System

Abstract

An object of a system according to an embodiment is to provide an optimal training plan according to a fitness level, a constraint, and a goal of a user.SOLUTION: A system according to an embodiment includes a user information collection unit, a training plan generation unit, a data analysis unit, and a correction proposal unit. The user information collector collects the user's fitness level, constraints and goals. The training plan generation unit generates a training plan based on the information collected by the user information collection unit. The data analysis unit analyzes user data and sensor data during training in real time. The correction proposal unit corrects the training plan based on the data analyzed by the data analysis unit.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 the drawback of making it difficult to provide optimal training plans tailored to a user's fitness level, constraints, and goals.

[0005] The system according to the embodiment aims to provide an optimal training plan according to the user's fitness level, constraints, and goals. [Means for solving the problem]

[0006] The system according to the embodiment includes a user information collection unit, a training plan generation unit, a data analysis unit, and a modification suggestion unit. The user information collection unit collects a user's fitness level, constraints, and goals. The training plan generation unit generates a training plan based on the information collected by the user information collection unit. The data analysis unit analyzes user data and sensor data during training in real time. The modification suggestion unit modifies the training plan based on the data analyzed by the data analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can provide an optimal training plan according to the user's fitness level, constraints, and goals. [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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[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 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[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) The fitness support system according to an embodiment of the present invention is a system in which a generation AI considers a user's fitness level, constraints, and goals and proposes an individually optimized training plan, thereby enabling the fitness support system to provide optimal training support tailored to the user.

[0029] A fitness support system according to an embodiment includes a user information collection unit, a training plan generation unit, a data analysis unit, and a modification suggestion unit. The user information collection unit collects the user's fitness level, constraints, and goals. For example, the user can set goals such as "I want to lose 5 kg" or "I want to run a marathon." The user information collection unit can also collect the user's physical constraints and health status. The training plan generation unit generates a training plan based on the information collected by the user information collection unit. For example, it may suggest lighter training for beginner users and more intense training for advanced users. The training plan generation unit also proposes meal plans that take into account calorie and nutritional balance according to the user's goals. The data analysis unit analyzes user data and sensor data during training in real time. For example, data such as the user's heart rate and exercise volume is collected via sensors, and the generation AI analyzes the data in real time. The modification suggestion unit modifies the training plan based on the data analyzed by the data analysis unit. For example, if the user feels fatigued during training, the generation AI modifies the training plan based on that data and suggests taking a break. As a result, the fitness support system according to the embodiment can provide the user with an optimal training plan and correct it in real time, thereby supporting effective training.

[0030] The user information collection unit can collect the user's past fitness history and health checkup data and generate a training plan based on the user's long-term health condition. For example, the user information collection unit collects the user's past fitness history and analyzes the frequency and intensity of training, goals achieved, etc. For example, the user information collection unit evaluates changes in the user's physical strength based on training data from the past year. The user information collection unit also collects health checkup data and analyzes health indicators such as the user's blood pressure, heart rate, and body fat percentage. For example, the user's health condition is evaluated based on the most recent health checkup results and the training plan is adjusted. The user information collection unit also generates a training plan taking the user's long-term health condition into consideration. For example, the user information collection unit suggests a reasonable training plan based on the user's past medical history and injury history. This makes it possible to provide a training plan that takes the user's long-term health condition into consideration.

[0031] The user information collection unit can analyze the user's lifestyle habits and suggest appropriate training time slots. The user information collection unit, for example, analyzes the user's sleep patterns and suggests optimal training time slots. For example, if the user has a habit of waking up early, it suggests morning training. The user information collection unit also analyzes the work schedule and adjusts training time. For example, if the user works the night shift, it suggests daytime training. The user information collection unit also adjusts the training plan taking into account the user's overall lifestyle habits. For example, if a user has time on the weekends, it suggests training that is concentrated on the weekends. This makes it possible to suggest optimal training time slots that suit the user's lifestyle habits.

[0032] The user information collection unit can also collect fitness data of the user's family or friends and propose a group training plan. For example, the user information collection unit collects fitness data of the user's family and friends and generates a group training plan. For example, it proposes a joint training plan that takes into account the fitness levels of all family members. When proposing a group training plan, the user information collection unit also takes into account the goals and constraints of each member. For example, it proposes a training plan for a group of friends to challenge a marathon. The user information collection unit also proposes a plan to increase motivation by training together with family and friends. For example, it suggests fitness activities to do with the whole family on the weekend. This allows motivation to be increased by training together with family and friends.

[0033] The user information collection unit can propose a training plan that combines fitness and hobbies based on the user's hobbies or interests. For example, the user information collection unit collects the user's hobbies and interests and proposes a training plan based on them. For example, dance exercises are proposed for a user whose hobby is dancing. The user information collection unit also generates a training plan that combines fitness and hobbies. For example, hiking or trail running is proposed for a user who likes the outdoors. The user information collection unit also suggests fitness activities related to hobbies to draw out the user's interest. For example, exercises set to music are proposed for a user who likes music. This makes it possible to provide a training plan based on the user's hobbies and interests.

[0034] The training plan generation unit can monitor the user's muscle growth or body fat loss in real time and fine-tune the training plan based on the monitoring. The training plan generation unit, for example, monitors the user's muscle growth in real time and fine-tunes the training plan. For example, the training intensity is increased according to an increase in muscle mass. The training plan generation unit also monitors the loss of body fat in real time and adjusts the training plan based on the monitoring. For example, if the body fat percentage decreases, the time for aerobic exercise is increased. The training plan generation unit also individually optimizes the training plan based on the muscle growth or body fat loss. For example, if muscle growth is slow, the frequency of strength training is increased. This allows the training plan to be fine-tuned based on the user's muscle growth or body fat loss.

[0035] The training plan generation unit can analyze the user's meal history and propose a meal plan that optimizes nutritional balance. The training plan generation unit, for example, analyzes the user's meal history and proposes a meal plan that optimizes nutritional balance. For example, it proposes a menu that supplements necessary nutrients based on meal data from the past week. The training plan generation unit also evaluates calorie and nutrient intake based on the meal history and generates an optimal meal plan. For example, if there is a protein deficiency, it proposes ingredients that are high in protein. The training plan generation unit also analyzes the user's meal history and proposes a healthy meal plan. For example, it proposes low-calorie ingredients to avoid excessive calorie intake. In this way, it is possible to provide a meal plan that optimizes nutritional balance based on the user's meal history.

[0036] The training plan generation unit can collect user feedback, evaluate the training plan, and suggest improvements. The training plan generation unit, for example, collects user feedback and evaluates satisfaction with the training plan. For example, it conducts a survey after training to collect user opinions. The training plan generation unit also suggests improvements to the training plan based on the feedback. For example, if the user is dissatisfied with a particular exercise, it suggests an alternative exercise. The training plan generation unit also analyzes the user feedback and flexibly adjusts the training plan. For example, if the user wants to increase the intensity of the training, it suggests increasing the intensity. This allows the training plan to be improved based on the user feedback.

[0037] The training plan generation unit can propose a cross-training plan that combines different sports or activities according to the user's fitness goals. The training plan generation unit, for example, proposes a cross-training plan that combines different sports or activities according to the user's fitness goals. For example, a plan that combines running and cycling is proposed. The training plan generation unit also takes the user's preferences and interests into consideration when proposing a cross-training plan. For example, a plan that combines swimming and strength training is proposed for a user who likes swimming. The training plan generation unit also proposes a training plan that takes into account whole-body balance by combining different sports or activities. For example, a plan that combines yoga and weight training is proposed. This makes it possible to provide a cross-training plan that matches the user's fitness goals.

[0038] The training plan generation unit can propose a training plan using virtual reality according to the user's fitness level. The training plan generation unit, for example, proposes a training plan using virtual reality (VR) according to the user's fitness level. For example, a beginner can experience basic exercises in VR. The training plan generation unit also takes the user's interests and preferences into consideration when proposing a training plan using VR. For example, it proposes a plan for fun training through a sports game. The training plan generation unit also gradually evolves the training plan using VR according to the user's fitness level. For example, it provides training programs ranging from beginner to advanced. This makes it possible to provide a VR training plan according to the user's fitness level.

[0039] The data analysis unit can analyze the user's muscle fatigue level and suggest appropriate rest timing. The data analysis unit, for example, uses an electromyogram (EMG) sensor to analyze the user's muscle fatigue level. For example, it measures the electrical activity of the muscles and evaluates the fatigue level. The data analysis unit also suggests appropriate rest timing based on the muscle fatigue level. For example, if the fatigue level is high, it suggests interrupting training and taking a break. The data analysis unit also monitors the user's muscle fatigue level in real time and adjusts the training plan. For example, if the fatigue level is low, it suggests increasing the intensity of training. This makes it possible to suggest appropriate rest timing based on the user's muscle fatigue level.

[0040] The data analysis unit can analyze the user's breathing pattern and suggest improvements to their breathing technique. The data analysis unit, for example, uses a breathing sensor to analyze the user's breathing pattern. For example, it measures the rhythm and depth of breathing and evaluates the breathing pattern. The data analysis unit also suggests improvements to their breathing technique based on their breathing pattern. For example, if they are breathing shallowly, it suggests encouraging them to take deep breaths. The data analysis unit also monitors the user's breathing pattern in real time and adjusts their training plan. For example, if their breathing is irregular, it suggests reducing the intensity of their training. This makes it possible to suggest improvements to their breathing technique based on the user's breathing pattern.

[0041] The data analysis unit can analyze the user's posture and form and provide feedback to encourage training with correct form. The data analysis unit, for example, uses motion capture technology to analyze the user's posture and form. For example, it uses a camera or sensor to analyze the user's movements and evaluates their posture and form. The data analysis unit also provides feedback to encourage training with correct form based on the results of the posture and form analysis. For example, if the form is poor, it points out areas that need correction. The data analysis unit also monitors the user's posture and form in real time and adjusts the training plan. For example, if the user is training with correct form, it suggests increasing the intensity of the training. This makes it possible to provide feedback to encourage correct training based on the user's posture and form.

[0042] The data analysis unit can compare the user's training data with other users and provide a standard. For example, the data analysis unit compares the user's training data with other users and provides a benchmark. For example, the data analysis unit evaluates training progress by comparing with users of the same age and gender. The data analysis unit also adjusts the user's training plan based on the benchmark data. For example, if the user's progress is lagging behind other users, the data analysis unit suggests increasing the intensity of training. The data analysis unit also provides motivational feedback by comparing the user's training data with other users. For example, it incorporates an element of competition with other users. This makes it possible to compare the user's training data with other users and provide a benchmark.

[0043] The data analysis unit can use the user's training data to propose a post-training recovery plan. The data analysis unit, for example, analyzes the user's training data and proposes a post-training recovery plan. For example, it suggests appropriate rest periods and recovery exercises depending on the intensity and duration of training. The data analysis unit also individually optimizes the recovery plan based on the training data. For example, it suggests recovery methods taking into account muscle fatigue levels and heart rate. The data analysis unit also monitors the user's training data in real time and adjusts the recovery plan. For example, if the heart rate is high after training, it suggests increasing recovery exercises. This makes it possible to provide a recovery plan based on the user's training data.

[0044] The correction suggestion unit can analyze the user's training progress and propose a specific action plan for achieving the goal. The correction suggestion unit, for example, analyzes the user's training progress and proposes a specific action plan for achieving the goal. For example, it specifically shows the steps to achieving the goal. The correction suggestion unit also individually optimizes the action plan based on the training progress. For example, if progress is slow, it suggests increasing the frequency or intensity of training. The correction suggestion unit also monitors the user's training progress in real time and adjusts the action plan. For example, if progress is going well, it suggests raising the goal. In this way, a specific action plan can be provided based on the user's training progress.

[0045] The modification suggestion unit can analyze the user's training history and modify the training plan based on past successes or failures. For example, the modification suggestion unit analyzes the user's training history and modifies the training plan based on past successes or failures. For example, it re-proposes training methods that were successful in the past. The modification suggestion unit also individually optimizes the training plan based on the training history. For example, it makes suggestions to avoid training methods that failed in the past. The modification suggestion unit also monitors the user's training history in real time and adjusts the training plan. For example, it adjusts the intensity and frequency of training based on past data. This allows the training plan to be modified based on the user's training history.

[0046] The modification suggestion unit can collect user feedback, evaluate the training plan, and suggest improvements. The modification suggestion unit, for example, collects user feedback and evaluates satisfaction with the training plan. For example, it conducts a survey after training to collect user opinions. The modification suggestion unit also suggests improvements to the training plan based on the feedback. For example, if the user is dissatisfied with a particular exercise, it suggests an alternative exercise. The modification suggestion unit also analyzes the user feedback and flexibly adjusts the training plan. For example, if the user wishes to increase the intensity of the training, it suggests increasing the intensity. This allows the training plan to be improved based on the user feedback.

[0047] The modification suggestion unit may share the user's training data with other users to obtain community-based feedback. For example, the modification suggestion unit may share the user's training data with other users to obtain community-based feedback. For example, the modification suggestion unit may collect other users' opinions and advice on the training plan. The modification suggestion unit may also adjust the training plan based on the community-based feedback. For example, the modification suggestion unit may incorporate advice from other users to improve the training plan. The modification suggestion unit may also provide motivational feedback by sharing the user's training data. For example, the modification suggestion unit may incorporate an element of competition with other users. This allows the user's training data to be shared with other users to obtain community-based feedback.

[0048] The modification suggestion unit can use the user's training data to propose a post-training recovery plan. The modification suggestion unit, for example, analyzes the user's training data and proposes a post-training recovery plan. For example, it suggests appropriate rest periods and recovery exercises depending on the intensity and duration of training. The modification suggestion unit also individually optimizes the recovery plan based on the training data. For example, it suggests recovery methods taking into account muscle fatigue levels and heart rate. The modification suggestion unit also monitors the user's training data in real time and adjusts the recovery plan. For example, if the heart rate is high after training, it suggests increasing recovery exercises. This makes it possible to provide a recovery plan based on the user's training data.

[0049] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0050] The user information collection unit collects the user's fitness level, constraints, and goals. For example, the user can set goals such as "I want to lose 5 kg" or "I want to run a marathon." The user information collection unit can also collect the user's physical constraints and health condition. The training plan generation unit generates a training plan based on the information collected by the user information collection unit. For example, it suggests lighter training for beginner users and more intense training for advanced users. The training plan generation unit also suggests meal plans that take into account calories and nutritional balance according to the user's goals. The data analysis unit analyzes user data and sensor data during training in real time. For example, data such as the user's heart rate and exercise volume is collected via sensors, and the generation AI analyzes the data in real time. The modification suggestion unit modifies the training plan based on the data analyzed by the data analysis unit. For example, if the user feels fatigued during training, the generation AI modifies the training plan based on that data and suggests a break. As a result, the fitness support system according to the embodiment can provide the user with an optimal training plan and modify it in real time to support effective training.

[0051] The user information collection unit can collect the user's past fitness history and health checkup data and generate a training plan based on the user's long-term health condition. For example, the unit collects the user's past fitness history and analyzes the frequency and intensity of training, goals achieved, etc. For example, the unit evaluates changes in the user's physical strength based on training data from the past year. The user information collection unit also collects health checkup data and analyzes health indicators such as the user's blood pressure, heart rate, and body fat percentage. For example, the unit evaluates the user's health condition based on the results of the most recent health checkup and adjusts the training plan. The user information collection unit also generates a training plan taking the user's long-term health condition into consideration. For example, the unit suggests a reasonable training plan based on the user's past medical history and injury history. This makes it possible to provide a training plan that takes the user's long-term health condition into consideration.

[0052] The user information collection unit can analyze the user's lifestyle and suggest appropriate training times. For example, it can analyze the user's sleep patterns and suggest the optimal training time. For example, if the user has a habit of waking up early, it can suggest morning training. The user information collection unit can also analyze the work schedule and adjust training times. For example, if the user works the night shift, it can suggest daytime training. The user information collection unit can also adjust the training plan taking into account the user's overall lifestyle. For example, it can suggest training that is concentrated on the weekend for a user who has time on the weekend. This makes it possible to suggest the optimal training time that suits the user's lifestyle.

[0053] The user information collection unit can also collect fitness data of the user's family or friends and propose group training plans. For example, the fitness data of the user's family and friends is collected to generate a group training plan. For example, a joint training plan that takes into account the fitness levels of all family members is proposed. The user information collection unit also takes into account the goals and constraints of each member when proposing a group training plan. For example, a training plan for a group of friends attempting a marathon is proposed. The user information collection unit also proposes a plan to increase motivation by training together with family and friends. For example, fitness activities to be done with the whole family on the weekend are proposed. This allows motivation to be increased by training together with family and friends.

[0054] The user information collection unit can propose a training plan that combines fitness and hobbies based on the user's hobbies or interests. For example, the user's hobbies and interests are collected and a training plan based on them is proposed. For example, dance exercises are proposed for a user whose hobby is dancing. The user information collection unit also generates a training plan that combines fitness and hobbies. For example, hiking or trail running is proposed for a user who enjoys the outdoors. The user information collection unit also suggests fitness activities related to hobbies to draw out the user's interest. For example, exercises set to music are proposed for a user who likes music. This makes it possible to provide a training plan based on the user's hobbies and interests.

[0055] The training plan generation unit can monitor the user's muscle growth or body fat loss in real time and fine-tune the training plan based on the monitoring. For example, the training plan can be fine-tuned by monitoring the user's muscle growth in real time. For example, the training intensity can be increased in accordance with an increase in muscle mass. The training plan generation unit can also monitor the loss of body fat in real time and adjust the training plan based on the monitoring. For example, if the body fat percentage decreases, the time for aerobic exercise can be increased. The training plan generation unit can also individually optimize the training plan based on the muscle growth or body fat loss. For example, if muscle growth is slow, the frequency of strength training can be increased. This allows the training plan to be fine-tuned based on the user's muscle growth or body fat loss.

[0056] The training plan generation unit can analyze the user's diet history and propose a meal plan that optimizes nutritional balance. For example, the training plan generation unit analyzes the user's diet history and proposes a meal plan that optimizes nutritional balance. For example, it proposes a menu that supplements necessary nutrients based on dietary data from the past week. The training plan generation unit also evaluates calorie and nutrient intake based on the diet history and generates an optimal meal plan. For example, if there is a protein deficiency, it proposes ingredients that are high in protein. The training plan generation unit also analyzes the user's diet history and proposes a healthy meal plan. For example, it proposes low-calorie ingredients to avoid excessive calorie intake. In this way, it is possible to provide a meal plan that optimizes nutritional balance based on the user's diet history.

[0057] The training plan generation unit can collect user feedback, evaluate the training plan, and suggest improvements. For example, the training plan generation unit collects user feedback and evaluates satisfaction with the training plan. For example, a survey is conducted after training to collect user opinions. The training plan generation unit also suggests improvements to the training plan based on the feedback. For example, if the user is dissatisfied with a particular exercise, the training plan generation unit suggests an alternative exercise. The training plan generation unit also analyzes the user feedback and flexibly adjusts the training plan. For example, if the user wishes to increase the intensity of the training, the training plan generation unit makes a suggestion to increase the intensity. This allows the training plan to be improved based on the user feedback.

[0058] The processing flow of the first embodiment will be briefly explained below.

[0059] Step 1: The user information collection unit collects the user's fitness level, constraints, and goals. For example, the user can set goals such as "I want to lose 5 kg" or "I want to run a marathon." The user information collection unit can also collect the user's physical constraints and health status. Step 2: The training plan generation unit generates a training plan based on the information collected by the user information collection unit. For example, it may suggest lighter training for beginner users and more intense training for advanced users. The training plan generation unit also suggests meal plans that take into account the user's goals, taking into account calorie and nutritional balance. Step 3: The data analysis unit analyzes user data and sensor data in real time during training. For example, data such as the user's heart rate and exercise volume is collected through sensors, and the generation AI analyzes it in real time. Step 4: The modification suggestion unit modifies the training plan based on the data analyzed by the data analysis unit. For example, if the user feels fatigued during training, the generation AI modifies the training plan based on that data and suggests taking a break.

[0060] (Example 2) The fitness support system according to an embodiment of the present invention is a system in which a generation AI considers a user's fitness level, constraints, and goals and proposes an individually optimized training plan, thereby enabling the fitness support system to provide optimal training support tailored to the user.

[0061] A fitness support system according to an embodiment includes a user information collection unit, a training plan generation unit, a data analysis unit, and a modification suggestion unit. The user information collection unit collects the user's fitness level, constraints, and goals. For example, the user can set goals such as "I want to lose 5 kg" or "I want to run a marathon." The user information collection unit can also collect the user's physical constraints and health status. The training plan generation unit generates a training plan based on the information collected by the user information collection unit. For example, it may suggest lighter training for beginner users and more intense training for advanced users. The training plan generation unit also proposes meal plans that take into account calorie and nutritional balance according to the user's goals. The data analysis unit analyzes user data and sensor data during training in real time. For example, data such as the user's heart rate and exercise volume is collected via sensors, and the generation AI analyzes the data in real time. The modification suggestion unit modifies the training plan based on the data analyzed by the data analysis unit. For example, if the user feels fatigued during training, the generation AI modifies the training plan based on that data and suggests taking a break. As a result, the fitness support system according to the embodiment can provide the user with an optimal training plan and correct it in real time, thereby supporting effective training.

[0062] The user information collection unit can collect the user's past fitness history and health checkup data and generate a training plan based on the user's long-term health condition. For example, the user information collection unit collects the user's past fitness history and analyzes the frequency and intensity of training, goals achieved, etc. For example, the user information collection unit evaluates changes in the user's physical strength based on training data from the past year. The user information collection unit also collects health checkup data and analyzes health indicators such as the user's blood pressure, heart rate, and body fat percentage. For example, the user's health condition is evaluated based on the most recent health checkup results and the training plan is adjusted. The user information collection unit also generates a training plan taking the user's long-term health condition into consideration. For example, the user information collection unit suggests a reasonable training plan based on the user's past medical history and injury history. This makes it possible to provide a training plan that takes the user's long-term health condition into consideration.

[0063] The user information collection unit can analyze the user's psychological state and stress level using the emotion estimation function and propose a training plan based on mental health. For example, the user information collection unit measures the user's daily stress level using the emotion estimation function to analyze the user's psychological state. For example, it analyzes the user's facial expressions and voice and calculates a stress score. The user information collection unit also proposes a training plan based on the stress level. For example, on days when stress is high, it suggests relaxing yoga or stretching, and on days when stress is low, it suggests high-intensity training. The user information collection unit also adjusts the training plan taking the user's mental health into consideration. For example, it periodically conducts mental health checks and changes the training content based on the results. This makes it possible to provide a training plan that takes the user's mental health into consideration.

[0064] The user information collection unit can analyze the user's lifestyle habits and suggest appropriate training time slots. The user information collection unit, for example, analyzes the user's sleep patterns and suggests optimal training time slots. For example, if the user has a habit of waking up early, it suggests morning training. The user information collection unit also analyzes the work schedule and adjusts training time. For example, if the user works the night shift, it suggests daytime training. The user information collection unit also adjusts the training plan taking into account the user's overall lifestyle habits. For example, if a user has time on the weekends, it suggests training that is concentrated on the weekends. This makes it possible to suggest optimal training time slots that suit the user's lifestyle habits.

[0065] The user information collection unit can also collect fitness data of the user's family or friends and propose a group training plan. For example, the user information collection unit collects fitness data of the user's family and friends and generates a group training plan. For example, it proposes a joint training plan that takes into account the fitness levels of all family members. When proposing a group training plan, the user information collection unit also takes into account the goals and constraints of each member. For example, it proposes a training plan for a group of friends to challenge a marathon. The user information collection unit also proposes a plan to increase motivation by training together with family and friends. For example, it suggests fitness activities to do with the whole family on the weekend. This allows motivation to be increased by training together with family and friends.

[0066] The user information collection unit can propose a training plan that combines fitness and hobbies based on the user's hobbies or interests. For example, the user information collection unit collects the user's hobbies and interests and proposes a training plan based on them. For example, dance exercises are proposed for a user whose hobby is dancing. The user information collection unit also generates a training plan that combines fitness and hobbies. For example, hiking or trail running is proposed for a user who likes the outdoors. The user information collection unit also suggests fitness activities related to hobbies to draw out the user's interest. For example, exercises set to music are proposed for a user who likes music. This makes it possible to provide a training plan based on the user's hobbies and interests.

[0067] The user information collection unit can use the emotion estimation function to analyze the user's emotions regarding training in real time and provide feedback to increase motivation. The user information collection unit, for example, uses the emotion estimation function to analyze the user's emotions regarding training. For example, it analyzes facial expressions and voice during training and calculates an emotion score. The user information collection unit also provides feedback to increase motivation based on the user's emotion data. For example, if the user has strong positive emotions, it displays an encouraging message. The user information collection unit also adjusts the training plan based on the emotion estimation data. For example, if the user has strong negative emotions, it suggests reducing the intensity of the training. This makes it possible to provide feedback based on the user's emotions and increase motivation.

[0068] The training plan generation unit can monitor the user's muscle growth or body fat loss in real time and fine-tune the training plan based on the monitoring. The training plan generation unit, for example, monitors the user's muscle growth in real time and fine-tunes the training plan. For example, the training intensity is increased according to an increase in muscle mass. The training plan generation unit also monitors the loss of body fat in real time and adjusts the training plan based on the monitoring. For example, if the body fat percentage decreases, the time for aerobic exercise is increased. The training plan generation unit also individually optimizes the training plan based on the muscle growth or body fat loss. For example, if muscle growth is slow, the frequency of strength training is increased. This allows the training plan to be fine-tuned based on the user's muscle growth or body fat loss.

[0069] The training plan generation unit can analyze the user's meal history and propose a meal plan that optimizes nutritional balance. The training plan generation unit, for example, analyzes the user's meal history and proposes a meal plan that optimizes nutritional balance. For example, it proposes a menu that supplements necessary nutrients based on meal data from the past week. The training plan generation unit also evaluates calorie and nutrient intake based on the meal history and generates an optimal meal plan. For example, if there is a protein deficiency, it proposes ingredients that are high in protein. The training plan generation unit also analyzes the user's meal history and proposes a healthy meal plan. For example, it proposes low-calorie ingredients to avoid excessive calorie intake. In this way, it is possible to provide a meal plan that optimizes nutritional balance based on the user's meal history.

[0070] The training plan generation unit can collect user feedback, evaluate the training plan, and suggest improvements. The training plan generation unit, for example, collects user feedback and evaluates satisfaction with the training plan. For example, it conducts a survey after training to collect user opinions. The training plan generation unit also suggests improvements to the training plan based on the feedback. For example, if the user is dissatisfied with a particular exercise, it suggests an alternative exercise. The training plan generation unit also analyzes the user feedback and flexibly adjusts the training plan. For example, if the user wants to increase the intensity of the training, it suggests increasing the intensity. This allows the training plan to be improved based on the user feedback.

[0071] The training plan generation unit can propose a cross-training plan that combines different sports or activities according to the user's fitness goals. The training plan generation unit, for example, proposes a cross-training plan that combines different sports or activities according to the user's fitness goals. For example, a plan that combines running and cycling is proposed. The training plan generation unit also takes the user's preferences and interests into consideration when proposing a cross-training plan. For example, a plan that combines swimming and strength training is proposed for a user who likes swimming. The training plan generation unit also proposes a training plan that takes into account whole-body balance by combining different sports or activities. For example, a plan that combines yoga and weight training is proposed. This makes it possible to provide a cross-training plan that matches the user's fitness goals.

[0072] The training plan generation unit can propose a training plan using virtual reality according to the user's fitness level. The training plan generation unit, for example, proposes a training plan using virtual reality (VR) according to the user's fitness level. For example, a beginner can experience basic exercises in VR. The training plan generation unit also takes the user's interests and preferences into consideration when proposing a training plan using VR. For example, it proposes a plan for fun training through a sports game. The training plan generation unit also gradually evolves the training plan using VR according to the user's fitness level. For example, it provides training programs ranging from beginner to advanced. This makes it possible to provide a VR training plan according to the user's fitness level.

[0073] The training plan generation unit can use the emotion estimation function to analyze the emotions felt by the user during training and suggest a training menu to elicit positive emotions. The training plan generation unit, for example, uses the emotion estimation function to analyze the emotions felt by the user during training. For example, it analyzes facial expressions and voice during training and calculates an emotion score. The training plan generation unit also suggests a training menu to elicit positive emotions based on the user's emotion data. For example, it suggests increasing the number of exercises that the user enjoys. The training plan generation unit also adjusts the training menu based on the emotion estimation data. For example, if negative emotions are strong, it suggests exercises that have a relaxing effect. In this way, a training menu based on the user's emotions can be provided to elicit positive emotions.

[0074] The data analysis unit can analyze the user's muscle fatigue level and suggest appropriate rest timing. The data analysis unit, for example, uses an electromyogram (EMG) sensor to analyze the user's muscle fatigue level. For example, it measures the electrical activity of the muscles and evaluates the fatigue level. The data analysis unit also suggests appropriate rest timing based on the muscle fatigue level. For example, if the fatigue level is high, it suggests interrupting training and taking a break. The data analysis unit also monitors the user's muscle fatigue level in real time and adjusts the training plan. For example, if the fatigue level is low, it suggests increasing the intensity of training. This makes it possible to suggest appropriate rest timing based on the user's muscle fatigue level.

[0075] The data analysis unit can analyze the user's breathing pattern and suggest improvements to their breathing technique. The data analysis unit, for example, uses a breathing sensor to analyze the user's breathing pattern. For example, it measures the rhythm and depth of breathing and evaluates the breathing pattern. The data analysis unit also suggests improvements to their breathing technique based on their breathing pattern. For example, if they are breathing shallowly, it suggests encouraging them to take deep breaths. The data analysis unit also monitors the user's breathing pattern in real time and adjusts their training plan. For example, if their breathing is irregular, it suggests reducing the intensity of their training. This makes it possible to suggest improvements to their breathing technique based on the user's breathing pattern.

[0076] The data analysis unit can analyze the user's posture and form and provide feedback to encourage training with correct form. The data analysis unit, for example, uses motion capture technology to analyze the user's posture and form. For example, it uses a camera or sensor to analyze the user's movements and evaluates their posture and form. The data analysis unit also provides feedback to encourage training with correct form based on the results of the posture and form analysis. For example, if the form is poor, it points out areas that need correction. The data analysis unit also monitors the user's posture and form in real time and adjusts the training plan. For example, if the user is training with correct form, it suggests increasing the intensity of the training. This makes it possible to provide feedback to encourage correct training based on the user's posture and form.

[0077] The data analysis unit can compare the user's training data with other users and provide a standard. For example, the data analysis unit compares the user's training data with other users and provides a benchmark. For example, the data analysis unit evaluates training progress by comparing with users of the same age and gender. The data analysis unit also adjusts the user's training plan based on the benchmark data. For example, if the user's progress is lagging behind other users, the data analysis unit suggests increasing the intensity of training. The data analysis unit also provides motivational feedback by comparing the user's training data with other users. For example, it incorporates an element of competition with other users. This makes it possible to compare the user's training data with other users and provide a benchmark.

[0078] The data analysis unit can use the user's training data to propose a post-training recovery plan. The data analysis unit, for example, analyzes the user's training data and proposes a post-training recovery plan. For example, it suggests appropriate rest periods and recovery exercises depending on the intensity and duration of training. The data analysis unit also individually optimizes the recovery plan based on the training data. For example, it suggests recovery methods taking into account muscle fatigue levels and heart rate. The data analysis unit also monitors the user's training data in real time and adjusts the recovery plan. For example, if the heart rate is high after training, it suggests increasing recovery exercises. This makes it possible to provide a recovery plan based on the user's training data.

[0079] The data analysis unit can use the emotion estimation function to analyze the emotions felt by the user during training in real time and provide feedback to maintain motivation. The data analysis unit, for example, uses the emotion estimation function to analyze the emotions felt by the user during training in real time. For example, it analyzes facial expressions and voice during training and calculates an emotion score. The data analysis unit also provides feedback to maintain motivation based on the user's emotion data. For example, if positive emotions are strong, it displays an encouraging message. The data analysis unit also adjusts the training plan based on the emotion estimation data. For example, if negative emotions are strong, it suggests reducing the intensity of the training. In this way, it is possible to provide feedback to maintain motivation based on the user's emotions.

[0080] The correction suggestion unit can analyze the user's training progress and propose a specific action plan for achieving the goal. The correction suggestion unit, for example, analyzes the user's training progress and proposes a specific action plan for achieving the goal. For example, it specifically shows the steps to achieving the goal. The correction suggestion unit also individually optimizes the action plan based on the training progress. For example, if progress is slow, it suggests increasing the frequency or intensity of training. The correction suggestion unit also monitors the user's training progress in real time and adjusts the action plan. For example, if progress is going well, it suggests raising the goal. In this way, a specific action plan can be provided based on the user's training progress.

[0081] The modification suggestion unit can analyze the user's training history and modify the training plan based on past successes or failures. For example, the modification suggestion unit analyzes the user's training history and modifies the training plan based on past successes or failures. For example, it re-proposes training methods that were successful in the past. The modification suggestion unit also individually optimizes the training plan based on the training history. For example, it makes suggestions to avoid training methods that failed in the past. The modification suggestion unit also monitors the user's training history in real time and adjusts the training plan. For example, it adjusts the intensity and frequency of training based on past data. This allows the training plan to be modified based on the user's training history.

[0082] The modification suggestion unit can collect user feedback, evaluate the training plan, and suggest improvements. The modification suggestion unit, for example, collects user feedback and evaluates satisfaction with the training plan. For example, it conducts a survey after training to collect user opinions. The modification suggestion unit also suggests improvements to the training plan based on the feedback. For example, if the user is dissatisfied with a particular exercise, it suggests an alternative exercise. The modification suggestion unit also analyzes the user feedback and flexibly adjusts the training plan. For example, if the user wishes to increase the intensity of the training, it suggests increasing the intensity. This allows the training plan to be improved based on the user feedback.

[0083] The modification suggestion unit may share the user's training data with other users to obtain community-based feedback. For example, the modification suggestion unit may share the user's training data with other users to obtain community-based feedback. For example, the modification suggestion unit may collect other users' opinions and advice on the training plan. The modification suggestion unit may also adjust the training plan based on the community-based feedback. For example, the modification suggestion unit may incorporate advice from other users to improve the training plan. The modification suggestion unit may also provide motivational feedback by sharing the user's training data. For example, the modification suggestion unit may incorporate an element of competition with other users. This allows the user's training data to be shared with other users to obtain community-based feedback.

[0084] The modification suggestion unit can use the user's training data to propose a post-training recovery plan. The modification suggestion unit, for example, analyzes the user's training data and proposes a post-training recovery plan. For example, it suggests appropriate rest periods and recovery exercises depending on the intensity and duration of training. The modification suggestion unit also individually optimizes the recovery plan based on the training data. For example, it suggests recovery methods taking into account muscle fatigue levels and heart rate. The modification suggestion unit also monitors the user's training data in real time and adjusts the recovery plan. For example, if the heart rate is high after training, it suggests increasing recovery exercises. This makes it possible to provide a recovery plan based on the user's training data.

[0085] The modification suggestion unit can use the emotion estimation function to analyze the emotions felt by the user during training in real time and provide feedback to maintain motivation. The modification suggestion unit, for example, uses the emotion estimation function to analyze the emotions felt by the user during training in real time. For example, it analyzes facial expressions and voice during training and calculates an emotion score. The modification suggestion unit also provides feedback to maintain motivation based on the user's emotion data. For example, if positive emotions are strong, it displays an encouraging message. The modification suggestion unit also adjusts the training plan based on the emotion estimation data. For example, if negative emotions are strong, it suggests reducing the intensity of the training. This makes it possible to provide feedback to maintain motivation based on the user's emotions.

[0086] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0087] The user information collection unit collects the user's fitness level, constraints, and goals. For example, the user can set goals such as "I want to lose 5 kg" or "I want to run a marathon." The user information collection unit can also collect the user's physical constraints and health condition. The training plan generation unit generates a training plan based on the information collected by the user information collection unit. For example, it suggests lighter training for beginner users and more intense training for advanced users. The training plan generation unit also suggests meal plans that take into account calories and nutritional balance according to the user's goals. The data analysis unit analyzes user data and sensor data during training in real time. For example, data such as the user's heart rate and exercise volume is collected via sensors, and the generation AI analyzes the data in real time. The modification suggestion unit modifies the training plan based on the data analyzed by the data analysis unit. For example, if the user feels fatigued during training, the generation AI modifies the training plan based on that data and suggests a break. As a result, the fitness support system according to the embodiment can provide the user with an optimal training plan and modify it in real time to support effective training.

[0088] The user information collection unit can collect the user's past fitness history and health checkup data and generate a training plan based on the user's long-term health condition. For example, the unit collects the user's past fitness history and analyzes the frequency and intensity of training, goals achieved, etc. For example, the unit evaluates changes in the user's physical strength based on training data from the past year. The user information collection unit also collects health checkup data and analyzes health indicators such as the user's blood pressure, heart rate, and body fat percentage. For example, the unit evaluates the user's health condition based on the results of the most recent health checkup and adjusts the training plan. The user information collection unit also generates a training plan taking the user's long-term health condition into consideration. For example, the unit suggests a reasonable training plan based on the user's past medical history and injury history. This makes it possible to provide a training plan that takes the user's long-term health condition into consideration.

[0089] The user information collection unit can analyze the user's psychological state and stress level using the emotion estimation function and propose a training plan based on mental health. For example, to analyze the user's psychological state, the emotion estimation function is used to measure the user's daily stress level. For example, the user's facial expressions and voice are analyzed to calculate a stress score. The user information collection unit also proposes a training plan based on the stress level. For example, on days when stress is high, it may suggest relaxing yoga or stretching, and on days when stress is low, it may suggest high-intensity training. The user information collection unit also adjusts the training plan taking the user's mental health into consideration. For example, it may periodically conduct mental health checks and change the training content based on the results. This makes it possible to provide a training plan that takes the user's mental health into consideration.

[0090] The user information collection unit can analyze the user's lifestyle and suggest appropriate training times. For example, it can analyze the user's sleep patterns and suggest the optimal training time. For example, if the user has a habit of waking up early, it can suggest morning training. The user information collection unit can also analyze the work schedule and adjust training times. For example, if the user works the night shift, it can suggest daytime training. The user information collection unit can also adjust the training plan taking into account the user's overall lifestyle. For example, it can suggest training that is concentrated on the weekend for a user who has time on the weekend. This makes it possible to suggest the optimal training time that suits the user's lifestyle.

[0091] The user information collection unit can also collect fitness data of the user's family or friends and propose group training plans. For example, the fitness data of the user's family and friends is collected to generate a group training plan. For example, a joint training plan that takes into account the fitness levels of all family members is proposed. The user information collection unit also takes into account the goals and constraints of each member when proposing a group training plan. For example, a training plan for a group of friends attempting a marathon is proposed. The user information collection unit also proposes a plan to increase motivation by training together with family and friends. For example, fitness activities to be done with the whole family on the weekend are proposed. This allows motivation to be increased by training together with family and friends.

[0092] The user information collection unit can propose a training plan that combines fitness and hobbies based on the user's hobbies or interests. For example, the user's hobbies and interests are collected and a training plan based on them is proposed. For example, dance exercises are proposed for a user whose hobby is dancing. The user information collection unit also generates a training plan that combines fitness and hobbies. For example, hiking or trail running is proposed for a user who enjoys the outdoors. The user information collection unit also suggests fitness activities related to hobbies to draw out the user's interest. For example, exercises set to music are proposed for a user who likes music. This makes it possible to provide a training plan based on the user's hobbies and interests.

[0093] The user information collection unit can use the emotion estimation function to analyze the user's emotions regarding training in real time and provide feedback to increase motivation. For example, the emotion estimation function is used to analyze the user's emotions regarding training. For example, facial expressions and voice during training are analyzed to calculate an emotion score. The user information collection unit also provides feedback to increase motivation based on the user's emotion data. For example, if positive emotions are strong, an encouraging message is displayed. The user information collection unit also adjusts the training plan based on the emotion estimation data. For example, if negative emotions are strong, a suggestion is made to reduce the intensity of the training. In this way, feedback based on the user's emotions can be provided to increase motivation.

[0094] The training plan generation unit can monitor the user's muscle growth or body fat loss in real time and fine-tune the training plan based on the monitoring. For example, the training plan can be fine-tuned by monitoring the user's muscle growth in real time. For example, the training intensity can be increased in accordance with an increase in muscle mass. The training plan generation unit can also monitor the loss of body fat in real time and adjust the training plan based on the monitoring. For example, if the body fat percentage decreases, the time for aerobic exercise can be increased. The training plan generation unit can also individually optimize the training plan based on the muscle growth or body fat loss. For example, if muscle growth is slow, the frequency of strength training can be increased. This allows the training plan to be fine-tuned based on the user's muscle growth or body fat loss.

[0095] The training plan generation unit can analyze the user's diet history and propose a meal plan that optimizes nutritional balance. For example, the training plan generation unit analyzes the user's diet history and proposes a meal plan that optimizes nutritional balance. For example, it proposes a menu that supplements necessary nutrients based on dietary data from the past week. The training plan generation unit also evaluates calorie and nutrient intake based on the diet history and generates an optimal meal plan. For example, if there is a protein deficiency, it proposes ingredients that are high in protein. The training plan generation unit also analyzes the user's diet history and proposes a healthy meal plan. For example, it proposes low-calorie ingredients to avoid excessive calorie intake. In this way, it is possible to provide a meal plan that optimizes nutritional balance based on the user's diet history.

[0096] The training plan generation unit can collect user feedback, evaluate the training plan, and suggest improvements. For example, the training plan generation unit collects user feedback and evaluates satisfaction with the training plan. For example, a survey is conducted after training to collect user opinions. The training plan generation unit also suggests improvements to the training plan based on the feedback. For example, if the user is dissatisfied with a particular exercise, the training plan generation unit suggests an alternative exercise. The training plan generation unit also analyzes the user feedback and flexibly adjusts the training plan. For example, if the user wishes to increase the intensity of the training, the training plan generation unit makes a suggestion to increase the intensity. This allows the training plan to be improved based on the user feedback.

[0097] The processing flow of the second embodiment will be briefly explained below.

[0098] Step 1: The user information collection unit collects the user's fitness level, constraints, and goals. For example, the user can set goals such as "I want to lose 5 kg" or "I want to run a marathon." The user information collection unit can also collect the user's physical constraints and health status. Step 2: The training plan generation unit generates a training plan based on the information collected by the user information collection unit. For example, it may suggest lighter training for beginner users and more intense training for advanced users. The training plan generation unit also suggests meal plans that take into account the user's goals, taking into account calorie and nutritional balance. Step 3: The data analysis unit analyzes user data and sensor data in real time during training. For example, data such as the user's heart rate and exercise volume is collected through sensors, and the generation AI analyzes it in real time. Step 4: The modification suggestion unit modifies the training plan based on the data analyzed by the data analysis unit. For example, if the user feels fatigued during training, the generation AI modifies the training plan based on that data and suggests taking a break.

[0099] 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.

[0100] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<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.

[0101] 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.

[0102] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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).

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0112] 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. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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).

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0127] In the headset type terminal 314, 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 headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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).

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0143] In the robot 414, 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. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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).

[0152] 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.

[0153] 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."

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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, to avoid confusion and 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.

[0165] 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]

[0166] 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 user information collection unit that collects a user's fitness level, constraints, and goals; a training plan generation unit that generates a training plan based on the information collected by the user information collection unit; a data analysis unit that analyzes user data and sensor data during training in real time; a modification suggestion unit that modifies the training plan based on the data analyzed by the data analysis unit. A system characterized by:

2. The user information collection unit Collects users' past fitness history and health check data to generate training plans based on their long-term health status.

2. The system of claim 1.

3. The user information collection unit Analyzes the user's psychological state and stress level using emotion estimation functionality and proposes training plans based on mental health 2. The system of claim 1.

4. The user information collection unit Analyzes the user's lifestyle and suggests appropriate training times 2. The system of claim 1.

5. The user information collection unit It also collects fitness data from the user's family or friends to suggest group training plans.

2. The system of claim 1.

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