System

Abstract

An object of a system according to an embodiment is to accurately recognize a posture during training and provide guidance in real time.SOLUTION: A system includes a posture recognition unit, a difference calculation unit, an instruction unit, a superimposition unit, a motivation improvement unit, a moving image generation unit, and a nutrition value analysis unit. The posture recognition unit recognizes a posture during training using an image recognition technique on a moving image captured by a camera of a smartphone. The difference calculation unit calculates a difference between the posture recognized by the posture recognition unit and the video of the trainer. The instructor instructs the posture in a natural language in real time on the basis of the difference calculated by the difference calculator. The superimposition unit superimposes the posture of the user on the moving image of the trainer. The motivation improving unit provides a cheering message in a natural language. The moving image generation unit automatically generates an optimal training moving image. The nutrition value analysis unit analyzes the photograph of the meal to analyze the nutrition value.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The technology of the present disclosure relates to a system. [Background technology]

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technology has had the problem of making it difficult to accurately recognize posture during training and provide real-time instruction, making it difficult to provide effective training.

[0005] The system according to the embodiment aims to accurately recognize posture during training and provide guidance in real time. [Means for solving the problem]

[0006] The system according to the embodiment includes a posture recognition unit, a difference calculation unit, an instruction unit, an overlay unit, a motivation improvement unit, a video generation unit, and a nutritional value analysis unit. The posture recognition unit recognizes posture during training from videos captured with a smartphone camera using image recognition technology. The difference calculation unit calculates the difference between the posture recognized by the posture recognition unit and the trainer's video. The instruction unit provides posture guidance in natural language in real time based on the difference calculated by the difference calculation unit. The overlay unit overlays the user's posture on the trainer's video. The motivation improvement unit provides encouraging messages in natural language. The video generation unit automatically generates optimal training videos. The nutritional value analysis unit analyzes photos of meals to analyze nutritional value. [Effects of the Invention]

[0007] The system according to the embodiment can accurately recognize posture during training and provide guidance in real time. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8]FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

[0011] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, the processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), or a TPU (Tensor Processing Unit).

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0016] [First embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0023] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0027] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) A training support system according to an embodiment of the present invention uses image recognition technology to recognize a user's posture during training from a video captured by a smartphone camera and provides real-time posture guidance. This enables the training support system to accurately recognize a user's posture and provide real-time guidance.

[0029] A training support system according to an embodiment includes a posture recognition unit, a difference calculation unit, an instruction unit, a superimposition unit, a motivation improvement unit, a video generation unit, and a nutritional value analysis unit. The posture recognition unit recognizes a user's posture during training using image recognition technology for a video captured by a smartphone camera. For example, the posture recognition unit uses deep learning technology to analyze the position and angle of each part of the user's body and accurately estimate the user's posture. The posture recognition unit can also analyze the user's movements in real time using computer vision technology. The difference calculation unit calculates the difference between the posture recognized by the posture recognition unit and the trainer's video. For example, the difference calculation unit calculates pixel-by-pixel differences to detect differences between the user's posture and the trainer's posture. The difference calculation unit can also calculate differences in the timing of movements. The instruction unit provides posture guidance in natural language in real time based on the differences calculated by the difference calculation unit. For example, the instruction unit uses a generation AI (such as a text generation AI or a multimodal generation AI) to provide specific instructions such as "Please stand up a little longer" or "Adjust the angle of your arms." The superimposition unit superimposes the user's posture on the trainer's video. For example, the superimposition unit superimposes the user's posture data on the trainer's video in real time, allowing the user to visually recognize the difference. The motivation improvement unit provides encouraging messages in natural language. For example, the motivation improvement unit uses a generation AI to provide encouraging words such as "Good luck!" or "Just a little more effort!" in real time. The video generation unit automatically generates optimal training videos. For example, the video generation unit proposes an effective training plan based on the user's training history and goals, and generates videos based on that plan. The nutritional value analysis unit analyzes photos of meals to analyze their nutritional value. For example, the nutritional value analysis unit uses image recognition technology to analyze photos of meals and calculate calorie and nutrient intake. This allows the training support system according to the embodiment to accurately recognize the user's posture and provide guidance in real time. For example, the user can train with the correct form, maintaining motivation while continuing.In addition, the app automatically generates optimal training videos to provide you with constantly updated training plans, and analyzes the nutritional value of your meals to help you maintain a healthy diet.

[0030] The posture recognition unit can estimate the user's muscle tension or fatigue level and suggest appropriate rest timing. For example, the posture recognition unit uses posture estimation technology to analyze the user's muscle tension in real time and suggest rest if excessive tension is detected. For example, it measures the muscle tension in the shoulders and back and notifies the user of appropriate rest timing. The posture recognition unit also estimates the user's fatigue level based on posture data and suggests rest when a certain level is reached. For example, it sends a notification encouraging rest when fatigue accumulates after long-term training. The posture recognition unit also combines posture estimation technology with a biosensor to obtain more accurate data to analyze muscle tension and fatigue. For example, it uses heart rate and electromyogram data in combination to suggest rest timing. This maximizes the effectiveness of training by analyzing the user's muscle tension and fatigue level in real time and suggesting appropriate rest.

[0031] The posture recognition unit can learn from the user's past posture data and develop a posture recognition algorithm optimized for each individual user. For example, the posture recognition unit collects the user's past posture data and uses a machine learning algorithm to build an individually optimized posture recognition model. For example, it generates a model tailored to the user's characteristics based on past training data. The posture recognition unit also periodically updates the user's posture data and improves the posture recognition algorithm based on the latest data. For example, it collects data after each training session and re-trains the model. The posture recognition unit also manages the posture recognition algorithm optimized for each individual user on the cloud and makes it accessible from multiple devices. For example, the same algorithm can be used from a smartphone or tablet. This enables more accurate posture recognition by providing an optimized posture recognition algorithm based on the user's past data.

[0032] The posture recognition unit can be considered for application in the fields of rehabilitation or physical therapy. For example, the posture recognition unit applies posture estimation technology to rehabilitation to develop a system that monitors a patient's rehabilitation progress in real time. For example, it analyzes the range of motion of joints and the recovery status of muscle strength. In addition, the posture recognition unit uses posture estimation technology to analyze a patient's movements in physical therapy and builds a system that proposes an appropriate treatment plan. For example, it performs gait analysis and balance evaluation. In addition, the posture recognition unit provides a training program using posture estimation technology in rehabilitation or physical therapy settings. For example, it provides feedback on the patient's movements in real time and teaches the correct form. As a result, application in the fields of rehabilitation and physical therapy makes it possible to monitor a patient's rehabilitation progress in real time and provide an appropriate treatment plan.

[0033] The posture recognition unit can develop a posture recognition system for supporting the improvement of specific movements or forms in sports or dance training. For example, in sports training, the posture recognition unit uses posture estimation technology to analyze an athlete's movements and develop a system that supports form improvement. For example, it analyzes running form and swing movements. In dance training, the posture recognition unit uses posture estimation technology to analyze a dancer's movements and build a system that provides guidance on accurate poses and movements. For example, it analyzes ballet and hip-hop movements. In addition, the posture recognition unit integrates posture estimation technology into sports or dance training programs to develop a system that provides feedback in real time. For example, it provides guidance on maintaining correct form during training. This helps improve specific movements or forms in sports or dance training, thereby increasing the effectiveness of the training.

[0034] The difference calculation unit can detect subtle deviations in the user's movements and provide more precise instruction. For example, the difference calculation unit uses difference calculation technology to detect subtle deviations in the user's movements in real time and provide specific correction instructions. For example, it may instruct the user to make fine adjustments to the angle of their arms or the position of their feet. The difference calculation unit also compares a trainer's video with the user's movements, analyzes the differences, and develops a system that provides precise feedback. For example, it highlights parts that do not match the trainer's movements. The difference calculation unit also provides specific instruction to correct the user's movements in real time based on the difference calculation results. For example, it provides instructions such as "Bend your knees a little more" or "Straighten your back." This allows the unit to detect subtle deviations in the user's movements and provide more precise instruction, thereby improving the effectiveness of training.

[0035] The differential calculation unit generates a 3D model based on a combination of the trainer's video and the user's posture data, enabling three-dimensional instruction. The differential calculation unit develops a system that generates a 3D model based on, for example, the trainer's video and the user's posture data and provides three-dimensional instruction. For example, it provides a 3D model that the user can rotate 360 ​​degrees to check. The differential calculation unit also uses the 3D model to three-dimensionally analyze the user's movements and provide specific correction instructions. For example, it provides detailed instruction on joint movement and muscle usage. The differential calculation unit also uses a 3D model that combines the trainer's video and the user's posture data to provide an interface that allows the user to three-dimensionally check their own movements. For example, the 3D model is displayed in real time during training. This allows three-dimensional instruction by using a 3D model that combines the trainer's video and the user's posture data, improving the effectiveness of training.

[0036] The difference calculation unit can also be applied to other sports or fitness activities to provide a wide range of training guidance. For example, the difference calculation unit applies the difference calculation technology with the trainer's video to other sports or fitness activities to develop a system that provides a wide range of training guidance. For example, it is applied to yoga or Pilates instruction. The difference calculation unit also uses the difference calculation technology to build a system that provides training guidance for different sports. For example, it analyzes golf or tennis form. The difference calculation unit also provides training guidance for general fitness activities based on the difference calculation with the trainer's video. For example, it analyzes aerobics or dance movements. In this way, a wide range of training guidance can be provided by applying the difference calculation technology to other sports and fitness activities.

[0037] The difference calculation unit can develop a system that evaluates long-term training effects by combining the difference calculation results with the user's training history. The difference calculation unit, for example, combines the difference calculation results with the user's training history to develop a system that evaluates long-term training effects. For example, it analyzes training progress based on past data. The difference calculation unit also analyzes the difference calculation results based on the user's training history to evaluate training effects. For example, it measures improvements in form and muscle strength. The difference calculation unit also integrates the difference calculation results with the training history to build a system that visualizes long-term training effects. For example, it displays training progress using graphs and charts. In this way, by integrating the difference calculation results with the training history, it is possible to evaluate long-term training effects and visualize training progress.

[0038] The superimposition unit provides an interface that allows users to freely rotate and scale the superimposed posture data, enabling detailed posture checks. For example, the superimposition unit displays the superimposed posture data as a 3D model and provides an interface that allows users to freely rotate and scale it. For example, detailed posture checks can be performed using fingertip controls. The superimposition unit will also develop an interface that allows users to check the superimposed posture data from any angle. For example, rotating it 360 degrees to check posture from all directions. The superimposition unit will also provide an interface that allows detailed checks of specific body parts by scaling the superimposed posture data. For example, detailed checks of joint movement and muscle usage can be performed. This allows users to freely manipulate the superimposed posture data, enabling detailed posture checks and improving the effectiveness of training.

[0039] The superimposition unit can display the user's muscle movements or joint range of motion on the superimposed posture data, thereby providing more detailed feedback. The superimposition unit develops a system that displays the user's muscle movements and joint range of motion on the superimposed posture data, for example. For example, it visually displays muscle contraction and extension. The superimposition unit also displays the joint range of motion in real time based on the user's posture data, providing detailed feedback. For example, it displays the angles of the knees and elbows to instruct the user on correct form. The superimposition unit also displays the muscle movements and joint range of motion, providing an interface that allows the user to check their own movements in detail. For example, it can visually indicate muscle movements by color, making them easier to understand. This allows for more detailed feedback by displaying the user's muscle movements and joint range of motion, thereby improving the effectiveness of training.

[0040] The superposition unit can be considered for application in the fields of rehabilitation or physical therapy. For example, the superposition function of the superposition unit is applied to rehabilitation to develop a system that monitors a patient's rehabilitation progress in real time. For example, it analyzes the range of motion of joints and the recovery status of muscle strength. In addition, the superposition unit uses the superposition function to analyze a patient's movements in physical therapy and builds a system that proposes an appropriate treatment plan. For example, it performs gait analysis and balance evaluation. In addition, the superposition unit provides a training program using the superposition function in rehabilitation or physical therapy settings. For example, it provides feedback on the patient's movements in real time and teaches the correct form. As a result, application in the fields of rehabilitation and physical therapy makes it possible to monitor a patient's rehabilitation progress in real time and provide an appropriate treatment plan.

[0041] The superimposition unit can provide a function for sharing the superimposed posture data with other users and receiving feedback within the community. For example, the superimposition unit develops a system that provides a function for sharing the superimposed posture data with other users and receiving feedback within the community. For example, receiving advice from fellow trainers or trainers. The superimposition unit also provides a function for a user to publish the superimposed posture data within the community and receive comments and evaluations from other users. For example, sharing training progress and increasing motivation. The superimposition unit also provides a function for competing and cooperating with other users based on the superimposed posture data. For example, holding a competition in which users compare training results. In this way, by sharing the superimposed posture data with other users, users can receive feedback within the community and improve the effectiveness of their training.

[0042] The motivation improvement unit can integrate the cheering message function into other fitness apps or training programs to provide them to a wide range of users. For example, the motivation improvement unit develops a system that integrates the cheering message function into other fitness apps or training programs to provide them to a wide range of users. For example, the motivation improvement unit adds a cheering message function to an existing fitness app. The motivation improvement unit also provides the cheering message function as an API to make it easy for other fitness apps and training programs to integrate it. For example, the motivation improvement unit provides documentation and sample code for developers. The motivation improvement unit also promotes the fitness app or training program that has integrated the cheering message function to encourage use by a wide range of users. For example, the motivation improvement unit conducts a marketing campaign. In this way, the cheering message function can be integrated into other fitness apps or training programs to provide it to a wide range of users.

[0043] The motivation improvement unit can adapt the content of the cheering message to different languages ​​or cultures, making it suitable for international users. The motivation improvement unit, for example, develops a system that translates the content of the cheering message into different languages ​​and accommodates international users. For example, it supports multiple languages ​​such as English, French, and Chinese. The motivation improvement unit also uses phrases and words that take cultural background into consideration to provide cheering message content that is tailored to different cultures. For example, it provides words of encouragement that are tailored to the culture of each country. The motivation improvement unit also builds a system that customizes the content of the cheering message based on feedback from international users. For example, it adjusts the content of the cheering message depending on the user's country or region. This allows the content of the cheering message to be adapted to different languages ​​and cultures, making it suitable for international users.

[0044] The video generation unit can generate individually customized training videos based on the user's training history or goals. The video generation unit, for example, develops a system that analyzes the user's training history and goals and generates individually customized training videos. For example, it generates optimal videos based on past training data. The video generation unit also generates training videos tailored to the user's goals based on the training history. For example, it provides videos suitable for users aiming to improve muscle strength or lose weight. The video generation unit also builds a system that analyzes the user's training data in real time and generates individually customized training videos. For example, it adjusts the video content according to the training progress. In this way, the effectiveness of training is improved by generating customized training videos based on the user's training history and goals.

[0045] The video generation unit can integrate the automatic training video generation function into other fitness apps or training programs to provide them to a wide range of users. For example, the video generation unit develops a system that integrates the automatic training video generation function into other fitness apps or training programs to provide them to a wide range of users. For example, the video generation unit adds an automatic generation function to an existing fitness app. The video generation unit also provides the automatic training video generation function as an API to make it easy for other fitness apps and training programs to integrate it. For example, the video generation unit provides documentation and sample code for developers. The video generation unit also promotes the fitness apps and training programs that have integrated the automatic training video generation function to encourage a wide range of users to use them. For example, the video generation unit conducts a marketing campaign. In this way, the automatic training video generation function can be integrated into other fitness apps and training programs to provide them to a wide range of users.

[0046] The video generation unit can adapt the training videos to different languages ​​or cultures to accommodate international users. The video generation unit, for example, develops a system that translates training videos into different languages ​​to accommodate international users. For example, it supports multiple languages ​​such as English, French, and Chinese. The video generation unit also uses content that takes cultural background into consideration to provide training videos tailored to different cultures. For example, it provides training programs tailored to the cultures of each country. The video generation unit also builds a system that customizes the content of training videos based on feedback from international users. For example, it adjusts the content of the training videos depending on the user's country or region. This allows the training videos to accommodate different languages ​​and cultures, making it possible to accommodate international users.

[0047] The nutritional value analysis unit can perform a detailed analysis of the types and amounts of ingredients from a photo of a meal, allowing for more accurate nutritional value analysis. For example, the nutritional value analysis unit develops a system that analyzes a photo of a meal and identifies the types and amounts of ingredients in detail. For example, it uses image recognition technology to identify ingredients and measure the amounts. The nutritional value analysis unit also builds a system that automatically analyzes the types and amounts of ingredients from a photo of a meal and calculates accurate nutritional value. For example, it calculates calorie and nutrient intake. The nutritional value analysis unit also develops a system that analyzes the types and amounts of ingredients in detail based on a photo of a meal and provides nutritional value in real time. For example, simply uploading a photo of a meal can display nutritional value. This allows for more accurate nutritional value analysis by analyzing the types and amounts of ingredients in detail from a photo of a meal.

[0048] The nutritional value analysis unit can integrate the nutritional value analysis function into other health management apps and fitness programs to provide them to a wide range of users. For example, the nutritional value analysis unit develops a system that integrates the nutritional value analysis function into other health management apps and fitness programs to provide them to a wide range of users. For example, it adds a nutritional value analysis function to an existing health management app. The nutritional value analysis unit also provides the nutritional value analysis function as an API to make it easy for other health management apps and fitness programs to integrate it. For example, it provides documentation and sample code for developers. The nutritional value analysis unit also promotes the health management apps and fitness programs that integrate the nutritional value analysis function to encourage a wide range of users to use it. For example, it conducts a marketing campaign. In this way, the nutritional value analysis function can be integrated into other health management apps and fitness programs to provide it to a wide range of users.

[0049] The nutritional value analysis unit can combine the nutritional value analysis results of meals with the user's training data to provide a comprehensive health management plan. The nutritional value analysis unit, for example, develops a system that combines the nutritional value analysis results of meals with the user's training data to provide a comprehensive health management plan. For example, it proposes a meal plan to maximize the effectiveness of training. The nutritional value analysis unit also analyzes the nutritional value analysis results based on the user's training data to provide a comprehensive health management plan. For example, it proposes a meal plan suitable for a user who is aiming to improve muscle strength or lose weight. The nutritional value analysis unit also integrates the nutritional value analysis results of meals with training data to build a system that visualizes the comprehensive health management plan. For example, it displays the progress of health management using graphs and charts. In this way, a comprehensive health management plan can be provided by integrating the nutritional value analysis results of meals with training data.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] The training support system can further analyze the user's sleep data and adjust the training plan. For example, if a lack of sleep is detected, it can recommend lighter exercise. It can also optimize the timing of training based on the sleep data. For example, it can suggest that the user train when they are most energetic. It can also analyze the sleep data to evaluate the user's recovery state and set an appropriate training load. In this way, it can utilize the user's sleep data to provide a more effective training plan.

[0052] The training support system can also analyze the user's heart rate data and adjust the intensity of the training. For example, if the heart rate is too high, it can instruct the user to reduce the intensity of the training. It can also evaluate the effectiveness of the user's aerobic exercise based on the heart rate data and suggest an appropriate training plan. For example, it can monitor whether the heart rate is within the target range and provide feedback in real time. It can also analyze the heart rate data to evaluate the user's physical fitness level and provide an individually customized training plan. This makes it possible to utilize the user's heart rate data to achieve more effective training.

[0053] The training support system can further analyze the user's breathing data to maximize the effectiveness of training. For example, if breathing becomes shallow, it can provide instructions to encourage deep breathing. It can also evaluate the user's state of relaxation based on the breathing data and suggest an appropriate training plan. For example, if breathing is stable, it can recommend high-intensity training. It can also analyze the breathing data to evaluate the user's stress level and suggest training that has a relaxing effect. In this way, it is possible to utilize the user's breathing data to provide more effective training.

[0054] The training support system can further analyze the user's body temperature data and adjust the training plan. For example, if the user's body temperature is too high, it can recommend cool-down exercises. It can also adjust the intensity of training based on the body temperature data. For example, if the user's body temperature is rising, it can suggest low-intensity exercises. It can also analyze the body temperature data to evaluate the user's physical condition and provide an appropriate training plan. In this way, it can utilize the user's body temperature data to provide a more effective training plan.

[0055] The training support system can further analyze the user's hydration data and adjust the training plan. For example, if the user's hydration intake is insufficient, the system will encourage the user to take a break and hydrate. The system can also adjust the intensity of training based on the hydration data. For example, if the user's hydration intake is insufficient, the system will suggest low-intensity exercise. The system can also analyze the hydration data to evaluate the user's physical condition and provide an appropriate training plan. This makes it possible to utilize the user's hydration intake data to provide a more effective training plan.

[0056] The training support system can further analyze the user's environmental data and adjust the training plan. For example, if the room temperature is too high, it can recommend cool-down exercises. It can also adjust the intensity of training based on the environmental data. For example, if the humidity is high, it can suggest low-intensity exercises. It can also analyze the environmental data to evaluate the user's physical condition and provide an appropriate training plan. This makes it possible to utilize the user's environmental data to provide a more effective training plan.

[0057] The processing flow of the first embodiment will be briefly explained below.

[0058] Step 1: The posture recognition unit uses image recognition technology to recognize the user's posture during training from videos taken with the smartphone camera. For example, deep learning technology can be used to analyze the position and angle of each part of the user's body and estimate the user's posture accurately. Computer vision technology can also be used to analyze the user's movements in real time. Step 2: The difference calculation unit calculates the difference between the posture recognized by the posture recognition unit and the video of the trainer. For example, it calculates the difference in pixel units to detect the difference between the user's posture and the trainer's posture. It can also calculate the difference in the timing of movements. Step 3: The instructor provides posture guidance in real time using natural language based on the difference calculated by the difference calculation unit. For example, using a generative AI, the instructor provides specific instructions such as "Please stand up a little straighter" or "Adjust the angle of your arms." Step 4: The superimposition unit superimposes the user's posture on the trainer's video. For example, the user's posture data is superimposed on the trainer's video in real time, allowing the difference to be visually recognized. Step 5: The motivational section provides encouraging messages in natural language, such as "Good luck!" or "Keep it up!", in real time using generative AI. Step 6: The video generator automatically generates optimal training videos. For example, it proposes an effective training plan based on the user's training history and goals, and generates videos based on that plan. Step 7: The nutritional value analysis unit analyzes the food photos to analyze their nutritional value. For example, it uses image recognition technology to analyze the food photos and calculate calorie and nutrient intake.

[0059] (Example 2) A training support system according to an embodiment of the present invention uses image recognition technology to recognize a user's posture during training from a video captured by a smartphone camera and provides real-time posture guidance. This enables the training support system to accurately recognize a user's posture and provide real-time guidance.

[0060] A training support system according to an embodiment includes a posture recognition unit, a difference calculation unit, an instruction unit, a superimposition unit, a motivation improvement unit, a video generation unit, and a nutritional value analysis unit. The posture recognition unit recognizes a user's posture during training using image recognition technology for a video captured by a smartphone camera. For example, the posture recognition unit uses deep learning technology to analyze the position and angle of each part of the user's body and accurately estimate the user's posture. The posture recognition unit can also analyze the user's movements in real time using computer vision technology. The difference calculation unit calculates the difference between the posture recognized by the posture recognition unit and the trainer's video. For example, the difference calculation unit calculates pixel-by-pixel differences to detect differences between the user's posture and the trainer's posture. The difference calculation unit can also calculate differences in the timing of movements. The instruction unit provides posture guidance in natural language in real time based on the differences calculated by the difference calculation unit. For example, the instruction unit uses a generation AI (such as a text generation AI or a multimodal generation AI) to provide specific instructions such as "Please stand up a little longer" or "Adjust the angle of your arms." The superimposition unit superimposes the user's posture on the trainer's video. For example, the superimposition unit superimposes the user's posture data on the trainer's video in real time, allowing the user to visually recognize the difference. The motivation improvement unit provides encouraging messages in natural language. For example, the motivation improvement unit uses a generation AI to provide encouraging words such as "Good luck!" or "Just a little more effort!" in real time. The video generation unit automatically generates optimal training videos. For example, the video generation unit proposes an effective training plan based on the user's training history and goals, and generates videos based on that plan. The nutritional value analysis unit analyzes photos of meals to analyze their nutritional value. For example, the nutritional value analysis unit uses image recognition technology to analyze photos of meals and calculate calorie and nutrient intake. This allows the training support system according to the embodiment to accurately recognize the user's posture and provide guidance in real time. For example, the user can train with the correct form, maintaining motivation while continuing.In addition, the app automatically generates optimal training videos to provide you with constantly updated training plans, and analyzes the nutritional value of your meals to help you maintain a healthy diet.

[0061] The posture recognition unit can estimate the user's muscle tension or fatigue level and suggest appropriate rest timing. For example, the posture recognition unit uses posture estimation technology to analyze the user's muscle tension in real time and suggest rest if excessive tension is detected. For example, it measures the muscle tension in the shoulders and back and notifies the user of appropriate rest timing. The posture recognition unit also estimates the user's fatigue level based on posture data and suggests rest when a certain level is reached. For example, it sends a notification encouraging rest when fatigue accumulates after long-term training. The posture recognition unit also combines posture estimation technology with a biosensor to obtain more accurate data to analyze muscle tension and fatigue. For example, it uses heart rate and electromyogram data in combination to suggest rest timing. This maximizes the effectiveness of training by analyzing the user's muscle tension and fatigue level in real time and suggesting appropriate rest.

[0062] The posture recognition unit can learn from the user's past posture data and develop a posture recognition algorithm optimized for each individual user. For example, the posture recognition unit collects the user's past posture data and uses a machine learning algorithm to build an individually optimized posture recognition model. For example, it generates a model tailored to the user's characteristics based on past training data. The posture recognition unit also periodically updates the user's posture data and improves the posture recognition algorithm based on the latest data. For example, it collects data after each training session and re-trains the model. The posture recognition unit also manages the posture recognition algorithm optimized for each individual user on the cloud and makes it accessible from multiple devices. For example, the same algorithm can be used from a smartphone or tablet. This enables more accurate posture recognition by providing an optimized posture recognition algorithm based on the user's past data.

[0063] The posture recognition unit can use the emotion estimation function to provide posture guidance based on the user's emotional state. For example, the posture recognition unit uses the emotion estimation function to analyze the user's stress level and recommend a relaxing posture. For example, it suggests deep breathing and stretching. The posture recognition unit also monitors the user's emotional state in real time and provides posture guidance that elicits positive emotions. For example, it suggests postures and movements that encourage a smile. The posture recognition unit also customizes a training program based on the user's emotional state based on the emotion estimation data. For example, if stress is high, it recommends yoga or meditation, which have a relaxing effect. In this way, posture guidance based on the user's emotional state is provided, thereby enhancing the effectiveness of training.

[0064] The posture recognition unit can be considered for application in the fields of rehabilitation or physical therapy. For example, the posture recognition unit applies posture estimation technology to rehabilitation to develop a system that monitors a patient's rehabilitation progress in real time. For example, it analyzes the range of motion of joints and the recovery status of muscle strength. In addition, the posture recognition unit uses posture estimation technology to analyze a patient's movements in physical therapy and builds a system that proposes an appropriate treatment plan. For example, it performs gait analysis and balance evaluation. In addition, the posture recognition unit provides a training program using posture estimation technology in rehabilitation or physical therapy settings. For example, it provides feedback on the patient's movements in real time and teaches the correct form. As a result, application in the fields of rehabilitation and physical therapy makes it possible to monitor a patient's rehabilitation progress in real time and provide an appropriate treatment plan.

[0065] The posture recognition unit can develop a posture recognition system for supporting the improvement of specific movements or forms in sports or dance training. For example, in sports training, the posture recognition unit uses posture estimation technology to analyze an athlete's movements and develop a system that supports form improvement. For example, it analyzes running form and swing movements. In dance training, the posture recognition unit uses posture estimation technology to analyze a dancer's movements and build a system that provides guidance on accurate poses and movements. For example, it analyzes ballet and hip-hop movements. In addition, the posture recognition unit integrates posture estimation technology into sports or dance training programs to develop a system that provides feedback in real time. For example, it provides guidance on maintaining correct form during training. This helps improve specific movements or forms in sports or dance training, thereby increasing the effectiveness of the training.

[0066] The posture recognition unit can use the emotion estimation function to suggest a training program based on the user's emotions. For example, the posture recognition unit uses the emotion estimation function to analyze the user's motivation level and suggest an appropriate training program. For example, if motivation is low, it may recommend light exercises. The posture recognition unit also monitors the user's emotional state in real time and customizes a training program according to the emotion. For example, it may suggest fun training to bring out positive emotions. The posture recognition unit will also develop a system that automatically generates a training plan according to the user's emotional state based on the emotion estimation data. For example, if stress is high, it may recommend training that has a relaxing effect. In this way, the effectiveness of training is improved by suggesting a training program according to the user's emotions.

[0067] The difference calculation unit can detect subtle deviations in the user's movements and provide more precise instruction. For example, the difference calculation unit uses difference calculation technology to detect subtle deviations in the user's movements in real time and provide specific correction instructions. For example, it may instruct the user to make fine adjustments to the angle of their arms or the position of their feet. The difference calculation unit also compares a trainer's video with the user's movements, analyzes the differences, and develops a system that provides precise feedback. For example, it highlights parts that do not match the trainer's movements. The difference calculation unit also provides specific instruction to correct the user's movements in real time based on the difference calculation results. For example, it provides instructions such as "Bend your knees a little more" or "Straighten your back." This allows the unit to detect subtle deviations in the user's movements and provide more precise instruction, thereby improving the effectiveness of training.

[0068] The differential calculation unit generates a 3D model based on a combination of the trainer's video and the user's posture data, enabling three-dimensional instruction. The differential calculation unit develops a system that generates a 3D model based on, for example, the trainer's video and the user's posture data and provides three-dimensional instruction. For example, it provides a 3D model that the user can rotate 360 ​​degrees to check. The differential calculation unit also uses the 3D model to three-dimensionally analyze the user's movements and provide specific correction instructions. For example, it provides detailed instruction on joint movement and muscle usage. The differential calculation unit also uses a 3D model that combines the trainer's video and the user's posture data to provide an interface that allows the user to three-dimensionally check their own movements. For example, the 3D model is displayed in real time during training. This allows three-dimensional instruction by using a 3D model that combines the trainer's video and the user's posture data, improving the effectiveness of training.

[0069] The difference calculation unit can use the emotion estimation function to customize instruction content based on the user's emotional state. For example, the difference calculation unit uses the emotion estimation function to analyze the user's state of tension and provide instruction content to help them relax. For example, it may give instructions such as, "Take a deep breath and relax." The difference calculation unit also monitors the user's emotional state in real time and customizes instruction content according to the emotion. For example, it may provide words of encouragement to elicit positive emotions. The difference calculation unit also develops a system that provides training instruction according to the user's emotional state based on the emotion estimation data. For example, if the user is tense, it may suggest training that has a relaxing effect. In this way, the effectiveness of training can be improved by customizing instruction content according to the user's emotional state.

[0070] The difference calculation unit can also be applied to other sports or fitness activities to provide a wide range of training guidance. For example, the difference calculation unit applies the difference calculation technology with the trainer's video to other sports or fitness activities to develop a system that provides a wide range of training guidance. For example, it is applied to yoga or Pilates instruction. The difference calculation unit also uses the difference calculation technology to build a system that provides training guidance for different sports. For example, it analyzes golf or tennis form. The difference calculation unit also provides training guidance for general fitness activities based on the difference calculation with the trainer's video. For example, it analyzes aerobics or dance movements. In this way, a wide range of training guidance can be provided by applying the difference calculation technology to other sports and fitness activities.

[0071] The difference calculation unit can develop a system that evaluates long-term training effects by combining the difference calculation results with the user's training history. The difference calculation unit, for example, combines the difference calculation results with the user's training history to develop a system that evaluates long-term training effects. For example, it analyzes training progress based on past data. The difference calculation unit also analyzes the difference calculation results based on the user's training history to evaluate training effects. For example, it measures improvements in form and muscle strength. The difference calculation unit also integrates the difference calculation results with the training history to build a system that visualizes long-term training effects. For example, it displays training progress using graphs and charts. In this way, by integrating the difference calculation results with the training history, it is possible to evaluate long-term training effects and visualize training progress.

[0072] The superimposition unit provides an interface that allows users to freely rotate and scale the superimposed posture data, enabling detailed posture checks. For example, the superimposition unit displays the superimposed posture data as a 3D model and provides an interface that allows users to freely rotate and scale it. For example, detailed posture checks can be performed using fingertip controls. The superimposition unit will also develop an interface that allows users to check the superimposed posture data from any angle. For example, rotating it 360 degrees to check posture from all directions. The superimposition unit will also provide an interface that allows detailed checks of specific body parts by scaling the superimposed posture data. For example, detailed checks of joint movement and muscle usage can be performed. This allows users to freely manipulate the superimposed posture data, enabling detailed posture checks and improving the effectiveness of training.

[0073] The superimposition unit can display the user's muscle movements or joint range of motion on the superimposed posture data, thereby providing more detailed feedback. The superimposition unit develops a system that displays the user's muscle movements and joint range of motion on the superimposed posture data, for example. For example, it visually displays muscle contraction and extension. The superimposition unit also displays the joint range of motion in real time based on the user's posture data, providing detailed feedback. For example, it displays the angles of the knees and elbows to instruct the user on correct form. The superimposition unit also displays the muscle movements and joint range of motion, providing an interface that allows the user to check their own movements in detail. For example, it can visually indicate muscle movements by color, making them easier to understand. This allows for more detailed feedback by displaying the user's muscle movements and joint range of motion, thereby improving the effectiveness of training.

[0074] The overlay unit can use the emotion estimation function to provide visual feedback based on the user's emotional state. The overlay unit, for example, uses the emotion estimation function to develop a system that provides visual feedback according to the user's emotional state. For example, it uses bright colors and effects to elicit positive emotions. The overlay unit also monitors the user's emotional state in real time and customizes the visual feedback according to the emotion. For example, it uses colors and effects that have a relaxing effect. The overlay unit also develops an interface that provides visual feedback according to the user's emotional state based on the emotion estimation data. For example, it dynamically changes colors and effects according to the emotion score. This provides visual feedback according to the user's emotional state, thereby enhancing the effectiveness of training.

[0075] The superposition unit can be considered for application in the fields of rehabilitation or physical therapy. For example, the superposition function of the superposition unit is applied to rehabilitation to develop a system that monitors a patient's rehabilitation progress in real time. For example, it analyzes the range of motion of joints and the recovery status of muscle strength. In addition, the superposition unit uses the superposition function to analyze a patient's movements in physical therapy and builds a system that proposes an appropriate treatment plan. For example, it performs gait analysis and balance evaluation. In addition, the superposition unit provides a training program using the superposition function in rehabilitation or physical therapy settings. For example, it provides feedback on the patient's movements in real time and teaches the correct form. As a result, application in the fields of rehabilitation and physical therapy makes it possible to monitor a patient's rehabilitation progress in real time and provide an appropriate treatment plan.

[0076] The superimposition unit can provide a function for sharing the superimposed posture data with other users and receiving feedback within the community. For example, the superimposition unit develops a system that provides a function for sharing the superimposed posture data with other users and receiving feedback within the community. For example, receiving advice from fellow trainers or trainers. The superimposition unit also provides a function for a user to publish the superimposed posture data within the community and receive comments and evaluations from other users. For example, sharing training progress and increasing motivation. The superimposition unit also provides a function for competing and cooperating with other users based on the superimposed posture data. For example, holding a competition in which users compare training results. In this way, by sharing the superimposed posture data with other users, users can receive feedback within the community and improve the effectiveness of their training.

[0077] The overlay unit can use the emotion estimation function to customize the training environment based on the user's emotions. The overlay unit, for example, uses the emotion estimation function to develop a system that customizes the training environment according to the user's emotional state. For example, it provides music and lighting that have a relaxing effect. The overlay unit also monitors the user's emotional state in real time and dynamically adjusts the training environment according to the emotion. For example, if the user is highly stressed, it provides an environment that has a relaxing effect. The overlay unit also provides an interface that customizes the training environment according to the user's emotional state based on the emotion estimation data. For example, it automatically changes the environment settings according to the emotion score. In this way, the training effect is enhanced by customizing the training environment according to the user's emotions.

[0078] The motivation improvement unit can use the emotion estimation function to provide cheering messages based on the user's emotional state. For example, the motivation improvement unit uses the emotion estimation function to analyze the user's motivation level and develop a system that provides appropriate interjections. For example, if motivation is low, more encouraging words are provided. The motivation improvement unit also monitors the user's emotional state in real time and customizes interjections according to the emotion. For example, encouraging words are provided to elicit positive emotions. The motivation improvement unit also develops an interface that provides interjections according to the user's emotional state based on the emotion estimation data. For example, the content of the interjections is dynamically changed according to the emotion score. In this way, interjections according to the user's emotional state are provided, thereby improving motivation.

[0079] The motivation improvement unit can integrate the cheering message function into other fitness apps or training programs to provide them to a wide range of users. For example, the motivation improvement unit develops a system that integrates the cheering message function into other fitness apps or training programs to provide them to a wide range of users. For example, the motivation improvement unit adds a cheering message function to an existing fitness app. The motivation improvement unit also provides the cheering message function as an API to make it easy for other fitness apps and training programs to integrate it. For example, the motivation improvement unit provides documentation and sample code for developers. The motivation improvement unit also promotes the fitness app or training program that has integrated the cheering message function to encourage use by a wide range of users. For example, the motivation improvement unit conducts a marketing campaign. In this way, the cheering message function can be integrated into other fitness apps or training programs to provide it to a wide range of users.

[0080] The motivation improvement unit can adapt the content of the cheering message to different languages ​​or cultures, making it suitable for international users. The motivation improvement unit, for example, develops a system that translates the content of the cheering message into different languages ​​and accommodates international users. For example, it supports multiple languages ​​such as English, French, and Chinese. The motivation improvement unit also uses phrases and words that take cultural background into consideration to provide cheering message content that is tailored to different cultures. For example, it provides words of encouragement that are tailored to the culture of each country. The motivation improvement unit also builds a system that customizes the content of the cheering message based on feedback from international users. For example, it adjusts the content of the cheering message depending on the user's country or region. This allows the content of the cheering message to be adapted to different languages ​​and cultures, making it suitable for international users.

[0081] The motivation improvement unit can use the emotion estimation function to suggest a training program based on the user's emotions. The motivation improvement unit develops a system that, for example, uses the emotion estimation function to analyze the user's stress level and suggest a relaxing training program. For example, yoga or meditation may be recommended. The motivation improvement unit also monitors the user's emotional state in real time and customizes a training program according to the emotion. For example, it may suggest a fun training program to bring out positive emotions. The motivation improvement unit also develops a system that automatically generates a training plan according to the user's emotional state based on the emotion estimation data. For example, if stress is high, it may recommend training that has a relaxing effect. In this way, the effectiveness of the training is improved by suggesting a training program according to the user's emotions.

[0082] The video generation unit can generate individually customized training videos based on the user's training history or goals. The video generation unit, for example, develops a system that analyzes the user's training history and goals and generates individually customized training videos. For example, it generates optimal videos based on past training data. The video generation unit also generates training videos tailored to the user's goals based on the training history. For example, it provides videos suitable for users aiming to improve muscle strength or lose weight. The video generation unit also builds a system that analyzes the user's training data in real time and generates individually customized training videos. For example, it adjusts the video content according to the training progress. In this way, the effectiveness of training is improved by generating customized training videos based on the user's training history and goals.

[0083] The video generation unit can use the emotion estimation function to generate training videos based on the user's emotional state. The video generation unit, for example, develops a system that uses the emotion estimation function to generate training videos according to the user's emotional state. For example, if the user wants to relax, it generates a yoga video. The video generation unit also monitors the user's emotional state in real time and customizes training videos according to the emotion. For example, it generates a fun training video to elicit positive emotions. The video generation unit also builds a system that automatically generates training videos according to the user's emotional state based on the emotion estimation data. For example, if the user is highly stressed, it generates a training video that has a relaxing effect. In this way, training videos are generated according to the user's emotional state, thereby enhancing the effectiveness of training.

[0084] The video generation unit can integrate the automatic training video generation function into other fitness apps or training programs to provide them to a wide range of users. For example, the video generation unit develops a system that integrates the automatic training video generation function into other fitness apps or training programs to provide them to a wide range of users. For example, the video generation unit adds an automatic generation function to an existing fitness app. The video generation unit also provides the automatic training video generation function as an API to make it easy for other fitness apps and training programs to integrate it. For example, the video generation unit provides documentation and sample code for developers. The video generation unit also promotes the fitness apps and training programs that have integrated the automatic training video generation function to encourage a wide range of users to use them. For example, the video generation unit conducts a marketing campaign. In this way, the automatic training video generation function can be integrated into other fitness apps and training programs to provide them to a wide range of users.

[0085] The video generation unit can adapt the training videos to different languages ​​or cultures to accommodate international users. The video generation unit, for example, develops a system that translates training videos into different languages ​​to accommodate international users. For example, it supports multiple languages ​​such as English, French, and Chinese. The video generation unit also uses content that takes cultural background into consideration to provide training videos tailored to different cultures. For example, it provides training programs tailored to the cultures of each country. The video generation unit also builds a system that customizes the content of training videos based on feedback from international users. For example, it adjusts the content of the training videos depending on the user's country or region. This allows the training videos to accommodate different languages ​​and cultures, making it possible to accommodate international users.

[0086] The video generation unit can use the emotion estimation function to suggest a training program based on the user's emotions. The video generation unit, for example, uses the emotion estimation function to develop a system that analyzes the user's stress level and suggests a relaxing training program. For example, yoga or meditation may be recommended. The video generation unit also monitors the user's emotional state in real time and customizes a training program according to the emotion. For example, it may suggest a fun training program to bring out positive emotions. The video generation unit also develops a system that automatically generates a training plan according to the user's emotional state based on the emotion estimation data. For example, if stress is high, it may recommend a training program that has a relaxing effect. In this way, the training effect is enhanced by suggesting a training program according to the user's emotions.

[0087] The nutritional value analysis unit can perform a detailed analysis of the types and amounts of ingredients from a photo of a meal, allowing for more accurate nutritional value analysis. For example, the nutritional value analysis unit develops a system that analyzes a photo of a meal and identifies the types and amounts of ingredients in detail. For example, it uses image recognition technology to identify ingredients and measure the amounts. The nutritional value analysis unit also builds a system that automatically analyzes the types and amounts of ingredients from a photo of a meal and calculates accurate nutritional value. For example, it calculates calorie and nutrient intake. The nutritional value analysis unit also develops a system that analyzes the types and amounts of ingredients in detail based on a photo of a meal and provides nutritional value in real time. For example, simply uploading a photo of a meal can display nutritional value. This allows for more accurate nutritional value analysis by analyzing the types and amounts of ingredients in detail from a photo of a meal.

[0088] The nutritional value analysis unit can integrate the nutritional value analysis function into other health management apps and fitness programs to provide them to a wide range of users. For example, the nutritional value analysis unit develops a system that integrates the nutritional value analysis function into other health management apps and fitness programs to provide them to a wide range of users. For example, it adds a nutritional value analysis function to an existing health management app. The nutritional value analysis unit also provides the nutritional value analysis function as an API to make it easy for other health management apps and fitness programs to integrate it. For example, it provides documentation and sample code for developers. The nutritional value analysis unit also promotes the health management apps and fitness programs that integrate the nutritional value analysis function to encourage a wide range of users to use it. For example, it conducts a marketing campaign. In this way, the nutritional value analysis function can be integrated into other health management apps and fitness programs to provide it to a wide range of users.

[0089] The nutritional value analysis unit can combine the nutritional value analysis results of meals with the user's training data to provide a comprehensive health management plan. The nutritional value analysis unit, for example, develops a system that combines the nutritional value analysis results of meals with the user's training data to provide a comprehensive health management plan. For example, it proposes a meal plan to maximize the effectiveness of training. The nutritional value analysis unit also analyzes the nutritional value analysis results based on the user's training data to provide a comprehensive health management plan. For example, it proposes a meal plan suitable for a user who is aiming to improve muscle strength or lose weight. The nutritional value analysis unit also integrates the nutritional value analysis results of meals with training data to build a system that visualizes the comprehensive health management plan. For example, it displays the progress of health management using graphs and charts. In this way, a comprehensive health management plan can be provided by integrating the nutritional value analysis results of meals with training data.

[0090] The nutritional value analysis unit can use the emotion estimation function to make meal suggestions based on the user's emotions. For example, the nutritional value analysis unit uses the emotion estimation function to develop a system that analyzes the user's emotional state and suggests ingredients that elicit positive emotions. For example, dark chocolate and berries are recommended. The nutritional value analysis unit also monitors the user's emotional state in real time and customizes meal suggestions according to the emotion. For example, ingredients that have a relaxing effect or ingredients that increase energy are suggested. The nutritional value analysis unit also builds a system that makes meal suggestions according to the user's emotional state based on the emotion estimation data. For example, ingredients that elicit positive emotions are suggested. This makes it possible to support a healthy diet by making meal suggestions according to the user's emotions.

[0091] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0092] The training support system can further analyze the user's sleep data and adjust the training plan. For example, if a lack of sleep is detected, it can recommend lighter exercise. It can also optimize the timing of training based on the sleep data. For example, it can suggest that the user train when they are most energetic. It can also analyze the sleep data to evaluate the user's recovery state and set an appropriate training load. In this way, it can utilize the user's sleep data to provide a more effective training plan.

[0093] The training support system can also analyze the user's heart rate data and adjust the intensity of the training. For example, if the heart rate is too high, it can instruct the user to reduce the intensity of the training. It can also evaluate the effectiveness of the user's aerobic exercise based on the heart rate data and suggest an appropriate training plan. For example, it can monitor whether the heart rate is within the target range and provide feedback in real time. It can also analyze the heart rate data to evaluate the user's physical fitness level and provide an individually customized training plan. This makes it possible to utilize the user's heart rate data to achieve more effective training.

[0094] The training support system can further analyze the user's breathing data to maximize the effectiveness of training. For example, if breathing becomes shallow, it can provide instructions to encourage deep breathing. It can also evaluate the user's state of relaxation based on the breathing data and suggest an appropriate training plan. For example, if breathing is stable, it can recommend high-intensity training. It can also analyze the breathing data to evaluate the user's stress level and suggest training that has a relaxing effect. In this way, it is possible to utilize the user's breathing data to provide more effective training.

[0095] The training support system can also use the user's emotion estimation function to customize the music played during training. For example, if the user is feeling stressed, it can play music that has a relaxing effect. It can also monitor the user's emotional state in real time and automatically select music that corresponds to the emotion. For example, it can provide up-tempo music that elicits positive emotions. It can also generate a music playlist based on the user's emotional state based on the emotion estimation data. For example, it can play music that increases motivation at the start of training. This can improve the effectiveness of training by providing music that corresponds to the user's emotional state.

[0096] The training support system can also use the user's emotion estimation function to customize lighting during training. For example, if the user wants to relax, it can provide warm lighting. It can also monitor the user's emotional state in real time and automatically adjust lighting settings according to the emotion. For example, it can provide soft light if the user is feeling high in stress. It can also generate lighting scenarios according to the user's emotional state based on the emotion estimation data. For example, it can provide lighting with a relaxing effect at the end of training. This can enhance the effectiveness of training by providing lighting that suits the user's emotional state.

[0097] The training support system can also use the user's emotion estimation function to customize the interface during training. For example, if the user is feeling stressed, it will provide a simple and intuitive interface. It can also monitor the user's emotional state in real time and automatically adjust the interface design according to the emotion. For example, it can provide a colorful design to elicit positive emotions. It can also generate interface themes according to the user's emotional state based on the emotion estimation data. For example, it can provide a design that increases motivation at the start of training. This can improve the effectiveness of training by providing an interface that suits the user's emotional state.

[0098] The training support system can further customize feedback messages during training using the user's emotion estimation function. For example, if the user is losing motivation, it can provide an encouraging message. It can also monitor the user's emotional state in real time and automatically generate feedback messages according to the emotion. For example, it can provide words of praise to elicit positive emotions. It can also customize feedback messages according to the user's emotional state based on the emotion estimation data. For example, it can provide a message that enhances a sense of accomplishment at the end of training. This makes it possible to improve the effectiveness of training by providing feedback messages according to the user's emotional state.

[0099] The training support system can further analyze the user's body temperature data and adjust the training plan. For example, if the user's body temperature is too high, it can recommend cool-down exercises. It can also adjust the intensity of training based on the body temperature data. For example, if the user's body temperature is rising, it can suggest low-intensity exercises. It can also analyze the body temperature data to evaluate the user's physical condition and provide an appropriate training plan. In this way, it can utilize the user's body temperature data to provide a more effective training plan.

[0100] The training support system can further analyze the user's hydration data and adjust the training plan. For example, if the user's hydration intake is insufficient, the system will encourage the user to take a break and hydrate. The system can also adjust the intensity of training based on the hydration data. For example, if the user's hydration intake is insufficient, the system will suggest low-intensity exercise. The system can also analyze the hydration data to evaluate the user's physical condition and provide an appropriate training plan. This makes it possible to utilize the user's hydration intake data to provide a more effective training plan.

[0101] The training support system can further analyze the user's environmental data and adjust the training plan. For example, if the room temperature is too high, it can recommend cool-down exercises. It can also adjust the intensity of training based on the environmental data. For example, if the humidity is high, it can suggest low-intensity exercises. It can also analyze the environmental data to evaluate the user's physical condition and provide an appropriate training plan. This makes it possible to utilize the user's environmental data to provide a more effective training plan.

[0102] The processing flow of the second embodiment will be briefly explained below.

[0103] Step 1: The posture recognition unit uses image recognition technology to recognize the user's posture during training from videos taken with the smartphone camera. For example, deep learning technology can be used to analyze the position and angle of each part of the user's body and estimate the user's posture accurately. Computer vision technology can also be used to analyze the user's movements in real time. Step 2: The difference calculation unit calculates the difference between the posture recognized by the posture recognition unit and the video of the trainer. For example, it calculates the difference in pixel units to detect the difference between the user's posture and the trainer's posture. It can also calculate the difference in the timing of movements. Step 3: The instructor provides posture guidance in real time using natural language based on the difference calculated by the difference calculation unit. For example, using a generative AI, the instructor provides specific instructions such as "Please stand up a little straighter" or "Adjust the angle of your arms." Step 4: The superimposition unit superimposes the user's posture on the trainer's video. For example, the user's posture data is superimposed on the trainer's video in real time, allowing the difference to be visually recognized. Step 5: The motivational section provides encouraging messages in natural language, such as "Good luck!" or "Keep it up!", in real time using generative AI. Step 6: The video generator automatically generates optimal training videos. For example, it proposes an effective training plan based on the user's training history and goals, and generates videos based on that plan. Step 7: The nutritional value analysis unit analyzes the food photos to analyze their nutritional value. For example, it uses image recognition technology to analyze the food photos and calculate calorie and nutrient intake.

[0104] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0105] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0106] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0107] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0108] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0109] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0110] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0111] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0112] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0113] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0114] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0115] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0116] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0117] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0118] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0119] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0120] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0121] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0122] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0123] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0124] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0125] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0126] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0127] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0128] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0129] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0130] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0131] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0132] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0133] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0134] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0135] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0136] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0137] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0138] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0139] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0140] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0141] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0142] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0143] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0144] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0145] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0146] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0147] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0148] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0149] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0150] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0151] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0152] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0153] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0154] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0155] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0156] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0157] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0158] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0159] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0160] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0161] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0162] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0163] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0164] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0165] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0166] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0167] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0168] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0169] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0170] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0171] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A posture recognition unit that uses image recognition technology to recognize posture during training from videos taken with a smartphone camera; a difference calculation unit that calculates a difference between the posture recognized by the posture recognition unit and a video of the trainer; an instruction unit that provides posture instruction in real time in natural language based on the difference calculated by the difference calculation unit; an overlay unit that overlays the user's posture on the trainer's video; A motivational section that provides supportive messages in natural language; a video generation unit that automatically generates optimal training videos; a nutritional value analysis unit that analyzes the photograph of the meal and analyzes the nutritional value; A system characterized by:

2. The posture recognition unit Develop a posture recognition algorithm optimized for each individual user by learning from the user's past posture data.

2. The system of claim 1.

3. The difference calculation unit A 3D model is generated based on the trainer's video and the user's posture data, and three-dimensional instruction is provided.

2. The system of claim 1.

4. The overlapping portion is Displaying the user's muscle movements or joint range of motion on top of the overlaid posture data provides more detailed feedback 2. The system of claim 1.

5. The video generation unit Generate personalized training videos based on the user's training history or goals.

2. The system of claim 1.

6. The nutritional value analysis unit Providing meal suggestions based on the user's emotions 2. The system of claim 1.

7. The posture recognition unit Providing posture guidance based on the emotional state of the user 2. The system of claim 1.

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