limited to the embodiments described herein. In relation to the description of the drawings, the same or similar reference numerals may be used for identical or similar components. Furthermore, in the drawings and related descriptions, descriptions of well-known functions and configurations may be omitted for clarity and brevity.

[0044] FIG. 1 is a block diagram of an electronic device (101) in a network environment (100) according to one embodiment of the present disclosure.

[0045] Referring to FIG. 1, in a network environment (100), an electronic device (101) may communicate with an electronic device (102) through a first network (198) (e.g., a short-range wireless communication network) or with at least one of an electronic device (104) or a server (108) through a second network (199) (e.g., a long-range wireless communication network). According to one embodiment, the electronic device (101) may communicate with the electronic device (104) through a server (108). According to one embodiment, the electronic device (101) may include a processor (120), memory (130), input module (150), sound output module (155), display module (160), audio module (170), sensor module (176), interface (177), connection terminal (178), haptic module (179), camera module (180), power management module (188), battery (189), communication module (190), subscriber identification module (196), or antenna module (197). In some embodiments, at least one of these components (e.g., connection terminal (178)) may be omitted from the electronic device (101), or one or more other components may be added. In some embodiments, some of these components (e.g., sensor module (176), camera module (180), or antenna module (197)) may be integrated into a single component (e.g., display module (160)).

[0046] The processor (120) can control at least one other component (e.g., a hardware or software component) of the electronic device (101) connected to the processor (120) by executing software (e.g., a program (140)), and can perform various data processing or operations. According to one embodiment, as at least part of the data processing or operations, the processor (120) can store commands or data received from other components (e.g., a sensor module (176) or a communication module (190)) in volatile memory (132), process the commands or data stored in volatile memory (132), and store the resulting data in non-volatile memory (134). According to one embodiment, the processor (120) may include a main processor (121) (e.g., a central processing unit or an application processor) or an auxiliary processor (123) that can operate independently or together with it (e.g., a graphics processing unit, a neural processing unit (NPU), an image signal processor, a sensor hub processor, or a communication processor). For example, if the electronic device (101) includes a main processor (121) and an auxiliary processor (123), the auxiliary processor (123) may be configured to use lower power than the main processor (121) or to be specialized for a designated function. The auxiliary processor (123) may be implemented separately from the main processor (121) or as part thereof.

[0047] The auxiliary processor (123) may control at least some of the functions or states associated with at least one component of the electronic device (101) (e.g., display module (160), sensor module (176), or communication module (190)) on behalf of the main processor (121) while the main processor (121) is in an inactive (e.g., sleep) state, or together with the main processor (121) while the main processor (121) is in an active (e.g., application execution) state. According to one embodiment, the auxiliary processor (123) (e.g., image signal processor or communication processor) may be implemented as part of another functionally related component (e.g., camera module (180) or communication module (190)). According to one embodiment, the auxiliary processor (123) (e.g., neural network processing unit) may include a hardware structure specialized for processing an artificial intelligence model. The artificial intelligence model may be generated through machine learning. Such learning may be performed, for example, on the electronic device (101) itself where the artificial intelligence model is executed, or through a separate server (e.g., server (108)). The learning algorithm may include, for example, supervised learning, unsupervised learning, semi-supervised learning, or reinforcement learning, but is not limited to the examples described above. The artificial intelligence model may include a plurality of artificial neural network layers.An artificial neural network may be a deep neural network (DNN), a convolutional neural network (CNN), a recurrent neural network (RNN), a restricted Boltzmann machine (RBM), a deep belief network (DBN), a bidirectional recurrent deep neural network (BRDNN), a deep Q-network, or a combination of two or more of the above, but is not limited to the examples described above. In addition to the hardware structure, the artificial intelligence model may include a software structure, either additionally or substantially.

[0048] The memory (130) can store various data used by at least one component of the electronic device (101) (e.g., processor (120) or sensor module (176)). The data may include, for example, input data or output data for software (e.g., program (140)) and related commands. The memory (130) may include volatile memory (132) or non-volatile memory (134).

[0049] The program (140) may be stored as software in memory (130) and may include, for example, an operating system (142), middleware (144), or an application (146).

[0050] The input module (150) can receive commands or data to be used for a component of the electronic device (101) (e.g., processor (120)) from outside the electronic device (101) (e.g., user). The input module (150) may include, for example, a microphone, a mouse, a keyboard, a key (e.g., a button), or a digital pen (e.g., a stylus pen).

[0051] The sound output module (155) can output a sound signal to the outside of the electronic device (101). The sound output module (155) may include, for example, a speaker or a receiver. The speaker may be used for general purposes, such as multimedia playback or recording playback. The receiver may be used to receive incoming calls. According to one embodiment, the receiver may be implemented separately from the speaker or as part thereof.

[0052] The display module (160) can visually provide information to an external (e.g., user) of the electronic device (101). The display module (160) may include, for example, a display, a holographic device, or a projector and a control circuit for controlling said device. According to one embodiment, the display module (160) may include a touch sensor configured to detect a touch, or a pressure sensor configured to measure the intensity of the force generated by said touch.

[0053] The audio module (170) can convert sound into an electrical signal or, conversely, convert an electrical signal into sound. According to one embodiment, the audio module (170) can acquire sound through the input module (150) or output sound through the sound output module (155) or an external electronic device (e.g., electronic device (102)) (e.g., speaker or headphones) connected directly or wirelessly to the electronic device (101).

[0054] The sensor module (176) can detect the operating state of the electronic device (101) (e.g., power or temperature) or the external environmental state (e.g., user state) and generate an electrical signal or data value corresponding to the detected state. According to one embodiment, the sensor module (176) may include, for example, a gesture sensor, a gyroscope sensor, a barometric pressure sensor, a magnetic sensor, an accelerometer sensor, a grip sensor, a proximity sensor, a color sensor, an IR (infrared) sensor, a biosensor, a temperature sensor, a humidity sensor, or an illuminance sensor.

[0055] The interface (177) may support one or more specified protocols that can be used for the electronic device (101) to be connected directly or wirelessly to an external electronic device (e.g., electronic device (102)). According to one embodiment, the interface (177) may include, for example, a high definition multimedia interface (HDMI), a universal serial bus (USB) interface, an SD card interface, or an audio interface.

[0056] The connection terminal (178) may include a connector through which the electronic device (101) can be physically connected to an external electronic device (e.g., electronic device (102)). According to one embodiment, the connection terminal (178) may include, for example, an HDMI connector, a USB connector, an SD card connector, or an audio connector (e.g., a headphone connector).

[0057] The haptic module (179) can convert an electrical signal into a mechanical stimulus (e.g., vibration or movement) or an electrical stimulus that can be perceived by the user through tactile or kinesthetic senses. According to one embodiment, the haptic module (179) may include, for example, a motor, a piezoelectric element, or an electric stimulation device.

[0058] The camera module (180) can capture still images and video. According to one embodiment, the camera module (180) may include one or more lenses, image sensors, image signal processors, or flashes.

[0059] The power management module (188) can manage power supplied to the electronic device (101). According to one embodiment, the power management module (188) can be implemented, for example, as at least part of a power management integrated circuit (PMIC).

[0060] The battery (189) can supply power to at least one component of the electronic device (101). According to one embodiment, the battery (189) may include, for example, a non-rechargeable primary battery, a rechargeable secondary battery, or a fuel cell.

[0061] The communication module (190) can support the establishment of a direct (e.g., wired) communication channel or a wireless communication channel between an electronic device (101) and an external electronic device (e.g., electronic device (102), electronic device (104), or server (108)), and the performance of communication through the established communication channel. The communication module (190) may include one or more communication processors that operate independently of the processor (120) (e.g., application processor) and support direct (e.g., wired) communication or wireless communication. According to one embodiment, the communication module (190) may include a wireless communication module (192) (e.g., cellular communication module, short-range wireless communication module, or GNSS (global navigation satellite system) communication module) or a wired communication module (194) (e.g., LAN (local area network) communication module, or power line communication module). The corresponding communication module among these communication modules can communicate with an external electronic device (104) through a first network (198) (e.g., a short-range communication network such as Bluetooth, WiFi (wireless fidelity) direct, or IrDA (infrared data association)) or a second network (199) (e.g., a legacy cellular network, a 5G network, a next-generation communication network, the Internet, or a computer network (e.g., a LAN or WAN). These various types of communication modules may be integrated into a single component (e.g., a single chip) or implemented as multiple separate components (e.g., multiple chips). The wireless communication module (192) can identify or authenticate the electronic device (101) within a communication network such as the first network (198) or the second network (199) using subscriber information (e.g., International Mobile Subscriber Identifier (IMSI)) stored in the subscriber identification module (196).

[0062] The wireless communication module (192) can support 5G networks and next-generation communication technologies following 4G networks, for example, new radio access technology. NR access technology can support high-speed transmission of high-capacity data (enhanced mobile broadband (eMBB)), minimization of terminal power and connection of multiple terminals (massive machine type communications (mMTC)), or high reliability and low latency (ultra-reliable and low-latency communications (URLLC)). The wireless communication module (192) can support a high-frequency band (e.g., mmWave band) to achieve a high data transmission rate, for example. The wireless communication module (192) can support various technologies for securing performance in the high-frequency band, such as beamforming, massive MIMO (multiple-input and multiple-output), full-dimensional MIMO (FD-MIMO), array antenna, analog beam-forming, or large-scale antenna. The wireless communication module (192) can support various requirements specified in the electronic device (101), external electronic device (e.g., electronic device (104)), or network system (e.g., second network (199)). According to one embodiment, the wireless communication module (192) may support a Peak data rate (e.g., 20 Gbps or more) for eMBB realization, loss coverage (e.g., 164 dB or less) for mMTC realization, or U-plane latency (e.g., downlink (DL) and uplink (UL) each 0.5 ms or less, or round trip 1 ms or less) for URLLC realization.

[0063] An antenna module (197) can transmit a signal or power to or from an external source (e.g., an external electronic device). According to one embodiment, the antenna module (197) may include an antenna comprising a radiator made of a conductor or a conductive pattern formed on a substrate (e.g., a printed circuit board (PCB)). According to one embodiment, the antenna module (197) may include a plurality of antennas (e.g., an array antenna). In this case, at least one antenna suitable for a communication method used in a communication network, such as a first network (198) or a second network (199), may be selected from the plurality of antennas, for example, by a communication module (190). A signal or power may be transmitted or received between the communication module (190) and an external electronic device through the selected at least one antenna. According to some embodiments, in addition to the radiator, other components (e.g., a radio frequency integrated circuit (RFIC)) may be additionally formed as part of the antenna module (197).

[0064] According to various embodiments, the antenna module (197) may form a mmWave antenna module. According to one embodiment, the mmWave antenna module may include a printed circuit board, an RFIC disposed on or adjacent to a first surface (e.g., bottom surface) of the printed circuit board and capable of supporting a specified high frequency band (e.g., mmWave band), and a plurality of antennas (e.g., array antennas) disposed on or adjacent to a second surface (e.g., top surface or side surface) of the printed circuit board and capable of transmitting or receiving a signal of the specified high frequency band.

[0065] At least some of the above components can be connected to each other via a communication method between peripheral devices (e.g., bus, GPIO (general purpose input and output), SPI (serial peripheral interface), or MIPI (mobile industry processor interface)) and exchange signals (e.g., commands or data) with each other.

[0066] According to one embodiment, commands or data may be transmitted or received between an electronic device (101) and an external electronic device (104) through a server (108) connected to a second network (199). Each of the external electronic devices (102, or 104) may be the same or a different type of device as the electronic device (101). According to one embodiment, all or part of the operations performed on the electronic device (101) may be performed on one or more of the external electronic devices (102, 104, or 108). For example, if the electronic device (101) needs to perform a function or service automatically or in response to a request from a user or another device, the electronic device (101) may request one or more external electronic devices to perform at least part of the function or service instead of performing the function or service itself or additionally. One or more external electronic devices that receive the above request may execute at least part of the requested function or service, or additional function or service related to the request, and transmit the result of the execution to the electronic device (101). The electronic device (101) may provide the result as is or additionally processed as at least part of the response to the request. For this purpose, for example, cloud computing, distributed computing, mobile edge computing (MEC), or client-server computing technology may be used. The electronic device (101) may provide ultra-low latency services using, for example, distributed computing or mobile edge computing. In another embodiment, the external electronic device (104) may include an Internet of Things (IoT) device. The server (108) may be an intelligent server using machine learning and / or neural networks. According to one embodiment, the external electronic device (104) or the server (108) may be included within a second network (199).The electronic device (101) can be applied to intelligent services (e.g., smart home, smart city, smart car, or healthcare) based on 5G communication technology and IoT-related technology.

[0067] FIG. 2 is a cross-sectional view of a wearable electronic device (200) according to one embodiment of the present disclosure, viewed from the top surface.

[0068] The wearable electronic device (200) illustrated in FIG. 2 may be at least partially similar to the electronic device (101) of FIG. 1, or may further include other embodiments of the electronic device. A wearable electronic device (201) according to one embodiment is an accessory device that can be worn on a user's body, and may include a wearable electronic device that can be worn on a part of the body (e.g., an ear). However, it is not limited thereto.

[0069] In describing the wearable electronic device (200) of the present disclosure, it is illustrated as a hook-type wearable electronic device (e.g., open loop type, hook type) that is worn on both ears of a user, but is not limited thereto. For example, the wearable electronic device (200) may include smart glasses (AI glass) that can be worn on a user's face, smart headphones, a spherical type wearable electronic device that can be worn in ear canals, an open loop-type electronic device with a part open, or a curved or non-curved electronic device.

[0070] Referring to FIG. 2, the wearable electronic device (200) may include a first housing (201), a second housing (202), and a connecting part (203) connecting the first housing and the second housing.

[0071] FIG. 3 is a cross-sectional view of a wearable electronic device viewed from the front and a user wearing the wearable electronic device according to one embodiment of the present disclosure.

[0072] Referring to FIGS. 2 and 3, in one embodiment, the first housing (201) may be positioned in the user's ear canal while the wearable electronic device (200) is worn on a part of the body (e.g., ear). The first housing (201) may include a voice sensor (e.g., microphone) (212) for detecting sound generated by the user and an output module (e.g., speaker). The first housing (201) may include an electromyography sensor (213) for detecting muscle movement generated in the muscles around the user's ear.

[0073] In one embodiment, the second housing (202) may be positioned on the user's earlobe while the wearable electronic device (200) is worn on a part of the body (e.g., the ear). The second housing (201) may include an electromyography sensor (213) for detecting muscle movements occurring in the muscles around the user's ear. The second housing (201) may include a vibration sensor (211) for detecting vibrations occurring from the user.

[0074] In one embodiment, the wearable electronic device (200) may include a connecting portion (203) connecting a first housing and a second housing. The entire or part of the connecting portion (230) may be made of an elastic material and may function to be worn securely by expanding or contracting to fit the size of the user's ear.

[0075] In one embodiment, a power management module (188) (e.g., the power management module (188) of FIG. 1) can manage power supplied to at least one electrical component included in a wearable electronic device (200). For example, the power management module (188) can manage power supplied to each component from a battery (189) (e.g., the battery (189) of FIG. 1) through a charging interface (213).

[0076] According to one embodiment, a communication module (190) (e.g., the communication module (190) of FIG. 1) can perform a communication connection between a wearable electronic device (200) and an external electronic device (e.g., the electronic device (102, 104) of FIG. 1). For example, the communication module (190) may be electrically connected to an antenna module (197) and, through the antenna module (197), transmit a signal or power to the external electronic device (102, 104) or receive from the external electronic device (102, 104). For example, the antenna module (197) may include a radiator made of a conductor or a conductive pattern formed on a substrate (240).

[0077] In one embodiment, the wearable electronic device (200) may include an audio module (not shown) for providing auditory output information to a user (e.g., audio module (170) of FIG. 1, audio module (241) of FIG. 4), and / or a haptic module (not shown) for providing tactile output information to a user (e.g., haptic module (179) of FIG. 1, haptic module (242) of FIG. 4).

[0078] FIG. 4 is a block diagram illustrating a wearable electronic device and an external electronic device according to one embodiment of the present disclosure.

[0079] Referring to FIG. 4, a wearable electronic device (200) (e.g., the electronic device (101) of FIG. 1) may include a plurality of sensors (vibration sensor (211), voice sensor (212), electromyography sensor (213), sensor module (176) of FIG. 1), memory (230) (e.g., memory (130) of FIG. 1), output module (240) (e.g., acoustic output module (155) of FIG. 1, audio module (170), haptic module (179), audio module (241) of FIG. 4, haptic module (242), communication module (250) (e.g., communication module (190) of FIG. 1), and / or a processor (220) (e.g., processor (120) of FIG. 1).

[0080] According to one embodiment of the present disclosure, a wearable electronic device (200) may include a plurality of sensors (vibration sensor (211), voice sensor (212), electromyography sensor (213)).

[0081] According to one embodiment of the present disclosure, a vibration sensor (211) can measure (or sense, detect) vibrations of the body during a user's eating activity while the wearable electronic device (200) is worn on the user's body (e.g., ear). The vibration sensor (211) can accurately detect the user's posture, movement, impact, and minute vibrations occurring during eating. The vibration sensor (211) may include an accelerometer capable of measuring the speed and / or acceleration of the wearable electronic device (200) and / or a gyroscope capable of measuring the posture of the wearable electronic device (200).

[0082] In one embodiment, the vibration sensor (211) can acquire vibration information related to movement during the eating activity of the wearable electronic device (200) (or the user of the wearable electronic device (200)) and transmit it to the processor (220).

[0083] According to one embodiment of the present disclosure, the voice sensor (212) can accurately measure (or sense, detect) sounds occurring from the user and the surroundings of the user during eating activities while the wearable electronic device (200) is worn on the user's body (e.g., ear).

[0084] In one embodiment, the voice sensor (212) can acquire sound information related to the eating activity of the wearable electronic device (200) (or the user of the wearable electronic device (200)) and transmit it to the processor (220).

[0085] According to one embodiment of the present disclosure, the electromyography sensor (213) can accurately measure (or sense, detect) electrical signal values ​​that occur when the muscles around the user's ear move while the wearable electronic device (200) is worn on the user's body (e.g., ear). The electromyography sensor (213) can detect direct or indirect movements, such as muscle relaxation and contraction, by measuring the electrical signals of the muscles.

[0086] In one embodiment, the electromyography sensor (213) can acquire electrical signal information related to muscle movement occurring during the eating activity of the wearable electronic device (200) (or the user of the wearable electronic device (200)) and transmit it to the processor (220).

[0087] According to one embodiment of the present disclosure, a memory (230) (e.g., memory (130) of FIG. 1) performs the function of storing a program (e.g., program (140) of FIG. 1) for processing and controlling a processor (220) of a wearable electronic device (200), an operating system (OS) (e.g., operating system (142) of FIG. 1), various applications, and / or input / output data, and can store a program that controls the overall operation of the wearable electronic device (200). The memory (230) can store various configuration information required for processing functions related to various embodiments of the present disclosure in the wearable electronic device (200). The memory (230) can store executable instructions. For example, the memory (230) can store instructions that cause the wearable electronic device (200) to perform operations when executed by the processor (220). For example, instructions may be stored on a computer-readable recording medium. The recording medium may be tangible and non-transitory. The memory (230) and / or the recording medium may store one or more programs containing instructions.

[0088] In one embodiment, the memory (230) may store sensor information (e.g., sensor information related to movement of the wearable electronic device (200) and / or sensor information related to the biometric information of the user wearing the wearable electronic device (200)) for analyzing user activity data and generating eating information. For example, user activity data may include chewing activity, eating activity such as drinking activity, and dynamic state (e.g., exercise state). However, it is not limited thereto.

[0089] In one embodiment, the memory (230) may store instructions for determining whether a user has started a feeding activity under the control of the processor (220). For example, the memory (230) may store instructions for determining that the user has started a feeding activity if at least one pattern is included, after analyzing user activity data obtained from at least one of a plurality of sensors (vibration sensor (212), voice sensor (212), electromyography sensor (213)) under the control of the processor (220).

[0090] In one embodiment, the memory (230), under the control of the processor (220), may store user activity data as eating data when it is determined that a user has started a eating activity, and may store instructions for generating at least one user eating information based on the eating data.

[0091] In one embodiment, the memory (230) may store instructions for analyzing user activity data under the control of the processor (220) and determining that the user has finished eating activity if at least one pattern is not included.

[0092] In one embodiment, the memory (230) may store instructions for calculating the number of chewings and the chewing speed during a user eating activity using the eating data stored in the memory (230) from the time the user eating activity starts, under the control of the processor (220).

[0093] In one embodiment, the memory (230) can store instructions for providing a notification to the user through an output module (240) within the wearable electronic device (200) when the number of works and the speed of works are compared with a reference number of works and a reference speed of works under the control of the processor (220).

[0094] In one embodiment, the memory (230) can store instructions for transmitting feeding data and user feeding information to an external electronic device (300) through a communication module (250) under the control of a processor (220).

[0095] According to one embodiment of the present disclosure, the output module (240) may include an audio module (241) and a haptic module (242).

[0096] According to one embodiment of the present disclosure, the audio module (241) can output an acoustic signal generated from the wearable electronic device (200) to the electronic device (200). The audio module (240) can automatically provide a notification, such as providing a sound to the user, when the number of chewings and the chewing speed differ from the reference number of chewings and the reference chewing speed by more than a predetermined ratio during the user's eating activity, thereby allowing the user to immediately change the number of chewings and the chewing speed.

[0097] According to one embodiment of the present disclosure, a haptic module (242) can convert an electrical signal generated from a wearable electronic device (200) into a mechanical stimulus (e.g., vibration or movement) or an electrical stimulus so that the user can perceive it through tactile or kinesthetic senses. The haptic module (242) can automatically provide a notification, such as providing a vibration or electrical stimulus to the user, when the number of chewings and the chewing speed differ from a reference number of chewings and a reference chewing speed by more than a predetermined ratio during the user's eating activity, thereby allowing the user to immediately change the number of chewings and the chewing speed.

[0098] According to one embodiment of the present disclosure, a communication module (250) (e.g., the communication module (190) of FIG. 1) can control a communication connection between a wearable electronic device (200) and at least one external electronic device (e.g., the electronic device (102), the electronic device (104), the external electronic device (300) of FIG. 1) and / or a server (e.g., the server (108) of FIG. 1) under the control of a processor (220).

[0099] In one embodiment, the communication module (250) may support the establishment of a wireless communication channel with an external electronic device (300) and the performance of communication through the established communication channel. The communication module (250) may support short-range wireless communication such as UWB, Bluetooth, and low-power Bluetooth. However, it is not limited thereto.

[0100] According to one embodiment of the present disclosure, the processor (220) may include, for example, a microcontroller unit (MCU) and may control a plurality of hardware components connected to the processor (220) by running an operating system (OS) or an embedded software program. The processor (220) may control a plurality of hardware components according to, for example, instructions stored in memory (230) (e.g., program (140) of FIG. 1).

[0101] In one embodiment, the processor (220) may acquire user activity data through at least one of a plurality of sensors. The processor (220) may analyze the acquired user activity data for a predetermined period of time, and if at least one pattern is included, determine the time of acquisition of user activity data containing at least one pattern as the time of start of the user's eating activity.

[0102] In one embodiment, the processor (220) can remove noise to analyze whether at least one pattern exists in the user activity data. That is, the noise can be removed by processing the user activity data obtained from a plurality of sensors. The processor (220) can obtain user activity data with reduced variability by measuring the average value of the user activity data and removing the noise present in the data using methods such as additive decomposition and multiplicative decomposition.

[0103] The predetermined time can be adjusted based on the history of eating data obtained from the user in the past and / or user eating information. For example, the time for analyzing user activity data can be adjusted based on the user's usual eating time and duration. That is, if the user's usual eating duration is long, the time for analyzing user activity data can be adjusted to be longer.

[0104] When the processor (220) determines that the user has started a eating activity, it may store the user activity data as eating data and generate at least one user eating information based thereon. The user eating information may include the time of the meal, the duration of the meal, the number of chewings, the speed of chewing, or the direction of the teeth mainly used during the chewing activity. However, it is not limited to these.

[0105] In one embodiment, if the processor (220) analyzes that at least one pattern is not included in the user activity data, it may determine the time of acquiring the user activity data that does not include at least one pattern as the time of ending the user eating activity.

[0106] In one embodiment, the processor (220) can calculate the number of times or speed of the user’s chewing based on the chewing data stored from the start of the user’s chewing activity. For example, the processor (220) can calculate the speed of the chewing by dividing the chewing time by the number of times the chewing is done.

[0107] In one embodiment, the processor (220) can provide a notification to the user through the output module (240) if the number of works or the speed of works differs from the reference number of works or the reference speed of works by more than a predetermined ratio.

[0108] The standard number of chews or standard chewing speed may be based on major research findings regarding dietary habits, and users can also set them directly.

[0109] In one embodiment, when the wearable electronic device (200) is in a state of communication connection with an external electronic device (300), the processor (220) can transmit feeding data and user feeding information to the external electronic device (300) through the communication module (250).

[0110] In one embodiment, an external electronic device (300) may provide a first UI to a user by analyzing eating data and user eating information received from a wearable electronic device (200). The first UI may include advice regarding the user's meal time, meal duration, number of chewings, chewing speed, or the direction of teeth mainly used during chewing activities.

[0111] In one embodiment, when the wearable electronic device (200) is in a state of communication connection with an external electronic device (300), the processor (220) can transmit feeding data to the external electronic device (300) through the communication module (250).

[0112] In one embodiment, an external electronic device (300) can generate user eating information by analyzing eating data received from a wearable electronic device (200). The external electronic device (300) can provide a first UI to the user by analyzing the generated user eating information. The first UI may include advice regarding the user's meal time, meal duration, number of chewings, chewing speed, or the direction of teeth primarily used during chewing activities.

[0113] According to one embodiment of the present disclosure, the external electronic device (300) may include a watch, a smartphone, a tablet PC, a desktop PC, a laptop PC, and / or a smart TV.

[0114] In one embodiment, although not illustrated, the external electronic device (300) may include a communication circuit (e.g., the communication module (190) of FIG. 1), a memory (e.g., the memory (130) of FIG. 1), a display (e.g., the display module (160) of FIG. 1), and / or a processor (e.g., the processor (120) of FIG. 1). The processor of the external electronic device (300) may receive eating data or user eating information from the wearable electronic device (200) through the communication circuit.

[0115] The processor of the external electronic device (300) can store food data or user food information received from the wearable electronic device (200) in memory. In one embodiment, the processor of the external electronic device (300) can analyze food data or user food information obtained from the wearable electronic device (200) and provide a first UI to the user.

[0116] The processor of the external electronic device (300) can generate user eating information by analyzing eating data received from the wearable electronic device (200). The processor of the external electronic device (300) can provide a first UI to the user by analyzing the generated eating information. The first UI may include advice regarding the user's meal time, meal duration, number of chewings, chewing speed, or the direction of teeth mainly used during chewing activities.

[0117] FIG. 5 is a flowchart illustrating a method for generating user eating information using user activity data according to one embodiment of the present disclosure.

[0118] In the following embodiments, each operation of FIG. 5 may be performed sequentially, but is not necessarily performed sequentially. For example, the order of each operation of FIG. 5 may be changed, and at least two operations may be performed in parallel.

[0119] According to one embodiment, the 1000 to 1500 operations of FIG. 5 may be understood to be performed in a processor (e.g., processor (220) of FIG. 4) of a wearable electronic device (e.g., electronic device (101) of FIG. 1, wearable electronic device (200) of FIG. 4).

[0120] In various embodiments, before performing the operation, the wearable electronic device (200) may perform the operation of establishing a communication connection (e.g., pairing) with an external electronic device (e.g., the external electronic device (300) of FIG. 3).

[0121] In one embodiment, the processor (220) can acquire user activity data from a plurality of sensors (1000). For example, the processor (220) can acquire user activity data from the vibration sensor (211) based on the vibration sensor (211) detecting body vibrations occurring during the user's eating activity. The processor (220) can acquire user activity data from the voice sensor (212) based on the voice sensor (212) detecting sounds occurring during the user's eating activity. The processor (220) can acquire user activity data from the electromyography sensor (213) based on the electromyography sensor (213) detecting electrical signals of the muscles around the ear occurring during the user's eating activity.

[0122] In one embodiment, the processor (220) can analyze user activity data acquired over a predetermined period of time based on acquiring user activity data from a plurality of sensors (e.g., 1100).

[0123] The predetermined time can be adjusted based on the history of eating data obtained from the user in the past and / or user eating information. For example, the time for analyzing user activity data can be adjusted based on the user's usual eating time and duration. That is, if the user's usual eating duration is long, the time for analyzing user activity data can be adjusted to be longer.

[0124] In one embodiment, the processor (220) can analyze whether at least one pattern is included in the user activity data (1200). For example, the processor (220) can remove noise to analyze whether at least one pattern exists in the user activity data. That is, noise can be removed by processing user activity data obtained from a plurality of sensors. The processor (220) can obtain user activity data with reduced variability by measuring the average value of the user activity data and removing noise present in the data using methods such as additive decomposition and multiplicative decomposition.

[0125] If the user activity data contains at least one pattern (e.g., 1200), the processor (220) can determine the time when the user activity data containing at least one pattern is acquired as the time when the user eating activity starts (1300).

[0126] The processor (220) stores user activity data as eating data in memory (230) and can generate user eating information based on the stored eating data (1400, 1500). User eating information may include the time of meal, meal duration, number of chewings, chewing speed, or the direction of teeth mainly used during chewing activity. However, it is not limited to these.

[0127] FIG. 6 is a graph showing that at least one pattern is included in user activity data according to one embodiment of the present disclosure.

[0128] Graph A is a graph representing user activity data obtained from a vibration sensor (211) processed by methods such as removing noise.

[0129] Referring to graph A, the vibration intensity is large at a1 and then decreases at a2. Similarly, at a3, the vibration intensity increases to the same magnitude as the vibration at a1, and at a4, the vibration intensity decreases to the same magnitude as the vibration at a3. In other words, it can be seen that a vibration pattern of a certain shape appears repeatedly over time. For example, if a user repeatedly engages in a chewing activity, a regular vibration graph like graph A may appear. If the processor (220) analyzes that the user activity data contains a regular pattern like the shape of graph A, it can determine that the user has started eating activity.

[0130] Graphs B to D are graphs representing user activity data obtained from an electromyography sensor (213) processed by methods such as removing noise.

[0131] Graph B is a user activity graph that detects muscle movements occurring when chewing food. Referring to Graph B, the strength of the right masseter muscle is at its maximum at point b1, and the strength of the left masseter muscle is at its maximum at point b2. Also, the strength of the lateral pterygoid muscle is at its maximum at point c1. (The same applies to b4, b5, and b6.) During the activity of chewing food, that is, mastication, the teeth act like millstones, so the right and left muscles are used sequentially. Therefore, a pattern in the shape of Graph B may appear repeatedly. That is, if the processor (220) analyzes that the user activity data obtained from the electromyography sensor (213) contains a regular pattern in the shape of Graph B, it can determine that the user has started the eating activity of chewing food.

[0132] Graph C is a user activity graph that detects muscle movements occurring when eating food by melting. Referring to Graph C, the strength of the masseter muscle is at its maximum at point c1, and the strength of the lateral pterygoid muscle is at its maximum at point c3. Similarly, the strength of the masseter muscle is at its maximum at point c5, and the strength of the lateral pterygoid muscle is at its maximum at point c7. When eating by melting food, the mouth is closed and the lateral pterygoid muscle is used to open the mouth, so the pressure inside the mouth is lowered, and a pattern similar to that of Graph C may appear repeatedly. That is, if the processor (220) analyzes that the user activity data obtained from the electromyography sensor (213) contains a regular pattern similar to that of Graph C, it can determine that the user has started the eating activity of eating by melting food.

[0133] Graph D is a user activity graph that detects muscle movements occurring when drinking food. Referring to Graph D, the strength of the masseter muscle is at its maximum at point d1, and the strength of the lateral pterygoid muscle is at its maximum at point d3. Similarly, the strength of the masseter muscle is at its maximum at point d5, and the strength of the lateral pterygoid muscle is at its maximum at point d7. When drinking food, the opening and closing of the mouth is controlled consistently to swallow the food, so a pattern of the shape of Graph D may appear repeatedly. That is, if the processor (220) analyzes that the user activity data obtained from the electromyography sensor (213) contains a regular pattern of the shape of Graph D, it can determine that the user has started a feeding activity of drinking food.

[0134] At least one pattern may be a pattern model related to eating activity pre-built into the wearable electronic device (200) or external electronic device (300), and if the processor (200) of the wearable electronic device (200) analyzes that a pattern exists in the user activity data, it may be further learned as a pattern according to the user's personal eating data history.

[0135] FIG. 7 is a flowchart illustrating a method for determining the start and end of a user's eating activity according to one embodiment of the present disclosure.

[0136] In the following embodiments, each operation of FIG. 7 may be performed sequentially, but is not necessarily performed sequentially. For example, the order of each operation of FIG. 7 may be changed, and at least two operations may be performed in parallel.

[0137] According to one embodiment, operations 2000 to 2700 of FIG. 7 may be understood to be performed in a processor (e.g., processor (220) of FIG. 4) of a wearable electronic device (e.g., electronic device (101) of FIG. 1, wearable electronic device (200) of FIG. 2).

[0138] Since the 2000 to 2500 operations of FIG. 7 according to one embodiment are substantially the same as the 1000 to 1500 operations of FIG. 5 described above, a detailed description thereof may be replaced by the description of FIG. 5.

[0139] Referring to FIG. 7, in one embodiment, the processor (220) can analyze whether at least one pattern is included in the user activity data (2600).

[0140] If the user activity data does not contain at least one pattern (e.g., 2600), the processor (220) can determine the time when the user activity data that does not contain at least one pattern is acquired as the time when the user eating activity ends (2700). Even if user activity data is detected through multiple sensors, if the analysis result does not include at least one regular and repetitive pattern, it cannot be considered as eating activity, so the processor (220) can determine the time when the user activity data that does not contain at least one pattern is acquired as the time when the user eating activity ends.

[0141] FIG. 8 is a flowchart illustrating a method for generating user eating information and providing notifications to the user using user activity data according to one embodiment of the present disclosure.

[0142] In the following embodiments, each operation of FIG. 8 may be performed sequentially, but is not necessarily performed sequentially. For example, the order of each operation of FIG. 8 may be changed, and at least two operations may be performed in parallel.

[0143] According to one embodiment, the 3000 to 3800 operations of FIG. 8 may be understood to be performed in a processor (e.g., processor (220) of FIG. 4) of a wearable electronic device (e.g., electronic device (101) of FIG. 1, wearable electronic device (200) of FIG. 4).

[0144] Since the 3000 to 3400 operations of FIG. 8 according to one embodiment are substantially the same as the 1000 to 1400 operations of FIG. 5 described above, a detailed description thereof may be replaced by the description of FIG. 5.

[0145] Referring to FIG. 8, the processor (220) can calculate the number of times or the speed of chewing during the user's eating activity based on the eating data stored in the memory (230) from the time the user's eating activity starts (3500). For example, the processor (220) can calculate the speed of chewing by dividing the chewing time by the number of times chewing.

[0146] The processor (220) can compare the number of chewing operations or the chewing speed with a reference number of chewing operations or a reference chewing speed (3600). The reference number of chewing operations or the reference chewing speed may be based on major research results regarding dietary habits, and may also be set directly by the user.

[0147] The processor (220) can provide a notification to the user through the output module (240) when the value obtained by comparing the number of works or the work speed with the reference number of works or the reference work speed is greater than or equal to a predetermined ratio (e.g., 3700, 3800). For example, if the user's work speed differs from the reference work speed by more than a predetermined ratio, the processor can provide a notification to the user by outputting a warning sound through the audio module (240) or by outputting a vibration through the haptic module (242). Additionally, if the user's number of works differs from the reference number of works by more than a predetermined ratio, the processor can provide an immediate notification to the user by outputting a warning sound through the audio module (240) or by outputting a vibration through the haptic module (242). That is, the user can be made to immediately change the work speed and the number of works.

[0148] FIG. 9 is a diagram illustrating the flow of feeding data and user feeding information being transmitted from a wearable electronic device to an external electronic device according to one embodiment of the present disclosure.

[0149] In the following embodiments, each operation of FIG. 9 may be performed sequentially, but is not necessarily performed sequentially. For example, the order of each operation of FIG. 9 may be changed, and at least two operations may be performed in parallel.

[0150] According to one embodiment, the 4000 to 4800 operations of FIG. 9 may be understood to be performed in a processor (e.g., processor (220) of FIG. 4) of a wearable electronic device (e.g., electronic device (101) of FIG. 1, wearable electronic device (200) of FIG. 4) and in an external electronic device (e.g., processor of electronic device (102, 104) of FIG. 1, external electronic device (300) of FIG. 4).

[0151] Referring to FIG. 9, the wearable electronic device (200) can be connected to an external electronic device (300) for communication (4000). For example, the wearable electronic device (200) can be connected to an external electronic device (300) for communication using short-range wireless communication such as UWB, Bluetooth, or low-power Bluetooth.

[0152] Since the 4100 to 4600 operations of FIG. 9 according to one embodiment are substantially the same as the 1000 to 1500 operations of FIG. 5 described above, a detailed description thereof may be replaced by the description of FIG. 5.

[0153] A processor (220) of a wearable electronic device (200) according to one embodiment can transmit food data and user food information stored in memory (230) to an external electronic device (300) (4700).

[0154] According to one embodiment, the processor of an external electronic device (300) may provide a first UI to a user based on eating data received from a wearable electronic device (200) and the degree of user eating (4800). The first UI may include dietary advice regarding user eating information, such as the time of meal, meal duration, number of chewings, chewing speed, or the direction of teeth mainly used during chewing activity.

[0155] FIG. 10 is a diagram illustrating the flow in which feeding data is transmitted from a wearable electronic device to an external electronic device, and user feeding information generated from the external electronic device is transmitted to the wearable electronic device.

[0156] In the following embodiments, each operation of FIG. 10 may be performed sequentially, but is not necessarily performed sequentially. For example, the order of each operation of FIG. 10 may be changed, and at least two operations may be performed in parallel.

[0157] According to one embodiment, operations 5000 to 5900 of FIG. 10 may be understood to be performed in a processor (e.g., processor (220) of FIG. 4) of a wearable electronic device (e.g., electronic device (101) of FIG. 1, wearable electronic device (200) of FIG. 4) and in an external electronic device (e.g., processor of electronic device (102, 104) of FIG. 1, external electronic device (300) of FIG. 4).

[0158] Referring to FIG. 10, the wearable electronic device (200) can be connected to an external electronic device (300) for communication (5000). For example, the wearable electronic device (200) can be connected to an external electronic device (300) for communication using short-range wireless communication such as UWB, Bluetooth, or low-power Bluetooth.

[0159] Since the 5000 to 5500 operations of FIG. 10 according to one embodiment are substantially the same as the 1000 to 1400 operations of FIG. 5 described above, a detailed description thereof may be replaced by the description of FIG. 5.

[0160] A processor (220) of a wearable electronic device (200) according to one embodiment can transmit food data stored in memory (230) to an external electronic device (300) (5600).

[0161] According to one embodiment, the processor of an external electronic device (300) can generate user eating information by utilizing an artificial intelligence model related to eating based on eating data received from a wearable electronic device (200) (5700). User eating information may include the time of meal, meal duration, number of chewings, chewing speed, or the direction of teeth mainly used during chewing activity. However, it is not limited thereto.

[0162] The artificial intelligence model related to eating may be embedded in memory within an external electronic device (300), further learned by eating data and generated eating information, and updated by a server. The artificial intelligence model related to eating may be a model for average eating habits and may be a personalized eating habit model for each user.

[0163] According to one embodiment, the processor of the external electronic device (300) can transmit the generated feeding information to the wearable electronic device (200) (5800).

[0164] According to one embodiment, the processor of an external electronic device (300) can provide a first UI to a user based on eating data received from a wearable electronic device (200) and user eating information generated (5900). The first UI may include dietary advice regarding user eating information, such as the time of meal, meal duration, number of chewings, chewing speed, or the direction of teeth mainly used during chewing activity.

[0165] FIG. 11 is a flowchart illustrating a method for an external electronic device to generate user feeding information according to one embodiment of the present disclosure.

[0166] In the following embodiments, each operation of FIG. 11 may be performed sequentially, but is not necessarily performed sequentially. For example, the order of each operation of FIG. 11 may be changed, and at least two operations may be performed in parallel.

[0167] According to one embodiment, the 6000 to 6300 operations of FIG. 11 can be understood as being performed in a processor of an external electronic device (e.g., electronic device (102, 104) in FIG. 1, external electronic device (300) in FIG. 4).

[0168] In one embodiment, the processor of the external electronic device (300) can determine whether there is an existing meal record related to the user in memory (6000).

[0169] In one embodiment, the processor of the external electronic device (300) can compare whether the current user's meal time is shorter than the recommended meal time (6210) when there is no existing meal record related to the user in memory (6000 no).

[0170] In one embodiment, the processor of the external electronic device (300) may provide a first UI that advises the user to increase the current meal time to the recommended time (6300) if the current user's meal time is shorter than the recommended time (6211).

[0171] In one embodiment, the processor of the external electronic device (300) may provide a first UI that advises the user to reduce the current meal time to the recommended time (6300) if the current user's meal time is longer than the recommended time (6212).

[0172] In one embodiment, the processor of the external electronic device (300) can compare whether the current user's meal time is shorter than the existing meal time (6100) if there is an existing meal record related to the user in memory (6000 example).

[0173] In one embodiment, the processor of the external electronic device (300) can compare whether the current user's meal time is shorter than the recommended meal time (6220) when the current user's meal time is shorter than the existing meal time (6100 example).

[0174] In one embodiment, the processor of the external electronic device (300) may provide a first UI that advises the user to increase the current meal time to the existing meal time or the recommended meal time (6300) if the current user's meal time is shorter than the existing meal time and shorter than the recommended meal time (6221).

[0175] In one embodiment, the processor of the external electronic device (300) can compare whether the current user's meal time is shorter than the recommended meal time (6230) when the current user's meal time is longer than the existing meal time (6100 No).

[0176] In one embodiment, the processor of the external electronic device (300) may provide a first UI that advises the user to increase the current meal time to the recommended meal time (6300) when the user's current meal time is longer than the existing meal time and shorter than the recommended meal time (6231).

[0177] In one embodiment, the processor of the external electronic device (300) may provide a first UI that advises the user to reduce the current meal time to the recommended meal time (6300) if the user's current meal time is longer than the existing meal time and longer than the recommended meal time (6232).

[0178] FIG. 12 is a drawing illustrating an example of a first UI provided to a user by an external electronic device based on feeding data and user feeding information, according to one embodiment of the present disclosure.

[0179] The first UI displayed on the external electronic device (300) illustrated in FIG. 12 may correspond to the first UI that advises the user to increase the current meal time by the recommended meal time according to the 6231 operation in FIG. 11.

[0180] According to one embodiment, the processor of the external electronic device (300) can receive the user's eating data and generate user eating information. By referring to the graph (7300) showing the food intake flow (7200), it can be seen that the user's eating speed generated by the processor of the external electronic device (300) is faster than the normal speed. Accordingly, the external electronic device (300) can provide the user with an analysis result (7400, 7500) regarding the current eating activity and a phrase (7000, 7100) containing advice on proper eating habits based on the generated user eating information.

[0181] For example, referring to FIG. 12, it can be seen that the user's current meal time is about 1 minute 30 seconds faster than the previous meal time of 12 minutes 2 minutes 40 seconds, totaling 18 minutes 40 seconds (7400, 7500). According to this analysis result, the user's current meal time is longer than the previous meal time but does not reach the recommended meal time of 20 minutes. Therefore, the wearable electronic device (200) can provide the user with a message containing advice for proper eating habits, such as, “Your meal time has become longer than last time and is approaching the recommended meal time. Shall we try eating a little more slowly until the recommended meal time?” (7000, 7100).

[0182] The food information generated by the processor of the external electronic device (300) using food data received from the wearable electronic device (200) may include the time of meal, meal duration, number of chewings, chewing speed, or the direction of teeth primarily used during chewing activities, and based on this user food information, advice on proper eating habits and medical consultation can be provided to the user. Additionally, the generated food information can be stored in memory, an artificial intelligence model for the user's individual eating activities can be trained, and the information can be utilized to improve the user's eating habits.

[0183] According to one embodiment of the present disclosure, a wearable electronic device may include a plurality of sensors for acquiring user activity data; a communication module including at least one communication circuit; a memory for storing instructions; and at least one processor operatively connected to the communication module and the memory. According to one embodiment of the present disclosure, when the instructions are executed by the at least one processor, the wearable electronic device may acquire user activity data from at least one of the plurality of sensors, analyze the user activity data for a predetermined period of time, and if it is analyzed that at least one pattern is included in the user activity data, determine the time of acquiring the user activity data including the at least one pattern as the time of starting the user eating activity, store the user activity data as eating data, and generate at least one user eating information based on the eating data.

[0184] According to one embodiment of the present disclosure, when the instructions are executed by the at least one processor, if it is analyzed that the at least one pattern is not included in the user activity data, the time of acquiring the user activity data that does not include the at least one pattern can be determined as the time of end of the user eating activity.

[0185] According to one embodiment of the present disclosure, when the instructions are executed by the at least one processor, the wearable electronic device can calculate the number of chewings or the chewing speed based on the feeding data stored from the start of the user's feeding activity.

[0186] According to one embodiment of the present disclosure, when the instructions are executed by the at least one processor, the number of works or the speed of works is compared with a reference number of works or a reference speed of works, and if the comparison value is higher than a predetermined ratio, a notification can be provided to the user through an output module.

[0187] According to one embodiment of the present disclosure, the eating data can be analyzed as containing at least one pattern in the user activity data from which noise has been removed using the average value of the user activity data.

[0188] According to one embodiment of the present disclosure, the plurality of sensors may include at least one of a vibration sensor, a voice sensor, or an electromyography sensor.

[0189] According to one embodiment of the present disclosure, the user eating information may include at least one of meal time, number of chewings, or chewing speed.

[0190] According to one embodiment of the present disclosure, the predetermined time may be adjusted based on at least one of the history of the stored feeding data and the feeding information.

[0191] According to one embodiment of the present disclosure, when the instructions are executed by the at least one processor, the wearable electronic device can transmit the feeding data and the user feeding information to an external electronic device through the communication module.

[0192] According to one embodiment of the present disclosure, the feeding data transmitted from the wearable electronic device and the user feeding information can cause the external electronic device to display a first UI.

[0193] According to one embodiment of the present disclosure, a method for generating user eating information may include: acquiring user activity data from at least one of a plurality of sensors; analyzing the user activity data for a predetermined period of time; determining the time of acquiring the user activity data containing at least one pattern as the time of starting the user eating activity when it is analyzed that the user activity data contains at least one pattern; storing the user activity data as eating data; and generating at least one user eating information based on the eating data.

[0194] According to one embodiment of the present disclosure, a method for generating user eating information may further include, when it is determined that the user activity data does not contain the at least one pattern, determining the time of acquiring user activity data that does not contain the at least one pattern as the time of ending the user eating activity.

[0195] According to one embodiment of the present disclosure, a method for generating user eating information may further include an operation of calculating the number of times or the speed of eating based on eating data stored from the start of the user eating activity.

[0196] According to one embodiment of the present disclosure, a method for generating user eating information may further include an operation of comparing the number of times or the speed of times to a reference number of times or the speed of times to a reference speed of times to a reference speed of times to a user, and an operation of providing a notification to the user through an output module when the comparison value is higher than a predetermined ratio.

[0197] The electronic device according to the various embodiments disclosed in this document may be of various forms. The electronic device may include, for example, a portable communication device (e.g., a smartphone), a computer device, a portable multimedia device, a portable medical device, a camera, a wearable device, or a consumer electronics device. The electronic device according to the embodiments of this document is not limited to the devices described above.

[0198] The various embodiments of this document and the terms used therein are not intended to limit the technical features described in this document to specific embodiments, and should be understood to include various modifications, equivalents, or substitutions of said embodiments. In connection with the description of the drawings, similar reference numerals may be used for similar or related components. The singular form of a noun corresponding to an item may include one or more of said items unless the relevant context clearly indicates otherwise. In this document, phrases such as “A or B,” “at least one of A and B,” “at least one of A or B,” “A, B, or C,” “at least one of A, B, and C,” and “at least one of A, B, or C” each may include any one of the items listed together in the corresponding phrase, or all possible combinations thereof. Terms such as “first,” “second,” or “first” or “second” may be used simply to distinguish a component from another corresponding component and do not limit the components in any other aspect (e.g., importance or order). Where any (e.g., 1st) component is referred to as “coupled” or “connected” to another (e.g., 2nd) component, with or without the terms “functionally” or “communicationly,” it means that said any component may be connected to said other component directly (e.g., via a wire), wirelessly, or through a third component.

[0199] The term “module” as used in the various embodiments of this document may include a unit implemented in hardware, software, or firmware, and may be used interchangeably with terms such as logic, logic block, component, or circuit, for example. A module may be a component formed integrally, or a minimum unit of said component or a part thereof that performs one or more functions. According to one embodiment, a module may be implemented in the form of an application-specific integrated circuit (ASIC).

[0200] Various embodiments of the present document may be implemented as software (e.g., program (140)) comprising one or more instructions stored in a storage medium (e.g., internal memory (136) or external memory (138)) readable by a machine (e.g., electronic device (101)). For example, a processor (e.g., processor (120)) of the machine (e.g., electronic device (101)) may call at least one of the one or more instructions stored in the storage medium and execute it. This enables the machine to be operated to perform at least one function according to the at least one called instruction. The one or more instructions may include code generated by a compiler or code that can be executed by an interpreter. The storage medium readable by the machine may be provided in the form of a non-transitory storage medium. Here, 'non-temporary' simply means that the storage medium is a tangible device and does not contain a signal (e.g., electromagnetic waves), and the term does not distinguish between cases where data is stored semi-permanently and cases where it is stored temporarily.

[0201] According to one embodiment, the method according to the various embodiments disclosed herein may be provided as included in a computer program product. The computer program product may be traded between a seller and a buyer as a product. The computer program product may be distributed in the form of a device-readable storage medium (e.g., compact disc read-only memory (CD-ROM)), or distributed online (e.g., download or upload) through an application store (e.g., Play Store™) or directly between two user devices (e.g., smartphones). In the case of online distribution, at least a portion of the computer program product may be temporarily stored or temporarily created on a device-readable storage medium, such as the memory of a manufacturer's server, an application store's server, or a relay server.

[0202] According to various embodiments, each component (e.g., module or program) of the components described above may include a singular or multiple entities, and some of the multiple entities may be separated and placed in other components. According to various embodiments, one or more of the components or operations of the aforementioned components may be omitted, or one or more other components or operations may be added. Generally or additionally, multiple components (e.g., module or program) may be integrated into a single component. In this case, the integrated component may perform one or more functions of each of the multiple components in the same or similar manner as those performed by the corresponding component among the multiple components prior to integration. According to various embodiments, operations performed by the module, program, or other components may be executed sequentially, in parallel, iteratively, or heuristically, or one or more of the operations may be executed in a different order, omitted, or one or more other operations may be added.

Claims

In a wearable electronic device, Multiple sensors for acquiring user activity data; A communication module comprising at least one communication circuit; Memory for storing instructions; and It includes at least one processor operatively connected to the communication module and the memory; When the above instructions are executed by the at least one processor, the wearable electronic device, User activity data is obtained from at least one of the plurality of sensors mentioned above, and Analyze the above user activity data for a predetermined period of time, and If it is analyzed that at least one pattern is included in the above user activity data, the time of acquisition of the user activity data including the at least one pattern is determined as the start time of the user eating activity, and The above user activity data is stored as eating data, and A wearable electronic device that generates at least one user eating information based on the above eating data. In Article 1, When the above instructions are executed by the at least one processor, the wearable electronic device, A wearable electronic device that determines the time of acquiring user activity data not containing the at least one pattern as the time of termination of user eating activity when it is analyzed that the user activity data does not contain the at least one pattern. In Article 1, When the above instructions are executed by the at least one processor, the wearable electronic device, A wearable electronic device that calculates the number of chewing operations or the chewing speed based on feeding data stored from the start of the user's feeding activity. In Paragraph 3, Including an output module; further When the above instructions are executed by the at least one processor, the wearable electronic device, Compare the above number of works or the above speed of works with a reference number of works or a reference speed of works, and A wearable electronic device that provides a notification to the user through the output module when the above comparison value is higher than a predetermined ratio. In Article 1, The above dietary data is, An electronic device comprising data in which at least one pattern is analyzed to be included in the user activity data from which noise has been removed using the average value of the user activity data. In Article 1, A wearable electronic device comprising at least one of a vibration sensor, a voice sensor, or an electromyography sensor, wherein the plurality of sensors described above. In Article 1, The above user eating information is a wearable electronic device comprising at least one of meal time, number of chewings, or chewing speed. In Article 1, A wearable electronic device that adjusts the above-determined time based on at least one of the history of the stored eating data and the eating information. In Article 1, When the above instructions are executed by the at least one processor, the wearable electronic device, A wearable electronic device that transmits the feeding data and the user feeding information to an external electronic device through the communication module. In Article 9, The food intake data transmitted from the above-mentioned wearable electronic device and the user food intake information cause the external electronic device to display a first UI. In a method for generating user's eating information using a wearable electronic device, The operation of acquiring user activity data from at least one of a plurality of sensors; An operation to analyze the above user activity data for a predetermined period of time; If it is analyzed that the user activity data includes at least one pattern, the operation of determining the time of acquisition of the user activity data containing the at least one pattern as the start time of the user eating activity; The operation of storing the above user activity data as eating data; and A method comprising the operation of generating at least one user eating information based on the above eating data. In Paragraph 11, The above method A method further comprising, when it is determined that the user activity data does not include at least one pattern, determining the time of acquiring user activity data that does not include at least one pattern as the time of termination of user eating activity. In Paragraph 11, The above method A method further comprising an operation to calculate the number of chewing operations or the chewing speed based on feeding data stored from the start of the user's feeding activity. In Paragraph 13, The above method The operation of comparing the above number of works or the above speed of works with a reference number of works or a reference speed of works; and A method further comprising an action of providing a notification to the user through an output module when the above comparison value is higher than a predetermined ratio. In Paragraph 11, The above method A method further comprising the operation of transmitting the feeding data and the user feeding information to an external electronic device through a communication module.

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