system

Abstract

The system according to this embodiment aims to propose an optimal training menu and meal plan based on the user's lifestyle and physical data, and to maintain motivation while monitoring progress. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a proposal unit, a monitoring unit, and a support unit. The collection unit collects the user's lifestyle and physical data. The analysis unit analyzes the data collected by the collection unit. The proposal unit proposes an optimal training menu and meal plan based on the analysis results obtained by the analysis unit. The monitoring unit monitors the progress of the plan proposed by the proposal unit. The support unit provides support and alerts to maintain motivation based on the progress monitored by the monitoring 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, the method including the 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 in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there was a problem that an optimal training menu and diet plan were not sufficiently proposed based on the user's living habits and physical data, and motivation was not maintained while monitoring the progress.

[0005] The system according to the embodiment aims to propose an optimal training menu and diet plan based on the user's living habits and physical data, and maintain motivation while monitoring the progress.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, a proposal unit, a monitoring unit, and a support unit. The data collection unit collects the user's lifestyle and physical data. The analysis unit analyzes the data collected by the data collection unit. The proposal unit proposes an optimal training menu and meal plan based on the analysis results obtained by the analysis unit. The monitoring unit monitors the progress of the plan proposed by the proposal unit. The support unit provides support and alerts to maintain motivation based on the progress monitored by the monitoring unit. [Effects of the Invention]

[0007] The system according to this embodiment can propose an optimal training menu and meal plan based on the user's lifestyle and physical data, and can maintain motivation while monitoring progress. [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, etc. 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 reception 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 reception 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) An AI smart fitness partner system according to an embodiment of the present invention is a system that autonomously collects and analyzes a user's lifestyle, physical data, and performance information, and proposes the optimal training menu and meal plan in real time. The AI ​​smart fitness partner system works by having the user wear a wearable device to collect daily lifestyle and training data. Next, the user inputs their goals, current physical condition, and training objectives. Based on the collected data and input information, the AI ​​agent proposes the optimal training menu and meal plan for the user. Furthermore, the AI ​​agent monitors the user's progress and provides alerts and motivational support as needed. For example, the AI ​​smart fitness partner system works by having the user wear a wearable device to collect daily lifestyle and training data. For example, the user inputs their goals, current physical condition, and training objectives. Based on the collected data and input information, the AI ​​agent proposes the optimal training menu and meal plan for the user. Furthermore, the AI ​​agent monitors the user's progress and provides alerts and motivational support as needed. This allows the user to perform health management and training optimized for them, enabling them to achieve effective results in a short period of time. This enables the AI ​​smart fitness partner system to efficiently collect, analyze, suggest, monitor, and support users' lifestyle and physical data.

[0029] The AI ​​smart fitness partner system according to this embodiment comprises a data collection unit, an analysis unit, a suggestion unit, a monitoring unit, and a support unit. The data collection unit collects the user's lifestyle and physical data. The data collection unit collects the user's lifestyle and physical data, for example, using a wearable device. The data collection unit can collect data such as the user's heart rate, steps taken, and sleep patterns, for example, using a smartwatch or fitness tracker. The data collection unit can also collect data manually entered by the user. For example, the data collection unit can collect data such as the user's diet and exercise details when the user enters them into an application. Furthermore, the data collection unit can automatically collect data related to the user's lifestyle. For example, the data collection unit can measure the user's distance traveled and activity level using the user's smartphone sensors. The analysis unit analyzes the data collected by the data collection unit. The analysis unit analyzes the collected data, for example, using AI to evaluate the user's physical fitness and skill level. The analysis unit can analyze the user's heart rate data to evaluate the user's cardiopulmonary function. The analysis unit can also analyze the user's step count data and evaluate the user's activity level. Furthermore, the analysis unit can analyze the user's sleep data and evaluate the user's sleep quality. The suggestion unit proposes optimal training menus and meal plans based on the analysis results obtained by the analysis unit. For example, the suggestion unit can use AI to propose the optimal training menu for the user. For example, the suggestion unit can propose appropriate exercise intensity and exercise time according to the user's physical fitness and skill level. The suggestion unit can also propose appropriate training menus according to the user's goals. Furthermore, the suggestion unit can propose appropriate meal plans based on the user's meal data. For example, the suggestion unit can propose appropriate meal menus considering the user's nutritional balance. The monitoring unit monitors the progress of the plan proposed by the suggestion unit. For example, the monitoring unit uses AI to monitor the user's progress in real time.The monitoring unit can, for example, monitor the user's training data and verify whether the user is following the suggested training menu. It can also monitor the user's meal data and verify whether the user is following the suggested meal plan. Furthermore, the monitoring unit can provide appropriate feedback based on the user's progress. The support unit provides motivational support and alerts based on the progress monitored by the monitoring unit. The support unit, for example, uses AI to provide appropriate support to the user. For example, the support unit can send encouraging messages to help the user continue training. It can also send reminders to help the user continue their meal plan. Furthermore, the support unit can provide appropriate alerts based on the user's progress. This enables the AI ​​smart fitness partner system according to the embodiment to efficiently collect, analyze, suggest, monitor, and support the user's lifestyle and physical data.

[0030] The data collection unit collects user lifestyle and physical data. For example, it can collect user lifestyle and physical data using wearable devices. Specifically, it can use smartwatches and fitness trackers to collect data such as the user's heart rate, steps taken, and sleep patterns. These devices are worn on the user's wrist and collect data 24 hours a day. Heart rate sensors measure the user's heart rate in real time and record fluctuations during exercise and at rest. Pedometers detect the user's walking movements and measure the total number of steps and distance traveled per day. Sleep trackers detect the user's movements during sleep and evaluate sleep quality and duration. The data collection unit can also collect data manually entered by the user. For example, the unit can collect data when the user enters their meal and exercise details into an application. Users can use smartphones or tablets to input detailed information such as meal types, calorie intake, exercise types, and exercise duration. Furthermore, the data collection unit can automatically collect data related to the user's lifestyle. For example, the data collection unit can use the user's smartphone sensors to measure the user's travel distance and activity level. Using the smartphone's GPS function, it records the user's travel route and speed, and evaluates their daily activity level. This allows the data collection unit to comprehensively collect user lifestyle and physical data, building a comprehensive database. The collected data is sent to a cloud server, making it accessible to the analysis and proposal units. This enables the data collection unit to collect data efficiently and accurately, improving the overall system performance.

[0031] The analysis unit analyzes the data collected by the data collection unit. For example, the analysis unit uses AI to analyze the collected data and evaluate the user's physical fitness and skill level. Specifically, the AI ​​can use machine learning algorithms to analyze the user's heart rate data and evaluate the user's cardiopulmonary function. For example, it can analyze heart rate variability patterns to evaluate the user's exercise intensity and fatigue level. It can also analyze step count data to evaluate the user's activity level. Based on the user's total daily steps and distance traveled, the AI ​​classifies the user's activity level and sets appropriate exercise goals. Furthermore, the analysis unit can analyze the user's sleep data and evaluate the quality of the user's sleep. The AI ​​analyzes movement and heart rate variability during sleep and evaluates the proportion of deep sleep and light sleep. This allows the analysis unit to provide advice to improve the user's sleep quality. The analysis unit comprehensively analyzes this data to evaluate the user's health status and fitness level. Furthermore, the analysis unit can also utilize historical data and statistical information to analyze long-term health trends. For example, based on past exercise data, the system evaluates changes in the user's physical fitness and improvements in exercise habits, and predicts future health risks. Furthermore, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data, issuing early warnings. This allows the analysis unit to not only monitor 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 suggestion unit proposes optimal training menus and meal plans based on the analysis results obtained by the analysis unit. For example, the suggestion unit uses AI to propose the optimal training menu for the user. Specifically, it can suggest appropriate exercise intensity and duration according to the user's physical fitness and skill level. For example, it can suggest light aerobic exercise and stretching for beginner users, and high-intensity interval training and strength training for advanced users. The suggestion unit can also propose appropriate training menus according to the user's goals. For example, it can suggest exercise menus that focus on calorie consumption for users aiming to lose weight, and menus that focus on strength training for users aiming to increase muscle mass. Furthermore, the suggestion unit can also propose appropriate meal plans based on the user's dietary data. The AI ​​proposes appropriate meal menus considering the user's nutritional balance. For example, it analyzes the user's calorie intake and nutrient balance and proposes a balanced meal menu. It can also suggest ingredients and recipes to supplement specific nutrients if there is a deficiency. The suggestion unit notifies the user of these suggestions and encourages them to take action. Users can check and implement the proposed training menus and meal plans via their smartphones or tablets. This allows the proposal department to support users in achieving their health goals and provide effective fitness plans.

[0033] The monitoring unit monitors the progress of the plans proposed by the proposal unit. For example, the monitoring unit uses AI to monitor the user's progress in real time. Specifically, it can monitor the user's training data to verify whether the user is following the proposed training menu. The AI ​​analyzes the user's exercise data and evaluates the degree of achievement against the proposed exercise intensity and duration. The monitoring unit can also monitor the user's dietary data to verify whether the user is following the proposed meal plan. The AI ​​analyzes the user's dietary data and evaluates the degree of achievement against the proposed nutritional balance and calorie intake. Furthermore, the monitoring unit can provide appropriate feedback based on the user's progress. For example, if the user completes the training menu, it can send an encouraging message to maintain motivation. If the user completes the meal plan, it can provide advice to help maintain a healthy diet. Through this feedback, the monitoring unit supports the user's progress and promotes the achievement of health goals. Additionally, the monitoring unit can accumulate user progress data and analyze long-term health trends. This allows the monitoring unit to comprehensively support the user's health management and maximize the overall effectiveness of the system.

[0034] The support department provides motivational support and alerts based on progress monitored by the monitoring department. For example, the support department uses AI to provide appropriate support to users. Specifically, it can send encouraging messages to help users continue their training. The AI ​​analyzes the user's progress and sends encouraging messages at the appropriate time. For example, if a user completes a training menu, it sends a message to enhance their sense of accomplishment and maintain their motivation towards the next goal. It can also send reminders to help users stick to their meal plans. The AI ​​analyzes the user's meal data and sends reminders based on the timing and content of meals. For example, when mealtime approaches, it suggests appropriate meal menus to support healthy eating habits. Furthermore, the support department can provide appropriate alerts based on the user's progress. For example, if a user is skipping training, it sends a warning alert to encourage them to resume training. Also, if a user is not following their meal plan, it sends an alert warning of health risks and encourages improvements to their eating habits. In this way, the support department can maintain user motivation and support the achievement of their health goals. Furthermore, the support department can collect user feedback and continuously improve the accuracy and effectiveness of its support services. This allows the support department to provide users with prompt and reliable support, thereby improving the overall reliability and effectiveness of the system.

[0035] The data collection unit can collect user lifestyle and physical data from wearable devices. For example, the data collection unit can use a smartwatch to collect data such as the user's heart rate, steps, and sleep patterns. The data collection unit can also use a fitness tracker to collect the user's exercise data. The data collection unit can use a smartwatch to collect data such as the user's heart rate, steps, and sleep patterns. The data collection unit can also use a fitness tracker to collect the user's exercise data. The data collection unit can use a smartwatch to collect data such as the user's heart rate, steps, and sleep patterns. The data collection unit can also use a fitness tracker to collect the user's exercise data. This allows for the efficient collection of user lifestyle and physical data using wearable devices. Some or all of the above-described processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data acquired from wearable devices into a generating AI and have the generating AI perform data analysis.

[0036] The analysis unit can analyze the user's physical fitness and skill level based on the collected data. For example, the analysis unit can analyze the user's physical fitness based on the collected data. The analysis unit can also analyze the user's skill level based on the collected data. For example, the analysis unit can analyze the user's physical fitness based on the collected data. The analysis unit can also analyze the user's skill level based on the collected data. For example, the analysis unit can analyze the user's physical fitness based on the collected data. The analysis unit can also analyze the user's skill level based on the collected data. This makes it possible to make more appropriate suggestions by analyzing the user's physical fitness and skill level based on the collected data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the collected data into a generating AI and have the generating AI perform an analysis of the user's physical fitness and skill level.

[0037] The suggestion unit can propose the optimal training menu and meal plan to the user based on the analysis results. For example, the suggestion unit can propose the optimal training menu to the user based on the analysis results. The suggestion unit can also propose the optimal meal plan to the user based on the analysis results. For example, the suggestion unit can propose the optimal training menu to the user based on the analysis results. The suggestion unit can also propose the optimal meal plan to the user based on the analysis results. For example, the suggestion unit can propose the optimal training menu to the user based on the analysis results. The suggestion unit can also propose the optimal meal plan to the user based on the analysis results. This improves the user's health management by proposing the optimal training menu and meal plan based on the analysis results. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the analysis results into a generating AI and have the generating AI execute the proposal of the optimal training menu and meal plan.

[0038] The monitoring unit can monitor the user's progress in real time. The monitoring unit can, for example, monitor the user's progress in real time. The monitoring unit can, for example, monitor the user's progress in real time. The monitoring unit can, for example, monitor the user's progress in real time. The monitoring unit can, for example, monitor the user's progress in real time. The monitoring unit can, for example, monitor the user's progress in real time. This enables appropriate support by monitoring the user's progress in real time. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input the user's progress into a generating AI and have the generating AI perform real-time monitoring.

[0039] The support unit can provide users with alerts and support to maintain their motivation based on monitoring results. The support unit can, for example, provide users with alerts based on monitoring results. The support unit can, for example, provide users with support to maintain their motivation based on monitoring results. The support unit can, for example, provide users with alerts based on monitoring results. The support unit can, for example, provide users with alerts based on monitoring results. The support unit can, for example, provide users with support to maintain their motivation based on monitoring results. This improves user motivation by providing appropriate support based on monitoring results. Some or all of the above processing in the support unit may be performed using AI, for example, or without AI. For example, the support unit can input monitoring results into a generating AI and have the generating AI execute alerts and support to maintain motivation.

[0040] The data collection unit can analyze the user's past data collection history and select the optimal collection method. For example, the data collection unit can analyze the time periods when the user frequently collected data in the past and perform data collection during those times. The data collection unit can also analyze the types of devices the user has used in the past and suggest the most suitable device. The data collection unit can also select the most effective collection method from the user's past data collection history. In this way, the optimal collection method can be selected by analyzing the user's past data collection history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past data collection history into a generating AI and have the generating AI select the optimal collection method.

[0041] The data collection unit can filter data based on the user's current activity status and environment during data collection. For example, if the user is exercising, the data collection unit can collect only data related to exercise. For example, if the user is resting, the data collection unit can collect only data related to resting. For example, if the user is out, the data collection unit can collect only data related to going out. By filtering the data based on the user's current activity status and environment, more relevant data can be collected. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's current activity status and environment data into a generating AI and have the generating AI perform data filtering.

[0042] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information during data collection. For example, if the user is at a gym, the data collection unit can prioritize the collection of data related to training at the gym. For example, if the user is at home, the data collection unit can prioritize the collection of data related to lifestyle habits at home. For example, if the user is out, the data collection unit can prioritize the collection of data related to activities while out. This allows for the priority collection of highly relevant data by considering the user's geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into a generating AI and have the generating AI perform the priority collection of highly relevant data.

[0043] The data collection unit can analyze the user's social media activity and collect relevant data during data collection. For example, the data collection unit can collect exercise-related data based on exercise records shared by the user on social media. The data collection unit can also collect diet-related data based on meal records shared by the user on social media. The data collection unit can also collect lifestyle-related data based on lifestyle habits shared by the user on social media. This allows for the efficient collection of relevant data by analyzing the user's social media activity. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media activity data into a generating AI and have the generating AI perform the collection of relevant data.

[0044] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit can perform a detailed analysis on data with high importance. For example, the analysis unit can perform a simplified analysis on data with low importance. For example, the analysis unit can perform an analysis with an appropriate level of detail on data with moderate importance. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0045] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply an exercise analysis algorithm to training data. For example, the analysis unit can also apply a nutrition analysis algorithm to dietary data. For example, the analysis unit can also apply a lifestyle analysis algorithm to lifestyle data. By applying different analysis algorithms depending on the data category, more accurate analysis becomes possible. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data category into a generating AI and have the generating AI execute the application of different analysis algorithms.

[0046] The analysis unit can determine the priority of analysis based on the data collection period during analysis. For example, the analysis unit may prioritize the analysis of the most recent data. The analysis unit may also analyze the most recent data while referring to past data. The analysis unit may also prioritize the analysis of data collected during a specific period. This enables efficient analysis by determining the priority of analysis based on the data collection period. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data collection period into a generating AI and have the generating AI determine the priority of analysis.

[0047] The analysis unit can adjust the order of analysis based on the relevance of the data during the analysis. For example, the analysis unit may prioritize the analysis of highly relevant data. For example, the analysis unit may also postpone the analysis of less relevant data. For example, the analysis unit may dynamically adjust the order of analysis according to the relevance of the data. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance of the data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0048] The suggestion unit can adjust the level of detail in its suggestions based on the importance of the training menu and meal plan. For example, the suggestion unit will provide detailed suggestions for training menus of high importance. For example, it can provide simplified suggestions for training menus of low importance. For example, it can provide suggestions with an appropriate level of detail for training menus of moderate importance. This allows for efficient suggestions by adjusting the level of detail in the suggestions based on the importance of the training menu and meal plan. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the importance of the training menu and meal plan into a generating AI and have the generating AI adjust the level of detail in the suggestions.

[0049] The suggestion unit can apply different suggestion algorithms depending on the user's goals and physical condition when making suggestions. For example, if the user's goal is to improve muscle strength, the suggestion unit will make suggestions specifically for strength training. If the user's goal is weight loss, the suggestion unit can also make suggestions specifically for aerobic exercise. If the user is in good physical condition, the suggestion unit can also make suggestions for high-intensity training. By applying different suggestion algorithms according to the user's goals and physical condition, more appropriate suggestions can be made. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's goals and physical condition data into a generating AI and have the generating AI execute the application of different suggestion algorithms.

[0050] The suggestion unit can determine the priority of suggestions based on the user's past training history when making suggestions. For example, the suggestion unit can make optimal suggestions based on the training menus the user has previously performed. For example, the suggestion unit can also prioritize suggesting effective menus based on the user's past training history. For example, the suggestion unit can analyze the user's past training history and make the most effective suggestions. This makes it possible to make more effective suggestions by determining the priority of suggestions based on the user's past training history. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's past training history into a generating AI and have the generating AI perform the task of determining the priority of suggestions.

[0051] The suggestion unit can adjust the order of suggestions based on the user's lifestyle habits when making suggestions. For example, the suggestion unit can determine the optimal order of suggestions to match the user's lifestyle habits. The suggestion unit can also dynamically adjust the order of suggestions, taking into account the user's lifestyle habits. The suggestion unit can also customize the order of suggestions based on the user's lifestyle habits. This allows for more appropriate suggestions by adjusting the order of suggestions based on the user's lifestyle habits. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input user lifestyle data into a generating AI and have the generating AI adjust the order of suggestions.

[0052] The monitoring unit can improve the accuracy of monitoring by considering user interactions during monitoring. For example, if a user is training with other users, the monitoring unit will consider their interactions when monitoring. For example, if a user is training in a group, the monitoring unit can also perform monitoring based on the data of the entire group. For example, if a user is training with a partner, the monitoring unit can also consider the partner's data when monitoring. This improves the accuracy of monitoring by considering user interactions. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input user interaction data into a generating AI and have the generating AI perform the task of improving the accuracy of monitoring.

[0053] The monitoring unit can perform monitoring while considering the user's attribute information. For example, the monitoring unit can perform age-appropriate monitoring by considering the user's age. For example, the monitoring unit can also perform gender-appropriate monitoring by considering the user's gender. For example, the monitoring unit can also perform sport-appropriate monitoring by considering the user's sport. This makes it possible to perform more appropriate monitoring by considering the user's attribute information. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without using AI. For example, the monitoring unit can input the user's attribute information into a generating AI and have the generating AI perform the monitoring.

[0054] The monitoring unit can perform monitoring while considering the geographical distribution of users. For example, if users are in different regions, the monitoring unit can perform monitoring based on data for each region. For example, if users are traveling, the monitoring unit can perform monitoring based on data from their travel destination. For example, if users are active in multiple regions, the monitoring unit can perform monitoring based on data from each region. This allows for more appropriate monitoring by considering the geographical distribution of users. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input user geographical distribution data into a generating AI and have the generating AI perform the monitoring.

[0055] The monitoring unit can improve the accuracy of monitoring by referring to relevant literature for the user during monitoring. For example, the monitoring unit may refer to the latest research papers related to the user's training. The monitoring unit may also refer to nutritional literature related to the user's meal plan. The monitoring unit may also refer to health management literature related to the user's lifestyle. This improves the accuracy of monitoring by referring to relevant literature for the user. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input the user's relevant literature data into a generating AI and have the generating AI perform the improvement of monitoring accuracy.

[0056] The support unit can analyze the user's past behavior during support to select the optimal support method. For example, the support unit can provide optimal support based on support methods that have been effective for the user in the past. The support unit can also analyze the user's past behavior patterns to select the most effective support method. For example, the support unit can propose the optimal support method based on the user's past support history. In this way, the optimal support method can be selected by analyzing the user's past behavior. Some or all of the above processes in the support unit may be performed using AI, for example, or without AI. For example, the support unit can input the user's past behavior data into a generating AI and have the generating AI select the optimal support method.

[0057] The support unit can customize the means of support based on the user's current lifestyle when providing support. For example, if the user is busy, the support unit can provide a quick and effective support method. For example, if the user is relaxed, the support unit can also provide a detailed support method. For example, if the user is exhilarated during exercise, the support unit can also provide a support method to boost motivation. By customizing the means of support based on the user's current lifestyle, more appropriate support becomes possible. Some or all of the above processing in the support unit may be performed using AI, for example, or without AI. For example, the support unit can input the user's current lifestyle data into a generating AI and have the generating AI perform the customization of the support methods.

[0058] The support unit can select the optimal support method by considering the user's geographical location information during support. For example, if the user is at a gym, the support unit can provide support related to training at the gym. For example, if the user is at home, the support unit can also provide support related to lifestyle habits at home. For example, if the user is out, the support unit can also provide support related to activities while out. This allows the support unit to select the optimal support method by considering the user's geographical location information. Some or all of the above processing in the support unit may be performed using AI, for example, or without AI. For example, the support unit can input the user's geographical location information into a generating AI and have the generating AI select the optimal support method.

[0059] The support unit can analyze the user's social media activity and propose support methods during support. For example, the support unit can provide exercise-related support based on exercise records shared by the user on social media. The support unit can also provide diet-related support based on meal records shared by the user on social media. The support unit can also provide lifestyle-related support based on lifestyle habits shared by the user on social media. In this way, by analyzing the user's social media activity, the support unit can propose the most suitable support method. Some or all of the above processing in the support unit may be performed using AI, for example, or without AI. For example, the support unit can input the user's social media activity data into a generating AI and have the generating AI execute the proposal of support methods.

[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 data collection unit can analyze the user's past data collection history and select the optimal collection method. For example, it can analyze the time periods when the user frequently collected data in the past and perform data collection during those times. It can also analyze the types of devices the user has used in the past and suggest the most suitable device. Furthermore, it can select the most effective collection method based on the user's past data collection history. In this way, the optimal collection method can be selected by analyzing the user's past data collection history.

[0062] The analysis unit can adjust the level of detail of the analysis based on the importance of the data. For example, it can perform a detailed analysis on highly important data, a simplified analysis on less important data, and an analysis with an appropriate level of detail on moderately important data. By adjusting the level of detail of the analysis based on the importance of the data, efficient analysis becomes possible.

[0063] The proposal department can adjust the level of detail in its proposals based on the importance of the training menu and meal plan. For example, it can provide detailed proposals for highly important training menus, simplified proposals for less important ones, and proposals with an appropriate level of detail for moderately important training menus. By adjusting the level of detail in proposals based on the importance of the training menu and meal plan, efficient proposals become possible.

[0064] The monitoring unit can perform monitoring while considering user attribute information. For example, it can perform age-appropriate monitoring by considering the user's age. It can also perform gender-appropriate monitoring by considering the user's gender. Furthermore, it can perform monitoring appropriate for the sport the user participates in by considering their sport. In this way, more appropriate monitoring becomes possible by considering user attribute information.

[0065] The support department can analyze a user's past behavior during support to select the most appropriate support method. For example, it can provide optimal support based on support methods that have been effective for the user in the past. It can also analyze the user's past behavior patterns to select the most effective support method. Furthermore, it can propose the most appropriate support method based on the user's past support history. In this way, the optimal support method can be selected by analyzing the user's past behavior.

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

[0067] Step 1: The data collection unit collects the user's lifestyle and physical data. The data collection unit collects the user's lifestyle and physical data, for example, using wearable devices. For example, the data collection unit can collect data such as the user's heart rate, steps taken, and sleep patterns using smartwatches or fitness trackers. The data collection unit can also collect data manually entered by the user. For example, the data collection unit can collect data on the user's diet and exercise when the user enters this information into an application. Furthermore, the data collection unit can automatically collect data related to the user's lifestyle. For example, the data collection unit can measure the user's distance traveled and activity level using the sensors on the user's smartphone. Step 2: The analysis unit analyzes the data collected by the data collection unit. The analysis unit can, for example, use AI to analyze the collected data and evaluate the user's physical fitness and skill level. The analysis unit can, for example, analyze the user's heart rate data to evaluate the user's cardiopulmonary function. The analysis unit can also analyze the user's step count data to evaluate the user's activity level. Furthermore, the analysis unit can analyze the user's sleep data to evaluate the user's sleep quality. Step 3: The suggestion unit proposes the optimal training menu and meal plan based on the analysis results obtained by the analysis unit. For example, the suggestion unit can use AI to propose the optimal training menu for the user. For example, the suggestion unit can propose appropriate exercise intensity and duration according to the user's physical fitness and skill level. The suggestion unit can also propose an appropriate training menu according to the user's goals. Furthermore, the suggestion unit can propose an appropriate meal plan based on the user's meal data. For example, the suggestion unit can propose an appropriate meal menu considering the user's nutritional balance. Step 4: The monitoring unit monitors the progress of the plan proposed by the proposal unit. The monitoring unit can, for example, use AI to monitor the user's progress in real time. The monitoring unit can, for example, monitor the user's training data to confirm whether the user is following the proposed training menu. The monitoring unit can also monitor the user's meal data to confirm whether the user is following the proposed meal plan. Furthermore, the monitoring unit can provide appropriate feedback according to the user's progress. Step 5: The support team provides motivational support and alerts based on the progress monitored by the monitoring team. The support team, for example, uses AI to provide appropriate support to users. For example, the support team can send encouraging messages to help users continue their training. The support team can also send reminders to help users stick to their meal plans. Furthermore, the support team can provide appropriate alerts based on the user's progress.

[0068] (Example of form 2) An AI smart fitness partner system according to an embodiment of the present invention is a system that autonomously collects and analyzes a user's lifestyle, physical data, and performance information, and proposes the optimal training menu and meal plan in real time. The AI ​​smart fitness partner system works by having the user wear a wearable device to collect daily lifestyle and training data. Next, the user inputs their goals, current physical condition, and training objectives. Based on the collected data and input information, the AI ​​agent proposes the optimal training menu and meal plan for the user. Furthermore, the AI ​​agent monitors the user's progress and provides alerts and motivational support as needed. For example, the AI ​​smart fitness partner system works by having the user wear a wearable device to collect daily lifestyle and training data. For example, the user inputs their goals, current physical condition, and training objectives. Based on the collected data and input information, the AI ​​agent proposes the optimal training menu and meal plan for the user. Furthermore, the AI ​​agent monitors the user's progress and provides alerts and motivational support as needed. This allows the user to perform health management and training optimized for them, enabling them to achieve effective results in a short period of time. This enables the AI ​​smart fitness partner system to efficiently collect, analyze, suggest, monitor, and support users' lifestyle and physical data.

[0069] The AI ​​smart fitness partner system according to this embodiment comprises a data collection unit, an analysis unit, a suggestion unit, a monitoring unit, and a support unit. The data collection unit collects the user's lifestyle and physical data. The data collection unit collects the user's lifestyle and physical data, for example, using a wearable device. The data collection unit can collect data such as the user's heart rate, steps taken, and sleep patterns, for example, using a smartwatch or fitness tracker. The data collection unit can also collect data manually entered by the user. For example, the data collection unit can collect data such as the user's diet and exercise details when the user enters them into an application. Furthermore, the data collection unit can automatically collect data related to the user's lifestyle. For example, the data collection unit can measure the user's distance traveled and activity level using the user's smartphone sensors. The analysis unit analyzes the data collected by the data collection unit. The analysis unit analyzes the collected data, for example, using AI to evaluate the user's physical fitness and skill level. The analysis unit can analyze the user's heart rate data to evaluate the user's cardiopulmonary function. The analysis unit can also analyze the user's step count data and evaluate the user's activity level. Furthermore, the analysis unit can analyze the user's sleep data and evaluate the user's sleep quality. The suggestion unit proposes optimal training menus and meal plans based on the analysis results obtained by the analysis unit. For example, the suggestion unit can use AI to propose the optimal training menu for the user. For example, the suggestion unit can propose appropriate exercise intensity and exercise time according to the user's physical fitness and skill level. The suggestion unit can also propose appropriate training menus according to the user's goals. Furthermore, the suggestion unit can propose appropriate meal plans based on the user's meal data. For example, the suggestion unit can propose appropriate meal menus considering the user's nutritional balance. The monitoring unit monitors the progress of the plan proposed by the suggestion unit. For example, the monitoring unit uses AI to monitor the user's progress in real time.The monitoring unit can, for example, monitor the user's training data and verify whether the user is following the suggested training menu. It can also monitor the user's meal data and verify whether the user is following the suggested meal plan. Furthermore, the monitoring unit can provide appropriate feedback based on the user's progress. The support unit provides motivational support and alerts based on the progress monitored by the monitoring unit. The support unit, for example, uses AI to provide appropriate support to the user. For example, the support unit can send encouraging messages to help the user continue training. It can also send reminders to help the user continue their meal plan. Furthermore, the support unit can provide appropriate alerts based on the user's progress. This enables the AI ​​smart fitness partner system according to the embodiment to efficiently collect, analyze, suggest, monitor, and support the user's lifestyle and physical data.

[0070] The data collection unit collects user lifestyle and physical data. For example, it can collect user lifestyle and physical data using wearable devices. Specifically, it can use smartwatches and fitness trackers to collect data such as the user's heart rate, steps taken, and sleep patterns. These devices are worn on the user's wrist and collect data 24 hours a day. Heart rate sensors measure the user's heart rate in real time and record fluctuations during exercise and at rest. Pedometers detect the user's walking movements and measure the total number of steps and distance traveled per day. Sleep trackers detect the user's movements during sleep and evaluate sleep quality and duration. The data collection unit can also collect data manually entered by the user. For example, the unit can collect data when the user enters their meal and exercise details into an application. Users can use smartphones or tablets to input detailed information such as meal types, calorie intake, exercise types, and exercise duration. Furthermore, the data collection unit can automatically collect data related to the user's lifestyle. For example, the data collection unit can use the user's smartphone sensors to measure the user's travel distance and activity level. Using the smartphone's GPS function, it records the user's travel route and speed, and evaluates their daily activity level. This allows the data collection unit to comprehensively collect user lifestyle and physical data, building a comprehensive database. The collected data is sent to a cloud server, making it accessible to the analysis and proposal units. This enables the data collection unit to collect data efficiently and accurately, improving the overall system performance.

[0071] The analysis unit analyzes the data collected by the data collection unit. For example, the analysis unit uses AI to analyze the collected data and evaluate the user's physical fitness and skill level. Specifically, the AI ​​can use machine learning algorithms to analyze the user's heart rate data and evaluate the user's cardiopulmonary function. For example, it can analyze heart rate variability patterns to evaluate the user's exercise intensity and fatigue level. It can also analyze step count data to evaluate the user's activity level. Based on the user's total daily steps and distance traveled, the AI ​​classifies the user's activity level and sets appropriate exercise goals. Furthermore, the analysis unit can analyze the user's sleep data and evaluate the quality of the user's sleep. The AI ​​analyzes movement and heart rate variability during sleep and evaluates the proportion of deep sleep and light sleep. This allows the analysis unit to provide advice to improve the user's sleep quality. The analysis unit comprehensively analyzes this data to evaluate the user's health status and fitness level. Furthermore, the analysis unit can also utilize historical data and statistical information to analyze long-term health trends. For example, based on past exercise data, the system evaluates changes in the user's physical fitness and improvements in exercise habits, and predicts future health risks. Furthermore, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data, issuing early warnings. This allows the analysis unit to not only monitor 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 suggestion unit proposes optimal training menus and meal plans based on the analysis results obtained by the analysis unit. For example, the suggestion unit uses AI to propose the optimal training menu for the user. Specifically, it can suggest appropriate exercise intensity and duration according to the user's physical fitness and skill level. For example, it can suggest light aerobic exercise and stretching for beginner users, and high-intensity interval training and strength training for advanced users. The suggestion unit can also propose appropriate training menus according to the user's goals. For example, it can suggest exercise menus that focus on calorie consumption for users aiming to lose weight, and menus that focus on strength training for users aiming to increase muscle mass. Furthermore, the suggestion unit can also propose appropriate meal plans based on the user's dietary data. The AI ​​proposes appropriate meal menus considering the user's nutritional balance. For example, it analyzes the user's calorie intake and nutrient balance and proposes a balanced meal menu. It can also suggest ingredients and recipes to supplement specific nutrients if there is a deficiency. The suggestion unit notifies the user of these suggestions and encourages them to take action. Users can check and implement the proposed training menus and meal plans via their smartphones or tablets. This allows the proposal department to support users in achieving their health goals and provide effective fitness plans.

[0073] The monitoring unit monitors the progress of the plans proposed by the proposal unit. For example, the monitoring unit uses AI to monitor the user's progress in real time. Specifically, it can monitor the user's training data to verify whether the user is following the proposed training menu. The AI ​​analyzes the user's exercise data and evaluates the degree of achievement against the proposed exercise intensity and duration. The monitoring unit can also monitor the user's dietary data to verify whether the user is following the proposed meal plan. The AI ​​analyzes the user's dietary data and evaluates the degree of achievement against the proposed nutritional balance and calorie intake. Furthermore, the monitoring unit can provide appropriate feedback based on the user's progress. For example, if the user completes the training menu, it can send an encouraging message to maintain motivation. If the user completes the meal plan, it can provide advice to help maintain a healthy diet. Through this feedback, the monitoring unit supports the user's progress and promotes the achievement of health goals. Additionally, the monitoring unit can accumulate user progress data and analyze long-term health trends. This allows the monitoring unit to comprehensively support the user's health management and maximize the overall effectiveness of the system.

[0074] The support department provides motivational support and alerts based on progress monitored by the monitoring department. For example, the support department uses AI to provide appropriate support to users. Specifically, it can send encouraging messages to help users continue their training. The AI ​​analyzes the user's progress and sends encouraging messages at the appropriate time. For example, if a user completes a training menu, it sends a message to enhance their sense of accomplishment and maintain their motivation towards the next goal. It can also send reminders to help users stick to their meal plans. The AI ​​analyzes the user's meal data and sends reminders based on the timing and content of meals. For example, when mealtime approaches, it suggests appropriate meal menus to support healthy eating habits. Furthermore, the support department can provide appropriate alerts based on the user's progress. For example, if a user is skipping training, it sends a warning alert to encourage them to resume training. Also, if a user is not following their meal plan, it sends an alert warning of health risks and encourages improvements to their eating habits. In this way, the support department can maintain user motivation and support the achievement of their health goals. Furthermore, the support department can collect user feedback and continuously improve the accuracy and effectiveness of its support services. This allows the support department to provide users with prompt and reliable support, thereby improving the overall reliability and effectiveness of the system.

[0075] The data collection unit can collect user lifestyle and physical data from wearable devices. For example, the data collection unit can use a smartwatch to collect data such as the user's heart rate, steps, and sleep patterns. The data collection unit can also use a fitness tracker to collect the user's exercise data. The data collection unit can use a smartwatch to collect data such as the user's heart rate, steps, and sleep patterns. The data collection unit can also use a fitness tracker to collect the user's exercise data. The data collection unit can use a smartwatch to collect data such as the user's heart rate, steps, and sleep patterns. The data collection unit can also use a fitness tracker to collect the user's exercise data. This allows for the efficient collection of user lifestyle and physical data using wearable devices. Some or all of the above-described processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data acquired from wearable devices into a generating AI and have the generating AI perform data analysis.

[0076] The analysis unit can analyze the user's physical fitness and skill level based on the collected data. For example, the analysis unit can analyze the user's physical fitness based on the collected data. The analysis unit can also analyze the user's skill level based on the collected data. For example, the analysis unit can analyze the user's physical fitness based on the collected data. The analysis unit can also analyze the user's skill level based on the collected data. For example, the analysis unit can analyze the user's physical fitness based on the collected data. The analysis unit can also analyze the user's skill level based on the collected data. This makes it possible to make more appropriate suggestions by analyzing the user's physical fitness and skill level based on the collected data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the collected data into a generating AI and have the generating AI perform an analysis of the user's physical fitness and skill level.

[0077] The suggestion unit can propose the optimal training menu and meal plan to the user based on the analysis results. For example, the suggestion unit can propose the optimal training menu to the user based on the analysis results. The suggestion unit can also propose the optimal meal plan to the user based on the analysis results. For example, the suggestion unit can propose the optimal training menu to the user based on the analysis results. The suggestion unit can also propose the optimal meal plan to the user based on the analysis results. For example, the suggestion unit can propose the optimal training menu to the user based on the analysis results. The suggestion unit can also propose the optimal meal plan to the user based on the analysis results. This improves the user's health management by proposing the optimal training menu and meal plan based on the analysis results. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the analysis results into a generating AI and have the generating AI execute the proposal of the optimal training menu and meal plan.

[0078] The monitoring unit can monitor the user's progress in real time. The monitoring unit can, for example, monitor the user's progress in real time. The monitoring unit can, for example, monitor the user's progress in real time. The monitoring unit can, for example, monitor the user's progress in real time. The monitoring unit can, for example, monitor the user's progress in real time. The monitoring unit can, for example, monitor the user's progress in real time. This enables appropriate support by monitoring the user's progress in real time. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input the user's progress into a generating AI and have the generating AI perform real-time monitoring.

[0079] The support unit can provide users with alerts and support to maintain their motivation based on monitoring results. The support unit can, for example, provide users with alerts based on monitoring results. The support unit can, for example, provide users with support to maintain their motivation based on monitoring results. The support unit can, for example, provide users with alerts based on monitoring results. The support unit can, for example, provide users with alerts based on monitoring results. The support unit can, for example, provide users with support to maintain their motivation based on monitoring results. This improves user motivation by providing appropriate support based on monitoring results. Some or all of the above processing in the support unit may be performed using AI, for example, or without AI. For example, the support unit can input monitoring results into a generating AI and have the generating AI execute alerts and support to maintain motivation.

[0080] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can reduce the frequency of data collection and collect data when the user is relaxed. For example, if the user is relaxed, the data collection unit can increase the frequency of data collection and collect more detailed data. For example, if the user is exhilarated during exercise, the data collection unit can collect data during the cool-down period after exercise. This allows for more appropriate data collection by adjusting the timing of data collection based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI adjust the timing of data collection.

[0081] The data collection unit can analyze the user's past data collection history and select the optimal collection method. For example, the data collection unit can analyze the time periods when the user frequently collected data in the past and perform data collection during those times. The data collection unit can also analyze the types of devices the user has used in the past and suggest the most suitable device. The data collection unit can also select the most effective collection method from the user's past data collection history. In this way, the optimal collection method can be selected by analyzing the user's past data collection history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past data collection history into a generating AI and have the generating AI select the optimal collection method.

[0082] The data collection unit can filter data based on the user's current activity status and environment during data collection. For example, if the user is exercising, the data collection unit can collect only data related to exercise. For example, if the user is resting, the data collection unit can collect only data related to resting. For example, if the user is out, the data collection unit can collect only data related to going out. By filtering the data based on the user's current activity status and environment, more relevant data can be collected. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's current activity status and environment data into a generating AI and have the generating AI perform data filtering.

[0083] The data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated user emotions. For example, if the user is stressed, the data collection unit will prioritize collecting stress-related data. For example, if the user is relaxed, the data collection unit may also prioritize collecting relaxation-related data. For example, if the user is exhilarated during exercise, the data collection unit may also prioritize collecting exercise-related data. This allows for the priority collection of more important data by determining the priority of data to collect based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, or not using AI. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI determine the priority of the data.

[0084] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information during data collection. For example, if the user is at a gym, the data collection unit can prioritize the collection of data related to training at the gym. For example, if the user is at home, the data collection unit can prioritize the collection of data related to lifestyle habits at home. For example, if the user is out, the data collection unit can prioritize the collection of data related to activities while out. This allows for the priority collection of highly relevant data by considering the user's geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into a generating AI and have the generating AI perform the priority collection of highly relevant data.

[0085] The data collection unit can analyze the user's social media activity and collect relevant data during data collection. For example, the data collection unit can collect exercise-related data based on exercise records shared by the user on social media. The data collection unit can also collect diet-related data based on meal records shared by the user on social media. The data collection unit can also collect lifestyle-related data based on lifestyle habits shared by the user on social media. This allows for the efficient collection of relevant data by analyzing the user's social media activity. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media activity data into a generating AI and have the generating AI perform the collection of relevant data.

[0086] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is stressed, the analysis unit can provide simple and highly visual analysis results. For example, if the user is relaxed, the analysis unit can also provide detailed analysis results. For example, if the user is exhilarated during exercise, the analysis unit can also provide visually stimulating analysis results. By adjusting the presentation of the analysis based on the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into the generative AI and have the generative AI adjust the presentation of the analysis.

[0087] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit can perform a detailed analysis on data with high importance. For example, the analysis unit can perform a simplified analysis on data with low importance. For example, the analysis unit can perform an analysis with an appropriate level of detail on data with moderate importance. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0088] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply an exercise analysis algorithm to training data. For example, the analysis unit can also apply a nutrition analysis algorithm to dietary data. For example, the analysis unit can also apply a lifestyle analysis algorithm to lifestyle data. By applying different analysis algorithms depending on the data category, more accurate analysis becomes possible. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data category into a generating AI and have the generating AI execute the application of different analysis algorithms.

[0089] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, the analysis unit can provide a short, concise analysis result. For example, if the user is relaxed, the analysis unit can also provide a detailed analysis result. For example, if the user is excited, the analysis unit can also provide a visually stimulating analysis result. By adjusting the length of the analysis based on the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into the generative AI and have the generative AI adjust the length of the analysis.

[0090] The analysis unit can determine the priority of analysis based on the data collection period during analysis. For example, the analysis unit may prioritize the analysis of the most recent data. The analysis unit may also analyze the most recent data while referring to past data. The analysis unit may also prioritize the analysis of data collected during a specific period. This enables efficient analysis by determining the priority of analysis based on the data collection period. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data collection period into a generating AI and have the generating AI determine the priority of analysis.

[0091] The analysis unit can adjust the order of analysis based on the relevance of the data during the analysis. For example, the analysis unit may prioritize the analysis of highly relevant data. For example, the analysis unit may also postpone the analysis of less relevant data. For example, the analysis unit may dynamically adjust the order of analysis according to the relevance of the data. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance of the data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0092] The suggestion unit can estimate the user's emotions and adjust the way suggestions are presented based on those emotions. For example, if the user is stressed, the suggestion unit will provide simple and easily understandable suggestions. If the user is relaxed, the suggestion unit may provide more detailed suggestions. If the user is exhilarated during exercise, the suggestion unit may provide visually stimulating suggestions. By adjusting the way suggestions are presented based on the user's emotions, more appropriate suggestions can be made. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI adjust the way suggestions are presented.

[0093] The suggestion unit can adjust the level of detail in its suggestions based on the importance of the training menu and meal plan. For example, the suggestion unit will provide detailed suggestions for training menus of high importance. For example, it can provide simplified suggestions for training menus of low importance. For example, it can provide suggestions with an appropriate level of detail for training menus of moderate importance. This allows for efficient suggestions by adjusting the level of detail in the suggestions based on the importance of the training menu and meal plan. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the importance of the training menu and meal plan into a generating AI and have the generating AI adjust the level of detail in the suggestions.

[0094] The suggestion unit can apply different suggestion algorithms depending on the user's goals and physical condition when making suggestions. For example, if the user's goal is to improve muscle strength, the suggestion unit will make suggestions specifically for strength training. If the user's goal is weight loss, the suggestion unit can also make suggestions specifically for aerobic exercise. If the user is in good physical condition, the suggestion unit can also make suggestions for high-intensity training. By applying different suggestion algorithms according to the user's goals and physical condition, more appropriate suggestions can be made. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's goals and physical condition data into a generating AI and have the generating AI execute the application of different suggestion algorithms.

[0095] The suggestion unit can estimate the user's emotions and adjust the length of the suggestions based on the estimated emotions. For example, if the user is in a hurry, the suggestion unit will make short, to-the-point suggestions. If the user is relaxed, the suggestion unit may make detailed suggestions. If the user is excited, the suggestion unit may make visually stimulating suggestions. By adjusting the length of suggestions based on the user's emotions, more appropriate suggestions can be made. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI adjust the length of the suggestions.

[0096] The suggestion unit can determine the priority of suggestions based on the user's past training history when making suggestions. For example, the suggestion unit can make optimal suggestions based on the training menus the user has previously performed. For example, the suggestion unit can also prioritize suggesting effective menus based on the user's past training history. For example, the suggestion unit can analyze the user's past training history and make the most effective suggestions. This makes it possible to make more effective suggestions by determining the priority of suggestions based on the user's past training history. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's past training history into a generating AI and have the generating AI perform the task of determining the priority of suggestions.

[0097] The suggestion unit can adjust the order of suggestions based on the user's lifestyle habits when making suggestions. For example, the suggestion unit can determine the optimal order of suggestions to match the user's lifestyle habits. The suggestion unit can also dynamically adjust the order of suggestions, taking into account the user's lifestyle habits. The suggestion unit can also customize the order of suggestions based on the user's lifestyle habits. This allows for more appropriate suggestions by adjusting the order of suggestions based on the user's lifestyle habits. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input user lifestyle data into a generating AI and have the generating AI adjust the order of suggestions.

[0098] The monitoring unit can estimate the user's emotions and adjust the monitoring criteria based on the estimated emotions. For example, if the user is stressed, the monitoring unit can reduce the frequency of monitoring and perform monitoring when the user is relaxed. For example, if the user is relaxed, the monitoring unit can increase the frequency of monitoring and collect more detailed data. For example, if the user is exhilarated during exercise, the monitoring unit can perform monitoring during the cool-down period after exercise. This allows for more appropriate monitoring by adjusting the monitoring criteria based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the monitoring unit may be performed using AI or not. For example, the monitoring unit can input user emotion data into the generative AI and have the generative AI adjust the monitoring criteria.

[0099] The monitoring unit can improve the accuracy of monitoring by considering user interactions during monitoring. For example, if a user is training with other users, the monitoring unit will consider their interactions when monitoring. For example, if a user is training in a group, the monitoring unit can also perform monitoring based on the data of the entire group. For example, if a user is training with a partner, the monitoring unit can also consider the partner's data when monitoring. This improves the accuracy of monitoring by considering user interactions. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input user interaction data into a generating AI and have the generating AI perform the task of improving the accuracy of monitoring.

[0100] The monitoring unit can perform monitoring while considering the user's attribute information. For example, the monitoring unit can perform age-appropriate monitoring by considering the user's age. For example, the monitoring unit can also perform gender-appropriate monitoring by considering the user's gender. For example, the monitoring unit can also perform sport-appropriate monitoring by considering the user's sport. This makes it possible to perform more appropriate monitoring by considering the user's attribute information. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without using AI. For example, the monitoring unit can input the user's attribute information into a generating AI and have the generating AI perform the monitoring.

[0101] The monitoring unit can estimate the user's emotions and adjust the order in which monitoring results are displayed based on the estimated emotions. For example, if the user is stressed, the monitoring unit may display important results first and detailed results later. If the user is relaxed, the monitoring unit may also display detailed results first and important results later. If the user is exhilarated during exercise, the monitoring unit may also display visually stimulating results first. By adjusting the order in which monitoring results are displayed based on the user's emotions, more appropriate results can be displayed. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the monitoring unit may be performed using AI, or not using AI. For example, the monitoring unit can input user emotion data into a generative AI and have the generative AI adjust the display order of the monitoring results.

[0102] The monitoring unit can perform monitoring while considering the geographical distribution of users. For example, if users are in different regions, the monitoring unit can perform monitoring based on data for each region. For example, if users are traveling, the monitoring unit can perform monitoring based on data from their travel destination. For example, if users are active in multiple regions, the monitoring unit can perform monitoring based on data from each region. This allows for more appropriate monitoring by considering the geographical distribution of users. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input user geographical distribution data into a generating AI and have the generating AI perform the monitoring.

[0103] The monitoring unit can improve the accuracy of monitoring by referring to relevant literature for the user during monitoring. For example, the monitoring unit may refer to the latest research papers related to the user's training. The monitoring unit may also refer to nutritional literature related to the user's meal plan. The monitoring unit may also refer to health management literature related to the user's lifestyle. This improves the accuracy of monitoring by referring to relevant literature for the user. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input the user's relevant literature data into a generating AI and have the generating AI perform the improvement of monitoring accuracy.

[0104] The support unit can estimate the user's emotions and adjust its support methods based on the estimated emotions. For example, if the user is feeling stressed, the support unit can provide a relaxing support method. For example, if the user is relaxed, the support unit can also provide a more proactive support method. For example, if the user is feeling exhilarated during exercise, the support unit can also provide a more motivating support method. By adjusting the support method based on the user's emotions, more appropriate support becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the support unit may be performed using AI, for example, or not using AI. For example, the support unit can input user emotion data into a generative AI and have the generative AI adjust the support method.

[0105] The support unit can analyze the user's past behavior during support to select the optimal support method. For example, the support unit can provide optimal support based on support methods that have been effective for the user in the past. The support unit can also analyze the user's past behavior patterns to select the most effective support method. For example, the support unit can propose the optimal support method based on the user's past support history. In this way, the optimal support method can be selected by analyzing the user's past behavior. Some or all of the above processes in the support unit may be performed using AI, for example, or without AI. For example, the support unit can input the user's past behavior data into a generating AI and have the generating AI select the optimal support method.

[0106] The support unit can customize the means of support based on the user's current lifestyle when providing support. For example, if the user is busy, the support unit can provide a quick and effective support method. For example, if the user is relaxed, the support unit can also provide a detailed support method. For example, if the user is exhilarated during exercise, the support unit can also provide a support method to boost motivation. By customizing the means of support based on the user's current lifestyle, more appropriate support becomes possible. Some or all of the above processing in the support unit may be performed using AI, for example, or without AI. For example, the support unit can input the user's current lifestyle data into a generating AI and have the generating AI perform the customization of the support methods.

[0107] The support unit can estimate the user's emotions and determine the priority of support based on the estimated emotions. For example, if the user is feeling stressed, the support unit will prioritize stress reduction support. For example, if the user is relaxed, the support unit may also prioritize support to maintain relaxation. For example, if the user is exhilarated during exercise, the support unit may also prioritize support to increase motivation. This allows for more appropriate support by determining the priority of support 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 is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the support unit may be performed using AI or not using AI. For example, the support unit can input user emotion data into a generative AI and have the generative AI determine the priority of support.

[0108] The support unit can select the optimal support method by considering the user's geographical location information during support. For example, if the user is at a gym, the support unit can provide support related to training at the gym. For example, if the user is at home, the support unit can also provide support related to lifestyle habits at home. For example, if the user is out, the support unit can also provide support related to activities while out. This allows the support unit to select the optimal support method by considering the user's geographical location information. Some or all of the above processing in the support unit may be performed using AI, for example, or without AI. For example, the support unit can input the user's geographical location information into a generating AI and have the generating AI select the optimal support method.

[0109] The support unit can analyze the user's social media activity and propose support methods during support. For example, the support unit can provide exercise-related support based on exercise records shared by the user on social media. The support unit can also provide diet-related support based on meal records shared by the user on social media. The support unit can also provide lifestyle-related support based on lifestyle habits shared by the user on social media. In this way, by analyzing the user's social media activity, the support unit can propose the most suitable support method. Some or all of the above processing in the support unit may be performed using AI, for example, or without AI. For example, the support unit can input the user's social media activity data into a generating AI and have the generating AI execute the proposal of support methods.

[0110] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0111] The data collection unit can estimate the user's emotions when collecting user lifestyle and physical data, and adjust the timing of data collection based on the estimated emotions. For example, if the user is stressed, the frequency of data collection can be reduced, and data can be collected when the user is relaxed. Conversely, if the user is relaxed, the frequency of data collection can be increased to collect more detailed data. Furthermore, if the user is exhilarated during exercise, data can be collected during the cool-down period after exercise. In this way, adjusting the timing of data collection based on the user's emotions enables more appropriate data collection.

[0112] The analysis unit can estimate the user's emotions when analyzing the user's physical fitness and skill level based on collected data, and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is feeling stressed, it can provide simple and easy-to-understand analysis results. If the user is relaxed, it can provide detailed analysis results. Furthermore, if the user is exhilarated during exercise, it can provide visually stimulating analysis results. In this way, by adjusting the presentation of the analysis based on the user's emotions, more appropriate analysis results can be provided.

[0113] The suggestion function can estimate the user's emotions when proposing optimal training menus and meal plans based on analysis results, and adjust the presentation of the suggestions based on those emotions. For example, if the user is feeling stressed, it can provide simple and highly visual suggestions. If the user is relaxed, it can provide more detailed suggestions. Furthermore, if the user is exhilarated during exercise, it can provide visually stimulating suggestions. By adjusting the presentation of suggestions based on the user's emotions, it becomes possible to provide more appropriate suggestions.

[0114] The monitoring unit can estimate the user's emotions when monitoring the user's progress in real time, and adjust the monitoring criteria based on the estimated emotions. For example, if the user is stressed, the monitoring frequency can be reduced, and monitoring can be performed when the user is relaxed. Conversely, if the user is relaxed, the monitoring frequency can be increased to collect more detailed data. Furthermore, if the user is exhilarated during exercise, monitoring can be performed during the cool-down period after exercise. In this way, adjusting the monitoring criteria based on the user's emotions enables more appropriate monitoring.

[0115] The support department can estimate the user's emotions when providing alerts and motivational support based on monitoring results, and adjust the support method based on the estimated emotions. For example, if the user is feeling stressed, it can provide support methods that help them relax. If the user is relaxed, it can provide support methods that encourage them. Furthermore, if the user is feeling exhilarated during exercise, it can provide support methods that boost their motivation. By adjusting the support method based on the user's emotions, more appropriate support becomes possible.

[0116] The data collection unit can analyze the user's past data collection history and select the optimal collection method. For example, it can analyze the time periods when the user frequently collected data in the past and perform data collection during those times. It can also analyze the types of devices the user has used in the past and suggest the most suitable device. Furthermore, it can select the most effective collection method based on the user's past data collection history. In this way, the optimal collection method can be selected by analyzing the user's past data collection history.

[0117] The analysis unit can adjust the level of detail of the analysis based on the importance of the data. For example, it can perform a detailed analysis on highly important data, a simplified analysis on less important data, and an analysis with an appropriate level of detail on moderately important data. By adjusting the level of detail of the analysis based on the importance of the data, efficient analysis becomes possible.

[0118] The proposal department can adjust the level of detail in its proposals based on the importance of the training menu and meal plan. For example, it can provide detailed proposals for highly important training menus, simplified proposals for less important ones, and proposals with an appropriate level of detail for moderately important training menus. By adjusting the level of detail in proposals based on the importance of the training menu and meal plan, efficient proposals become possible.

[0119] The monitoring unit can perform monitoring while considering user attribute information. For example, it can perform age-appropriate monitoring by considering the user's age. It can also perform gender-appropriate monitoring by considering the user's gender. Furthermore, it can perform monitoring appropriate for the sport the user participates in by considering their sport. In this way, more appropriate monitoring becomes possible by considering user attribute information.

[0120] The support department can analyze a user's past behavior during support to select the most appropriate support method. For example, it can provide optimal support based on support methods that have been effective for the user in the past. It can also analyze the user's past behavior patterns to select the most effective support method. Furthermore, it can propose the most appropriate support method based on the user's past support history. In this way, the optimal support method can be selected by analyzing the user's past behavior.

[0121] The following briefly describes the processing flow for example form 2.

[0122] Step 1: The data collection unit collects the user's lifestyle and physical data. The data collection unit collects the user's lifestyle and physical data, for example, using wearable devices. For example, the data collection unit can collect data such as the user's heart rate, steps taken, and sleep patterns using smartwatches or fitness trackers. The data collection unit can also collect data manually entered by the user. For example, the data collection unit can collect data on the user's diet and exercise when the user enters this information into an application. Furthermore, the data collection unit can automatically collect data related to the user's lifestyle. For example, the data collection unit can measure the user's distance traveled and activity level using the sensors on the user's smartphone. Step 2: The analysis unit analyzes the data collected by the data collection unit. The analysis unit can, for example, use AI to analyze the collected data and evaluate the user's physical fitness and skill level. The analysis unit can, for example, analyze the user's heart rate data to evaluate the user's cardiopulmonary function. The analysis unit can also analyze the user's step count data to evaluate the user's activity level. Furthermore, the analysis unit can analyze the user's sleep data to evaluate the user's sleep quality. Step 3: The suggestion unit proposes the optimal training menu and meal plan based on the analysis results obtained by the analysis unit. For example, the suggestion unit can use AI to propose the optimal training menu for the user. For example, the suggestion unit can propose appropriate exercise intensity and duration according to the user's physical fitness and skill level. The suggestion unit can also propose an appropriate training menu according to the user's goals. Furthermore, the suggestion unit can propose an appropriate meal plan based on the user's meal data. For example, the suggestion unit can propose an appropriate meal menu considering the user's nutritional balance. Step 4: The monitoring unit monitors the progress of the plan proposed by the proposal unit. The monitoring unit can, for example, use AI to monitor the user's progress in real time. The monitoring unit can, for example, monitor the user's training data to confirm whether the user is following the proposed training menu. The monitoring unit can also monitor the user's meal data to confirm whether the user is following the proposed meal plan. Furthermore, the monitoring unit can provide appropriate feedback according to the user's progress. Step 5: The support team provides motivational support and alerts based on the progress monitored by the monitoring team. The support team, for example, uses AI to provide appropriate support to users. For example, the support team can send encouraging messages to help users continue their training. The support team can also send reminders to help users stick to their meal plans. Furthermore, the support team can provide appropriate alerts based on the user's progress.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] Each of the multiple elements described above, including the data collection unit, analysis unit, proposal unit, monitoring unit, and support unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the data collection unit collects data such as the user's heart rate, steps, and sleep patterns using a smartwatch or fitness tracker of the smart device 14. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and analyzes the collected data to evaluate the user's physical fitness and skill level. The proposal unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and proposes an optimal training menu or meal plan based on the analysis results. The monitoring unit is implemented in the control unit 46A of the smart device 14, for example, and monitors the progress of the proposed plan. The support unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and provides appropriate support to the user based on the monitoring results. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0127] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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).

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.).

[0139] 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.

[0140] 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.

[0141] 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.

[0142] Each of the multiple elements described above, including the data collection unit, analysis unit, proposal unit, monitoring unit, and support 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 the user's lifestyle and physical data using the camera and sensors of the smart glasses 214. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and analyzes the collected data to evaluate the user's physical fitness and skill level. The proposal unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and proposes an optimal training menu and meal plan based on the analysis results. The monitoring unit is implemented in the control unit 46A of the smart glasses 214, for example, and monitors the progress of the proposed plan. The support unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and provides appropriate support to the user based on the monitoring results. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0143] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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).

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.).

[0155] 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.

[0156] 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.

[0157] 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.

[0158] Each of the multiple elements described above, including the data collection unit, analysis unit, proposal unit, monitoring unit, and support 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 the user's lifestyle and physical data using the camera and sensors of the headset terminal 314. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and analyzes the collected data to evaluate the user's physical fitness and skill level. The proposal unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and proposes an optimal training menu and meal plan based on the analysis results. The monitoring unit is implemented in the control unit 46A of the headset terminal 314, for example, and monitors the progress of the proposed plan. The support unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and provides appropriate support to the user based on the monitoring results. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0159] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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).

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.).

[0172] 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.

[0173] 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.

[0174] 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.

[0175] Each of the multiple elements described above, including the data collection unit, analysis unit, proposal unit, monitoring unit, and support unit, is implemented in at least one of the following: the robot 414 and the data processing unit 12. For example, the data collection unit collects the user's lifestyle and physical data using the camera and sensors of the robot 414. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and analyzes the collected data to evaluate the user's physical fitness and skill level. The proposal unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and proposes an optimal training menu and meal plan based on the analysis results. The monitoring unit is implemented in the control unit 46A of the robot 414, for example, and monitors the progress of the proposed plan. The support unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and provides appropriate support to the user based on the monitoring results. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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."

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] (Note 1) A data collection unit that collects users' lifestyle habits and physical data, An analysis unit analyzes the data collected by the aforementioned collection unit, Based on the analysis results obtained by the aforementioned analysis unit, a proposal unit proposes an optimal training menu and meal plan. A monitoring unit that monitors the progress of the plan proposed by the aforementioned proposal unit, The system includes a support unit that provides support and alerts to maintain motivation based on the progress monitored by the monitoring unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is Collect user lifestyle and physical data from wearable devices. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, Based on the collected data, the user's physical fitness and skill level are analyzed. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned proposal section is, Based on the analysis results, we propose the most suitable training menu and meal plan for the user. The system described in Appendix 1, characterized by the features described herein. (Note 5) The monitoring unit, Monitor user progress in real time. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned support unit is Based on monitoring results, provide users with alerts and support to help them maintain motivation. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is Analyze the user's past data collection history and select the optimal collection method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When collecting data, filtering is performed based on the user's current activity status and environment. The system described in Appendix 1, characterized by the features described herein. (Note 10) 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 11) The aforementioned collection unit is When collecting data, the system prioritizes the collection of highly relevant data, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is During data collection, the system analyzes users' social media activity and collects relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, adjust the level of detail based on the importance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the data category. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, the priority of the analysis is determined based on when the data was collected. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, adjust the order of analysis based on the relevance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned proposal section is, It estimates the user's emotions and adjusts the way suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned proposal section is, When making a proposal, adjust the level of detail based on the importance of the training menu and meal plan. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned proposal section is, When making suggestions, different suggestion algorithms are applied depending on the user's goals and physical condition. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned proposal section is, It estimates the user's emotions and adjusts the length of the suggestion based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned proposal section is, When making suggestions, the system prioritizes suggestions based on the user's past training history. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned proposal section is, When making suggestions, adjust the order of suggestions based on the user's lifestyle. The system described in Appendix 1, characterized by the features described herein. (Note 25) The monitoring unit, The system estimates user sentiment and adjusts monitoring criteria based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 26) The monitoring unit, During monitoring, consider user interactions to improve monitoring accuracy. The system described in Appendix 1, characterized by the features described herein. (Note 27) The monitoring unit, During monitoring, the monitoring process takes user attribute information into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 28) The monitoring unit, It estimates the user's emotions and adjusts the order in which monitoring results are displayed based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The monitoring unit, During monitoring, the monitoring process takes into account the geographical distribution of users. The system described in Appendix 1, characterized by the features described herein. (Note 30) The monitoring unit, During monitoring, referencing relevant user literature improves the accuracy of the monitoring. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned support unit is It estimates the user's emotions and adjusts the support method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned support unit is During support, we analyze the user's past behavior to select the most appropriate support method. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned support unit is During support, customize the support methods based on the user's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned support unit is The system estimates the user's emotions and determines support priorities based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned support unit is During support, the optimal support method will be selected considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned support unit is During support, we analyze the user's social media activity and suggest support methods. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0195] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A data collection unit that collects users' lifestyle habits and physical data, An analysis unit analyzes the data collected by the aforementioned collection unit, Based on the analysis results obtained by the aforementioned analysis unit, a proposal unit proposes an optimal training menu and meal plan. A monitoring unit that monitors the progress of the plan proposed by the aforementioned proposal unit, The system includes a support unit that provides support and alerts to maintain motivation based on the progress monitored by the monitoring unit. A system characterized by the following features.

2. The aforementioned collection unit is Collect user lifestyle and physical data from wearable devices. The system according to feature 1.

3. The aforementioned analysis unit, Based on the collected data, the user's physical fitness and skill level are analyzed. The system according to feature 1.

4. The aforementioned proposal section is, Based on the analysis results, we propose the most suitable training menu and meal plan for the user. The system according to feature 1.

5. The monitoring unit, Monitor user progress in real time. The system according to feature 1.

6. The aforementioned support unit is Based on monitoring results, provide users with alerts and support to help them maintain motivation. The system according to feature 1.

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

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

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

10. The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system according to feature 1.

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