system

Abstract

The system according to this embodiment aims to provide a training program and meal plan optimized for the user's lifestyle. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a generation unit, and a feedback unit. The collection unit collects healthcare data from the user's smartphone. The analysis unit uses a generation AI to analyze the data collected by the collection unit. The generation unit generates a training program and a meal plan based on the analysis results obtained by the analysis unit. The feedback unit provides real-time feedback based on the plan generated by the generation 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, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of the chatbot's character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance as a 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] In the prior art, there is a problem that it is difficult to provide a training program and a diet plan optimized for the user's lifestyle, and the effect of health management is limited.

[0005] The system according to the embodiment aims to provide a training program and a diet plan optimized for the user's lifestyle.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, a generation unit, and a feedback unit. The data collection unit collects healthcare data from the user's smartphone. The analysis unit uses a generation AI to analyze the data collected by the data collection unit. The generation unit generates a training program and a meal plan based on the analysis results obtained by the analysis unit. The feedback unit provides real-time feedback based on the plan generated by the generation unit. [Effects of the Invention]

[0007] The system according to this embodiment can provide a training program and meal plan optimized for the user's lifestyle. [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 labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages 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 healthcare system according to an embodiment of the present invention is a system that utilizes healthcare data from a smartphone to provide a training program and meal plan optimized for the user's lifestyle. This healthcare system collects healthcare data (heart rate, steps, sleep time, weight, calories burned, etc.) from the user's smartphone, and a generating AI analyzes the collected data to generate a training program and meal plan optimized for the user's lifestyle. For example, based on the user's heart rate and step count data, it suggests specific menus for walking and strength training. It also provides an appropriate meal plan based on the user's weight and calorie burn data. Furthermore, the generating AI provides real-time feedback and advice. For example, after the user has trained, the generating AI analyzes the results and provides advice for the next training session. Also, if the user records their meals, the generating AI analyzes the contents and points out areas for improvement in nutritional balance. This system allows users to easily receive training and meal plans tailored to their lifestyle, making health management easier. In addition, the real-time feedback and advice from the generating AI helps maintain the user's motivation and supports long-term health management. As a result, the healthcare system can efficiently support the user's health management.

[0029] The healthcare system according to this embodiment comprises a data collection unit, an analysis unit, a data generation unit, and a feedback unit. The data collection unit collects healthcare data from the user's smartphone. The data collection unit can collect data such as heart rate, steps taken, sleep duration, weight, and calories burned. The data collection unit acquires this data using the smartphone's sensors and applications. For example, the data collection unit can measure heart rate using the smartphone's built-in sensors and collect step count data through an application. The data collection unit can also collect weight and dietary information entered by the user. The analysis unit analyzes the data collected by the data collection unit using a data generation AI. The data generation AI analyzes the data using, for example, machine learning models and deep learning algorithms. Based on heart rate and step count data, the data generation AI can evaluate the user's exercise patterns and health status. For example, the data generation AI can analyze heart rate variability and estimate the user's stress level. The data generation AI can also analyze step count data and evaluate the user's activity level. Based on the analysis results obtained by the analysis unit, the data generation unit generates a training program and a meal plan. The generation unit can, for example, suggest specific walking and strength training menus based on the user's heart rate and step count data. It can also provide appropriate meal plans based on the user's weight and calorie expenditure data. For example, it can suggest calorie restrictions to maintain the user's weight and meal menus that consider nutritional balance. The feedback unit provides real-time feedback based on the plan generated by the generation unit. For example, after the user completes a workout, the feedback unit can analyze the results and provide advice for the next workout. Furthermore, if the user records their meals, the feedback unit can analyze the content and point out areas for improvement in nutritional balance. For example, the feedback unit can analyze the user's diet, evaluate vitamin and mineral intake, and provide advice on supplementing any deficient nutrients.As a result, the healthcare system according to this embodiment makes health management easier by utilizing the user's healthcare data and providing optimized training programs and meal plans.

[0030] The data collection unit collects healthcare data from the user's smartphone. For example, it can collect data such as heart rate, steps taken, sleep duration, weight, and calories burned. The unit acquires this data using the smartphone's sensors and applications. Specifically, it can measure heart rate using the smartphone's built-in sensors and collect steps through the application. An optical heart rate sensor is used to measure heart rate by shining light onto the user's skin and detecting the reflected light. Accelerometers and gyroscopes are used to collect steps by detecting the user's walking motion. The data collection unit can also collect weight and dietary data entered by the user. Weight data is collected when the user regularly enters their weight, and dietary data is collected when the user enters their meals and calorie intake into the application. Furthermore, the data collection unit can also collect data from wearable devices such as smartwatches and fitness trackers. This allows the data collection unit to centrally collect diverse healthcare data from the user and understand their health status in real time. The collected data is sent to a cloud server and stored securely. The cloud server is equipped with advanced security measures to protect user privacy. Furthermore, the data collection unit can adjust the frequency and accuracy of data collection, allowing for flexible responses to user needs and circumstances. For example, increasing the frequency of heart rate measurements allows for a more detailed understanding of stress level fluctuations. This enables the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0031] The analysis unit uses generative AI to analyze data collected by the data collection unit. Generative AI analyzes data using, for example, machine learning models and deep learning algorithms. Specifically, it can evaluate a user's exercise patterns and health status based on heart rate and step count data. Generative AI can analyze heart rate variability and estimate the user's stress level. For example, if the heart rate increases sharply, the generative AI will determine that the user is likely experiencing stress. Generative AI can also analyze step count data and evaluate the user's activity level. For example, if the step count is low, the generative AI will determine that the user is not getting enough exercise and will provide advice to increase their activity level. Furthermore, the generative AI can analyze sleep data and evaluate the quality of the user's sleep. For example, if the sleep duration is short, the generative AI will determine that the user is not getting enough rest and will provide advice to improve sleep quality. By comprehensively analyzing this data, the generative AI can comprehensively evaluate the user's health status. Additionally, the generative AI can utilize historical data and statistical information to analyze long-term health trends. For example, based on past heart rate data, it can predict fluctuations in the user's stress level and assess future health risks. Furthermore, the generating AI can use anomaly detection algorithms to detect unusual patterns and abnormal data, issuing early warnings. This allows the analysis unit to not only understand the situation in real time but also to handle long-term health management and anomaly detection, improving the overall reliability and safety of the system.

[0032] The generation unit generates training programs and meal plans based on the analysis results obtained by the analysis unit. For example, the generation unit can suggest specific menus for walking and strength training based on the user's heart rate and step count data. Specifically, the generation unit sets appropriate exercise intensity and repetitions according to the user's exercise level and goals, and provides detailed training menus. For example, the walking menu provides specific instructions for walking distance, time, and pace, and the strength training menu provides detailed instructions for the number of repetitions and sets of various exercises, as well as rest times. The generation unit can also provide appropriate meal plans based on the user's weight and calorie expenditure data. For example, the generation unit suggests calorie restrictions to maintain the user's weight and meal menus that consider nutritional balance. Specifically, the generation unit calculates the daily calorie intake according to the user's target weight and activity level, and provides specific menus for breakfast, lunch, and dinner. Furthermore, the generation unit can analyze the user's diet and evaluate the balance of nutrient intake. For example, it evaluates the intake of vitamins and minerals and suggests ingredients and recipes to supplement any deficient nutrients. This allows the generation unit to provide optimal training programs and meal plans tailored to the user's health condition and goals, thereby supporting their health management. Furthermore, the generation unit can collect user feedback and continuously improve the accuracy and effectiveness of the training programs and meal plans. This enables the generation unit to more effectively support the user's health management and improve the overall system performance.

[0033] The feedback unit provides real-time feedback based on the plan generated by the generation unit. For example, after a user completes a training session, the feedback unit can analyze the results and provide advice for the next training session. Specifically, the feedback unit analyzes the user's training data and evaluates the effectiveness of the exercise and areas for improvement. For example, it can provide advice on adjusting the intensity and duration of training based on the user's heart rate and calories burned. The feedback unit can also analyze the user's food log and point out areas for improvement in nutritional balance. Specifically, it analyzes the user's diet, evaluates the intake of vitamins and minerals, and provides advice on supplementing any deficient nutrients. For example, if a user consumes few vegetables, the feedback unit will suggest recipes and ingredients that are rich in vegetables. Furthermore, the feedback unit can collect user feedback and continuously improve the accuracy and effectiveness of the entire system. For example, by providing feedback on the advice given, the system can adjust the advice based on that feedback and provide more personalized support. The feedback unit can also provide a function to visualize achievement goals and progress to maintain user motivation. This allows the feedback unit to quickly provide users with appropriate feedback and support their health management.

[0034] The generation unit can generate training programs based on the user's heart rate and step count data. For example, the generation unit can analyze the user's heart rate data and set an appropriate exercise intensity. For instance, it can suggest walking or jogging menus to keep the heart rate within a certain range. The generation unit can also set daily walking goals based on the user's step count data. For example, it can suggest a daily step goal based on the user's current step count. Furthermore, the generation unit can combine the user's heart rate and step count data to generate a comprehensive training program. For example, it can analyze heart rate fluctuations and increases / decreases in step counts and suggest training menus tailored to the user's exercise patterns. This allows the system to provide an optimal training program based on the user's heart rate and step count data.

[0035] The generation unit can generate meal plans based on the user's weight and calorie expenditure data. For example, the generation unit can analyze the user's weight data and set an appropriate calorie intake. For instance, the generation unit can calculate the calorie intake necessary to maintain the user's weight and propose a meal plan based on that. The generation unit can also propose meal menus that match the user's calorie expenditure based on their calorie expenditure data. For example, the generation unit can provide a meal plan to replenish the calories the user has burned through exercise. Furthermore, the generation unit can combine the user's weight and calorie expenditure data to generate a comprehensive meal plan. For example, the generation unit can analyze the relationship between the user's weight fluctuations and calorie expenditure and propose a meal menu that considers nutritional balance. This allows the system to provide an optimal meal plan based on the user's weight and calorie expenditure data.

[0036] The feedback unit can analyze the results after a user completes a training session and provide advice for the next training session. For example, the feedback unit can analyze the user's training results and suggest adjustments to exercise intensity and duration. For instance, it can set the exercise intensity for the next training session based on the user's heart rate data. It can also suggest extending or shortening exercise time based on the user's calorie expenditure data. Furthermore, the feedback unit can comprehensively evaluate the user's training results and point out areas for improvement in the training menu. For example, it can analyze the user's exercise patterns and suggest more effective training methods. In this way, by analyzing training results and providing advice for the next training session, the feedback unit improves the effectiveness of the user's training.

[0037] The feedback unit can analyze the user's recorded meals and point out areas for improvement in nutritional balance. For example, the feedback unit can analyze the user's meals and evaluate the balance of calories and nutrients. For example, based on the user's meals, the feedback unit can evaluate the intake of vitamins and minerals and provide advice on supplementing any deficient nutrients. The feedback unit can also analyze the user's eating patterns and suggest adjustments to the timing and quantity of meals. For example, based on the user's meal records, the feedback unit can provide advice on improving the balance of breakfast, lunch, and dinner. Furthermore, the feedback unit can comprehensively evaluate the user's meals and point out areas for improvement in nutritional balance. For example, the feedback unit can analyze the user's meals and suggest healthy meal menus. In this way, by analyzing meals and pointing out areas for improvement in nutritional balance, the feedback unit improves the user's eating habits.

[0038] The adjustment unit can provide plans tailored to the user's lifestyle. For example, it adjusts training programs and meal plans considering the user's daily activity level, eating habits, and sleep patterns. For instance, if the user has a high activity level, the adjustment unit can suggest a training program that increases exercise intensity. It can also provide meal plans that consider nutritional balance based on the user's eating habits. For example, if the user does not consume enough vegetables, the adjustment unit can suggest meal menus that include plenty of vegetables. Furthermore, the adjustment unit can provide plans that emphasize the importance of rest, taking into account the user's sleep patterns. For example, if the user sleeps for a short time, the adjustment unit can provide advice on how to improve sleep. By providing plans tailored to the user's lifestyle, more effective health management becomes possible.

[0039] The communications department can provide communication to maintain user motivation. For example, the communications department can provide encouraging messages and advice to users. For instance, after a user completes a training session, the communications department can send a message praising their achievements. Similarly, if a user records their meals, the communications department can provide a message acknowledging their efforts. Furthermore, to maintain user motivation, the communications department can set regular goals and provide feedback on their progress. For example, the communications department can set weekly training goals for users and send messages evaluating their achievement. This provides communication to maintain user motivation and supports long-term health management.

[0040] The data collection unit can analyze the user's past healthcare data and select the optimal collection method. For example, the unit can analyze the user's past heart rate data and collect data during times when the heart rate is stable. For example, the unit can collect heart rate data at night when the user's heart rate is stable. The unit can also analyze the user's past step count data and collect data during times when the user takes many steps. For example, if the user walks a lot during the day, the unit will collect step count data during the day. Furthermore, the unit can analyze the user's past sleep data and collect data during times when the quality of sleep is good. For example, the unit will collect sleep data when the user is in deep sleep. By analyzing past data, the unit can select the optimal collection method and improve the accuracy of data collection.

[0041] The data collection unit can filter healthcare data based on the user's current activity level and health status. For example, if the user is exercising, the unit can collect data corresponding to the exercise intensity. For instance, if the user is performing high-intensity exercise, the unit can prioritize collecting heart rate data. Furthermore, if the user is resting, the unit can prioritize collecting heart rate and sleep data. For example, it can collect sleep data at night when the user is resting. Additionally, if the user is eating, the unit can collect calorie expenditure data. For example, it can collect calorie expenditure data during the time the user is eating. This allows for the collection of more relevant data by filtering it based on the user's current activity level and health status.

[0042] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location when collecting healthcare data. For example, if the user is at the gym, the data collection unit can prioritize the collection of exercise data. For example, the data collection unit can collect heart rate data during the time the user is training at the gym. The data collection unit can also prioritize the collection of rest data if the user is at home. For example, the data collection unit can collect sleep data during the time the user is resting at home. Furthermore, the data collection unit can prioritize the collection of step count data if the user is out. For example, the data collection unit can collect step count data during the time the user is out. In this way, by considering geographical location information, the data collection unit can prioritize the collection of highly relevant data.

[0043] The data collection unit can analyze a user's social media activity and collect relevant data when collecting healthcare data. For example, if a user posts about exercise on social media, the data collection unit can collect exercise data. For example, the data collection unit can collect heart rate data during the time the user made the exercise post. The data collection unit can also collect meal data if a user posts about food. For example, the data collection unit can collect calorie consumption data during the time the user made the meal post. Furthermore, if a user posts about sleep, the data collection unit can collect sleep data. For example, the data collection unit can collect sleep data during the time the user made the sleep post. This makes it easier to collect relevant data by analyzing social media activity.

[0044] The analysis unit can adjust the level of detail of the analysis based on the importance of the healthcare data during the analysis. For example, if heart rate data is important, the analysis unit can perform a detailed heart rate analysis. For instance, it can analyze heart rate variability in detail to assess the user's stress level. Similarly, if step count data is important, the analysis unit can perform a detailed step count analysis. For example, it can analyze the user's walking pattern in detail to evaluate the effects of exercise. Furthermore, if sleep data is important, the analysis unit can perform a detailed sleep analysis. For example, it can analyze the user's sleep stages in detail to assess sleep quality. This allows for efficient data analysis by adjusting the level of detail of the analysis based on the importance of the data.

[0045] The analysis unit can apply different analysis algorithms depending on the category of healthcare data during analysis. For example, the analysis unit can apply a heart rate variability analysis algorithm to heart rate data. For instance, it can apply an algorithm that analyzes heart rate variability and evaluates stress levels. Similarly, the analysis unit can apply a gait pattern analysis algorithm to step count data. For example, it can apply an algorithm that analyzes the user's gait pattern and evaluates the effects of exercise. Furthermore, the analysis unit can apply a sleep stage analysis algorithm to sleep data. For example, it can apply an algorithm that analyzes the user's sleep stages and evaluates sleep quality. This improves analysis accuracy by applying the appropriate analysis algorithm according to the data category.

[0046] The analytics department can prioritize analysis based on when healthcare data was collected. For example, it can prioritize analyzing recently collected data. For instance, it can prioritize analyzing a user's most recent heart rate data to assess their stress level. It can also prioritize analyzing data collected during specific time periods. For example, it can prioritize analyzing heart rate data immediately after a user exercises. Furthermore, it can prioritize analyzing data collected after specific events (exercise, meals, sleep). For example, it can prioritize analyzing calorie expenditure data after a user has eaten. This allows for prioritizing analysis based on the collection timing, ensuring that the most recent data is analyzed first.

[0047] The analysis unit can adjust the order of analysis based on the relevance of healthcare data during the analysis process. For example, the analysis unit can determine the order of analysis by considering the relationship between heart rate data and step count data. For instance, the analysis unit can simultaneously analyze a user's heart rate data and step count data to evaluate the effects of exercise. The analysis unit can also determine the order of analysis by considering the relationship between sleep data and weight data. For example, the analysis unit can simultaneously analyze a user's sleep data and weight data to evaluate the relationship between sleep quality and weight. Furthermore, the analysis unit can also determine the order of analysis by considering the relationship between calorie expenditure data and diet data. For example, the analysis unit can simultaneously analyze a user's calorie expenditure data and diet data to evaluate the effects of diet. By adjusting the order of analysis based on the relevance of the data, efficient data analysis becomes possible.

[0048] The generation unit can generate an optimal training program by referring to the user's past training history. For example, the generation unit can analyze the effectiveness of the user's past training and generate an optimal program. For example, the generation unit can analyze the user's past heart rate data and suggest an effective training menu. The generation unit can also generate an appropriate training plan by considering the user's past training frequency. For example, the generation unit can set weekly training goals based on the user's past training frequency. Furthermore, the generation unit can generate an appropriate training menu based on the user's past training intensity. For example, the generation unit can analyze the user's past training intensity and suggest a training menu with adjusted exercise intensity. In this way, by referring to past training history, the optimal training program can be provided.

[0049] The generation unit can generate an optimal meal plan by referring to the user's past eating history. For example, the generation unit can analyze the nutrients the user has consumed in the past and generate a balanced meal plan. For example, the generation unit can analyze the user's past eating data and suggest a nutritionally balanced meal menu. The generation unit can also generate an appropriate meal plan by considering the user's past eating patterns. For example, the generation unit can suggest a plan that adjusts the timing and amount of meals based on the user's past eating patterns. Furthermore, the generation unit can generate a meal plan that improves nutritional balance based on the user's past eating history. For example, the generation unit can analyze the user's past eating history and suggest a meal menu to supplement any deficient nutrients. In this way, the optimal meal plan can be provided by referring to past eating history.

[0050] The generation unit can generate an optimal training program by considering the user's geographical location information. For example, if the user is near a gym, the generation unit can generate a training program that can be done at the gym. For instance, based on the user's geographical location information, the generation unit can suggest a gym training menu. Similarly, if the user is near a park, the generation unit can generate a training program that can be done at the park. For example, based on the user's geographical location information, the generation unit can suggest walking or jogging menus in the park. Furthermore, if the user is at home, the generation unit can generate a training program that can be done at home. For example, based on the user's geographical location information, the generation unit can suggest strength training or stretching menus for home use. In this way, by considering geographical location information, the system can provide the most optimal training program.

[0051] The generation unit can generate optimal meal plans by analyzing the user's social media activity. For example, it can generate meal plans based on the content of meals shared by the user on social media. For instance, it can analyze the content of meals shared by the user on social media and suggest nutritionally balanced meal menus. It can also generate meal plans based on information from health-related accounts that the user follows on social media. For example, it can analyze the content of posts from health-related accounts that the user follows and suggest healthy meal menus. Furthermore, it can generate meal plans based on meal challenges that the user is participating in on social media. For example, it can analyze the content of meal challenges that the user is participating in and suggest meal menus suitable for the challenge. In this way, by analyzing social media activity, it can provide optimal meal plans.

[0052] The feedback unit can provide optimal advice by referring to the user's past training results when providing feedback. For example, the feedback unit can analyze the user's past training results and provide advice for the next training session. For example, the feedback unit can analyze the user's past heart rate data and suggest an exercise intensity for the next training session. The feedback unit can also provide appropriate advice by considering the user's past training frequency. For example, the feedback unit can set weekly training goals based on the user's past training frequency. Furthermore, the feedback unit can provide advice for the next training session based on the user's past training intensity. For example, the feedback unit can analyze the user's past training intensity and suggest a training menu with adjusted exercise intensity. In this way, the feedback unit can provide optimal advice by referring to past training results.

[0053] The feedback unit can refer to the user's past meal history when providing feedback to identify areas for improvement in nutritional balance. For example, the feedback unit can analyze the user's past meal history and identify areas for improvement in nutritional balance. For example, the feedback unit can analyze the user's past meal data and provide advice on supplementing any deficient nutrients. The feedback unit can also consider the user's past eating patterns to provide appropriate nutritional balance advice. For example, the feedback unit can provide advice on adjusting meal timing and quantity based on the user's past eating patterns. Furthermore, the feedback unit can make specific suggestions for improving nutritional balance based on the user's past meal history. For example, the feedback unit can analyze the user's past meal history and suggest healthy meal menus. This allows the feedback unit to identify areas for improvement in nutritional balance by referring to past meal history.

[0054] The feedback system can provide optimal advice by considering the user's geographical location when providing feedback. For example, if the user is at a gym, the feedback system can provide advice on training that can be done at the gym. For instance, based on the user's geographical location, the feedback system can suggest a gym training menu. Furthermore, if the user is at home, the feedback system can provide advice on training that can be done at home. For example, based on the user's geographical location, the feedback system can suggest a home workout or stretching menu. Additionally, if the user is out, the feedback system can provide advice on training that can be done outdoors. For example, based on the user's geographical location, the feedback system can suggest an outdoor walking or jogging menu. In this way, by considering geographical location, the system can provide optimal advice.

[0055] The feedback department can provide optimal advice by analyzing the user's social media activity when providing feedback. For example, the feedback department can provide advice based on the training content the user has shared on social media. For example, the feedback department can analyze the training content the user has shared on social media and provide advice to improve the effectiveness of the training. The feedback department can also provide advice based on information from health-related accounts the user follows on social media. For example, the feedback department can analyze the content of posts from health-related accounts the user follows and suggest a healthy training menu. Furthermore, the feedback department can provide advice based on training challenges the user is participating in on social media. For example, the feedback department can analyze the content of training challenges the user is participating in and suggest a training menu suitable for the challenge. In this way, by analyzing social media activity, the feedback department can provide optimal advice.

[0056] The adjustment unit can make optimal adjustments to the plan by referring to the user's past lifestyle data. For example, the adjustment unit can analyze the user's past lifestyle data and adjust it to create the optimal training plan. For instance, it can analyze the user's past activity levels and propose a training plan with adjusted exercise intensity. The adjustment unit can also analyze the user's past eating data and adjust it to create the optimal meal plan. For example, it can analyze the user's past eating patterns and propose a meal plan that considers nutritional balance. Furthermore, the adjustment unit can analyze the user's past sleep data and adjust it to create the optimal rest plan. For example, it can analyze the user's past sleep patterns and provide advice to improve sleep quality. In this way, optimal plan adjustments are possible by referring to past lifestyle data.

[0057] The adjustment unit can make optimal adjustments to the training plan by considering the user's geographical location. For example, if the user is near a gym, the adjustment unit will adjust the training plan to be something that can be done at the gym. For instance, based on the user's geographical location, the adjustment unit can suggest a gym training menu. Furthermore, if the user is at home, the adjustment unit can adjust the training plan to be something that can be done at home. For example, based on the user's geographical location, the adjustment unit can suggest a home workout or stretching menu. Additionally, if the user is out, the adjustment unit can adjust the training plan to be something that can be done outdoors. For example, based on the user's geographical location, the adjustment unit can suggest an outdoor walking or jogging menu. This allows for optimal plan adjustments by considering geographical location.

[0058] The communications department can provide optimal content by referring to the user's past communication history during communication. For example, the communications department can analyze the user's past communication history and provide optimal advice. For example, the communications department can analyze the content of the user's past communication and provide advice for the next step. The communications department can also provide appropriate messages by considering the user's past communication patterns. For example, the communications department can analyze the user's past communication patterns and provide advice tailored to the user's needs. Furthermore, the communications department can provide advice for the next step based on the user's past communication history. For example, the communications department can analyze the user's past communication history and provide advice regarding the next training or meal plan. In this way, the communications department can provide optimal content by referring to past communication history.

[0059] The communications department can provide optimal content during communication by taking into account the user's geographical location. For example, if the user is at a gym, the communications department can provide advice on training that can be done at the gym. For instance, based on the user's geographical location, the communications department can suggest a gym training menu. Furthermore, if the user is at home, the communications department can provide advice on training that can be done at home. For example, based on the user's geographical location, the communications department can suggest a home workout or stretching menu. Additionally, if the user is out, the communications department can provide advice on training that can be done outdoors. For example, based on the user's geographical location, the communications department can suggest an outdoor walking or jogging menu. In this way, by considering geographical location, the communications department can provide optimal content.

[0060] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0061] The healthcare system can analyze a user's past training data and adjust the training program according to their training progress. For example, it can set the next training goal based on the user's past achievements. It can also suggest an appropriate training menu considering the user's training frequency and intensity. Furthermore, it can refer to the user's training history to provide advice to maximize the effectiveness of their training. In this way, it can support effective training by providing an optimal training program based on the user's training history.

[0062] The healthcare system can adjust training programs based on the user's geographical location. For example, if the user is near a gym, it can suggest training menus that can be done at the gym. If the user is near a park, it can suggest walking or jogging menus that can be done in the park. Furthermore, if the user is at home, it can suggest strength training or stretching menus that can be done at home. This allows the system to provide an optimal training program based on the user's geographical location, thereby supporting effective training.

[0063] Healthcare systems can analyze a user's past dietary data and adjust meal plans accordingly. For example, they can suggest a nutritionally balanced meal plan based on the nutrients the user has consumed in the past. They can also suggest appropriate meal timings and quantities, taking into account the user's eating patterns. Furthermore, they can refer to the user's dietary history to suggest meal menus that address any nutritional deficiencies. By providing an optimal meal plan based on the user's past dietary data, they can support effective health management.

[0064] Healthcare systems can analyze a user's past lifestyle data and adjust training programs and meal plans accordingly. For example, they can set appropriate exercise intensity based on the user's past activity levels. They can also suggest nutritionally balanced meal plans based on the user's past eating data. Furthermore, they can refer to the user's past sleep data to provide an optimal rest plan. By providing optimal plans based on the user's past lifestyle data, they can support effective health management.

[0065] The healthcare system can analyze a user's social media activity and adjust training programs and meal plans accordingly. For example, it can suggest training menus based on the training content a user shares on social media. It can also suggest nutritionally balanced meal plans based on the meals a user shares. Furthermore, it can provide plans tailored to challenges a user is participating in on social media. By providing optimal plans based on the user's social media activity, it can support effective health management.

[0066] The following briefly describes the processing flow for example form 1.

[0067] Step 1: The data collection unit collects healthcare data from the user's smartphone. The data collection unit can collect data such as heart rate, steps taken, sleep duration, weight, and calories burned. The data collection unit uses the smartphone's sensors and applications to acquire this data. For example, the data collection unit measures heart rate using the smartphone's built-in sensors and collects step count data through the application. The data collection unit can also collect weight and dietary information entered by the user. Step 2: The analysis unit uses generative AI to analyze the data collected by the collection unit. The generative AI analyzes the data using machine learning models and deep learning algorithms. Based on heart rate and step count data, the generative AI evaluates the user's exercise patterns and health status. For example, the generative AI analyzes heart rate variability to estimate the user's stress level. It also analyzes step count data to evaluate the user's activity level. Step 3: The generation unit generates a training program and meal plan based on the analysis results obtained by the analysis unit. The generation unit proposes specific menus for walking and strength training based on the user's heart rate and step count data. The generation unit also provides an appropriate meal plan based on the user's weight and calorie expenditure data. For example, the generation unit proposes a meal menu that considers calorie restrictions to maintain the user's weight and nutritional balance. Step 4: The feedback unit provides real-time feedback based on the plan generated by the generation unit. After the user completes a training session, the feedback unit analyzes the results and provides advice for the next training session. Furthermore, if the user records their meals, the feedback unit analyzes the content and points out areas for improvement in nutritional balance. For example, the feedback unit analyzes the user's diet, evaluates their vitamin and mineral intake, and provides advice on supplementing any deficient nutrients.

[0068] (Example of form 2) The healthcare system according to an embodiment of the present invention is a system that utilizes healthcare data from a smartphone to provide a training program and meal plan optimized for the user's lifestyle. This healthcare system collects healthcare data (heart rate, steps, sleep time, weight, calories burned, etc.) from the user's smartphone, and a generating AI analyzes the collected data to generate a training program and meal plan optimized for the user's lifestyle. For example, based on the user's heart rate and step count data, it suggests specific menus for walking and strength training. It also provides an appropriate meal plan based on the user's weight and calorie burn data. Furthermore, the generating AI provides real-time feedback and advice. For example, after the user has trained, the generating AI analyzes the results and provides advice for the next training session. Also, if the user records their meals, the generating AI analyzes the contents and points out areas for improvement in nutritional balance. This system allows users to easily receive training and meal plans tailored to their lifestyle, making health management easier. In addition, the real-time feedback and advice from the generating AI helps maintain the user's motivation and supports long-term health management. As a result, the healthcare system can efficiently support the user's health management.

[0069] The healthcare system according to this embodiment comprises a data collection unit, an analysis unit, a data generation unit, and a feedback unit. The data collection unit collects healthcare data from the user's smartphone. The data collection unit can collect data such as heart rate, steps taken, sleep duration, weight, and calories burned. The data collection unit acquires this data using the smartphone's sensors and applications. For example, the data collection unit can measure heart rate using the smartphone's built-in sensors and collect step count data through an application. The data collection unit can also collect weight and dietary information entered by the user. The analysis unit analyzes the data collected by the data collection unit using a data generation AI. The data generation AI analyzes the data using, for example, machine learning models and deep learning algorithms. Based on heart rate and step count data, the data generation AI can evaluate the user's exercise patterns and health status. For example, the data generation AI can analyze heart rate variability and estimate the user's stress level. The data generation AI can also analyze step count data and evaluate the user's activity level. Based on the analysis results obtained by the analysis unit, the data generation unit generates a training program and a meal plan. The generation unit can, for example, suggest specific walking and strength training menus based on the user's heart rate and step count data. It can also provide appropriate meal plans based on the user's weight and calorie expenditure data. For example, it can suggest calorie restrictions to maintain the user's weight and meal menus that consider nutritional balance. The feedback unit provides real-time feedback based on the plan generated by the generation unit. For example, after the user completes a workout, the feedback unit can analyze the results and provide advice for the next workout. Furthermore, if the user records their meals, the feedback unit can analyze the content and point out areas for improvement in nutritional balance. For example, the feedback unit can analyze the user's diet, evaluate vitamin and mineral intake, and provide advice on supplementing any deficient nutrients.As a result, the healthcare system according to this embodiment makes health management easier by utilizing the user's healthcare data and providing optimized training programs and meal plans.

[0070] The data collection unit collects healthcare data from the user's smartphone. For example, it can collect data such as heart rate, steps taken, sleep duration, weight, and calories burned. The unit acquires this data using the smartphone's sensors and applications. Specifically, it can measure heart rate using the smartphone's built-in sensors and collect steps through the application. An optical heart rate sensor is used to measure heart rate by shining light onto the user's skin and detecting the reflected light. Accelerometers and gyroscopes are used to collect steps by detecting the user's walking motion. The data collection unit can also collect weight and dietary data entered by the user. Weight data is collected when the user regularly enters their weight, and dietary data is collected when the user enters their meals and calorie intake into the application. Furthermore, the data collection unit can also collect data from wearable devices such as smartwatches and fitness trackers. This allows the data collection unit to centrally collect diverse healthcare data from the user and understand their health status in real time. The collected data is sent to a cloud server and stored securely. The cloud server is equipped with advanced security measures to protect user privacy. Furthermore, the data collection unit can adjust the frequency and accuracy of data collection, allowing for flexible responses to user needs and circumstances. For example, increasing the frequency of heart rate measurements allows for a more detailed understanding of stress level fluctuations. This enables the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0071] The analysis unit uses generative AI to analyze data collected by the data collection unit. Generative AI analyzes data using, for example, machine learning models and deep learning algorithms. Specifically, it can evaluate a user's exercise patterns and health status based on heart rate and step count data. Generative AI can analyze heart rate variability and estimate the user's stress level. For example, if the heart rate increases sharply, the generative AI will determine that the user is likely experiencing stress. Generative AI can also analyze step count data and evaluate the user's activity level. For example, if the step count is low, the generative AI will determine that the user is not getting enough exercise and will provide advice to increase their activity level. Furthermore, the generative AI can analyze sleep data and evaluate the quality of the user's sleep. For example, if the sleep duration is short, the generative AI will determine that the user is not getting enough rest and will provide advice to improve sleep quality. By comprehensively analyzing this data, the generative AI can comprehensively evaluate the user's health status. Additionally, the generative AI can utilize historical data and statistical information to analyze long-term health trends. For example, based on past heart rate data, it can predict fluctuations in the user's stress level and assess future health risks. Furthermore, the generating AI can use anomaly detection algorithms to detect unusual patterns and abnormal data, issuing early warnings. This allows the analysis unit to not only understand the situation in real time but also to handle long-term health management and anomaly detection, improving the overall reliability and safety of the system.

[0072] The generation unit generates training programs and meal plans based on the analysis results obtained by the analysis unit. For example, the generation unit can suggest specific menus for walking and strength training based on the user's heart rate and step count data. Specifically, the generation unit sets appropriate exercise intensity and repetitions according to the user's exercise level and goals, and provides detailed training menus. For example, the walking menu provides specific instructions for walking distance, time, and pace, and the strength training menu provides detailed instructions for the number of repetitions and sets of various exercises, as well as rest times. The generation unit can also provide appropriate meal plans based on the user's weight and calorie expenditure data. For example, the generation unit suggests calorie restrictions to maintain the user's weight and meal menus that consider nutritional balance. Specifically, the generation unit calculates the daily calorie intake according to the user's target weight and activity level, and provides specific menus for breakfast, lunch, and dinner. Furthermore, the generation unit can analyze the user's diet and evaluate the balance of nutrient intake. For example, it evaluates the intake of vitamins and minerals and suggests ingredients and recipes to supplement any deficient nutrients. This allows the generation unit to provide optimal training programs and meal plans tailored to the user's health condition and goals, thereby supporting their health management. Furthermore, the generation unit can collect user feedback and continuously improve the accuracy and effectiveness of the training programs and meal plans. This enables the generation unit to more effectively support the user's health management and improve the overall system performance.

[0073] The feedback unit provides real-time feedback based on the plan generated by the generation unit. For example, after a user completes a training session, the feedback unit can analyze the results and provide advice for the next training session. Specifically, the feedback unit analyzes the user's training data and evaluates the effectiveness of the exercise and areas for improvement. For example, it can provide advice on adjusting the intensity and duration of training based on the user's heart rate and calories burned. The feedback unit can also analyze the user's food log and point out areas for improvement in nutritional balance. Specifically, it analyzes the user's diet, evaluates the intake of vitamins and minerals, and provides advice on supplementing any deficient nutrients. For example, if a user consumes few vegetables, the feedback unit will suggest recipes and ingredients that are rich in vegetables. Furthermore, the feedback unit can collect user feedback and continuously improve the accuracy and effectiveness of the entire system. For example, by providing feedback on the advice given, the system can adjust the advice based on that feedback and provide more personalized support. The feedback unit can also provide a function to visualize achievement goals and progress to maintain user motivation. This allows the feedback unit to quickly provide users with appropriate feedback and support their health management.

[0074] The generation unit can generate training programs based on the user's heart rate and step count data. For example, the generation unit can analyze the user's heart rate data and set an appropriate exercise intensity. For instance, it can suggest walking or jogging menus to keep the heart rate within a certain range. The generation unit can also set daily walking goals based on the user's step count data. For example, it can suggest a daily step goal based on the user's current step count. Furthermore, the generation unit can combine the user's heart rate and step count data to generate a comprehensive training program. For example, it can analyze heart rate fluctuations and increases / decreases in step counts and suggest training menus tailored to the user's exercise patterns. This allows the system to provide an optimal training program based on the user's heart rate and step count data.

[0075] The generation unit can generate meal plans based on the user's weight and calorie expenditure data. For example, the generation unit can analyze the user's weight data and set an appropriate calorie intake. For instance, the generation unit can calculate the calorie intake necessary to maintain the user's weight and propose a meal plan based on that. The generation unit can also propose meal menus that match the user's calorie expenditure based on their calorie expenditure data. For example, the generation unit can provide a meal plan to replenish the calories the user has burned through exercise. Furthermore, the generation unit can combine the user's weight and calorie expenditure data to generate a comprehensive meal plan. For example, the generation unit can analyze the relationship between the user's weight fluctuations and calorie expenditure and propose a meal menu that considers nutritional balance. This allows the system to provide an optimal meal plan based on the user's weight and calorie expenditure data.

[0076] The feedback unit can analyze the results after a user completes a training session and provide advice for the next training session. For example, the feedback unit can analyze the user's training results and suggest adjustments to exercise intensity and duration. For instance, it can set the exercise intensity for the next training session based on the user's heart rate data. It can also suggest extending or shortening exercise time based on the user's calorie expenditure data. Furthermore, the feedback unit can comprehensively evaluate the user's training results and point out areas for improvement in the training menu. For example, it can analyze the user's exercise patterns and suggest more effective training methods. In this way, by analyzing training results and providing advice for the next training session, the feedback unit improves the effectiveness of the user's training.

[0077] The feedback unit can analyze the user's recorded meals and point out areas for improvement in nutritional balance. For example, the feedback unit can analyze the user's meals and evaluate the balance of calories and nutrients. For example, based on the user's meals, the feedback unit can evaluate the intake of vitamins and minerals and provide advice on supplementing any deficient nutrients. The feedback unit can also analyze the user's eating patterns and suggest adjustments to the timing and quantity of meals. For example, based on the user's meal records, the feedback unit can provide advice on improving the balance of breakfast, lunch, and dinner. Furthermore, the feedback unit can comprehensively evaluate the user's meals and point out areas for improvement in nutritional balance. For example, the feedback unit can analyze the user's meals and suggest healthy meal menus. In this way, by analyzing meals and pointing out areas for improvement in nutritional balance, the feedback unit improves the user's eating habits.

[0078] The adjustment unit can provide plans tailored to the user's lifestyle. For example, it adjusts training programs and meal plans considering the user's daily activity level, eating habits, and sleep patterns. For instance, if the user has a high activity level, the adjustment unit can suggest a training program that increases exercise intensity. It can also provide meal plans that consider nutritional balance based on the user's eating habits. For example, if the user does not consume enough vegetables, the adjustment unit can suggest meal menus that include plenty of vegetables. Furthermore, the adjustment unit can provide plans that emphasize the importance of rest, taking into account the user's sleep patterns. For example, if the user sleeps for a short time, the adjustment unit can provide advice on how to improve sleep. By providing plans tailored to the user's lifestyle, more effective health management becomes possible.

[0079] The communications department can provide communication to maintain user motivation. For example, the communications department can provide encouraging messages and advice to users. For instance, after a user completes a training session, the communications department can send a message praising their achievements. Similarly, if a user records their meals, the communications department can provide a message acknowledging their efforts. Furthermore, to maintain user motivation, the communications department can set regular goals and provide feedback on their progress. For example, the communications department can set weekly training goals for users and send messages evaluating their achievement. This provides communication to maintain user motivation and supports long-term health management.

[0080] The data collection unit can estimate the user's emotions and adjust the timing of healthcare data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can collect healthcare data during times when the user is relaxed. For example, the data collection unit can collect heart rate data at night when the user is relaxed. Also, if the user is active, the data collection unit can prioritize collecting data after exercise. For example, the data collection unit can collect step count data immediately after the user exercises. Furthermore, if the user is tired, the data collection unit can collect data during rest. For example, the data collection unit can collect sleep data during times when the user is resting. By adjusting the collection timing based on the user's emotions, more accurate data collection becomes possible. 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.

[0081] The data collection unit can analyze the user's past healthcare data and select the optimal collection method. For example, the unit can analyze the user's past heart rate data and collect data during times when the heart rate is stable. For example, the unit can collect heart rate data at night when the user's heart rate is stable. The unit can also analyze the user's past step count data and collect data during times when the user takes many steps. For example, if the user walks a lot during the day, the unit will collect step count data during the day. Furthermore, the unit can analyze the user's past sleep data and collect data during times when the quality of sleep is good. For example, the unit will collect sleep data when the user is in deep sleep. By analyzing past data, the unit can select the optimal collection method and improve the accuracy of data collection.

[0082] The data collection unit can filter healthcare data based on the user's current activity level and health status. For example, if the user is exercising, the unit can collect data corresponding to the exercise intensity. For instance, if the user is performing high-intensity exercise, the unit can prioritize collecting heart rate data. Furthermore, if the user is resting, the unit can prioritize collecting heart rate and sleep data. For example, it can collect sleep data at night when the user is resting. Additionally, if the user is eating, the unit can collect calorie expenditure data. For example, it can collect calorie expenditure data during the time the user is eating. This allows for the collection of more relevant data by filtering it based on the user's current activity level and health status.

[0083] 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 heart rate data. For example, it may collect heart rate data during times when the user is stressed. The data collection unit may also prioritize collecting sleep data if the user is relaxed. For example, it may collect sleep data at night when the user is relaxed. Furthermore, the data collection unit may prioritize collecting step count data if the user is active. For example, it may collect step count data during times when the user is exercising. This allows for the priority collection of important data by determining data priority based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0084] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location when collecting healthcare data. For example, if the user is at the gym, the data collection unit can prioritize the collection of exercise data. For example, the data collection unit can collect heart rate data during the time the user is training at the gym. The data collection unit can also prioritize the collection of rest data if the user is at home. For example, the data collection unit can collect sleep data during the time the user is resting at home. Furthermore, the data collection unit can prioritize the collection of step count data if the user is out. For example, the data collection unit can collect step count data during the time the user is out. In this way, by considering geographical location information, the data collection unit can prioritize the collection of highly relevant data.

[0085] The data collection unit can analyze a user's social media activity and collect relevant data when collecting healthcare data. For example, if a user posts about exercise on social media, the data collection unit can collect exercise data. For example, the data collection unit can collect heart rate data during the time the user made the exercise post. The data collection unit can also collect meal data if a user posts about food. For example, the data collection unit can collect calorie consumption data during the time the user made the meal post. Furthermore, if a user posts about sleep, the data collection unit can collect sleep data. For example, the data collection unit can collect sleep data during the time the user made the sleep post. This makes it easier to collect relevant data by analyzing social media activity.

[0086] The analytics unit can estimate the user's emotions and adjust the data analysis method based on the estimated emotions. For example, if the user is stressed, the analytics unit will prioritize analyzing data related to stress reduction. For example, it can analyze the user's heart rate data to assess the stress level. If the user is relaxed, the analytics unit can also analyze their overall health status. For example, it can analyze the user's sleep data to assess sleep quality. Furthermore, if the user is active, the analytics unit can analyze exercise data in detail. For example, it can analyze the user's step count data to assess exercise patterns. This allows for more appropriate analysis by adjusting the analysis method based on the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0087] The analysis unit can adjust the level of detail of the analysis based on the importance of the healthcare data during the analysis. For example, if heart rate data is important, the analysis unit can perform a detailed heart rate analysis. For instance, it can analyze heart rate variability in detail to assess the user's stress level. Similarly, if step count data is important, the analysis unit can perform a detailed step count analysis. For example, it can analyze the user's walking pattern in detail to evaluate the effects of exercise. Furthermore, if sleep data is important, the analysis unit can perform a detailed sleep analysis. For example, it can analyze the user's sleep stages in detail to assess sleep quality. This allows for efficient data analysis by adjusting the level of detail of the analysis based on the importance of the data.

[0088] The analysis unit can apply different analysis algorithms depending on the category of healthcare data during analysis. For example, the analysis unit can apply a heart rate variability analysis algorithm to heart rate data. For instance, it can apply an algorithm that analyzes heart rate variability and evaluates stress levels. Similarly, the analysis unit can apply a gait pattern analysis algorithm to step count data. For example, it can apply an algorithm that analyzes the user's gait pattern and evaluates the effects of exercise. Furthermore, the analysis unit can apply a sleep stage analysis algorithm to sleep data. For example, it can apply an algorithm that analyzes the user's sleep stages and evaluates sleep quality. This improves analysis accuracy by applying the appropriate analysis algorithm according to the data category.

[0089] The analytics unit can estimate the user's emotions and determine the priority of analysis based on the estimated emotions. For example, if the user is stressed, the analytics unit will prioritize the analysis of stress-related data. For instance, it can prioritize the analysis of the user's heart rate data to assess the stress level. Similarly, if the user is relaxed, the analytics unit can prioritize the analysis of overall health data. For example, it can prioritize the analysis of the user's sleep data to assess sleep quality. Furthermore, if the user is active, the analytics unit can prioritize the analysis of exercise data. For example, it can prioritize the analysis of the user's step count data to assess the effects of exercise. This allows for the prioritization of important data by determining the priority of analysis based on the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0090] The analytics department can prioritize analysis based on when healthcare data was collected. For example, it can prioritize analyzing recently collected data. For instance, it can prioritize analyzing a user's most recent heart rate data to assess their stress level. It can also prioritize analyzing data collected during specific time periods. For example, it can prioritize analyzing heart rate data immediately after a user exercises. Furthermore, it can prioritize analyzing data collected after specific events (exercise, meals, sleep). For example, it can prioritize analyzing calorie expenditure data after a user has eaten. This allows for prioritizing analysis based on the collection timing, ensuring that the most recent data is analyzed first.

[0091] The analysis unit can adjust the order of analysis based on the relevance of healthcare data during the analysis process. For example, the analysis unit can determine the order of analysis by considering the relationship between heart rate data and step count data. For instance, the analysis unit can simultaneously analyze a user's heart rate data and step count data to evaluate the effects of exercise. The analysis unit can also determine the order of analysis by considering the relationship between sleep data and weight data. For example, the analysis unit can simultaneously analyze a user's sleep data and weight data to evaluate the relationship between sleep quality and weight. Furthermore, the analysis unit can also determine the order of analysis by considering the relationship between calorie expenditure data and diet data. For example, the analysis unit can simultaneously analyze a user's calorie expenditure data and diet data to evaluate the effects of diet. By adjusting the order of analysis based on the relevance of the data, efficient data analysis becomes possible.

[0092] The generation unit can estimate the user's emotions and adjust the generation method for training programs and meal plans based on the estimated emotions. For example, if the user is stressed, the generation unit can generate a training program with a relaxing effect. For example, it can analyze the user's heart rate data and suggest yoga or stretching exercises to reduce stress. Also, if the user is relaxed, the generation unit can generate a meal plan that promotes overall health. For example, it can analyze the user's dietary data and suggest a nutritionally balanced meal plan. Furthermore, if the user is active, the generation unit can generate a meal plan that emphasizes energy replenishment. For example, it can analyze the user's calorie expenditure data and suggest a high-energy meal plan. By adjusting the generation method based on the user's emotions, a more appropriate plan can be provided. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0093] The generation unit can generate an optimal training program by referring to the user's past training history. For example, the generation unit can analyze the effectiveness of the user's past training and generate an optimal program. For example, the generation unit can analyze the user's past heart rate data and suggest an effective training menu. The generation unit can also generate an appropriate training plan by considering the user's past training frequency. For example, the generation unit can set weekly training goals based on the user's past training frequency. Furthermore, the generation unit can generate an appropriate training menu based on the user's past training intensity. For example, the generation unit can analyze the user's past training intensity and suggest a training menu with adjusted exercise intensity. In this way, by referring to past training history, the optimal training program can be provided.

[0094] The generation unit can generate an optimal meal plan by referring to the user's past eating history. For example, the generation unit can analyze the nutrients the user has consumed in the past and generate a balanced meal plan. For example, the generation unit can analyze the user's past eating data and suggest a nutritionally balanced meal menu. The generation unit can also generate an appropriate meal plan by considering the user's past eating patterns. For example, the generation unit can suggest a plan that adjusts the timing and amount of meals based on the user's past eating patterns. Furthermore, the generation unit can generate a meal plan that improves nutritional balance based on the user's past eating history. For example, the generation unit can analyze the user's past eating history and suggest a meal menu to supplement any deficient nutrients. In this way, the optimal meal plan can be provided by referring to past eating history.

[0095] The generation unit can estimate the user's emotions and determine the priority of the plans it generates based on those emotions. For example, if the user is stressed, the generation unit will prioritize stress reduction plans. For instance, it can analyze the user's heart rate data and prioritize suggesting stress-reducing training plans. Similarly, if the user is relaxed, the generation unit can prioritize overall health plans. For example, it can analyze the user's dietary data and prioritize suggesting nutritionally balanced meal plans. Furthermore, if the user is active, the generation unit can prioritize energy replenishment plans. For example, it can analyze the user's calorie expenditure data and prioritize suggesting high-energy meal plans. This allows for the prioritization of important plans based on the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0096] The generation unit can generate an optimal training program by considering the user's geographical location information. For example, if the user is near a gym, the generation unit can generate a training program that can be done at the gym. For instance, based on the user's geographical location information, the generation unit can suggest a gym training menu. Similarly, if the user is near a park, the generation unit can generate a training program that can be done at the park. For example, based on the user's geographical location information, the generation unit can suggest walking or jogging menus in the park. Furthermore, if the user is at home, the generation unit can generate a training program that can be done at home. For example, based on the user's geographical location information, the generation unit can suggest strength training or stretching menus for home use. In this way, by considering geographical location information, the system can provide the most optimal training program.

[0097] The generation unit can generate optimal meal plans by analyzing the user's social media activity. For example, it can generate meal plans based on the content of meals shared by the user on social media. For instance, it can analyze the content of meals shared by the user on social media and suggest nutritionally balanced meal menus. It can also generate meal plans based on information from health-related accounts that the user follows on social media. For example, it can analyze the content of posts from health-related accounts that the user follows and suggest healthy meal menus. Furthermore, it can generate meal plans based on meal challenges that the user is participating in on social media. For example, it can analyze the content of meal challenges that the user is participating in and suggest meal menus suitable for the challenge. In this way, by analyzing social media activity, it can provide optimal meal plans.

[0098] The feedback unit can estimate the user's emotions and adjust the content of the feedback based on the estimated emotions. For example, if the user is feeling stressed, the feedback unit can provide encouraging messages. For example, the feedback unit can analyze the user's heart rate data and provide advice for stress reduction. The feedback unit can also provide detailed feedback if the user is relaxed. For example, the feedback unit can analyze the user's sleep data and provide advice for improving sleep quality. Furthermore, if the user is active, the feedback unit can provide advice for the next training session. For example, the feedback unit can analyze the user's step count data and provide advice for enhancing the effectiveness of exercise. This allows for more appropriate feedback to be provided by adjusting the content of the feedback based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0099] The feedback unit can provide optimal advice by referring to the user's past training results when providing feedback. For example, the feedback unit can analyze the user's past training results and provide advice for the next training session. For example, the feedback unit can analyze the user's past heart rate data and suggest an exercise intensity for the next training session. The feedback unit can also provide appropriate advice by considering the user's past training frequency. For example, the feedback unit can set weekly training goals based on the user's past training frequency. Furthermore, the feedback unit can provide advice for the next training session based on the user's past training intensity. For example, the feedback unit can analyze the user's past training intensity and suggest a training menu with adjusted exercise intensity. In this way, the feedback unit can provide optimal advice by referring to past training results.

[0100] The feedback unit can refer to the user's past meal history when providing feedback to identify areas for improvement in nutritional balance. For example, the feedback unit can analyze the user's past meal history and identify areas for improvement in nutritional balance. For example, the feedback unit can analyze the user's past meal data and provide advice on supplementing any deficient nutrients. The feedback unit can also consider the user's past eating patterns to provide appropriate nutritional balance advice. For example, the feedback unit can provide advice on adjusting meal timing and quantity based on the user's past eating patterns. Furthermore, the feedback unit can make specific suggestions for improving nutritional balance based on the user's past meal history. For example, the feedback unit can analyze the user's past meal history and suggest healthy meal menus. This allows the feedback unit to identify areas for improvement in nutritional balance by referring to past meal history.

[0101] The feedback unit can estimate the user's emotions and prioritize feedback based on those emotions. For example, if the user is stressed, the feedback unit will prioritize feedback related to stress reduction. For instance, it can analyze the user's heart rate data and prioritize providing advice for stress reduction. Similarly, if the user is relaxed, the feedback unit can prioritize feedback related to overall health. For example, it can analyze the user's sleep data and prioritize providing advice to improve sleep quality. Furthermore, if the user is active, the feedback unit can prioritize feedback for the next training session. For example, it can analyze the user's step count data and prioritize providing advice to enhance the effectiveness of exercise. This ensures that important feedback is prioritized by determining feedback priorities based on the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0102] The feedback system can provide optimal advice by considering the user's geographical location when providing feedback. For example, if the user is at a gym, the feedback system can provide advice on training that can be done at the gym. For instance, based on the user's geographical location, the feedback system can suggest a gym training menu. Furthermore, if the user is at home, the feedback system can provide advice on training that can be done at home. For example, based on the user's geographical location, the feedback system can suggest a home workout or stretching menu. Additionally, if the user is out, the feedback system can provide advice on training that can be done outdoors. For example, based on the user's geographical location, the feedback system can suggest an outdoor walking or jogging menu. In this way, by considering geographical location, the system can provide optimal advice.

[0103] The feedback department can provide optimal advice by analyzing the user's social media activity when providing feedback. For example, the feedback department can provide advice based on the training content the user has shared on social media. For example, the feedback department can analyze the training content the user has shared on social media and provide advice to improve the effectiveness of the training. The feedback department can also provide advice based on information from health-related accounts the user follows on social media. For example, the feedback department can analyze the content of posts from health-related accounts the user follows and suggest a healthy training menu. Furthermore, the feedback department can provide advice based on training challenges the user is participating in on social media. For example, the feedback department can analyze the content of training challenges the user is participating in and suggest a training menu suitable for the challenge. In this way, by analyzing social media activity, the feedback department can provide optimal advice.

[0104] The adjustment unit can estimate the user's emotions and determine how to adjust the plan based on the estimated emotions. For example, if the user is stressed, the adjustment unit will adjust the plan to focus on stress reduction. For example, the adjustment unit can analyze the user's heart rate data and suggest a training plan to reduce stress. Also, if the user is relaxed, the adjustment unit can adjust the plan to promote overall health. For example, the adjustment unit can analyze the user's dietary data and suggest a nutritionally balanced meal plan. Furthermore, if the user is active, the adjustment unit can adjust the plan to focus on energy replenishment. For example, the adjustment unit can analyze the user's calorie expenditure data and suggest a high-energy meal plan. In this way, by determining how to adjust the plan based on the user's emotions, a more appropriate plan can be provided. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0105] The adjustment unit can make optimal adjustments to the plan by referring to the user's past lifestyle data. For example, the adjustment unit can analyze the user's past lifestyle data and adjust it to create the optimal training plan. For instance, it can analyze the user's past activity levels and propose a training plan with adjusted exercise intensity. The adjustment unit can also analyze the user's past eating data and adjust it to create the optimal meal plan. For example, it can analyze the user's past eating patterns and propose a meal plan that considers nutritional balance. Furthermore, the adjustment unit can analyze the user's past sleep data and adjust it to create the optimal rest plan. For example, it can analyze the user's past sleep patterns and provide advice to improve sleep quality. In this way, optimal plan adjustments are possible by referring to past lifestyle data.

[0106] The adjustment unit can estimate the user's emotions and prioritize plans based on those emotions. For example, if the user is stressed, the adjustment unit will prioritize stress reduction plans. For instance, it can analyze the user's heart rate data and prioritize suggesting stress reduction training plans. Similarly, if the user is relaxed, the adjustment unit can prioritize overall health plans. For example, it can analyze the user's dietary data and prioritize suggesting nutritionally balanced meal plans. Furthermore, if the user is active, the adjustment unit can prioritize energy replenishment plans. For example, it can analyze the user's calorie expenditure data and prioritize suggesting high-energy meal plans. This allows for the prioritization of important plans based on the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0107] The adjustment unit can make optimal adjustments to the training plan by considering the user's geographical location. For example, if the user is near a gym, the adjustment unit will adjust the training plan to be something that can be done at the gym. For instance, based on the user's geographical location, the adjustment unit can suggest a gym training menu. Furthermore, if the user is at home, the adjustment unit can adjust the training plan to be something that can be done at home. For example, based on the user's geographical location, the adjustment unit can suggest a home workout or stretching menu. Additionally, if the user is out, the adjustment unit can adjust the training plan to be something that can be done outdoors. For example, based on the user's geographical location, the adjustment unit can suggest an outdoor walking or jogging menu. This allows for optimal plan adjustments by considering geographical location.

[0108] The communication unit can estimate the user's emotions and adjust the content of communication based on those emotions. For example, if the user is feeling stressed, the communication unit can provide encouraging messages. For example, it can analyze the user's heart rate data and provide advice to reduce stress. The communication unit can also provide detailed information if the user is relaxed. For example, it can analyze the user's sleep data and provide advice to improve sleep quality. Furthermore, if the user is active, the communication unit can provide advice for the next training session. For example, it can analyze the user's step count data and provide advice to enhance the effectiveness of exercise. This allows for more appropriate communication by adjusting the content of communication based on the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0109] The communications department can provide optimal content by referring to the user's past communication history during communication. For example, the communications department can analyze the user's past communication history and provide optimal advice. For example, the communications department can analyze the content of the user's past communication and provide advice for the next step. The communications department can also provide appropriate messages by considering the user's past communication patterns. For example, the communications department can analyze the user's past communication patterns and provide advice tailored to the user's needs. Furthermore, the communications department can provide advice for the next step based on the user's past communication history. For example, the communications department can analyze the user's past communication history and provide advice regarding the next training or meal plan. In this way, the communications department can provide optimal content by referring to past communication history.

[0110] The communication unit can estimate the user's emotions and prioritize communication based on those emotions. For example, if the user is stressed, the communication unit will prioritize communication related to stress reduction. For instance, it can analyze the user's heart rate data and prioritize providing advice on stress reduction. Similarly, if the user is relaxed, the communication unit can prioritize communication related to overall health. For example, it can analyze the user's sleep data and prioritize providing advice on improving sleep quality. Furthermore, if the user is active, the communication unit can prioritize communication related to their next workout. For example, it can analyze the user's step count data and prioritize providing advice on enhancing the effectiveness of their exercise. This allows for the prioritization of important communication based on the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0111] The communications department can provide optimal content during communication by taking into account the user's geographical location. For example, if the user is at a gym, the communications department can provide advice on training that can be done at the gym. For instance, based on the user's geographical location, the communications department can suggest a gym training menu. Furthermore, if the user is at home, the communications department can provide advice on training that can be done at home. For example, based on the user's geographical location, the communications department can suggest a home workout or stretching menu. Additionally, if the user is out, the communications department can provide advice on training that can be done outdoors. For example, based on the user's geographical location, the communications department can suggest an outdoor walking or jogging menu. In this way, by considering geographical location, the communications department can provide optimal content.

[0112] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0113] The healthcare system can estimate the user's emotions and adjust the difficulty of the training program based on those emotions. For example, if the user is feeling stressed, it can suggest light exercises with a relaxing effect. If the user is relaxed, it can provide a standard training program. Furthermore, if the user is highly motivated, it can suggest a challenging training program. By providing a training program tailored to the user's emotions, it can maintain the user's motivation and support effective training.

[0114] The healthcare system can analyze a user's past training data and adjust the training program according to their training progress. For example, it can set the next training goal based on the user's past achievements. It can also suggest an appropriate training menu considering the user's training frequency and intensity. Furthermore, it can refer to the user's training history to provide advice to maximize the effectiveness of their training. In this way, it can support effective training by providing an optimal training program based on the user's training history.

[0115] The healthcare system can estimate the user's emotions and adjust the meal plan based on those emotions. For example, if the user is stressed, it can suggest a meal plan that includes relaxing ingredients. If the user is relaxed, it can provide a regular, nutritionally balanced meal plan. Furthermore, if the user is highly motivated, it can suggest a meal plan that includes many healthy ingredients. In this way, by providing meal plans tailored to the user's emotions, it can support the user's health management.

[0116] The healthcare system can adjust training programs based on the user's geographical location. For example, if the user is near a gym, it can suggest training menus that can be done at the gym. If the user is near a park, it can suggest walking or jogging menus that can be done in the park. Furthermore, if the user is at home, it can suggest strength training or stretching menus that can be done at home. This allows the system to provide an optimal training program based on the user's geographical location, thereby supporting effective training.

[0117] Healthcare systems can estimate a user's emotions and tailor feedback based on those estimates. For example, if a user is stressed, it can provide encouraging messages. If a user is relaxed, it can provide more detailed feedback. Furthermore, if a user is highly motivated, it can offer challenging advice for their next training session. By providing feedback that matches the user's emotions, it can help maintain their motivation and support effective training.

[0118] Healthcare systems can analyze a user's past dietary data and adjust meal plans accordingly. For example, they can suggest a nutritionally balanced meal plan based on the nutrients the user has consumed in the past. They can also suggest appropriate meal timings and quantities, taking into account the user's eating patterns. Furthermore, they can refer to the user's dietary history to suggest meal menus that address any nutritional deficiencies. By providing an optimal meal plan based on the user's past dietary data, they can support effective health management.

[0119] Healthcare systems can estimate a user's emotions and adjust the content of their communication based on those estimates. For example, if a user is stressed, they can receive encouraging messages. If a user is relaxed, they can receive more detailed information. Furthermore, if a user is highly motivated, they can receive challenging advice for their next training session. By providing communication tailored to the user's emotions, this system can maintain their motivation and support effective training.

[0120] Healthcare systems can analyze a user's past lifestyle data and adjust training programs and meal plans accordingly. For example, they can set appropriate exercise intensity based on the user's past activity levels. They can also suggest nutritionally balanced meal plans based on the user's past eating data. Furthermore, they can refer to the user's past sleep data to provide an optimal rest plan. By providing optimal plans based on the user's past lifestyle data, they can support effective health management.

[0121] The healthcare system can estimate the user's emotions and prioritize training programs and meal plans based on those emotions. For example, if the user is stressed, a stress reduction plan can be prioritized. If the user is relaxed, an overall health plan can be prioritized. Furthermore, if the user is highly motivated, a challenging plan can be prioritized. This allows the system to prioritize important plans based on the user's emotions, ensuring that the most important plans are delivered to the user first.

[0122] The healthcare system can analyze a user's social media activity and adjust training programs and meal plans accordingly. For example, it can suggest training menus based on the training content a user shares on social media. It can also suggest nutritionally balanced meal plans based on the meals a user shares. Furthermore, it can provide plans tailored to challenges a user is participating in on social media. By providing optimal plans based on the user's social media activity, it can support effective health management.

[0123] The following briefly describes the processing flow for example form 2.

[0124] Step 1: The data collection unit collects healthcare data from the user's smartphone. The data collection unit can collect data such as heart rate, steps taken, sleep duration, weight, and calories burned. The data collection unit uses the smartphone's sensors and applications to acquire this data. For example, the data collection unit measures heart rate using the smartphone's built-in sensors and collects step count data through the application. The data collection unit can also collect weight and dietary information entered by the user. Step 2: The analysis unit uses generative AI to analyze the data collected by the collection unit. The generative AI analyzes the data using machine learning models and deep learning algorithms. Based on heart rate and step count data, the generative AI evaluates the user's exercise patterns and health status. For example, the generative AI analyzes heart rate variability to estimate the user's stress level. It also analyzes step count data to evaluate the user's activity level. Step 3: The generation unit generates a training program and meal plan based on the analysis results obtained by the analysis unit. The generation unit proposes specific menus for walking and strength training based on the user's heart rate and step count data. The generation unit also provides an appropriate meal plan based on the user's weight and calorie expenditure data. For example, the generation unit proposes a meal menu that considers calorie restrictions to maintain the user's weight and nutritional balance. Step 4: The feedback unit provides real-time feedback based on the plan generated by the generation unit. After the user completes a training session, the feedback unit analyzes the results and provides advice for the next training session. Furthermore, if the user records their meals, the feedback unit analyzes the content and points out areas for improvement in nutritional balance. For example, the feedback unit analyzes the user's diet, evaluates their vitamin and mineral intake, and provides advice on supplementing any deficient nutrients.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, feedback unit, adjustment unit, communication unit, and emotion estimation unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects healthcare data using the sensors and applications of the smart device 14. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the data using generation AI. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates training programs and meal plans. The feedback unit is implemented by the control unit 46A of the smart device 14 and provides real-time feedback. The adjustment unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides plans tailored to the user's lifestyle. The communication unit is implemented by the control unit 46A of the smart device 14 and provides communication to maintain the user's motivation. The emotion estimation unit is implemented by the specific processing unit 290 of the data processing unit 12 and estimates the user's emotions and adjusts the collection timing. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0129] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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).

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.).

[0141] 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.

[0142] 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.

[0143] 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.

[0144] Each of the multiple elements described above, including the data collection unit, analysis unit, generation unit, feedback unit, adjustment unit, communication unit, and emotion estimation 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 healthcare data using the sensors and applications of the smart glasses 214. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the data using generation AI. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates training programs and meal plans. The feedback unit is implemented by the control unit 46A of the smart glasses 214 and provides real-time feedback. The adjustment unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides plans tailored to the user's lifestyle. The communication unit is implemented by the control unit 46A of the smart glasses 214 and provides communication to maintain the user's motivation. The emotion estimation unit is implemented by the specific processing unit 290 of the data processing unit 12 and estimates the user's emotions and adjusts the data collection timing. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0145] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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).

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.).

[0157] 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.

[0158] 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.

[0159] 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.

[0160] Each of the multiple elements described above, including the data collection unit, analysis unit, generation unit, feedback unit, adjustment unit, communication unit, and emotion estimation 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 healthcare data using the sensors and applications of the headset terminal 314. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the data using generation AI. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates training programs and meal plans. The feedback unit is implemented by the control unit 46A of the headset terminal 314 and provides real-time feedback. The adjustment unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides plans tailored to the user's lifestyle. The communication unit is implemented by the control unit 46A of the headset terminal 314 and provides communication to maintain the user's motivation. The emotion estimation unit is implemented by the specific processing unit 290 of the data processing unit 12 and estimates the user's emotions and adjusts the data collection timing. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0161] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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).

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.).

[0174] 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.

[0175] 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.

[0176] 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.

[0177] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, feedback unit, adjustment unit, communication unit, and emotion estimation unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the collection unit collects healthcare data using the sensors and applications of the robot 414. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the data using generation AI. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates training programs and meal plans. The feedback unit is implemented by the control unit 46A of the robot 414 and provides real-time feedback. The adjustment unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides plans tailored to the user's lifestyle. The communication unit is implemented by the control unit 46A of the robot 414 and provides communication to maintain the user's motivation. The emotion estimation unit is implemented by the specific processing unit 290 of the data processing unit 12 and estimates the user's emotions and adjusts the collection timing. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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."

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] (Note 1) A collection unit that collects healthcare data from the user's smartphone, The data collected by the aforementioned collection unit is analyzed by an analysis unit, and the generation AI analyzes the data. A generation unit generates a training program and a meal plan based on the analysis results obtained by the aforementioned analysis unit, The system includes a feedback unit that provides real-time feedback based on the plan generated by the generation unit. A system characterized by the following features. (Note 2) The generating unit is It generates training programs based on the user's heart rate and step count data. The system described in Appendix 1, characterized by the features described herein. (Note 3) The generating unit is Generates meal plans based on the user's weight and calorie expenditure data. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned feedback unit is After a user completes a training session, the system analyzes the results and provides advice for the next training session. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned feedback unit is When a user records their meals, the system analyzes the data and points out areas for improvement in nutritional balance. The system described in Appendix 1, characterized by the features described herein. (Note 6) It features an adjustment unit that provides plans tailored to the user's lifestyle. The system described in Appendix 1, characterized by the features described herein. (Note 7) It includes a communication department to maintain user motivation. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is It estimates the user's emotions and adjusts the timing of healthcare data collection based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is Analyze the user's past healthcare data and select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is When collecting healthcare data, filtering is performed based on the user's current activity level and health status. 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 When collecting healthcare data, the system prioritizes the collection of highly relevant data by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned collection unit is When collecting healthcare data, analyze users' social media activity and collect relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit is We estimate user emotions and adjust the data analysis method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit is During analysis, adjust the level of detail based on the importance of the healthcare data. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit is During analysis, different analytical algorithms are applied depending on the category of healthcare data. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit is We estimate the user's emotions and prioritize the analysis based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit is During analysis, prioritize the analysis based on when the healthcare data was collected. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned analysis unit is During analysis, adjust the order of analysis based on the relevance of healthcare data. The system described in Appendix 1, characterized by the features described herein. (Note 20) The generating unit is It estimates the user's emotions and adjusts how training programs and meal plans are generated based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The generating unit is When generating a training program, the system references the user's past training history to generate the optimal program. The system described in Appendix 1, characterized by the features described herein. (Note 22) The generating unit is When generating a meal plan, the system references the user's past meal history to create the optimal plan. The system described in Appendix 1, characterized by the features described herein. (Note 23) The generating unit is It estimates the user's emotions and determines the priority of the plans generated based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The generating unit is When generating a training program, the system takes the user's geographical location into consideration to generate the optimal program. The system described in Appendix 1, characterized by the features described herein. (Note 25) The generating unit is When generating meal plans, the system analyzes the user's social media activity to create the optimal plan. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned feedback unit is It estimates the user's emotions and adjusts the content of the feedback based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned feedback unit is When providing feedback, we refer to the user's past training results to offer the best possible advice. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned feedback unit is When providing feedback, the system refers to the user's past meal history to identify areas for improvement in nutritional balance. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned feedback unit is It estimates the user's emotions and prioritizes feedback based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned feedback unit is When providing feedback, we take the user's geographical location into consideration to provide the most appropriate advice. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned feedback unit is When providing feedback, we analyze the user's social media activity to offer the best possible advice. The system described in Appendix 1, characterized by the features described herein. (Note 32) The adjustment unit is, The system estimates the user's emotions and determines how to adjust the plan based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 33) The adjustment unit is, When adjusting the plan, the system uses the user's past lifestyle data to make optimal adjustments. The system described in Appendix 1, characterized by the features described herein. (Note 34) The adjustment unit is, It estimates user sentiment and determines plan priorities based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 35) The adjustment unit is, When adjusting the plan, the optimal adjustments are made considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned communications department, It estimates the user's emotions and adjusts the content of communication based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 37) The aforementioned communications department, When communicating, we refer to the user's past communication history to provide the most relevant content. The system described in Appendix 1, characterized by the features described herein. (Note 38) The aforementioned communications department, It estimates the user's emotions and determines communication priorities based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 39) The aforementioned communications department, When communicating, we provide the most relevant content by taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0197] 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 collection unit that collects healthcare data from the user's smartphone, The data collected by the aforementioned collection unit is analyzed by an analysis unit using a generating AI. A generation unit generates a training program and a meal plan based on the analysis results obtained by the aforementioned analysis unit, The system includes a feedback unit that provides real-time feedback based on the plan generated by the generation unit. A system characterized by the following features.

2. The generating unit is It generates training programs based on the user's heart rate and step count data. The system according to feature 1.

3. The generating unit is Generates meal plans based on the user's weight and calorie expenditure data. The system according to feature 1.

4. The aforementioned feedback unit is After a user completes a training session, the system analyzes the results and provides advice for the next training session. The system according to feature 1.

5. The aforementioned feedback unit is When a user records their meals, the system analyzes the data and points out areas for improvement in nutritional balance. The system according to feature 1.

6. It features an adjustment unit that provides plans tailored to the user's lifestyle. The system according to feature 1.

7. It includes a communication department to maintain user motivation. The system according to feature 1.

8. The aforementioned collection unit is It estimates the user's emotions and adjusts the timing of healthcare data collection based on the estimated user emotions. The system according to feature 1.

9. The aforementioned collection unit is Analyze the user's past healthcare data and select the optimal data collection method. The system according to feature 1.

10. The aforementioned collection unit is When collecting healthcare data, filtering is performed based on the user's current activity level and health status. The system according to feature 1.

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