Electronic device for providing exercise plan and operation method therefor

Abstract

An electronic device according to an embodiment may comprise at least one processor and a memory for storing instructions. The instructions may cause, on the basis of being individually or collectively executed by the at least one processor, the electronic device to output a plurality of body type images on the basis of one or more of a user image and user information. The instructions may cause, on the basis of being individually or collectively executed by the at least one processor, the electronic device to determine, as a target body type, one or more body type images among the plurality of body type images on the basis of a user input. The instructions may cause, on the basis of being individually or collectively executed by the at least one processor, the electronic device to provide an exercise plan on the basis of one or more of the target body type and an exercise history of a user. Other various embodiments may also be possible.

Description

Electronic device providing exercise plan and method of operation thereof

[0001] Various embodiments of the present invention relate to an electronic device for providing an exercise plan and a method of operating the same.

[0002] With the recent rise in interest in health and fitness, various digital platforms offering personalized health and fitness coaching services have been proposed. These platforms provide personalized exercise plans based on users' physical data, lifestyle habits, health status, and exercise preferences, helping them maintain and improve their health.

[0003] Conventional digital health and fitness coaching services only provide users with general, abstract goals like weight loss, muscle gain, and physical strength improvement, making it difficult for users to set specific, ideal body goals. Furthermore, goals consisting solely of abstract numbers or categories often fail to accurately reflect users' individual desires and goals, hindering exercise adherence and reducing exercise effectiveness. A method for providing workout plans that reflect users' individual needs and provide ongoing motivation is needed.

[0004] The above information may be provided as background art to aid in understanding the present disclosure. No claim or determination is made as to whether any of the above is applicable as prior art related to the present disclosure.

[0005] The technical problems to be achieved in this document are not limited to the technical problems mentioned above, and other technical problems not mentioned can be clearly understood by a person having ordinary skill in the technical field to which the present invention belongs from the description below.

[0006] An electronic device according to one embodiment may include at least one processor and a memory storing instructions. The instructions, when individually or collectively executed by the at least one processor, may cause the electronic device to output a plurality of body images based on at least one of a user image and user information. The instructions, when individually or collectively executed by the at least one processor, may cause the electronic device to determine at least one of the plurality of body images as a target body type based on a user's selection input. The instructions, when individually or collectively executed by the at least one processor, may cause the electronic device to provide an exercise plan based on at least one of the target body type and the user's exercise history.

[0007] A method of operating an electronic device according to one embodiment may include an operation of outputting a plurality of body images based on at least one of a user image and user information.

[0008] The above operation method may include an operation of determining one or more body shape images among the plurality of body shape images as a target body shape based on a user's selection input.

[0009] The above method of operation may include an operation of providing an exercise plan based on at least one of the target body type and the exercise history of the user.

[0010] According to one embodiment, a computer-readable recording medium storing one or more computer programs may include instructions for performing the method in a processor.

[0011] FIG. 1 is a block diagram of an electronic device within a network environment, according to one embodiment.

[0012] FIG. 2 is a block diagram of an electronic device providing a workout plan, according to one embodiment.

[0013] FIG. 3 is a drawing for explaining a screen provided to a user according to one embodiment.

[0014] FIG. 4 is a diagram for explaining an operation of processing user information according to one embodiment.

[0015] FIG. 5a and FIG. 5b are flowcharts illustrating the operation of a body information modeling module according to one embodiment.

[0016] FIG. 6 is a diagram for explaining the operation of an image generation module according to one embodiment.

[0017] FIGS. 7A and 7B are drawings for explaining a plurality of body images according to one embodiment.

[0018] FIGS. 8A and 8B are drawings illustrating an interface provided to a user according to one embodiment.

[0019] FIG. 9 is a block diagram illustrating an exercise plan generation module according to one embodiment.

[0020] FIG. 10 is a diagram illustrating the operation of an exercise plan generation module according to one embodiment.

[0021] FIG. 11 is an example of an interface provided to a user according to one embodiment.

[0022] FIG. 12 is a diagram illustrating a user feedback interface module according to one embodiment.

[0023] FIGS. 13A and 13B are diagrams illustrating an exercise plan that changes according to user feedback, according to one embodiment.

[0024] FIG. 14 illustrates a flowchart of a method of operating an electronic device according to one embodiment.

[0025] Hereinafter, embodiments will be described in detail with reference to the attached drawings. In the description with reference to the attached drawings, identical components are assigned the same reference numerals regardless of the drawing numbers, and redundant descriptions thereof will be omitted.

[0026]

[0027] FIG. 1 is a block diagram of an electronic device (101) within a network environment (100) according to various embodiments. Referring to FIG. 1, in the network environment (100), the electronic device (101) may communicate with an electronic device (102) via a first network (198) (e.g., a short-range wireless communication network), or may communicate with at least one of an electronic device (104) or a server (108) via a second network (199) (e.g., a long-range wireless communication network). According to one embodiment, the electronic device (101) may communicate with the electronic device (104) via the server (108). According to one embodiment, the electronic device (101) may include a processor (120), a memory (130), an input module (150), an audio output module (155), a display module (160), an audio module (170), a sensor module (176), an interface (177), a connection terminal (178), a haptic module (179), a camera module (180), a power management module (188), a battery (189), a communication module (190), a subscriber identification module (196), or an antenna module (197). In some embodiments, the electronic device (101) may omit at least one of these components (e.g., the connection terminal (178)), or may have one or more other components added. In some embodiments, some of these components (e.g., the sensor module (176), the camera module (180), or the antenna module (197)) may be integrated into one component (e.g., the display module (160)).

[0028] The processor (120) may, for example, execute software (e.g., a program (140)) to control at least one other component (e.g., a hardware or software component) of the electronic device (101) connected to the processor (120) and perform various data processing or operations. According to one embodiment, as at least a part of the data processing or operations, the processor (120) may store commands or data received from other components (e.g., a sensor module (176) or a communication module (190)) in a volatile memory (132), process the commands or data stored in the volatile memory (132), and store result data in a non-volatile memory (134). According to one embodiment, the processor (120) may be implemented as a circuit (e.g., a processing circuit) such as a system on chip (SoC) or an integrated circuit (IC). The processor (120) may include one or more processors. For example, the processor (120) may include a combination of one or more processors, such as a CPU, a GPU, an MPU, an AP, and a CP. According to one embodiment, the processor (120) may include a main processor (121) (e.g., a central processing unit or an application processor) or an auxiliary processor (123) (e.g., a graphics processing unit, a neural processing unit (NPU), an image signal processor, a sensor hub processor, or a communication processor) that can operate independently or together therewith. For example, when the electronic device (101) includes the main processor (121) and the auxiliary processor (123), the auxiliary processor (123) may be configured to use less power than the main processor (121) or to be specialized for a given function. The auxiliary processor (123) may be implemented separately from the main processor (121) or as a part thereof.

[0029] The auxiliary processor (123) may control at least a portion of functions or states associated with at least one component (e.g., a display module (160), a sensor module (176), or a communication module (190)) of the electronic device (101), for example, on behalf of the main processor (121) while the main processor (121) is in an inactive (e.g., sleep) state, or together with the main processor (121) while the main processor (121) is in an active (e.g., application execution) state. In one embodiment, the auxiliary processor (123) (e.g., an image signal processor or a communication processor) may be implemented as a part of another functionally related component (e.g., a camera module (180) or a communication module (190)). In one embodiment, the auxiliary processor (123) (e.g., a neural network processing unit) may include a hardware structure specialized for processing artificial intelligence models. The artificial intelligence models may be generated through machine learning. This learning can be performed, for example, on the electronic device (101) itself where the artificial intelligence model is executed, or can be performed through a separate server (e.g., server (108)). The learning algorithm can include, for example, supervised learning, unsupervised learning, semi-supervised learning, or reinforcement learning, but is not limited to the examples described above. The artificial intelligence model can include multiple artificial neural network layers.The artificial neural network may be one of a deep neural network (DNN), a convolutional neural network (CNN), a recurrent neural network (RNN), a restricted Boltzmann machine (RBM), a deep belief network (DBN), a bidirectional recurrent deep neural network (BRDNN), a deep Q-network, or a combination of two or more of the above, but is not limited to the examples described above. In addition to, or alternatively to, a hardware structure, an artificial intelligence model may include a software structure.

[0030] The memory (130) can store various data used by at least one component (e.g., the processor (120) or the sensor module (176)) of the electronic device (101). The data can include, for example, software (e.g., the program (140)) and input data or output data for commands related thereto. The memory (130) can include a volatile memory (132) or a non-volatile memory (134). According to one embodiment, the memory (130) can include one or more memories. Instructions stored in the memory (130) can be stored in one memory. Instructions stored in the memory (130) can be divided and stored in multiple memories. According to one embodiment, instructions stored in the memory (130) may cause the electronic device (101) to perform one or more operations based on being individually or collectively executed by at least one processor (e.g., the main processor (121) and / or the auxiliary processor (123)). For example, the instructions stored in the memory (130) may be executed by one processor (e.g., the main processor (121) or an auxiliary processor (123) such as a communication processor) or by a plurality of processors operating cooperatively with each other (e.g., the main processor (121) and the auxiliary processor (123)).

[0031] The program (140) may be stored as software in the memory (130) and may include, for example, an operating system (142), middleware (144), or an application (146).

[0032] The input module (150) can receive commands or data to be used in a component of the electronic device (101) (e.g., a processor (120)) from an external source (e.g., a user) of the electronic device (101). The input module (150) can include, for example, a microphone, a mouse, a keyboard, a key (e.g., a button), or a digital pen (e.g., a stylus pen).

[0033] The audio output module (155) can output audio signals to the outside of the electronic device (101). The audio output module (155) can include, for example, a speaker or a receiver. The speaker can be used for general purposes, such as multimedia playback or recording playback. The receiver can be used to receive incoming calls. In one embodiment, the receiver can be implemented separately from the speaker or as part of the speaker.

[0034] The display module (160) can visually provide information to an external device (e.g., a user) of the electronic device (101). The display module (160) may include, for example, a display, a holographic device, or a projector, and a control circuit for controlling the device. According to one embodiment, the display module (160) may include a touch sensor configured to detect a touch, or a pressure sensor configured to measure the intensity of a force generated by the touch. The display module (160) may be implemented with an illustrative foldable structure and / or a rollable structure. For example, the size of the display screen of the display module (160) may be reduced when folded, and may be expanded when unfolded.

[0035] The audio module (170) can convert sound into an electrical signal, or vice versa, convert an electrical signal into sound. According to one embodiment, the audio module (170) can acquire sound through the input module (150), output sound through the sound output module (155), or an external electronic device (e.g., electronic device (102)) (e.g., speaker or headphone) directly or wirelessly connected to the electronic device (101).

[0036] The sensor module (176) can detect the operating status (e.g., power or temperature) of the electronic device (101) or the external environmental status (e.g., user status) and generate an electrical signal or data value corresponding to the detected status. According to one embodiment, the sensor module (176) can include, for example, a gesture sensor, a gyro sensor, a barometric pressure sensor, a magnetic sensor, an acceleration sensor, a grip sensor, a proximity sensor, a color sensor, an IR (infrared) sensor, a biometric sensor, a temperature sensor, a humidity sensor, or an illuminance sensor.

[0037] The interface (177) may support one or more designated protocols that may be used to directly or wirelessly connect the electronic device (101) with an external electronic device (e.g., the electronic device (102)). In one embodiment, the interface (177) may include, for example, a high definition multimedia interface (HDMI), a universal serial bus (USB) interface, an SD card interface, or an audio interface.

[0038] The connection terminal (178) may include a connector through which the electronic device (101) may be physically connected to an external electronic device (e.g., electronic device (102)). According to one embodiment, the connection terminal (178) may include, for example, an HDMI connector, a USB connector, an SD card connector, or an audio connector (e.g., a headphone connector).

[0039] A haptic module (179) can convert electrical signals into mechanical stimuli (e.g., vibration or movement) or electrical stimuli that a user can perceive through tactile or kinesthetic sensations. In one embodiment, the haptic module (179) can include, for example, a motor, a piezoelectric element, or an electrical stimulation device.

[0040] The camera module (180) can capture still images and videos. According to one embodiment, the camera module (180) may include one or more lenses, image sensors, image signal processors, or flashes.

[0041] 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 a part of a power management integrated circuit (PMIC).

[0042] A battery (189) may power at least one component of the electronic device (101). In one embodiment, the battery (189) may include, for example, a non-rechargeable primary battery, a rechargeable secondary battery, or a fuel cell.

[0043] The communication module (190) may support the establishment of a direct (e.g., wired) communication channel or a wireless communication channel between the electronic device (101) and an external electronic device (e.g., electronic device (102), electronic device (104), or server (108)), and the performance of communication through the established communication channel. The communication module (190) may operate independently from the processor (120) (e.g., application processor) and may include one or more communication processors that support direct (e.g., wired) communication or wireless communication. According to one embodiment, the communication module (190) may include a wireless communication module (192) (e.g., a cellular communication module, a short-range wireless communication module, or a global navigation satellite system (GNSS) communication module) or a wired communication module (194) (e.g., a local area network (LAN) communication module, or a power line communication module). Among these communication modules, the corresponding communication module can communicate with an external electronic device (104) via a first network (198) (e.g., a short-range communication network such as Bluetooth, wireless fidelity (WiFi) direct, or infrared data association (IrDA)) or a second network (199) (e.g., a long-range communication network such as a legacy cellular network, a 5G network, a next-generation communication network, the Internet, or a computer network (e.g., a LAN or WAN)). These various types of communication modules can be integrated into a single component (e.g., a single chip) or implemented as multiple separate components (e.g., multiple chips). The wireless communication module (192) can verify or authenticate the electronic device (101) within a communication network such as the first network (198) or the second network (199) by using subscriber information (e.g., an international mobile subscriber identity (IMSI)) stored in the subscriber identification module (196).

[0044] The wireless communication module (192) can support 5G networks and next-generation communication technologies following the 4G network, such as NR access technology (new radio access technology). The NR access technology can support high-speed transmission of high-capacity data (eMBB (enhanced mobile broadband)), minimization of terminal power and connection of multiple terminals (mMTC (massive machine type communications)), or high reliability and low latency (URLLC (ultra-reliable and low-latency communications)). The wireless communication module (192) can support, for example, a high-frequency band (e.g., mmWave band) to achieve a high data transmission rate. The wireless communication module (192) can support various technologies for securing performance in a high-frequency band, such as beamforming, massive multiple-input and multiple-output (MIMO), full dimensional MIMO (FD-MIMO), array antenna, analog beam-forming, or large scale antenna. The wireless communication module (192) can support various requirements specified in the electronic device (101), an external electronic device (e.g., the electronic device (104)), or a network system (e.g., the second network (199)). According to one embodiment, the wireless communication module (192) can support a peak data rate (e.g., 20 Gbps or more) for eMBB realization, a loss coverage (e.g., 164 dB or less) for mMTC realization, or a U-plane latency (e.g., 0.5 ms or less for downlink (DL) and uplink (UL), or 1 ms or less for round trip) for URLLC realization.

[0045] The antenna module (197) can transmit or receive signals or power to or from an external device (e.g., an external electronic device). According to one embodiment, the antenna module (197) may include an antenna including a radiator formed of a conductor or a conductive pattern formed on a substrate (e.g., a PCB). 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 the first network (198) or the second network (199), may be selected from the plurality of antennas by, for example, the 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, another component (e.g., a radio frequency integrated circuit (RFIC)) may be additionally formed as a part of the antenna module (197).

[0046] 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 side (e.g., a bottom side) of the printed circuit board and capable of supporting a designated high-frequency band (e.g., a mmWave band), and a plurality of antennas (e.g., an array antenna) disposed on or adjacent to a second side (e.g., a top side or a side side) of the printed circuit board and capable of transmitting or receiving signals in the designated high-frequency band.

[0047] At least some of the above components can be interconnected and exchange signals (e.g., commands or data) with each other via a communication method between peripheral devices (e.g., a bus, GPIO (general purpose input and output), SPI (serial peripheral interface), or MIPI (mobile industry processor interface)).

[0048] According to one embodiment, commands or data may be transmitted or received between the electronic device (101) and an external electronic device (104) via a server (108) connected to a second network (199). Each of the external electronic devices (102 or 104) may be the same or a different type of device as the electronic device (101). According to one embodiment, all or part of the operations executed in the electronic device (101) may be executed in one or more of the external electronic devices (102, 104, or 108). For example, when the electronic device (101) is to perform a certain function or service automatically or in response to a request from a user or another device, the electronic device (101) may, instead of or in addition to executing the function or service itself, request one or more external electronic devices to perform the function or at least a part of the service. One or more external electronic devices that receive the request may execute at least a portion of the requested function or service, or an additional function or service related to the request, and transmit the result of the execution to the electronic device (101). The electronic device (101) may process the result as is or additionally and provide it as at least a portion of a response to the request. For this purpose, cloud computing, distributed computing, mobile edge computing (MEC), or client-server computing technology may be used, for example. The electronic device (101) may provide an ultra-low latency service by using distributed computing or mobile edge computing, for example. In another embodiment, the external electronic device (104) may include an Internet of Things (IoT) device. The server (108) may be an intelligent server utilizing machine learning and / or a neural network. According to one embodiment, the external electronic device (104) or the server (108) may be included in the second network (199).The electronic device (101) can be applied to intelligent services (e.g., smart home, smart city, smart car, or healthcare) based on 5G communication technology and IoT-related technology.

[0049]

[0050] FIG. 2 is a block diagram of an electronic device providing a workout plan, according to one embodiment.

[0051] Referring to FIG. 2, according to one embodiment, an electronic device (200) (e.g., the electronic device (101) of FIG. 1 ) may provide a user with an exercise plan. The exercise plan may include a personalized exercise plan for the user. The exercise plan may be generated from information personalized to the user, such as the user's personalized goals (e.g., target body type), information about the user (e.g., user image, user information), and the user's exercise history. The electronic device (200) may utilize information about the user (e.g., exercise history) stored within the electronic device (200) to provide the user with a personalized exercise plan.

[0052] In one embodiment, the electronic device (200) may collaborate with an external server (201) (e.g., a health service provision server) and an artificial intelligence (AI) model to provide an exercise plan. The electronic device (200) may collaborate with an AI model (e.g., an AI model such as an inferential model and / or a generative model) generated from an AI model generator running on the server (201) (e.g., server (108) of FIG. 1) to generate an exercise plan.

[0053] According to one embodiment, the electronic device (200) and the server (201) may be connected via a local area network (LAN), a wide area network (WAN), a value added network (VAN), a mobile radio communication network, a satellite communication network, or a combination thereof. The electronic device (200) and the server (201) may communicate with each other via a wired communication method or a wireless communication method (e.g., wireless LAN (WiFi), Bluetooth, Bluetooth low energy, ZigBee, WiFi direct (WFD), ultra-wide band (UWB), infrared data association (IrDA), near field communication (NFC)).

[0054] According to one embodiment, the electronic device (200) may be implemented as at least one of a smartphone, a tablet personal computer, a mobile phone, a speaker (e.g., an AI speaker), a video phone, an e-book reader, a desktop personal computer, a laptop personal computer, a netbook computer, a workstation, a server, a personal digital assistant (PDA), a portable multimedia player (PMP), an MP3 player, a mobile medical device, a camera, smart glasses, or a wearable device. The smart glasses may provide a user with virtual reality, augmented reality, or mixed reality through a display.

[0055] According to one embodiment, the electronic device (200) may include a processor (210) (e.g., processor (120) of FIG. 1), a memory (230) (e.g., memory (130) of FIG. 1), and a display module (260) (e.g., display module (160) of FIG. 1). The processor (210) (e.g., application processor) may access the memory (230) to execute one or more instructions.

[0056] According to one embodiment, the processor (210) may be implemented as a circuit (e.g., a processing circuit) such as a system on chip (SoC) or an integrated circuit (IC). The processor (210) may include one or more processors. For example, the processor (210) may include a combination of one or more processors such as a CPU, a GPU, an MPU, an AP, and a CP. The instructions stored in the memory (230) may be executed by one processor to cause the communication device (200) to perform and / or control the operation of the electronic device (200) to be described with reference to FIGS. 2 to 15. The instructions stored in the memory (230) may be executed by multiple processors to cause the electronic device (200) to perform and / or control the operation of the electronic device (200) to be described with reference to FIGS.

[0057] According to one embodiment, the memory (230) may include one or more memories. Instructions stored in the memory (230) may be stored in a single memory. Instructions stored in the memory (230) may be divided and stored in multiple memories. The instructions stored in the memory (230) may be executed by the processor (520) to cause the electronic device (200) to perform and / or control the operations of the electronic device (200) described with reference to FIGS. 2 to 15. The memory (230) may store various data used by at least one component (e.g., the processor (210)) of the electronic device (200). The status information analysis module (220) and the AI ​​model module (250) are executable by the processor (210) and may be configured as one or more of a program code including instructions that can be stored in the memory (230), an application, an algorithm, a routine, a set of instructions, or an artificial intelligence learning model. At least one of the status information analysis module (220) and the AI ​​model module (250) may be implemented as hardware and / or a combination of hardware and software.

[0058] According to one embodiment, the electronic device (200) can obtain and store a user image (203), user information (205), and exercise history (207). The memory (230) can store one or more of the user image (203), the user information (205), and the exercise history (207), and according to an embodiment, a memory (not shown) other than the memory (230) included in the electronic device (200) can store one or more of the user image (203), the user information (205), and the exercise history (207).

[0059] According to one embodiment, the status information analysis module (220) may preprocess data (e.g., data to be used to generate an exercise plan). For example, the status information analysis module (220) may preprocess user images (203), user information (205), and exercise history (207) (e.g., including preprocessing methods such as vectorization and / or normalization). The status information analysis module (220) may output the preprocessed user images (203), user information (205), and exercise history (207) to the AI ​​model module (250).

[0060] According to one embodiment, the user image (203) may include an image retrieved from outside the electronic device (200) and an image captured by the user using a camera module of the electronic device (200) (e.g., the camera module (180) of FIG. 1). The user image (203) may include an image of the user's body. For example, the user image (203) may include an image captured so as to show the user's entire body.

[0061] According to one embodiment, user information (205) may include information about the user. For example, user information (205) may include data about the user's body (e.g., age, gender, height, weight, size of each body part, body fat percentage, body change data, etc.). Body change data may include data about changes in data about the user's body over time. User information (205) may include mandatory input information and optional input information. Mandatory input information may include information that is mandatory to be input by the user. Mandatory input information may include the user's age, gender, height, weight, etc. Optional input information may include information that is not mandatory to be input by the user. The electronic device (200) may estimate values ​​for items of optional input information that are not input by the user through the status information analysis module (220). The status information analysis module (220) may derive optional input information from mandatory input information. The operation of the status information analysis module (220) to derive optional input information from required input information will be described in detail later with reference to FIG. 4. Optional input information may include the user's body fat percentage, dimensions of each body part (e.g., waist circumference, horizontal shoulder length, shoulder thickness, chest circumference, arm thickness, thigh circumference, calf circumference, etc.), etc.

[0062] According to one embodiment, the exercise history (207) may include a user's exercise record stored in the electronic device (200). The exercise history (207) may include the user's exercise record, such as exercise frequency (e.g., exercise performance date, exercise time), exercise type, exercise intensity, calories consumed, etc. The exercise history (207) may include an exercise record entered by the user into the electronic device (200) and / or an exercise record of the user received by the electronic device (200) from an external electronic device (e.g., the electronic device (102) and / or the electronic device (104) of FIG. 1) (e.g., a wearable device owned by the user).

[0063] In one embodiment, the AI ​​model module (250) may provide a personalized exercise plan to a user. The AI ​​model module (250) may include a body information modeling module (253), an image generation module (255), and an exercise plan generation module (257).

[0064] According to one embodiment, the body information modeling module (253) can infer the ratios of possible changes for each body part of the user. The body information modeling module (253) can infer the ratios of possible changes for each body part of the user based on the user information (205) (e.g., the user's body change data). The body information modeling module (253) can infer the ratios of possible changes for each body part of the user by utilizing a learned AI model downloaded from the server (201). For example, the AI ​​model can include an AI model generated and learned through an AI model generator running on the server (201). The body information modeling module (253) can infer the ratios of possible changes for each body part of the user by utilizing an inference model (e.g., an inference model such as a Bayesian inference model). The operation of the body information modeling module (253) to infer variable ratios for each body part of the user will be described in detail later with reference to FIGS. 5A and 5B. The body information modeling module (253) can output variable ratios for each body part of the user to the exercise plan generation module (257).

[0065] According to one embodiment, the image generation module (255) can generate a plurality of body shape images. The image generation module (255) can generate a plurality of body shape images based on one or more of a user image (203) (e.g., a user image preprocessed by the status information analysis module (220)) and user information (205) (e.g., user information preprocessed by the status information analysis module (220). The image generation module (255) can receive variable ratios for each body part of the user from the body information modeling module (253). The image generation module (255) can obtain a plurality of segments corresponding to the body parts of the user from the user image (203). The image generation module (255) can generate a plurality of body shape images by transforming the plurality of segments. The image generation module (255) can generate multiple body images by transforming multiple segments based on variable ratios for each body part corresponding to each of the one or more segments. The operation of the image generation module (255) to generate multiple body images will be described in detail later with reference to FIGS. 6 to 8B.

[0066] According to one embodiment, the electronic device (200) can output a plurality of body images through the image generation module (255). The electronic device (200) can output a plurality of body images by displaying an interface including the plurality of body images on the display module (260). The electronic device (200) can receive a user's selection input for the plurality of body images, and determine one or more of the plurality of body images as a target body shape based on the user's selection input.

[0067] According to one embodiment, the exercise plan generation module (257) can generate an exercise plan. The exercise plan generation module (257) can generate an exercise plan based on one or more of a target body type and the user's exercise history (e.g., exercise history (207)). The exercise plan generation module (257) can generate a personalized exercise plan for the user based on the target body type and the user's exercise history (207) corresponding to the user's selection input. The operations performed by the exercise plan generation module (257) will be described in detail below with reference to FIGS. 11 and 12 .

[0068] According to one embodiment, the exercise plan generation module (257) can provide an exercise plan. The electronic device (200) can provide the exercise plan to the user by displaying an interface including the generated exercise plan on the display module (260). The electronic device (200) can receive user feedback on the exercise plan through a user feedback interface (e.g., the user feedback interface (1200) of FIG. 12). The electronic device (200) can generate an exercise plan that reflects the user feedback through the exercise plan generation module (257).

[0069]

[0070] FIG. 3 is a drawing for explaining a screen provided to a user according to one embodiment.

[0071] Referring to FIG. 3, according to one embodiment, the electronic device (200) may display screens (310) to (350) associated with the collection of data necessary to generate an exercise plan. The electronic device (200) may display the screens (310) to (350) on a display module (e.g., the display module (260) of FIG. 2).

[0072] According to one embodiment, the screen (310) and the screen (330) may include a user interface (UI) (303) (e.g., an input window) for inputting user information (205). The screen (330) may be a screen displayed on the display module (260) in response to a scroll input for scrolling the screen (310) from top to bottom or bottom to top. The UI (303) may be implemented so that essential input information among the user information (205) must be input. For example, if the user has not input one or more of the essential input information (e.g., age, gender, height, weight, etc.), the UI (303) may be implemented so that input is suggested and a screen corresponding to the next process is not provided. When the electronic device (200) receives all the essential input information through the UI (303), the electronic device (200) may derive and display selective input information in response. The electronic device (200) may derive selective input information from the essential input information through the status information analysis module (220).

[0073] According to one embodiment, the electronic device (200) can receive a user image (203). The screen (310) can include an icon (301) for allowing a user to input the user image (203). The icon (301) can include an icon (e.g., a gallery icon) for allowing a user to retrieve an image stored in the electronic device (200) and an icon (e.g., a camera icon) for allowing a user to take a new image via a camera module (e.g., the camera module (180) of FIG. 1).

[0074] According to one embodiment, the screen (350) illustrates an example of capturing a user's image. The electronic device (200) can acquire a user image (203) by having the user capture his / her own appearance (e.g., body) through the camera module (180).

[0075]

[0076] FIG. 4 is a diagram for explaining an operation of processing user information according to one embodiment.

[0077] Referring to FIG. 4, according to one embodiment, the state information analysis module (220) may preprocess user information (205). The user information (205) may include required input information (410) and optional input information (430). The user may not input a value for one or more items (e.g., waist circumference, body fat percentage, horizontal shoulder length, thigh length, etc.) included in the optional input information (430). The value for an item not input by the user may be determined as a null value. The state information analysis module (220) may process the required input information (410) to estimate a value corresponding to an item included in the optional input information (430). The state information analysis module (220) may utilize a predefined sample to estimate a value corresponding to an item (e.g., an item with a null value) (e.g., waist circumference, body fat percentage, etc.) included in the optional input information (430) and replace the null value. The predefined sample may include a standard numerical calculation method that has been previously defined. The state information analysis module (220) may estimate a value for an item of the optional input information that is a null value based on the user image (450) (e.g., the user image (203)). For example, the state information analysis module (220) may estimate a value for an item (e.g., an item with a null value) (e.g., a horizontal length of the shoulder, a circumference of the thigh, etc.) included in the optional input information (430) and replace the null value by substituting a value (e.g., 180 cm) corresponding to the height item included in the required input information (410) into the ratio of each body part obtained from the user image (450). The state information analysis module (220) may output user information including the preprocessed optional input information (470) to the body information modeling module (253).

[0078]

[0079] FIG. 5a and FIG. 5b are flowcharts illustrating the operation of a body information modeling module according to one embodiment.

[0080] FIG. 5a is a flowchart illustrating the operation of a body information modeling module according to one embodiment.

[0081] Referring to FIG. 5A, according to one embodiment, operations 510 to 590 may be operations performed by the electronic device (200) described with reference to FIG. 2 through the body information modeling module (253).

[0082] According to one embodiment, the server (201) may include an artificial intelligence (AI) model generator (not shown). The AI ​​model generator may generate an AI model by repeatedly training a time series model (e.g., a recurrent neural network (RNN), a long short term memory (LSTM), a gated recurrent unit (GRU), a transformer, etc.) based on the user's body change data. The AI ​​model may include an inference model. The AI ​​model generator may collect body change data over time (e.g., days) and preprocess the data (e.g., the number of days elapsed, the user's body data) through data embedding. The AI ​​model generator may generate an inference model by repeatedly training the time series model using the preprocessed data.

[0083] According to one embodiment, in operation 510, the body information modeling module (253) may receive user information (e.g., user information preprocessed by the state information analysis module (220)).

[0084] According to one embodiment, in operation 530, the body information modeling module (253) may infer a changeable ratio for each body part. The body information modeling module (253) may infer a changeable ratio for each body part of the user based on user information (e.g., user information such as age, gender, height, and race). For example, the body information modeling module (253) may infer a changeable ratio for each body part based on average body information (e.g., body information such as average weight, body type, skeletal structure, muscle mass, and body proportion) according to the user's age, gender, height, and race. The body information modeling module (253) may infer a changeable ratio for each body part of the user based on the user's body change data. The body information modeling module (253) may infer a changeable ratio for each body part of the user by utilizing an inference model. The AI ​​model generator may collect body change data according to time (e.g., day 1 to day n) and embedding-process the data to generate vector data capable of artificial intelligence learning. The vector data may include information that serves as an indicator of changes in the user's body. The AI ​​model generator may use the generated vector data to train a time series model (e.g., a recurrent neural network (RNN), a long short-term memory (LSTM), a gated recurrent unit (GRU), a transformer, etc.). The AI ​​model generator may repeatedly train the time series model to generate an inference model (e.g., an inference model such as a Bayesian inference model). The inference model may take a user image (203) and a specific period (e.g., m days) as input and output a changeable ratio for each body part of the user after a specific period of time has elapsed from the time the user image (203) is received.The AI ​​model generator can generate an inference model that reflects the user's latest physical change trends by retraining the time series model every n days (e.g., n is a natural number greater than or equal to 1). For example, the AI ​​model generator can reflect changes in the user's environment and changes in the user's physical change data in the inference model by retraining the time series model every n days (e.g., n is a natural number greater than or equal to 1). The body information modeling module (253) can utilize the inference model generated from the AI ​​model generator to infer the changeable ratio of each body part after a specific period of time (e.g., m days).

[0085] According to one embodiment, in operation 550, the body information modeling module (253) may randomly generate change values ​​for each body part. The body information modeling module (253) may generate multiple body change scenarios in which change values ​​are randomly reflected for each body part. The body information modeling module (253) may randomly generate change values ​​for each body part of the user based on the changeable ratio for each body part inferred in operation 530. For example, the body information modeling module (253) may randomly generate change values ​​within a range of changeable ratios after a specific period of time (e.g., after n days) obtained through an inference model. The change values ​​for each body part may include values ​​within the range of changeable ratios for each body part. The body information modeling module (253) may randomly select a body part for which to generate a change value and randomly generate a change value for the corresponding body part.

[0086] According to one embodiment, in operation 570, the body information modeling module (253) can determine whether data (e.g., multiple body change scenarios) to which change values ​​are randomly reflected reflect an actual improveable ratio. The body information modeling module (253) can determine whether data (e.g., multiple body change scenarios) to which change values ​​are randomly reflected reflect an actual improveable ratio and thus reflect a change value that is achievable by the user. For example, the body information modeling module (253) can determine whether an actual improveable ratio is reflected for multiple body change scenarios, thereby determining whether an unrealistic body ratio and / or excessive body distortion is likely to be included. The actual improveable ratio can be determined based on an embedding vector generated based on the user's existing body change data. The body information modeling module (253) can determine whether an actual improveable ratio is reflected by utilizing an inference model. For example, the body information modeling module (253) can load the embedding layer of the inference model, input data (e.g., multiple body change scenarios) with randomly reflected change values ​​in the vector space, and calculate the similarity distance with the existing vector data. The body information modeling module (253) can determine whether the data with randomly reflected change values ​​reflects an actual improveable ratio based on vector data including information that serves as an indicator of the user's body change. If the calculated similarity distance exceeds a predetermined threshold, the body information modeling module (253) can determine that it is not an improveable ratio and perform operation 550 again. If the body information modeling module (253) determines that it is an improveable ratio, the body information modeling module (253) can perform operation 590.

[0087] According to one embodiment, in operation 590, the body information modeling module (253) may adjust the ratio of surrounding body parts according to the body part. For example, the body information modeling module (253) may adjust the circumference of surrounding body parts, such as shoulders and / or arms, based on data corresponding to an increased chest circumference. By adjusting the ratio of surrounding body parts, the body information modeling module (253) may generate a plurality of reasonably balanced body shape images.

[0088]

[0089] Figure 5b is an example of a result reflecting the operation performed by the body information modeling module (253) described with reference to Figure 5a.

[0090] Referring to FIG. 5B, according to one embodiment, the electronic device (200) can infer a changeable ratio (535) for each body part based on user information (515) (e.g., user information (205)) through the body information modeling module (253). The electronic device (200) can generate a plurality of body change scenarios (555) by randomly generating change values ​​for each body part through the body information modeling module (253). The electronic device (200) can determine, through the body information modeling module (253), whether the generated plurality of body change scenarios (555) are improveable ratios and generate a final scenario (595) by adjusting the surrounding improvement ratio.

[0091]

[0092] FIG. 6 is a diagram for explaining the operation of an image generation module according to one embodiment.

[0093] Referring to FIG. 6, according to one embodiment, operations 610 to 670 may be operations for the electronic device (200) described with reference to FIG. 2 to generate a plurality of body images through an image generation module (e.g., the image generation module (255) of FIG. 2). The electronic device (200) may provide an image generated based on the user's image as a goal for health and / or exercise, thereby visualizing the goal and setting a goal that can stimulate the user's interest in exercise.

[0094] According to one embodiment, the image generation module (255) can output a plurality of body images. The plurality of body images can be generated by converting a plurality of segments obtained from a user image (203) (e.g., a user image preprocessed by the status information analysis module (220). The image generation module (255) can output a plurality of body images based on the user image (203), but in which segments corresponding to each body part are converted differently. The image generation module (255) can provide a visualization of health and / or exercise goals to the user by outputting a plurality of body images. The image generation module (255) can provide a personalized exercise plan based on an exercise goal that reflects the user's personal desires by outputting a plurality of body images.

[0095] According to one embodiment, the image generation module (255) may output multiple body images using images of other people rather than the user. The electronic device (200) may output multiple body images based on the user image using body images of other people (e.g., celebrities, influencers, etc.) received.

[0096] According to one embodiment, in operation 610, the image generation module (255) may receive a user image (203) and a variable ratio for each body part. The image generation module (255) may receive the user image (203) and a variable ratio for each body part to generate a plurality of body images. The image generation module (255) may receive the variable ratio for each body part from the body information modeling module (253).

[0097] According to one embodiment, in operation 630, the image generation module (255) can obtain a plurality of segments. The image generation module (255) can obtain a plurality of segments corresponding to body parts of the user from the user image (203). The image generation module (255) can perform segmentation on the user image (203) by utilizing deep learning technology (e.g., computer vision technology, etc.). The image generation module (255) can obtain a plurality of segments corresponding to body parts of the user (e.g., arms, shoulders, chest, stomach, thighs, calves, etc.) by performing segmentation on the user image (203).

[0098] According to one embodiment, in operation 650, the image generation module (255) can transform a plurality of segments. The image generation module (255) can transform the plurality of segments based on a changeable ratio for each body part.

[0099] According to one embodiment, in operation 670, the image generation module (255) can generate a plurality of body images. The image generation module (255) can generate a plurality of body images by utilizing a generative model (e.g., a stable diffusion model, etc.). When utilizing an image of another person to generate a body image, the image generation module (255) can analyze the body type of the other person in the image and generate a body image in which a plurality of segments of the user image (203) are converted to correspond to the body type of the other person.

[0100]

[0101] FIGS. 7A and 7B are drawings for explaining a plurality of body images according to one embodiment.

[0102] FIG. 7a is a drawing for explaining a plurality of body images according to one embodiment.

[0103] Referring to FIG. 7a, a plurality of body images (e.g., body image (751) to body image (753)) may represent an example of a plurality of body images generated by the image generation module (255).

[0104] According to one embodiment, the image generation module (255) may perform segmentation on a user image (710) (e.g., the user image (203) of FIG. 2). The image generation module (255) may perform segmentation on the user image (710) to obtain a plurality of segments. The image (730) may represent an example of a result of performing segmentation on the user image (710).

[0105] According to one embodiment, the image generation module (255) can transform a plurality of segments. The body image (751) to the body image (755) may include images in which a plurality of segments obtained from the user image (710) are transformed based on a changeable ratio for each body part. For example, the body image (755) may be a body image generated by transforming a plurality of segments corresponding to the upper body (e.g., shoulders, arms, chest, etc.) among the body parts based on the user image (710).

[0106]

[0107] Figure 7b is an example of multiple body shape images generated using clothing information.

[0108] Referring to FIG. 7B, according to one embodiment, the image generation module (255) may utilize outfit information when generating a plurality of body images. The outfit information may include outfit information obtained from an image of the user stored in the electronic device (200) and outfit information obtained from outside the electronic device (200). The image generation module (255) may output a plurality of body images in which the outfit information is reflected. For example, the body image (771) to the body image (775) may be examples of images generated by reflecting a suit image in the body image (751) to the body image (755), respectively. The electronic device (200) may provide the user with specific exercise goals by outputting a plurality of body images in which the outfit information is reflected.

[0109]

[0110] FIGS. 8A and 8B are drawings illustrating an interface provided to a user according to one embodiment.

[0111] FIG. 8A is an example of an interface provided to a user according to one embodiment.

[0112] Referring to FIG. 8A, according to one embodiment, the electronic device (200) can display screens (810) to (850) including a plurality of body images. The electronic device (200) can display screens (810) to (850) on a display module (e.g., display module (260) of FIG. 2). Screens (810) to (850) can be switched in response to a scroll input or a slide input. For example, the electronic device (200) can display screens (830) or (850) in response to a user's scroll input or slide input to screen (810). The user can select one or more body images (e.g., body image 1 to body image 3) corresponding to each of screens (810) to (850). The electronic device (200) may determine one or more body shape images among a plurality of body shape images as a target body shape based on a user's selection input. The electronic device (200) may determine the target body shape to provide an exercise plan. For example, the electronic device (200) may determine body shape image 2 as the target body shape in response to a user's selection input for body shape image 2 corresponding to the screen (830).

[0113]

[0114] FIG. 8b is an example for explaining an interface provided to a user according to one embodiment.

[0115] Referring to FIG. 8b, according to one embodiment, the electronic device (200) can reflect the user's settings when generating multiple body images through the image generation module (255).

[0116] According to one embodiment, the electronic device (200) may provide a UI to enable a user to select a segment to be converted through a screen (820). The electronic device (200) may display an image (e.g., image (730)) on which segmentation has been performed on a user image (e.g., user image (710), user image (203)) through the screen (820). The screen (820) may include a UI that may receive a user's selection input for a plurality of segments acquired from the user image. When the electronic device (200) receives a user's input for one or more of the segments (861) to (869), the electronic device (200) may set one or more segments corresponding to the user's selection input as a body part requiring improvement. For example, the electronic device (200) may determine segments (861) and (863) as body parts requiring improvement in response to a user's selection input for a segment (861) corresponding to an arm area and a segment (863) corresponding to a chest area. The electronic device (200) may determine one or more segments corresponding to the user's selection input as body parts requiring improvement, and may transform the segments corresponding to the body parts requiring improvement.

[0117] According to one embodiment, the electronic device (200) may receive, from the user, a change value for one or more segments corresponding to the user's selection input through the screen (825). The screen (825) may include a UI (843) that may receive, from the user, a change value to be converted for one or more segments (e.g., segment (861), segment (863)) corresponding to the user's selection input. The UJ (843) may be implemented so that the user may select a change value within a range of changeable ratios of a body part corresponding to the segment to be converted. For example, since the segment (861) corresponding to the user's selection input corresponds to an arm part, the UI (843) may provide a change value within a range of changeable ratios for the arm part.

[0118] According to one embodiment, the UI (843) may be implemented as a slide bar or an input window capable of receiving text input from a user. When the UI (843) is implemented as an input window capable of receiving text input, it may be implemented so that a change value can be input within a range of changeable proportions of a body part requiring improvement. After setting a segment corresponding to a body part requiring improvement and a desired degree of improvement, the user can generate a personalized target body image by inputting an additional icon (845). The electronic device (200) may display a screen (840) in response to an input to the additional icon (845). The screen (840) may include a body image generated by transforming one or more segments corresponding to a user's selection input by the change value set by the user.

[0119]

[0120] FIG. 9 is a block diagram illustrating an exercise plan generation module according to one embodiment.

[0121] Referring to FIG. 9, the exercise plan generation module (257) can generate an exercise plan.

[0122] According to one embodiment, the exercise plan generation module (957) may include an exercise history analyzer (910), an image-exercise mapping engine (930), an exercise plan generator (950), an exercise database (970), and a user feedback interface (990).

[0123] An exercise DB (970) according to one embodiment may include exercise data. The exercise DB (970) may include data indicating exercise types and the degree to which each exercise type helps improve each body part as a numerical value (e.g., a numerical value between 0 and 1). For example, the exercise DB (970) may be composed of data indicating exercise types and the degree to which each exercise type helps improve each body part as a numerical value (e.g., a numerical value between 0 and 1), as shown in Table 1.

[0124]

[0125]

[0126]

[0127]

[0128] According to one embodiment, the exercise history analyzer (910) can analyze the exercise history (e.g., the exercise history (207) of FIG. 2) to analyze the type of exercise, exercise intensity, exercise frequency, etc. preferred by the user. The exercise history analyzer (910) can utilize the obtained type of exercise, exercise intensity, exercise frequency, etc. preferred by the user to generate an exercise plan, thereby providing the user with an exercise plan with a high probability of achievement.

[0129] According to one embodiment, the image-to-exercise mapping engine (930) can obtain body parts requiring improvement and exercise data (e.g., type of exercise, amount of exercise, etc.). The image-to-exercise mapping engine (930) can determine body parts requiring improvement based on one or more of a user image (e.g., user image (203), user image (710)), user information (e.g., user information (205)), and a target body type (e.g., one or more body type images corresponding to a user's selection input). For example, the image-to-exercise mapping engine (930) can compare the user image (203) and the user information (205) with the target body type to determine body parts requiring improvement through changes. The image-to-exercise mapping engine (930) can obtain exercise data helpful for determining body parts requiring improvement from an exercise DB (970). For example, the image-movement mapping engine (930) can obtain movement data that is helpful for a body part in need of improvement based on data that indicates the types of movements included in the movement DB (970) and the degree to which each type of movement helps improve each body part as a number (e.g., a number between 0 and 1).

[0130] According to one embodiment, the image-exercise mapping engine (930) can generate priorities for exercise data. The image-exercise mapping engine (930) can provide a high-performance exercise plan for the user by generating priorities based on one or more of the user information (205) and the user's exercise history (207). The image-exercise mapping engine (930) can generate priorities for exercise data. The image-exercise mapping engine (930) can generate priorities based on the exercise history (207). For example, the image-exercise mapping engine (930) can generate priorities for exercises included in the exercise data in the order of the user's exercise history, the user's exercise volume relative to time, and the user-specified exercise. The criteria by which the image-exercise mapping engine (930) generates priorities are not limited to the above examples. The image-exercise mapping engine (930) can output the priorities to the exercise plan generator (950).

[0131] In one embodiment, the exercise plan generator (950) can generate an exercise plan. The exercise plan generator (950) can generate an exercise plan based on the priorities generated by the image-to-exercise mapping engine (930).

[0132] In one embodiment, the user feedback interface (990) can collect user feedback. The user feedback interface (990) can utilize the collected feedback to provide a workout plan with a high user satisfaction rating. The feedback can include the user's opinions regarding the difficulty, effectiveness, and satisfaction of the workout plan.

[0133]

[0134] FIG. 10 is a diagram illustrating the operation of an exercise plan generation module according to one embodiment.

[0135] Referring to FIG. 10, according to one embodiment, operations 1010 to 1050 may be operations in which the exercise plan generation module (957) generates an exercise plan.

[0136] According to one embodiment, in operation 1010, the exercise plan generation module (957) may obtain exercise data corresponding to a body part requiring improvement. The electronic device (200) may obtain the body part requiring improvement and exercise data corresponding to the body part requiring improvement through the image-motion mapping engine (930).

[0137] According to one embodiment, in operation 1030, the exercise plan generation module (957) may generate priorities for exercise data. The exercise plan generation module (957) may generate priorities based on exercise history (207).

[0138] According to one embodiment, in operation 1050, the exercise plan generation module (957) can generate an exercise plan. The electronic device (200) can generate an exercise plan based on a priority through the exercise plan generator (950).

[0139]

[0140] FIG. 11 is an example of an interface provided to a user according to one embodiment.

[0141] Referring to FIG. 11, according to one embodiment, an electronic device (200) may display a screen (1100) providing an exercise plan. The electronic device (200) may display the screen (1100) on a display module (e.g., the display module (260) of FIG. 2). The exercise plan may include one or more of an exercise type, an exercise frequency, and an exercise intensity. The exercise type may include one or more of an exercise name (e.g., barbell back squat, hip abduction machine, etc.) and an exercise part (e.g., lower body, shoulder, chest, etc.).

[0142]

[0143] FIG. 12 is a diagram illustrating a user feedback interface module according to one embodiment.

[0144] Referring to FIG. 12, according to one embodiment, a user feedback interface (1200) (e.g., user feedback interface (990)) may collect user feedback regarding an exercise plan. The user feedback interface (1200) may utilize the user feedback to provide an exercise plan with a high user satisfaction level.

[0145] According to one embodiment, the user feedback interface (1200) may include a feedback collector (1210), a change image analyzer (1230), and a feedback processor (1250).

[0146] In one embodiment, the feedback collector (1210) can collect user feedback on the exercise plan. The user's feedback may include opinions regarding the exercise plan's performance, difficulty, effectiveness, and satisfaction. The feedback collector (1210) can be implemented using methods such as evaluation scales, questionnaires, and free-form text input to collect user feedback.

[0147] In one embodiment, the change image analyzer (1230) may collect images of the user after exercise. The change image analyzer (1230) may utilize the images of the user after exercise to determine whether the exercise plan has had an impact on the user's body. The change image analyzer (1230) may utilize image analysis and machine learning techniques to analyze the impact of the exercise plan on the user's body.

[0148] In one embodiment, the feedback processor (1250) may modify the exercise plan based on the feedback. The feedback processor (1250) may modify the exercise plan based on one or more of the user's feedback received from the feedback collector (1210) and the user's post-exercise image received from the change image analyzer (1230). For example, the feedback processor (1250) may modify the exercise plan if it determines that the user's satisfaction with the exercise plan is low or the exercise effect is low.

[0149]

[0150] FIGS. 13A and 13B are diagrams illustrating an exercise plan that changes according to user feedback, according to one embodiment.

[0151] Referring to FIG. 13A, according to one embodiment, the electronic device (200) may display screens (1310) to (1350) associated with collecting user feedback. The electronic device (200) may display screens (1310) to (1350) on a display module (e.g., display module (260) of FIG. 2).

[0152] According to one embodiment, the screen (1310) may include a UI (e.g., a selection box (1315)) that allows a user to input whether or not to perform an exercise plan. The screen (1330) may include a UI (e.g., a star rating) that allows a user to rate the exercise plan and a UI (e.g., an input box (1335)) that allows a user to input feedback through free-form text input. It should be noted that the screens (1310) and (1330) are merely examples of screens that the feedback collector (1210) displays on the electronic device (200) to collect user feedback, and are not limited thereto.

[0153] A screen (1350) according to one embodiment may be an example of a screen for a change image analyzer (1230) to collect an image of a user after exercise.

[0154]

[0155] FIG. 13b is an example illustrating a modified exercise plan according to one embodiment.

[0156] Referring to FIG. 13B , according to one embodiment, the feedback processor (1250) may generate a modified exercise plan (e.g., exercise plan (1390)) based on user feedback. The user feedback may include user feedback such as a user feedback image (e.g., an image of the user after exercise collected through the screen (1350) of FIG. 13A ), whether the exercise was performed (e.g., whether the exercise was performed collected through the screen (1310) of FIG. 13A ), or a user evaluation (e.g., an evaluation collected through the screen (1330) of FIG. 13A ). The feedback processor (1250) may modify the exercise plan (e.g., exercise plan (1370)) based on the user feedback. For example, the feedback processor (1250) may modify at least one of an exercise type, an exercise frequency, an exercise time, and an exercise intensity based on the user feedback on the exercise plan (1370). The feedback processor (1250) may modify the overall period for which the exercise plan is set based on the user's feedback. For example, the feedback processor (1250) may modify an n-week (e.g., one-week) exercise plan to a longer-term or shorter-term exercise plan than n weeks based on the user's feedback (e.g., whether the user consented). The feedback processor (1250) may provide the user with visual and / or auditory notifications suggesting exercise performance based on the user's feedback. The feedback processor (1250) may provide the user with visual and / or auditory notifications suggesting exercise performance so that the user can achieve their target body shape. For example, if the feedback processor (1250) determines that it is difficult for the user to achieve their goal within the period based on the user's feedback, the feedback processor (1250) may increase the frequency of providing visual and / or auditory notifications suggesting exercise performance. The electronic device (200) may provide the modified exercise plan (1390) through the feedback processor (1250).

[0157]

[0158] FIG. 14 illustrates a flowchart of a method of operating an electronic device according to one embodiment.

[0159] Actions 1410 to 1450 may be performed sequentially, but are not necessarily performed sequentially. For example, the order of each action (1410 to 1450) may be changed, and at least two actions may be performed in parallel.

[0160] According to one embodiment, operations 1410 to 1450 may be understood to be performed in a processor (e.g., processor (220) of FIG. 3) of an electronic device (e.g., electronic device (200) of FIG. 2).

[0161] In operation 1410, an electronic device according to one embodiment may output a plurality of body images based on one or more of a user image and user information.

[0162] In operation 1430, an electronic device according to one embodiment may determine one or more body images among a plurality of body images as a target body type based on a user's selection input.

[0163] In operation 1450, an electronic device according to one embodiment may provide an exercise plan generated based on a target body type and the user's exercise history.

[0164]

[0165] An electronic device (e.g., the electronic device (101) of FIG. 1 or the electronic device (200) of FIG. 2) according to one embodiment may include at least one processor (e.g., the processor (120) of FIG. 1 or the processor (210) of FIG. 2) and a memory (e.g., the memory (130) of FIG. 1 or the memory (230) of FIG. 2)) that stores instructions. The instructions, based on being individually or collectively executed by the at least one processor, may cause the electronic device to output a plurality of body images based on one or more of a user image (e.g., the user image (203) of FIG. 2 or the user image (450) of FIG. 4 or the user image (710) of FIG. 7A) and user information (e.g., the user information (205) of FIG. 2).

[0166] The instructions, when individually or collectively executed by the at least one processor, may cause the electronic device to determine one or more of the plurality of body images as a target body type based on a user's selection input.

[0167] The above instructions, when individually or collectively executed by the at least one processor, may cause the electronic device to provide an exercise plan based on one or more of the target body type and the exercise history of the user (e.g., exercise history (207) of FIG. 2).

[0168] According to one embodiment, the instructions, based on being individually or collectively executed by the at least one processor, may cause the electronic device to obtain a plurality of segments corresponding to body parts of the user from the user image.

[0169] The instructions, when individually or collectively executed by the at least one processor, may cause the electronic device to transform the plurality of segments to generate the plurality of body images.

[0170] According to one embodiment, the user information may include body change data of the user.

[0171] According to one embodiment, the instructions, when individually or collectively executed by the at least one processor, may cause the electronic device to infer a changeable ratio for each body part of the user based on the body change data of the user.

[0172] The instructions, when individually or collectively executed by the at least one processor, may cause the electronic device to transform the plurality of segments based on the changeable ratio.

[0173] According to one embodiment, the instructions, based on being individually or collectively executed by the at least one processor, may cause the electronic device to determine one or more segments from among the plurality of segments corresponding to the user's selection input.

[0174] The instructions, when individually or collectively executed by the at least one processor, may cause the electronic device to output an image in which one or more segments corresponding to the user's selection input are transformed based on the changeable ratio.

[0175] According to one embodiment, the instructions, when individually or collectively executed by the at least one processor, may cause the electronic device to transform the plurality of segments based on a changeable ratio for each body part corresponding to each of the one or more segments.

[0176] According to one embodiment, the instructions, when individually or collectively executed by the at least one processor, may cause the electronic device to determine a body part of the user that requires improvement based on one or more of the user image, the user information, and the target body shape.

[0177] The above instructions, when individually or collectively executed by the at least one processor, may cause the electronic device to obtain movement data corresponding to the body part in need of improvement.

[0178] The instructions, when individually or collectively executed by the at least one processor, may cause the electronic device to provide the exercise plan based on one or more of the exercise data and the exercise history of the user.

[0179] According to one embodiment, when the user's selection input for the plurality of segments is received, the body part requiring improvement may include one or more segments corresponding to the user's selection input.

[0180] According to one embodiment, the instructions, when individually or collectively executed by the at least one processor, may cause the electronic device to generate a priority for the exercise data based on one or more of the user information and the user's exercise history.

[0181] The instructions, when individually or collectively executed by the at least one processor, may cause the electronic device to generate the motion plan based on the priority.

[0182] According to one embodiment, the exercise plan may include one or more of exercise type, exercise frequency, and exercise intensity.

[0183] In one embodiment, the exercise plan may be generated based on the body part in need of improvement.

[0184] A method of operating an electronic device (e.g., electronic device (101) of FIG. 1 or electronic device (200) of FIG. 2) according to one embodiment may include an operation of outputting a plurality of body images based on at least one of a user image (e.g., user image (203) of FIG. 2 or user image (450) of FIG. 4 or user image (710) of FIG. 7A) and user information (e.g., user information (205) of FIG. 2).

[0185] The above operating method may include an operation of determining one or more body images among the plurality of body images as a target body type based on a user's selection input.

[0186] The above method of operation may include an operation of providing an exercise plan based on at least one of the target body type and the user's exercise history (e.g., exercise history (207) of FIG. 2).

[0187] According to one embodiment, the operation of outputting the plurality of body images may include an operation of obtaining a plurality of segments corresponding to body parts of the user from the user image.

[0188] The operation of outputting the plurality of body shape images may include an operation of generating the plurality of body shape images by converting the plurality of segments.

[0189] According to one embodiment, the user information may include body change data of the user.

[0190] According to one embodiment, the method may include an operation of inferring a changeable ratio for each body part of the user based on the body change data of the user.

[0191] The above operating method may include an operation of converting the plurality of segments based on the changeable ratio.

[0192] According to one embodiment, the method of operation may include an operation of determining one or more segments corresponding to the user's selection input among the plurality of segments.

[0193] The above method of operation may include an operation of outputting an image in which one or more segments corresponding to the user's selection input are transformed based on the changeable ratio.

[0194] According to one embodiment, the method may include transforming the plurality of segments based on a changeable ratio for each body part corresponding to each of the one or more segments.

[0195] According to one embodiment, the method may include an operation of determining a body part of the user that requires improvement based on one or more of the user image, the user information, and the target body type.

[0196] The above method of operation may include an operation of acquiring movement data corresponding to the body part requiring improvement.

[0197] The above method of operation may include an operation of providing the exercise plan based on at least one of the exercise data and the exercise history of the user.

[0198] According to one embodiment, when the user's selection input for the plurality of segments is received, the body part requiring improvement may include one or more segments corresponding to the user's selection input.

[0199] According to one embodiment, the method may include an operation of generating a priority for the exercise data based on at least one of the user information and the user's exercise history.

[0200] The above method of operation may include an operation of generating the exercise plan based on the above priority.

[0201] In one embodiment, the exercise plan may include one or more of the following: exercise type, exercise frequency, and exercise intensity. In one embodiment, the exercise plan may be generated based on the body part in need of improvement.

[0202]

[0203] Electronic devices according to the various embodiments disclosed in this document may take various forms. Electronic devices may include, for example, portable communication devices (e.g., smartphones), computer devices, portable multimedia devices, portable medical devices, cameras, wearable devices, or home appliances. Electronic devices according to the embodiments of this document are not limited to the aforementioned devices.

[0204] The various embodiments of this document and the terminology used therein are not intended to limit the technical features described in this document to specific embodiments, but should be understood to include various modifications, equivalents, or substitutes of the embodiments. In connection with the description of the drawings, similar reference numerals may be used for similar or related components. The singular form of a noun corresponding to an item may include one or more of the items, unless the context clearly indicates otherwise. In this document, each of the phrases "A or B", "at least one of A and B", "at least one of A or B", "A, B, or C", "at least one of A, B, and C", and "at least one of A, B, or C" can include any one of the items listed together in the corresponding phrase among those phrases, or all possible combinations thereof. Terms such as "first," "second," or "first" or "second" may be used merely to distinguish one component from another, and do not limit the components in any other respect (e.g., importance or order). When a component (e.g., a first component) is referred to as "coupled" or "connected" to another (e.g., a second component), with or without the terms "functionally" or "communicatively," it means that the component can be connected to the other component directly (e.g., wired), wirelessly, or through a third component.

[0205] The term "module" used in various embodiments of this document may include a unit implemented in hardware, software, or firmware, and may be used interchangeably with terms such as logic, logic block, component, or circuit. A module may be an integral component, or a minimum unit or part of such a component that performs one or more functions. For example, according to one embodiment, a module may be implemented in the form of an application-specific integrated circuit (ASIC).

[0206] Various embodiments of the present document may be implemented as software (e.g., a program (1740)) including one or more instructions stored in a storage medium (e.g., an internal memory (1736) or an external memory (1738)) readable by a machine (e.g., an electronic device (1701)). For example, a processor (e.g., a processor (1720)) of the machine (e.g., an electronic device (1701)) may call at least one instruction among the one or more instructions stored from the storage medium and execute it. This enables the machine to operate to perform at least one function according to the at least one called instruction. The one or more instructions may include code generated by a compiler or code executable by an interpreter. The machine-readable storage medium may be provided in the form of a non-transitory storage medium. Here, 'non-transitory' simply means that the storage medium is a tangible device and does not contain signals (e.g., electromagnetic waves), and the term does not distinguish between cases where data is stored semi-permanently or temporarily on the storage medium.

[0207] According to one embodiment, the method according to various embodiments disclosed in this document may be provided as included in a computer program product. The computer program product may be traded as a product between a seller and a buyer. The computer program product may be distributed in the form of a machine-readable storage medium (e.g., compact disc read-only memory (CD-ROM)), or may be distributed online (e.g., downloaded or uploaded) through an application store (e.g., Play Store™) or directly between two user devices (e.g., smart phones). In the case of online distribution, at least a portion of the computer program product may be temporarily stored or temporarily generated in a machine-readable storage medium, such as the memory of a manufacturer's server, an application store's server, or an intermediary server.

[0208] According to various embodiments, each component (e.g., a module or a program) of the above-described components may include one or more entities, and some of the entities may be separated and placed in other components. According to various embodiments, one or more components or operations of the aforementioned components may be omitted, or one or more other components or operations may be added. Alternatively or additionally, a plurality of components (e.g., a module or a program) may be integrated into a single component. In such a case, the integrated component may perform one or more functions of each of the plurality of components identically or similarly to those performed by the corresponding component among the plurality of components prior to the integration. According to various embodiments, the operations performed by a module, program, or other component may be executed sequentially, in parallel, iteratively, or heuristically, or one or more of the operations may be executed in a different order, omitted, or one or more other operations may be added.

Claims

1. In an electronic device (101; 200), At least one processor (120; 210) and Memory for storing instructions (130; 230) Including, The above instructions, based on being individually or collectively executed by the at least one processor (120; 210), cause the electronic device (101; 200) to: Outputting multiple body images based on one or more of the user image (203; 450; 710) and the user information (205), Based on the user's selection input, one or more of the plurality of body images are determined as the target body type, To provide an exercise plan based on at least one of the above target body type and the user's exercise history (207). Electronic devices (101; 200).

2. In paragraph 1, The above instructions, based on being individually or collectively executed by the at least one processor (120; 210), cause the electronic device (101; 200) to: Obtaining multiple segments corresponding to body parts of the user from the user image (203; 450; 710), Converting the above plurality of segments to generate the above plurality of body shape images, Electronic devices (101; 200).

3. In any one of paragraphs 1 and 2, The above user information (205) is Containing the user's body change data, Electronic devices (101; 200).

4. In any one of paragraphs 1 to 3, The above instructions, based on being individually or collectively executed by the at least one processor (120; 210), cause the electronic device (101; 200) to: Based on the user's body change data, the changeable ratio for each body part of the user is inferred, To convert the plurality of segments based on the above changeable ratio, Electronic devices (101; 200).

5. In any one of paragraphs 1 to 4, The above instructions, based on being individually or collectively executed by the at least one processor (120; 210), cause the electronic device (101; 200) to: determining one or more segments corresponding to the user's selection input among the plurality of segments; Outputting an image in which one or more segments corresponding to the user's selection input are transformed based on the changeable ratio. Electronic devices (101; 200).

6. In any one of paragraphs 1 to 5, The above instructions, based on being individually or collectively executed by the at least one processor (120; 210), cause the electronic device (101; 200) to: Transforming said plurality of segments based on a changeable ratio for each body part corresponding to each of said one or more segments, Electronic devices (101; 200).

7. In any one of paragraphs 1 to 6, The above instructions, based on being individually or collectively executed by the at least one processor (120; 210), cause the electronic device (101; 200) to: Based on one or more of the user image (203; 450; 710), the user information (205), and the target body type, a body part of the user that needs improvement is determined, Obtain exercise data corresponding to the body part requiring improvement, Providing the exercise plan based on at least one of the above exercise data and the exercise history of the user; Electronic devices (101; 200).

8. In any one of paragraphs 1 to 7, When the user's selection input for the above multiple segments is received, The body parts that need improvement are: comprising one or more segments corresponding to the user's selection input; Electronic devices (101; 200).

9. In any one of paragraphs 1 to 8, The above instructions, based on being individually or collectively executed by the at least one processor (120; 210), cause the electronic device (101; 200) to: Based on one or more of the user information (205) and the user's exercise history (207), a priority is generated for the exercise data, Based on the above priorities, to generate the above exercise plan, Electronic devices (101; 200).

10. In any one of paragraphs 1 to 9, The above exercise plan is, Including one or more of the following: type of exercise, frequency of exercise, and intensity of exercise; The above exercise plan is, It is created based on the body part that needs improvement. Electronic devices (101; 200).

11. In the operating method of an electronic device (101; 200), An operation of outputting a plurality of body images based on one or more of a user image (203; 450; 710) and user information (205); An operation of determining one or more of the plurality of body images as a target body type based on a user's selection input; and An action that provides an exercise plan based on at least one of the above target body type and the user's exercise history (207). A method of operation, comprising:

12. In paragraph 11, The operation of outputting the above multiple body shape images is as follows: An operation of obtaining a plurality of segments corresponding to body parts of the user from the user image (203; 450; 710); and An operation of generating a plurality of body shape images by transforming the plurality of segments. A method of operation, comprising:

13. In any one of paragraphs 11 to 12, The above user information (205) is A method of operation, comprising the user's body change data.

14. In any one of paragraphs 11 to 13, An operation of inferring a changeable ratio for each body part of the user based on the body change data of the user; and An operation of converting the plurality of segments based on the above changeable ratio. A method of operation, comprising:

15. In any one of paragraphs 11 to 14, An operation of determining one or more segments corresponding to the user's selection input among the plurality of segments; and An operation of outputting an image in which one or more segments corresponding to the user's selection input are transformed based on the changeable ratio. A method of operation, comprising:

Images