system

Abstract

The system according to this embodiment aims to propose an individually optimized training program and dietary plan based on body composition data. [Solution] The system according to the embodiment comprises a data collection unit, a reception unit, an analysis unit, and a proposal unit. The data collection unit collects data from a body composition analyzer. The reception unit inputs the user's goals based on the data collected by the data collection unit. The analysis unit analyzes the information input by the reception unit. The proposal unit proposes a training program and meal plan based on the results analyzed by the analysis unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] Conventionally, it has not been sufficiently done to individually propose an optimal training program and diet content based on body composition data, and there is room for improvement.

[0005] The system according to the embodiment aims to individually propose an optimal training program and diet content based on body composition data.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, a reception unit, an analysis unit, and a suggestion unit. The data collection unit collects data from a body composition analyzer. The reception unit inputs the user's goals based on the data collected by the data collection unit. The analysis unit analyzes the information input by the reception unit. The suggestion unit proposes a training program and meal plan based on the results of the analysis performed by the analysis unit. [Effects of the Invention]

[0007] The system according to this embodiment can propose an individually optimized training program and diet based on body composition data. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the 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, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The training program suggestion system according to an embodiment of the present invention is a system that suggests an optimal training program and diet based on the user's current body composition, confirmed through a body composition analyzer, and inputs the ideal body shape, body composition, duration, and training frequency. The training program suggestion system allows the user to confirm their current body composition using a body composition analyzer, input their ideal body shape and body composition, the time to achieve the goal, and the training frequency, and the generating AI then suggests an optimal training program and diet. For example, the user measures data such as body fat percentage, muscle mass, and weight using a body composition analyzer, and the generating AI analyzes this data. Next, the user sets a goal such as "I want to reduce my body fat percentage to 15% and increase my muscle mass," inputs a duration of "3 months" to achieve this, and inputs a training frequency of "3 times a week." This information is input to the generating AI, which then suggests a specific program of strength training and aerobic exercise based on the user's goal. It also suggests a menu that takes into account calorie intake and nutritional balance. This allows the user to obtain specific guidance for efficiently achieving their goal. For example, by suggesting a training program and diet to reduce body fat percentage and increase muscle mass, the user can efficiently achieve their goal. This allows the training program suggestion system to propose an optimal training program and diet plan based on the user's body composition data.

[0029] The training program suggestion system according to this embodiment comprises a data collection unit, a reception unit, an analysis unit, and a suggestion unit. The data collection unit collects data from a body composition analyzer. The data collection unit collects data such as body fat percentage, muscle mass, and weight. The data collection unit can measure body fat percentage using dual-energy X-ray absorptiometry (DEXA) or bioimpedance. The data collection unit can measure muscle mass using MRI or ultrasound. The data collection unit can measure weight using a digital scale or an analog scale. The reception unit receives input from the user regarding their ideal body shape and body composition, the time to achieve the goal, and the training frequency. For example, the reception unit allows the user to set a goal such as "I want to reduce my body fat percentage to 15% and increase my muscle mass," set the time to achieve this to "3 months," and input a training frequency of "3 times a week." The reception unit can set the ideal body shape and body composition using BMI, body fat percentage, muscle mass, etc. The reception unit can set the time to achieve the goal in weekly or monthly units. The reception unit allows users to set training frequency, such as how many times a week and the duration of each training session. The analysis unit analyzes the data collected by the collection unit and the information entered by the reception unit. The analysis unit performs analysis using, for example, statistical analysis and machine learning algorithms. Based on the collected data and entered information, the analysis unit analyzes the optimal training program and diet plan for the user to achieve their goals. The suggestion unit proposes the optimal training program and diet plan to the user based on the results of the analysis unit's analysis. The suggestion unit proposes specific programs for strength training and aerobic exercise, for example. The suggestion unit proposes menus that take into account calorie intake and nutritional balance. The suggestion unit can propose strength training as weight training or resistance training. The suggestion unit can propose aerobic exercise as running or cycling. The suggestion unit can set calorie intake to the recommended daily calorie intake. The suggestion unit can set nutritional balance by the ratio of protein, fat, and carbohydrates. As a result, the training program suggestion system can propose the optimal training program and diet plan based on the user's body composition data.

[0030] The data acquisition unit collects data from the body composition analyzer. For example, the unit collects data such as body fat percentage, muscle mass, and weight. Specifically, body fat percentage can be measured using dual-energy X-ray absorptiometry (DEXA) or bioimpedance. DEXA is a method that uses X-rays to measure the density of fat, muscle, and bone in the body with high precision and is widely used in medical institutions. Bioimpedance, on the other hand, is a method that estimates body fat percentage from the resistance value when a weak electric current is passed through the body and is also widely used in home body composition analyzers. MRI and ultrasound are used to measure muscle mass. MRI is a method that uses magnetic resonance to acquire detailed images of muscles in the body, allowing for high-precision evaluation of muscle quality and quantity. Ultrasound is a method that uses ultrasound to image cross-sections of muscles, allowing for non-invasive and rapid measurement of muscle mass. Digital and analog scales are used to measure weight. Digital scales use electronic sensors to measure weight with high precision and display it digitally, while analog scales measure weight using spring displacement and display it analogously. This allows the data collection unit to collect diverse data about the user's body composition with high accuracy and speed. Furthermore, the data collection unit centrally manages this data, making it accessible to the analysis and proposal units. For example, the collected data is stored on a cloud server, allowing the analysis unit to access and analyze it in real time. In addition, the data collection unit can flexibly respond to user needs and circumstances by adjusting the frequency and accuracy of data collection. As a result, the data collection unit can collect data efficiently and effectively, improving the overall system performance.

[0031] The reception desk allows users to input their ideal body shape and composition, the timeframe for achieving their goal, and their training frequency. Specifically, a user might set a goal such as "I want to reduce my body fat percentage to 15% and increase my muscle mass," set the timeframe to achieve this to "3 months," and input a training frequency of "3 times a week." The reception desk allows users to set their ideal body shape and composition using BMI, body fat percentage, muscle mass, etc. BMI is an index calculated from the ratio of weight to height and is widely used to evaluate body shape. Body fat percentage indicates the percentage of fat in relation to body weight, and muscle mass indicates the total amount of muscle in the body. Based on these indicators, users can set specific goals. The timeframe for achieving the goal can be set in weeks or months. For example, by setting a specific timeframe such as "reduce body fat percentage to 15% in 3 months," users can more easily plan towards achieving their goal. Training frequency can be set by the number of times per week and the duration of each training session. For example, users can plan their training by setting a specific frequency, such as "training three times a week for one hour each time." This allows the reception department to input detailed information about the user's specific goals and plans, providing the analysis and proposal departments with the foundational data to suggest the most suitable training program based on this information. Furthermore, the reception department can monitor the user's progress by saving the user's input and comparing it with past data. This enables the reception department to effectively support users in achieving their goals.

[0032] The analysis unit analyzes data collected by the data collection unit and information entered by the data reception unit. Specifically, it performs analysis using statistical analysis and machine learning algorithms. In statistical analysis, it calculates the mean, standard deviation, and correlation of the collected data to understand the user's current body composition. In machine learning algorithms, it builds predictive models based on past data and analyzes the optimal training program and dietary content for achieving the user's goals. For example, based on the collected body fat percentage and muscle mass data, it predicts changes in the user's body composition and proposes the optimal training program for achieving the goal. It also analyzes dietary content, taking into account calorie intake and nutritional balance. Based on this data, the analysis unit creates a concrete action plan for achieving the user's goals. Furthermore, the analysis unit can continuously revise the analysis results based on data that is updated in real time, allowing it to respond to the latest situation. For example, if the user's weight or body fat percentage changes rapidly, the analysis unit immediately incorporates the new data and updates the analysis results. In addition, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data and issue warnings early. This allows the analysis unit to not only grasp the situation in real time, but also to handle long-term risk management and anomaly detection, thereby improving the reliability and safety of the entire system.

[0033] The proposal department proposes the optimal training program and diet plan for the user based on the results analyzed by the analysis department. Specifically, it proposes specific programs for strength training and aerobic exercise. Strength training can be proposed as weight training or resistance training. Weight training includes training using dumbbells and barbells, aiming to increase muscle mass. Resistance training includes training using resistance bands and body weight, aiming to improve muscle strength. Aerobic exercise can be proposed as running or cycling. For running, distance, time, and pace are set to improve cardiovascular function. For cycling, distance, time, and load are set to improve endurance. The proposal department customizes these training programs according to the user's goals and body composition to provide the optimal plan. Furthermore, the proposal department proposes menus that take into account calorie intake and nutritional balance. Calorie intake is set to the recommended daily intake, and energy management is performed to help the user achieve their goals. Nutritional balance is set in the proportion of protein, fat, and carbohydrates to support muscle growth and fat reduction. Based on this information, the suggestion department proposes specific meal plans to users and supports them in achieving their goals. For example, it might suggest high-protein foods for breakfast, a balanced meal for lunch, and a low-calorie menu for dinner. This allows the suggestion department to propose an optimal training program and diet plan based on the user's body composition data, supporting the user in achieving their goals. Furthermore, the suggestion department can collect user feedback and continuously improve the accuracy and effectiveness of its suggestions. This enables the suggestion department to provide users with optimal support and effectively assist them in achieving their goals.

[0034] The data collection unit can collect data such as body fat percentage, muscle mass, and weight. For example, the data collection unit can measure body fat percentage using dual-energy X-ray absorptiometry (DEXA) or bioimpedance. The data collection unit can measure muscle mass using MRI or ultrasound. The data collection unit can measure weight using a digital scale or an analog scale. This allows for the collection of detailed body composition data of the user. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input body fat percentage data into an AI, which can then analyze the data and calculate the body fat percentage.

[0035] The reception desk allows users to input their ideal physique and body composition, the timeframe to achieve their goals, and their training frequency. For example, a user might set a goal such as "I want to reduce my body fat percentage to 15% and increase my muscle mass," set the timeframe to achieve this to "3 months," and input a training frequency of "3 times a week." The reception desk can define the ideal physique and body composition using BMI, body fat percentage, muscle mass, etc. The reception desk can set the timeframe to achieve the goal in weeks or months. The reception desk can define the training frequency by the number of times per week and the duration of each training session. This allows users to input their goals in detail. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the goals entered by the user into the AI, which can analyze the goals and propose an optimal training program.

[0036] The analysis unit can analyze data collected by the collection unit and information entered by the reception unit. The analysis unit performs analysis using, for example, statistical analysis or machine learning algorithms. Based on the collected data and entered information, the analysis unit analyzes the optimal training program and diet plan for achieving the user's goals. This allows the analysis to be performed based on the collected data and entered information. Some or all of the above processing in the analysis unit is performed using a generative AI. For example, the analysis unit inputs the collected data and entered information into the generative AI, which analyzes the data and proposes the optimal training program and diet plan.

[0037] The suggestion unit can propose the optimal training program and diet plan to the user based on the results analyzed by the analysis unit. For example, the suggestion unit can propose specific programs for strength training and aerobic exercise. The suggestion unit can propose menus that take into account calorie intake and nutritional balance. The suggestion unit can propose strength training as weight training or resistance training. The suggestion unit can propose aerobic exercise as running or cycling. The suggestion unit can set calorie intake to the recommended daily calorie intake. The suggestion unit can set nutritional balance in terms of the ratio of protein, fat, and carbohydrates. This allows the unit to make optimal suggestions based on the analysis results. Some or all of the above processing in the suggestion unit is performed using a generation AI. For example, the suggestion unit inputs the results analyzed by the analysis unit into the generation AI, which can then propose the optimal training program and diet plan.

[0038] The suggestion unit can propose specific programs for strength training and aerobic exercise. For example, the suggestion unit can suggest strength training as weight training or resistance training. For aerobic exercise, it can suggest running or cycling. This allows it to propose specific training programs. Some or all of the above processing in the suggestion unit is performed using a generative AI. For example, the suggestion unit can input programs for strength training and aerobic exercise into the generative AI, which can then propose the optimal program.

[0039] The suggestion unit can propose menus that take into account calorie intake and nutritional balance. For example, the suggestion unit can set calorie intake to the recommended daily intake. The suggestion unit can set nutritional balance by the ratio of protein, fat, and carbohydrates. This allows it to propose meal content that takes nutritional balance into consideration. Some or all of the above processing in the suggestion unit is performed using a generation AI. For example, the suggestion unit inputs data on calorie intake and nutritional balance into the generation AI, which can then propose an optimal menu.

[0040] The data collection unit can optimize the data collection method by referring to the user's past body composition data during collection. For example, the data collection unit can analyze the user's past fluctuations in body fat percentage to determine the optimal collection timing. The data collection unit can adjust the data collection method by considering the user's past increases and decreases in muscle mass. The data collection unit can optimize the data collection frequency based on the user's past weight data. This allows the data collection method to be optimized based on past data. Some or all of the above processes in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's past body composition data into AI, which can then optimize the data collection method.

[0041] The data collection unit can filter data based on the user's lifestyle and activity level during collection. For example, if the user has a high activity level, the data collection unit can filter and collect data after exercise. If the user has a low activity level, the data collection unit can filter and collect data during daily life. The data collection unit can filter and collect data after meals based on the user's eating habits. This allows data to be filtered based on lifestyle and activity level. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input data on the user's lifestyle and activity level into an AI, which can then filter the data.

[0042] The data collection unit can prioritize the collection of highly relevant data, taking into account the user's geographical location information during data collection. For example, if the user is at a gym, the data collection unit can prioritize the collection of data after exercise. If the user is at home, the data collection unit can prioritize the collection of data during daily life. If the user is traveling, the data collection unit can prioritize the collection of data in different environments. This allows for the priority collection of data, taking geographical location information into consideration. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's geographical location information into the AI, which can then prioritize the collection of highly relevant data.

[0043] The data collection unit can analyze the user's social media activity and collect relevant data during the collection process. For example, if a user posts about exercise on social media, the data collection unit can collect data related to that activity. If a user posts about food, the data collection unit can collect data related to the content of that meal. If a user posts about health, the data collection unit can collect data related to their health status. This allows for the collection of relevant data based on social media activity. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input data on the user's social media activity into an AI, which can then collect relevant data.

[0044] The reception desk can suggest the optimal input method by referring to the user's past goal-setting history at the time of reception. For example, the reception desk can automatically display goals previously set by the user as candidates. The reception desk can prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. The reception desk can predict and suggest goals to be used during a specific time period based on the user's past goal-setting history. This allows the reception desk to suggest the optimal input method based on past goal-setting history. Some or all of the above processes in the reception desk may be performed using AI or not. For example, the reception desk can input the user's past goal-setting history into AI, and the AI ​​can suggest the optimal input method.

[0045] The reception desk can customize the input content based on the user's current health status and lifestyle habits at the time of registration. For example, when the user enters their current health status, the reception desk can suggest the optimal input method based on past health data. The reception desk can customize the input content and set appropriate goals based on the user's lifestyle habits. The reception desk can adjust the input content and set realistic goals according to the user's current health status. This allows the input content to be customized based on the current health status and lifestyle habits. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input data on the user's current health status and lifestyle habits into the AI, which can then customize the input content.

[0046] The reception desk can prioritize the input of highly relevant goals, taking into account the user's geographical location information, at the time of registration. For example, if the user is at a gym, the reception desk can prioritize the input of exercise-related goals. If the user is at home, the reception desk can prioritize the input of goals related to daily life. If the user is traveling, the reception desk can prioritize the input of goals that can be achieved at the travel destination. This allows for the input of goals that take geographical location information into consideration. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's geographical location information into the AI, which can then prioritize the input of highly relevant goals.

[0047] The reception desk can analyze the user's social media activity and input relevant goals at the time of registration. For example, if the user posts about exercise on social media, the reception desk can input goals related to that activity. If the user posts about food, the reception desk can input goals related to the content of that meal. If the user posts about health, the reception desk can input goals related to their health status. In this way, relevant goals can be input based on social media activity. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input data on the user's social media activity into the AI, and the AI ​​can input relevant goals.

[0048] The analysis unit can improve the accuracy of the analysis by comparing the collected data with past data during the analysis process. For example, the analysis unit can perform a highly accurate analysis by comparing the collected body fat percentage data with past data. The analysis unit can perform a highly accurate analysis by comparing the collected muscle mass data with past data. The analysis unit can perform a highly accurate analysis by comparing the collected weight data with past data. This allows the accuracy of the analysis to be improved based on past data. Some or all of the above processing in the analysis unit is performed using a generation AI. For example, the analysis unit inputs the collected data and past data into the generation AI, which then compares the data to improve the accuracy of the analysis.

[0049] The analysis unit can customize the analysis content based on the user's lifestyle and activity level during the analysis process. For example, the analysis unit can perform a detailed analysis of exercise data based on the user's high activity level. The analysis unit can perform a detailed analysis of daily life data based on the user's low activity level. The analysis unit can perform a detailed analysis of dietary data based on the user's eating habits. This allows the analysis content to be customized based on lifestyle and activity level. Some or all of the above processing in the analysis unit is performed using a generating AI. For example, the analysis unit inputs data on the user's lifestyle and activity level into the generating AI, which can then customize the analysis content.

[0050] The analysis unit can optimize the analysis content by taking into account the user's geographical location information during analysis. For example, if the user is at a gym, the analysis unit can prioritize analyzing exercise data. If the user is at home, the analysis unit can prioritize analyzing daily life data. If the user is traveling, the analysis unit can prioritize analyzing data from different environments. This allows the analysis content to be optimized by taking geographical location information into account. Some or all of the above processing in the analysis unit is performed using a generating AI. For example, the analysis unit can input the user's geographical location information into the generating AI, which can then optimize the analysis content.

[0051] The analysis unit can analyze a user's social media activity during analysis and reflect relevant data in the analysis. For example, if a user posts about exercise on social media, the analysis unit can reflect data related to that activity in the analysis. If a user posts about food, the analysis unit can reflect data related to the content of that meal in the analysis. If a user posts about health, the analysis unit can reflect data related to their health status in the analysis. In this way, relevant data can be reflected in the analysis based on social media activity. Some or all of the above processing in the analysis unit is performed using a generation AI. For example, the analysis unit can input data on the user's social media activity into the generation AI, and the generation AI can reflect relevant data in the analysis.

[0052] The suggestion unit can make optimal suggestions by referring to the user's past training and dietary history. For example, the suggestion unit can suggest an effective training program based on the user's past training history. The suggestion unit can suggest a nutritionally balanced meal plan based on the user's past dietary history. The suggestion unit can comprehensively analyze the user's past training and dietary history and propose the optimal plan. This allows the unit to make optimal suggestions based on past history. Some or all of the above processing in the suggestion unit is performed using a generative AI. For example, the suggestion unit inputs data on the user's past training and dietary history into the generative AI, which can then make optimal suggestions.

[0053] The suggestion unit can customize the suggested content based on the user's current health condition and lifestyle. For example, the suggestion unit can suggest a manageable training program considering the user's current health condition. The suggestion unit can suggest a feasible meal plan based on the user's lifestyle. The suggestion unit can comprehensively analyze the user's health condition and lifestyle to suggest the optimal training and meal plan. This allows the suggested content to be customized based on the user's current health condition and lifestyle. Some or all of the above processing in the suggestion unit is performed using a generative AI. For example, the suggestion unit inputs data on the user's current health condition and lifestyle into the generative AI, which can then customize the suggested content.

[0054] The suggestion unit can make optimal suggestions by considering the user's geographical location information. For example, if the user is at a gym, the suggestion unit can suggest a training program that can be done at the gym. If the user is at home, the suggestion unit can suggest a training program that can be done at home. If the user is traveling, the suggestion unit can suggest a training program that can be done at their travel destination. This allows the system to make optimal suggestions by considering geographical location information. Some or all of the above processing in the suggestion unit is performed using a generative AI. For example, the suggestion unit inputs the user's geographical location information into the generative AI, which then makes the optimal suggestion.

[0055] The suggestion unit can analyze a user's social media activity and make relevant suggestions when making suggestions. For example, if a user posts about exercise on social media, the suggestion unit can suggest a training program related to that activity. If a user posts about food, the suggestion unit can suggest a menu related to that meal. If a user posts about health, the suggestion unit can make suggestions related to their health status. In this way, relevant suggestions can be made based on social media activity. Some or all of the above processing in the suggestion unit is performed using a generative AI. For example, the suggestion unit inputs data on the user's social media activity into the generative AI, which can then make relevant suggestions.

[0056] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0057] The training program suggestion system can also collect and analyze user sleep data. The data collection unit measures the user's sleep duration and sleep quality and collects this data. For example, users can record their sleep duration each night and fill out questionnaires to evaluate their sleep quality. The analysis unit adjusts the user's training program and diet based on the collected sleep data. For example, if a user is not getting enough sleep, the system can suggest a program that reduces training intensity and emphasizes recovery. The suggestion unit can also suggest dietary and lifestyle improvements to enhance the user's sleep quality. This allows the system to suggest an optimal training program and diet that takes into account the user's overall health.

[0058] The training program suggestion system can also collect and analyze the user's water intake. The collection unit records the amount of water the user consumes in a day and collects this data. For example, the user can input the amount of water they drink into the app. The analysis unit adjusts the user's training program and diet based on the collected water intake data. For example, if the user is not drinking enough water, the system can suggest a training program to encourage hydration. The suggestion unit can also suggest specific methods to increase the user's water intake and appropriate timing for hydration. This allows the system to suggest an optimal training program and diet that takes into account the user's water intake status.

[0059] The training program suggestion system can also monitor the user's activity level in real time and incorporate it into its analysis. The data collection unit measures the user's activity level and collects this data in real time. For example, it can record steps and heart rate through a wearable device worn by the user. The analysis unit adjusts the user's training program and diet based on the collected activity data. For example, if the user shows a high activity level, it can suggest a meal plan to compensate for energy expenditure. The suggestion unit can also adjust the training program in real time according to the user's activity level and suggest the optimal amount of exercise. This allows the system to suggest an optimal training program and diet that takes the user's activity level into consideration.

[0060] The training program suggestion system can also collect and analyze the user's dietary preferences. The data collection unit measures the user's dietary preferences and collects this data. For example, the user can input their favorite and disliked foods. The analysis unit adjusts the user's meals based on the collected dietary preference data. For example, if the user likes a particular food, the system can suggest a menu that includes that food. The suggestion unit can also suggest a menu that avoids the food the user dislikes. This allows the system to suggest an optimal meal plan that takes the user's dietary preferences into account.

[0061] The training program suggestion system can also collect and analyze the user's exercise history. The collection unit collects the user's past exercise history and incorporates this data into the analysis. For example, it can record the type and frequency of training the user has performed in the past. The analysis unit adjusts the user's training program based on the collected exercise history data. For example, it can analyze the effectiveness of the user's past training and suggest an effective training program. Furthermore, the suggestion unit can suggest a training program that can be performed continuously based on the user's exercise history. This allows the system to suggest an optimal training program that takes the user's exercise history into consideration.

[0062] The training program suggestion system can also collect and analyze the user's geographical location information. The data collection unit collects the user's geographical location information and incorporates this data into the analysis. For example, if the user is at a gym, the system can suggest a training program based on the gym's facilities. The analysis unit adjusts the user's training program based on the collected geographical location data. For example, if the user is at home, the system can suggest a training program that can be done at home. Furthermore, if the user is traveling, the suggestion unit can suggest a training program that can be done at their travel destination. This allows the system to suggest an optimal training program that takes the user's geographical location into consideration.

[0063] The following briefly describes the processing flow for example form 1.

[0064] Step 1: The data acquisition unit collects data from the body composition analyzer. The data acquisition unit collects data such as body fat percentage, muscle mass, and weight. The data acquisition unit can measure body fat percentage using dual-energy X-ray absorptiometry (DEXA) or bioimpedance. The data acquisition unit can measure muscle mass using MRI or ultrasound. The data acquisition unit can measure weight using a digital scale or an analog scale. Step 2: The reception desk receives input from the user regarding their ideal body shape and composition, the timeframe to achieve the goal, and the training frequency. For example, a user might set a goal of "reducing body fat percentage to 15% and increasing muscle mass," specify a timeframe of "3 months" to achieve this, and input a training frequency of "3 times per week." The reception desk can specify the ideal body shape and composition using BMI, body fat percentage, muscle mass, etc. The reception desk can set the timeframe to achieve the goal in weeks or months. The reception desk can set the training frequency by the number of times per week and the duration of each training session. Step 3: The analysis unit analyzes the data collected by the collection unit and the information entered by the reception unit. The analysis unit performs the analysis using, for example, statistical analysis or machine learning algorithms. Based on the collected data and entered information, the analysis unit analyzes the optimal training program and diet plan to help the user achieve their goals. Step 4: The suggestion unit proposes an optimal training program and diet plan to the user based on the results analyzed by the analysis unit. The suggestion unit proposes specific programs for strength training and aerobic exercise, for example. The suggestion unit proposes menus that take into account calorie intake and nutritional balance. The suggestion unit can propose strength training as weight training or resistance training. The suggestion unit can propose aerobic exercise as running or cycling. The suggestion unit can set calorie intake to the recommended daily calorie intake. The suggestion unit can set nutritional balance in terms of the ratio of protein, fat, and carbohydrates.

[0065] (Example of form 2) The training program suggestion system according to an embodiment of the present invention is a system that suggests an optimal training program and diet based on the user's current body composition, confirmed through a body composition analyzer, and inputs the ideal body shape, body composition, duration, and training frequency. The training program suggestion system allows the user to confirm their current body composition using a body composition analyzer, input their ideal body shape and body composition, the time to achieve the goal, and the training frequency, and the generating AI then suggests an optimal training program and diet. For example, the user measures data such as body fat percentage, muscle mass, and weight using a body composition analyzer, and the generating AI analyzes this data. Next, the user sets a goal such as "I want to reduce my body fat percentage to 15% and increase my muscle mass," inputs a duration of "3 months" to achieve this, and inputs a training frequency of "3 times a week." This information is input to the generating AI, which then suggests a specific program of strength training and aerobic exercise based on the user's goal. It also suggests a menu that takes into account calorie intake and nutritional balance. This allows the user to obtain specific guidance for efficiently achieving their goal. For example, by suggesting a training program and diet to reduce body fat percentage and increase muscle mass, the user can efficiently achieve their goal. This allows the training program suggestion system to propose an optimal training program and diet plan based on the user's body composition data.

[0066] The training program suggestion system according to this embodiment comprises a data collection unit, a reception unit, an analysis unit, and a suggestion unit. The data collection unit collects data from a body composition analyzer. The data collection unit collects data such as body fat percentage, muscle mass, and weight. The data collection unit can measure body fat percentage using dual-energy X-ray absorptiometry (DEXA) or bioimpedance. The data collection unit can measure muscle mass using MRI or ultrasound. The data collection unit can measure weight using a digital scale or an analog scale. The reception unit receives input from the user regarding their ideal body shape and body composition, the time to achieve the goal, and the training frequency. For example, the reception unit allows the user to set a goal such as "I want to reduce my body fat percentage to 15% and increase my muscle mass," set the time to achieve this to "3 months," and input a training frequency of "3 times a week." The reception unit can set the ideal body shape and body composition using BMI, body fat percentage, muscle mass, etc. The reception unit can set the time to achieve the goal in weekly or monthly units. The reception unit allows users to set training frequency, such as how many times a week and the duration of each training session. The analysis unit analyzes the data collected by the collection unit and the information entered by the reception unit. The analysis unit performs analysis using, for example, statistical analysis and machine learning algorithms. Based on the collected data and entered information, the analysis unit analyzes the optimal training program and diet plan for the user to achieve their goals. The suggestion unit proposes the optimal training program and diet plan to the user based on the results of the analysis unit's analysis. The suggestion unit proposes specific programs for strength training and aerobic exercise, for example. The suggestion unit proposes menus that take into account calorie intake and nutritional balance. The suggestion unit can propose strength training as weight training or resistance training. The suggestion unit can propose aerobic exercise as running or cycling. The suggestion unit can set calorie intake to the recommended daily calorie intake. The suggestion unit can set nutritional balance by the ratio of protein, fat, and carbohydrates. As a result, the training program suggestion system can propose the optimal training program and diet plan based on the user's body composition data.

[0067] The data acquisition unit collects data from the body composition analyzer. For example, the unit collects data such as body fat percentage, muscle mass, and weight. Specifically, body fat percentage can be measured using dual-energy X-ray absorptiometry (DEXA) or bioimpedance. DEXA is a method that uses X-rays to measure the density of fat, muscle, and bone in the body with high precision and is widely used in medical institutions. Bioimpedance, on the other hand, is a method that estimates body fat percentage from the resistance value when a weak electric current is passed through the body and is also widely used in home body composition analyzers. MRI and ultrasound are used to measure muscle mass. MRI is a method that uses magnetic resonance to acquire detailed images of muscles in the body, allowing for high-precision evaluation of muscle quality and quantity. Ultrasound is a method that uses ultrasound to image cross-sections of muscles, allowing for non-invasive and rapid measurement of muscle mass. Digital and analog scales are used to measure weight. Digital scales use electronic sensors to measure weight with high precision and display it digitally, while analog scales measure weight using spring displacement and display it analogously. This allows the data collection unit to collect diverse data about the user's body composition with high accuracy and speed. Furthermore, the data collection unit centrally manages this data, making it accessible to the analysis and proposal units. For example, the collected data is stored on a cloud server, allowing the analysis unit to access and analyze it in real time. In addition, the data collection unit can flexibly respond to user needs and circumstances by adjusting the frequency and accuracy of data collection. As a result, the data collection unit can collect data efficiently and effectively, improving the overall system performance.

[0068] The reception desk allows users to input their ideal body shape and composition, the timeframe for achieving their goal, and their training frequency. Specifically, a user might set a goal such as "I want to reduce my body fat percentage to 15% and increase my muscle mass," set the timeframe to achieve this to "3 months," and input a training frequency of "3 times a week." The reception desk allows users to set their ideal body shape and composition using BMI, body fat percentage, muscle mass, etc. BMI is an index calculated from the ratio of weight to height and is widely used to evaluate body shape. Body fat percentage indicates the percentage of fat in relation to body weight, and muscle mass indicates the total amount of muscle in the body. Based on these indicators, users can set specific goals. The timeframe for achieving the goal can be set in weeks or months. For example, by setting a specific timeframe such as "reduce body fat percentage to 15% in 3 months," users can more easily plan towards achieving their goal. Training frequency can be set by the number of times per week and the duration of each training session. For example, users can plan their training by setting a specific frequency, such as "training three times a week for one hour each time." This allows the reception department to input detailed information about the user's specific goals and plans, providing the analysis and proposal departments with the foundational data to suggest the most suitable training program based on this information. Furthermore, the reception department can monitor the user's progress by saving the user's input and comparing it with past data. This enables the reception department to effectively support users in achieving their goals.

[0069] The analysis unit analyzes data collected by the data collection unit and information entered by the data reception unit. Specifically, it performs analysis using statistical analysis and machine learning algorithms. In statistical analysis, it calculates the mean, standard deviation, and correlation of the collected data to understand the user's current body composition. In machine learning algorithms, it builds predictive models based on past data and analyzes the optimal training program and dietary content for achieving the user's goals. For example, based on the collected body fat percentage and muscle mass data, it predicts changes in the user's body composition and proposes the optimal training program for achieving the goal. It also analyzes dietary content, taking into account calorie intake and nutritional balance. Based on this data, the analysis unit creates a concrete action plan for achieving the user's goals. Furthermore, the analysis unit can continuously revise the analysis results based on data that is updated in real time, allowing it to respond to the latest situation. For example, if the user's weight or body fat percentage changes rapidly, the analysis unit immediately incorporates the new data and updates the analysis results. In addition, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data and issue warnings early. This allows the analysis unit to not only grasp the situation in real time, but also to handle long-term risk management and anomaly detection, thereby improving the reliability and safety of the entire system.

[0070] The proposal department proposes the optimal training program and diet plan for the user based on the results analyzed by the analysis department. Specifically, it proposes specific programs for strength training and aerobic exercise. Strength training can be proposed as weight training or resistance training. Weight training includes training using dumbbells and barbells, aiming to increase muscle mass. Resistance training includes training using resistance bands and body weight, aiming to improve muscle strength. Aerobic exercise can be proposed as running or cycling. For running, distance, time, and pace are set to improve cardiovascular function. For cycling, distance, time, and load are set to improve endurance. The proposal department customizes these training programs according to the user's goals and body composition to provide the optimal plan. Furthermore, the proposal department proposes menus that take into account calorie intake and nutritional balance. Calorie intake is set to the recommended daily intake, and energy management is performed to help the user achieve their goals. Nutritional balance is set in the proportion of protein, fat, and carbohydrates to support muscle growth and fat reduction. Based on this information, the suggestion department proposes specific meal plans to users and supports them in achieving their goals. For example, it might suggest high-protein foods for breakfast, a balanced meal for lunch, and a low-calorie menu for dinner. This allows the suggestion department to propose an optimal training program and diet plan based on the user's body composition data, supporting the user in achieving their goals. Furthermore, the suggestion department can collect user feedback and continuously improve the accuracy and effectiveness of its suggestions. This enables the suggestion department to provide users with optimal support and effectively assist them in achieving their goals.

[0071] The data collection unit can collect data such as body fat percentage, muscle mass, and weight. For example, the data collection unit can measure body fat percentage using dual-energy X-ray absorptiometry (DEXA) or bioimpedance. The data collection unit can measure muscle mass using MRI or ultrasound. The data collection unit can measure weight using a digital scale or an analog scale. This allows for the collection of detailed body composition data of the user. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input body fat percentage data into an AI, which can then analyze the data and calculate the body fat percentage.

[0072] The reception desk allows users to input their ideal physique and body composition, the timeframe to achieve their goals, and their training frequency. For example, a user might set a goal such as "I want to reduce my body fat percentage to 15% and increase my muscle mass," set the timeframe to achieve this to "3 months," and input a training frequency of "3 times a week." The reception desk can define the ideal physique and body composition using BMI, body fat percentage, muscle mass, etc. The reception desk can set the timeframe to achieve the goal in weeks or months. The reception desk can define the training frequency by the number of times per week and the duration of each training session. This allows users to input their goals in detail. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the goals entered by the user into the AI, which can analyze the goals and propose an optimal training program.

[0073] The analysis unit can analyze data collected by the collection unit and information entered by the reception unit. The analysis unit performs analysis using, for example, statistical analysis or machine learning algorithms. Based on the collected data and entered information, the analysis unit analyzes the optimal training program and diet plan for achieving the user's goals. This allows the analysis to be performed based on the collected data and entered information. Some or all of the above processing in the analysis unit is performed using a generative AI. For example, the analysis unit inputs the collected data and entered information into the generative AI, which analyzes the data and proposes the optimal training program and diet plan.

[0074] The suggestion unit can propose the optimal training program and diet plan to the user based on the results analyzed by the analysis unit. For example, the suggestion unit can propose specific programs for strength training and aerobic exercise. The suggestion unit can propose menus that take into account calorie intake and nutritional balance. The suggestion unit can propose strength training as weight training or resistance training. The suggestion unit can propose aerobic exercise as running or cycling. The suggestion unit can set calorie intake to the recommended daily calorie intake. The suggestion unit can set nutritional balance in terms of the ratio of protein, fat, and carbohydrates. This allows the unit to make optimal suggestions based on the analysis results. Some or all of the above processing in the suggestion unit is performed using a generation AI. For example, the suggestion unit inputs the results analyzed by the analysis unit into the generation AI, which can then propose the optimal training program and diet plan.

[0075] The suggestion unit can propose specific programs for strength training and aerobic exercise. For example, the suggestion unit can suggest strength training as weight training or resistance training. For aerobic exercise, it can suggest running or cycling. This allows it to propose specific training programs. Some or all of the above processing in the suggestion unit is performed using a generative AI. For example, the suggestion unit can input programs for strength training and aerobic exercise into the generative AI, which can then propose the optimal program.

[0076] The suggestion unit can propose menus that take into account calorie intake and nutritional balance. For example, the suggestion unit can set calorie intake to the recommended daily intake. The suggestion unit can set nutritional balance by the ratio of protein, fat, and carbohydrates. This allows it to propose meal content that takes nutritional balance into consideration. Some or all of the above processing in the suggestion unit is performed using a generation AI. For example, the suggestion unit inputs data on calorie intake and nutritional balance into the generation AI, which can then propose an optimal menu.

[0077] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can delay the timing to collect data when the user is relaxed. If the user is relaxed, the data collection unit can collect data immediately to obtain accurate body composition data. If the user is tired after exercise, the data collection unit can collect data after the user has rested. This allows the timing of data collection to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's emotion data into an AI, which can then adjust the timing of data collection.

[0078] The data collection unit can optimize the data collection method by referring to the user's past body composition data during collection. For example, the data collection unit can analyze the user's past fluctuations in body fat percentage to determine the optimal collection timing. The data collection unit can adjust the data collection method by considering the user's past increases and decreases in muscle mass. The data collection unit can optimize the data collection frequency based on the user's past weight data. This allows the data collection method to be optimized based on past data. Some or all of the above processes in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's past body composition data into AI, which can then optimize the data collection method.

[0079] The data collection unit can filter data based on the user's lifestyle and activity level during collection. For example, if the user has a high activity level, the data collection unit can filter and collect data after exercise. If the user has a low activity level, the data collection unit can filter and collect data during daily life. The data collection unit can filter and collect data after meals based on the user's eating habits. This allows data to be filtered based on lifestyle and activity level. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input data on the user's lifestyle and activity level into an AI, which can then filter the data.

[0080] The data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit may prioritize collecting body fat percentage data. If the user is relaxed, the data collection unit may prioritize collecting muscle mass data. If the user is tired after exercise, the data collection unit may prioritize collecting weight data. This allows the data to be prioritized according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into an AI, which can then determine the data priority.

[0081] The data collection unit can prioritize the collection of highly relevant data, taking into account the user's geographical location information during data collection. For example, if the user is at a gym, the data collection unit can prioritize the collection of data after exercise. If the user is at home, the data collection unit can prioritize the collection of data during daily life. If the user is traveling, the data collection unit can prioritize the collection of data in different environments. This allows for the priority collection of data, taking geographical location information into consideration. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's geographical location information into the AI, which can then prioritize the collection of highly relevant data.

[0082] The data collection unit can analyze the user's social media activity and collect relevant data during the collection process. For example, if a user posts about exercise on social media, the data collection unit can collect data related to that activity. If a user posts about food, the data collection unit can collect data related to the content of that meal. If a user posts about health, the data collection unit can collect data related to their health status. This allows for the collection of relevant data based on social media activity. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input data on the user's social media activity into an AI, which can then collect relevant data.

[0083] The reception desk can estimate the user's emotions and adjust the goal input interface based on the estimated emotions. For example, if the user is stressed, the reception desk can provide a simple interface and minimize the input steps. If the user is relaxed, the reception desk can provide detailed input options and suggest a customizable input method. If the user is in a hurry, the reception desk can prioritize voice input to allow for quick goal input. This allows the goal input interface to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input user emotion data into an AI, which can then adjust the goal input interface.

[0084] The reception desk can suggest the optimal input method by referring to the user's past goal-setting history at the time of reception. For example, the reception desk can automatically display goals previously set by the user as candidates. The reception desk can prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. The reception desk can predict and suggest goals to be used during a specific time period based on the user's past goal-setting history. This allows the reception desk to suggest the optimal input method based on past goal-setting history. Some or all of the above processes in the reception desk may be performed using AI or not. For example, the reception desk can input the user's past goal-setting history into AI, and the AI ​​can suggest the optimal input method.

[0085] The reception desk can customize the input content based on the user's current health status and lifestyle habits at the time of registration. For example, when the user enters their current health status, the reception desk can suggest the optimal input method based on past health data. The reception desk can customize the input content and set appropriate goals based on the user's lifestyle habits. The reception desk can adjust the input content and set realistic goals according to the user's current health status. This allows the input content to be customized based on the current health status and lifestyle habits. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input data on the user's current health status and lifestyle habits into the AI, which can then customize the input content.

[0086] The reception desk can estimate the user's emotions and determine the priority of the goals to be entered based on the estimated emotions. For example, if the user is stressed, the reception desk may prioritize short-term goals. If the user is relaxed, the reception desk may prioritize long-term goals. If the user is in a hurry, the reception desk may prioritize easily achievable goals. This allows the priority of goals to be determined according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input user emotion data into an AI, which can then determine the priority of goals.

[0087] The reception desk can prioritize the input of highly relevant goals, taking into account the user's geographical location information, at the time of registration. For example, if the user is at a gym, the reception desk can prioritize the input of exercise-related goals. If the user is at home, the reception desk can prioritize the input of goals related to daily life. If the user is traveling, the reception desk can prioritize the input of goals that can be achieved at the travel destination. This allows for the input of goals that take geographical location information into consideration. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's geographical location information into the AI, which can then prioritize the input of highly relevant goals.

[0088] The reception desk can analyze the user's social media activity and input relevant goals at the time of registration. For example, if the user posts about exercise on social media, the reception desk can input goals related to that activity. If the user posts about food, the reception desk can input goals related to the content of that meal. If the user posts about health, the reception desk can input goals related to their health status. In this way, relevant goals can be input based on social media activity. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input data on the user's social media activity into the AI, and the AI ​​can input relevant goals.

[0089] The analysis unit can estimate the user's emotions and adjust the analysis algorithm based on the estimated emotions. For example, if the user is relaxed, the analysis unit can perform a detailed analysis and provide highly accurate results. If the user is in a hurry, the analysis unit can perform a rapid analysis and provide immediate results. If the user is stressed, the analysis unit can perform a concise analysis and provide easy-to-understand results. This allows the analysis algorithm to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit are performed using generative AI. For example, the analysis unit can input user emotion data into the generative AI, and the generative AI can adjust the analysis algorithm.

[0090] The analysis unit can improve the accuracy of the analysis by comparing the collected data with past data during the analysis process. For example, the analysis unit can perform a highly accurate analysis by comparing the collected body fat percentage data with past data. The analysis unit can perform a highly accurate analysis by comparing the collected muscle mass data with past data. The analysis unit can perform a highly accurate analysis by comparing the collected weight data with past data. This allows the accuracy of the analysis to be improved based on past data. Some or all of the above processing in the analysis unit is performed using a generation AI. For example, the analysis unit inputs the collected data and past data into the generation AI, which then compares the data to improve the accuracy of the analysis.

[0091] The analysis unit can customize the analysis content based on the user's lifestyle and activity level during the analysis process. For example, the analysis unit can perform a detailed analysis of exercise data based on the user's high activity level. The analysis unit can perform a detailed analysis of daily life data based on the user's low activity level. The analysis unit can perform a detailed analysis of dietary data based on the user's eating habits. This allows the analysis content to be customized based on lifestyle and activity level. Some or all of the above processing in the analysis unit is performed using a generating AI. For example, the analysis unit inputs data on the user's lifestyle and activity level into the generating AI, which can then customize the analysis content.

[0092] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated emotions. For example, if the user is nervous, the analysis unit can provide a simple and highly visible display method. If the user is relaxed, the analysis unit can provide a display method that includes detailed information. If the user is in a hurry, the analysis unit can provide a display method that gets straight to the point. This allows the display method of the analysis results to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the analysis unit is performed using the generative AI. For example, the analysis unit inputs the user's emotion data into the generative AI, and the generative AI can adjust the display method of the analysis results.

[0093] The analysis unit can optimize the analysis content by taking into account the user's geographical location information during analysis. For example, if the user is at a gym, the analysis unit can prioritize analyzing exercise data. If the user is at home, the analysis unit can prioritize analyzing daily life data. If the user is traveling, the analysis unit can prioritize analyzing data from different environments. This allows the analysis content to be optimized by taking geographical location information into account. Some or all of the above processing in the analysis unit is performed using a generating AI. For example, the analysis unit can input the user's geographical location information into the generating AI, which can then optimize the analysis content.

[0094] The analysis unit can analyze a user's social media activity during analysis and reflect relevant data in the analysis. For example, if a user posts about exercise on social media, the analysis unit can reflect data related to that activity in the analysis. If a user posts about food, the analysis unit can reflect data related to the content of that meal in the analysis. If a user posts about health, the analysis unit can reflect data related to their health status in the analysis. In this way, relevant data can be reflected in the analysis based on social media activity. Some or all of the above processing in the analysis unit is performed using a generation AI. For example, the analysis unit can input data on the user's social media activity into the generation AI, and the generation AI can reflect relevant data in the analysis.

[0095] The suggestion unit can estimate the user's emotions and adjust the suggestions based on those emotions. For example, if the user is relaxed, the suggestion unit can suggest a detailed training program. If the user is in a hurry, the suggestion unit can suggest a concise training program. If the user is stressed, the suggestion unit can suggest a training program with a relaxing effect. This allows the suggestions to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit is performed using generative AI. For example, the suggestion unit can input user emotion data into the generative AI, which can then adjust the suggestions.

[0096] The suggestion unit can make optimal suggestions by referring to the user's past training and dietary history. For example, the suggestion unit can suggest an effective training program based on the user's past training history. The suggestion unit can suggest a nutritionally balanced meal plan based on the user's past dietary history. The suggestion unit can comprehensively analyze the user's past training and dietary history and propose the optimal plan. This allows the unit to make optimal suggestions based on past history. Some or all of the above processing in the suggestion unit is performed using a generative AI. For example, the suggestion unit inputs data on the user's past training and dietary history into the generative AI, which can then make optimal suggestions.

[0097] The suggestion unit can customize the suggested content based on the user's current health condition and lifestyle. For example, the suggestion unit can suggest a manageable training program considering the user's current health condition. The suggestion unit can suggest a feasible meal plan based on the user's lifestyle. The suggestion unit can comprehensively analyze the user's health condition and lifestyle to suggest the optimal training and meal plan. This allows the suggested content to be customized based on the user's current health condition and lifestyle. Some or all of the above processing in the suggestion unit is performed using a generative AI. For example, the suggestion unit inputs data on the user's current health condition and lifestyle into the generative AI, which can then customize the suggested content.

[0098] The suggestion unit can estimate the user's emotions and determine the priority of suggestions based on the estimated emotions. For example, if the user is stressed, the suggestion unit may prioritize suggesting relaxing exercises. If the user is relaxed, the suggestion unit may prioritize suggesting strength training. If the user is in a hurry, the suggestion unit may prioritize suggesting short, effective exercises. This allows the suggestion unit to determine the priority of suggestions according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit is performed using generative AI. For example, the suggestion unit can input user emotion data into the generative AI, which can then determine the priority of suggestions.

[0099] The suggestion unit can make optimal suggestions by considering the user's geographical location information. For example, if the user is at a gym, the suggestion unit can suggest a training program that can be done at the gym. If the user is at home, the suggestion unit can suggest a training program that can be done at home. If the user is traveling, the suggestion unit can suggest a training program that can be done at their travel destination. This allows the system to make optimal suggestions by considering geographical location information. Some or all of the above processing in the suggestion unit is performed using a generative AI. For example, the suggestion unit inputs the user's geographical location information into the generative AI, which then makes the optimal suggestion.

[0100] The suggestion unit can analyze a user's social media activity and make relevant suggestions when making suggestions. For example, if a user posts about exercise on social media, the suggestion unit can suggest a training program related to that activity. If a user posts about food, the suggestion unit can suggest a menu related to that meal. If a user posts about health, the suggestion unit can make suggestions related to their health status. In this way, relevant suggestions can be made based on social media activity. Some or all of the above processing in the suggestion unit is performed using a generative AI. For example, the suggestion unit inputs data on the user's social media activity into the generative AI, which can then make relevant suggestions.

[0101] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0102] The training program suggestion system can also collect and analyze user sleep data. The data collection unit measures the user's sleep duration and sleep quality and collects this data. For example, users can record their sleep duration each night and fill out questionnaires to evaluate their sleep quality. The analysis unit adjusts the user's training program and diet based on the collected sleep data. For example, if a user is not getting enough sleep, the system can suggest a program that reduces training intensity and emphasizes recovery. The suggestion unit can also suggest dietary and lifestyle improvements to enhance the user's sleep quality. This allows the system to suggest an optimal training program and diet that takes into account the user's overall health.

[0103] The training program suggestion system can also collect and analyze the user's water intake. The collection unit records the amount of water the user consumes in a day and collects this data. For example, the user can input the amount of water they drink into the app. The analysis unit adjusts the user's training program and diet based on the collected water intake data. For example, if the user is not drinking enough water, the system can suggest a training program to encourage hydration. The suggestion unit can also suggest specific methods to increase the user's water intake and appropriate timing for hydration. This allows the system to suggest an optimal training program and diet that takes into account the user's water intake status.

[0104] The training program suggestion system can also collect and analyze the user's stress level. The data collection unit measures the user's stress level and collects this data. For example, the user can input a questionnaire to assess their stress level. The analysis unit adjusts the user's training program and diet based on the collected stress data. For instance, if a user shows a high stress level, the system can suggest a training program with relaxing effects. The suggestion unit can also suggest dietary and lifestyle improvements to reduce the user's stress. This allows the system to propose an optimal training program and diet that takes the user's stress level into consideration.

[0105] The training program suggestion system can also monitor the user's activity level in real time and incorporate it into its analysis. The data collection unit measures the user's activity level and collects this data in real time. For example, it can record steps and heart rate through a wearable device worn by the user. The analysis unit adjusts the user's training program and diet based on the collected activity data. For example, if the user shows a high activity level, it can suggest a meal plan to compensate for energy expenditure. The suggestion unit can also adjust the training program in real time according to the user's activity level and suggest the optimal amount of exercise. This allows the system to suggest an optimal training program and diet that takes the user's activity level into consideration.

[0106] The training program suggestion system can further estimate the user's emotions and adjust the difficulty level of the training program based on the estimated emotions. The data collection unit estimates the user's emotions and collects this data. For example, the user can input a questionnaire to evaluate their emotions. The analysis unit adjusts the difficulty level of the user's training program based on the collected emotion data. For example, if the user is relaxed, a more difficult training program can be suggested. Conversely, if the user is stressed, the suggestion unit can suggest a less difficult training program. This allows the system to suggest an optimal training program tailored to the user's emotions.

[0107] The training program suggestion system can also collect and analyze the user's dietary preferences. The data collection unit measures the user's dietary preferences and collects this data. For example, the user can input their favorite and disliked foods. The analysis unit adjusts the user's meals based on the collected dietary preference data. For example, if the user likes a particular food, the system can suggest a menu that includes that food. The suggestion unit can also suggest a menu that avoids the food the user dislikes. This allows the system to suggest an optimal meal plan that takes the user's dietary preferences into account.

[0108] The training program suggestion system can further estimate the user's emotions and adjust meal content based on those emotions. The data collection unit estimates the user's emotions and collects this data. For example, the user can input a questionnaire to evaluate their emotions. The analysis unit adjusts the user's meal content based on the collected emotion data. For example, if the user is relaxed, it can suggest a nutritionally balanced meal. Also, if the user is stressed, the suggestion unit can suggest a menu that includes ingredients with relaxing effects. This allows the system to suggest the optimal meal content according to the user's emotions.

[0109] The training program suggestion system can also collect and analyze the user's exercise history. The collection unit collects the user's past exercise history and incorporates this data into the analysis. For example, it can record the type and frequency of training the user has performed in the past. The analysis unit adjusts the user's training program based on the collected exercise history data. For example, it can analyze the effectiveness of the user's past training and suggest an effective training program. Furthermore, the suggestion unit can suggest a training program that can be performed continuously based on the user's exercise history. This allows the system to suggest an optimal training program that takes the user's exercise history into consideration.

[0110] The training program suggestion system can further estimate the user's emotions and suggest types of training based on those emotions. The data collection unit estimates the user's emotions and collects this data. For example, the user can input a questionnaire to evaluate their emotions. The analysis unit adjusts the user's training program based on the collected emotional data. For example, if the user is relaxed, it can suggest relaxing exercises such as yoga or Pilates. If the user is stressed, the suggestion unit can suggest stress-relieving aerobic exercises. This allows the system to suggest an optimal training program tailored to the user's emotions.

[0111] The training program suggestion system can also collect and analyze the user's geographical location information. The data collection unit collects the user's geographical location information and incorporates this data into the analysis. For example, if the user is at a gym, the system can suggest a training program based on the gym's facilities. The analysis unit adjusts the user's training program based on the collected geographical location data. For example, if the user is at home, the system can suggest a training program that can be done at home. Furthermore, if the user is traveling, the suggestion unit can suggest a training program that can be done at their travel destination. This allows the system to suggest an optimal training program that takes the user's geographical location into consideration.

[0112] The following briefly describes the processing flow for example form 2.

[0113] Step 1: The data acquisition unit collects data from the body composition analyzer. The data acquisition unit collects data such as body fat percentage, muscle mass, and weight. The data acquisition unit can measure body fat percentage using dual-energy X-ray absorptiometry (DEXA) or bioimpedance. The data acquisition unit can measure muscle mass using MRI or ultrasound. The data acquisition unit can measure weight using a digital scale or an analog scale. Step 2: The reception desk receives input from the user regarding their ideal body shape and composition, the timeframe to achieve the goal, and the training frequency. For example, a user might set a goal of "reducing body fat percentage to 15% and increasing muscle mass," specify a timeframe of "3 months" to achieve this, and input a training frequency of "3 times per week." The reception desk can specify the ideal body shape and composition using BMI, body fat percentage, muscle mass, etc. The reception desk can set the timeframe to achieve the goal in weeks or months. The reception desk can set the training frequency by the number of times per week and the duration of each training session. Step 3: The analysis unit analyzes the data collected by the collection unit and the information entered by the reception unit. The analysis unit performs the analysis using, for example, statistical analysis or machine learning algorithms. Based on the collected data and entered information, the analysis unit analyzes the optimal training program and diet plan to help the user achieve their goals. Step 4: The suggestion unit proposes an optimal training program and diet plan to the user based on the results analyzed by the analysis unit. The suggestion unit proposes specific programs for strength training and aerobic exercise, for example. The suggestion unit proposes menus that take into account calorie intake and nutritional balance. The suggestion unit can propose strength training as weight training or resistance training. The suggestion unit can propose aerobic exercise as running or cycling. The suggestion unit can set calorie intake to the recommended daily calorie intake. The suggestion unit can set nutritional balance in terms of the ratio of protein, fat, and carbohydrates.

[0114] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0115] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0116] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0117] Each of the multiple elements described above, including the data collection unit, reception unit, analysis unit, and proposal unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the data collection unit collects body composition data using the camera 42 and sensors of the smart device 14, and this data is analyzed by the identification processing unit 290 of the data processing unit 12. The reception unit inputs the user's goals and training frequency using the touch panel 38A and microphone 38B of the smart device 14. The analysis unit analyzes the collected data and input information using the identification processing unit 290 of the data processing unit 12. The proposal unit proposes an optimal training program and diet based on the analysis results using the identification processing unit 290 of the data processing unit 12. For example, the data collection unit can input the user's emotional data into an AI, which can then adjust the timing of data collection. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0118] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0119] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0120] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0121] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0122] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0123] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0124] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0125] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0126] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0127] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0128] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0129] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0130] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0131] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0132] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0133] Each of the multiple elements described above, including the data collection unit, reception unit, analysis unit, and proposal unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit collects body composition data using the camera 42 and sensors of the smart glasses 214, and this data is analyzed by the identification processing unit 290 of the data processing unit 12. The reception unit inputs the user's goals and training frequency using the microphone 238 of the smart glasses 214. The analysis unit analyzes the collected data and input information using the identification processing unit 290 of the data processing unit 12. The proposal unit proposes an optimal training program and diet based on the analysis results using the identification processing unit 290 of the data processing unit 12. For example, the data collection unit can input the user's emotional data into an AI, which can then adjust the timing of data collection. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0134] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0135] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0136] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0137] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0138] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0139] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0140] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0141] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0142] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0143] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0144] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0145] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0146] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0147] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0148] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0149] Each of the multiple elements described above, including the data collection unit, reception unit, analysis unit, and proposal unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the data collection unit collects body composition data using the camera 42 and sensors of the headset terminal 314, and this data is analyzed by the identification processing unit 290 of the data processing unit 12. The reception unit inputs the user's goals and training frequency using the microphone 238 of the headset terminal 314. The analysis unit analyzes the collected data and input information using the identification processing unit 290 of the data processing unit 12. The proposal unit proposes an optimal training program and diet based on the analysis results using the identification processing unit 290 of the data processing unit 12. For example, the data collection unit can input the user's emotional data into the AI, which can then adjust the timing of data collection. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0150] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0151] As shown in Figure 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.

[0152] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0153] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0154] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0155] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0156] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0157] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0158] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0159] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0160] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0161] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0162] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0163] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0164] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0165] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0166] Each of the multiple elements described above, including the data collection unit, reception unit, analysis unit, and proposal unit, is implemented, for example, in at least one of the robot 414 and the data processing unit 12. For example, the data collection unit collects body composition data using the camera 42 and sensors of the robot 414, and this data is analyzed by the specific processing unit 290 of the data processing unit 12. The reception unit inputs the user's goals and training frequency using the microphone 238 of the robot 414. The analysis unit analyzes the collected data and input information using the specific processing unit 290 of the data processing unit 12. The proposal unit proposes an optimal training program and diet based on the analysis results using the specific processing unit 290 of the data processing unit 12. For example, the data collection unit can input the user's emotional data into an AI, which can then adjust the timing of data collection. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0167] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0168] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0169] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0170] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0171] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0172] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0173] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0174] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0175] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0176] 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.

[0177] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0178] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0179] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0180] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0181] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0182] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0183] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0184] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0185] (Note 1) A data collection unit that collects data from the body composition analyzer, A reception unit inputs the user's goals based on the data collected by the aforementioned collection unit, An analysis unit analyzes the information input by the reception unit, The system includes a proposal unit that proposes a training program and meal plan based on the results of the analysis performed by the aforementioned analysis unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is Collect data such as body fat percentage, muscle mass, and weight. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned reception unit is The user enters their ideal body shape and composition, the timeframe for achieving that goal, and the frequency of training. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned analysis unit, The data collected by the collection unit and the information entered by the reception unit are analyzed. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned proposal section is, Based on the results analyzed by the analysis unit, the system proposes the optimal training program and diet plan for the user. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned proposal section is, We propose specific programs for strength training and aerobic exercise. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned proposal section is, We propose menus that take into account calorie intake and nutritional balance. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is During data collection, the collection method is optimized by referring to the user's past body composition data. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is During data collection, the data is filtered based on the user's lifestyle and activity level. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is During data collection, the system prioritizes collecting highly relevant data, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned collection unit is During data collection, the system analyzes the user's social media activity and collects relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned reception unit is It estimates the user's emotions and adjusts the target input interface based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned reception unit is During registration, we will refer to the user's past goal-setting history to suggest the most suitable input method. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned reception unit is At registration, the input information is customized based on the user's current health status and lifestyle. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned reception unit is It estimates the user's emotions and determines the priority of input goals based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned reception unit is During registration, the system prioritizes inputting highly relevant goals, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned reception unit is During registration, the system analyzes the user's social media activity and inputs relevant goals. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the analysis algorithm based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned analysis unit, During analysis, the collected data is compared with historical data to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned analysis unit, During analysis, the analysis content is customized based on the user's lifestyle and activity level. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned analysis unit, During analysis, the analysis content is optimized by taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned analysis unit, During analysis, the system analyzes users' social media activity and incorporates relevant data into the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned proposal section is, It estimates the user's emotions and adjusts the suggestions based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned proposal section is, When making suggestions, the system will refer to the user's past training and dietary history to provide the most suitable recommendations. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned proposal section is, When making suggestions, the suggestions are customized based on the user's current health status and lifestyle. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned proposal section is, It estimates the user's emotions and determines the priority of suggestions based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned proposal section is, When making a proposal, we take the user's geographical location into consideration to provide the most suitable suggestion. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned proposal section is, When making a proposal, we analyze the user's social media activity and make relevant suggestions. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0186] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A data collection unit that collects data from the body composition analyzer, A reception unit inputs the user's goals based on the data collected by the aforementioned collection unit, An analysis unit analyzes the information input by the reception unit, The system includes a proposal unit that proposes a training program and meal plan based on the results of the analysis performed by the aforementioned analysis unit. A system characterized by the following features.

2. The aforementioned collection unit is Collect data such as body fat percentage, muscle mass, and weight. The system according to feature 1.

3. The aforementioned reception unit is The user enters their ideal body shape and composition, the timeframe for achieving that goal, and the frequency of training. The system according to feature 1.

4. The aforementioned analysis unit, The data collected by the collection unit and the information entered by the reception unit are analyzed. The system according to feature 1.

5. The aforementioned proposal section is, Based on the results analyzed by the aforementioned analysis unit, the system proposes an optimal training program and diet plan to the user. The system according to feature 1.

6. The aforementioned proposal section is, We propose specific programs for strength training and aerobic exercise. The system according to feature 1.

7. The aforementioned proposal section is, We propose menus that take into account calorie intake and nutritional balance. The system according to feature 1.

8. The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system according to feature 1.

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