system

Abstract

The system according to this embodiment aims to provide a personalized training plan based on the user's health status and fitness goals. [Solution] The system according to the embodiment comprises a data collection unit, an analysis unit, a creation unit, and a support unit. The data collection unit collects the user's lifestyle data. The analysis unit analyzes the data collected by the data collection unit to understand the user's health status and fitness goals. The creation unit creates a personalized training plan based on the analysis results obtained by the analysis unit. The support unit provides support for daily training based on the training plan created by the creation 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 steps of: receiving a user utterance; adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot; encoding the prompt; and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, an optimal training plan based on an individual health condition and fitness goal of a user has not been sufficiently provided, and there is room for improvement.

[0005] The system according to the embodiment aims to provide a personalized training plan based on a user's health condition and fitness goal.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, a data creation unit, and a support unit. The data collection unit collects the user's lifestyle data. The analysis unit analyzes the data collected by the data collection unit to understand the user's health status and fitness goals. The data creation unit creates a personalized training plan based on the analysis results obtained by the analysis unit. The support unit provides support for daily training based on the training plan created by the data creation unit. [Effects of the Invention]

[0007] The system according to this embodiment can provide a personalized training plan based on the user's health status and fitness goals. [Brief explanation of the drawing]

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

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

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

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

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

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

[0014] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F controls communication between a plurality of computers. Examples of communication standards applied 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] [[ID=***10]]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] Note: There seems to be a formatting issue in the original text where the tag in line 10 might be incorrect as it has "***" in it. I've translated it as it is, but it might need to be corrected in the original source.The smart device 14 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

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

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

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

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

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

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

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

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

[0028] (Example of form 1) An AI agent system according to an embodiment of the present invention is a system that provides an optimal training plan based on the user's individual health status and fitness goals. This AI agent system creates a personalized fitness plan based on the user's lifestyle data (e.g., sleep patterns, food records), fitness level, and exercise history, and supports daily training. Furthermore, the AI ​​agent system provides encouragement, automates comparisons of the user with their past self, and increases motivation through visualization of progress and competition with other users. First, the AI ​​agent system collects the user's lifestyle data. In this process, it collects detailed data such as the user's sleep patterns, food records, fitness level, and exercise history. For example, the AI ​​agent system collects data such as how many hours the user sleeps each day, what they eat, and how much exercise they do. Next, the AI ​​agent system analyzes the collected data. Based on the collected data, the AI ​​understands the user's health status and fitness goals and creates an optimal training plan. For example, if the user wants to lose weight, the AI ​​analyzes the user's food records and exercise history and creates a training plan to increase calorie consumption. Furthermore, the AI ​​agent system supports daily training. Specifically, the AI ​​agent system provides encouraging messages to the user. Furthermore, the AI ​​agent system automates comparisons between the user's past performance and current performance, boosting motivation through visualization of progress and competition with other users. For example, the AI ​​agent system tracks the user's training progress in real time, visualizing their progress. It also provides a competitive feature with other users to increase user motivation. As a result, the AI ​​agent system can provide personalized training plans based on the user's lifestyle data, supporting their daily training.

[0029] The AI ​​agent system according to this embodiment comprises a collection unit, an analysis unit, a creation unit, and a support unit. The collection unit collects user lifestyle data. User lifestyle data includes, but is not limited to, sleep patterns, meal records, fitness levels, and exercise history. For example, the collection unit collects data such as how many hours the user sleeps each day, what they eat, and how much exercise they do. The collection unit can collect user lifestyle data through sensors or applications. For example, the collection unit acquires data from the user's smartphone or wearable device. The collection unit can also collect data manually entered by the user. The analysis unit analyzes the data collected by the collection unit to understand the user's health status and fitness goals. The analysis unit analyzes the data using, for example, statistical analysis or machine learning algorithms. For example, the analysis unit analyzes the user's sleep patterns and meal records to evaluate the user's health status. The analysis unit can also analyze the user's exercise history to evaluate the user's fitness level. The analysis unit can integrate and analyze multiple data sources to understand the user's health status and fitness goals. The creation unit creates a personalized training plan based on the analysis results obtained by the analysis unit. For example, the creation unit adjusts the type, frequency, and intensity of exercise according to the user's health status and fitness goals. For example, if the user wants to lose weight, the creation unit will create a training plan to increase calorie expenditure. The creation unit can also create a plan that includes strength training if the user wants to increase muscle strength. The creation unit can periodically update the training plan according to the user's fitness goals. The support unit supports daily training based on the training plan created by the creation unit. For example, the support unit provides encouraging messages to the user. The support unit tracks the user's training progress in real time and visualizes the user's progress. The support unit can also provide a competitive function with other users to increase user motivation.For example, the support unit displays the user's training progress using graphs and charts, visually showing the user's progress. The support unit also compares the user's past training data with current data to evaluate the user's progress. As a result, the AI ​​agent system according to this embodiment can provide a personalized training plan based on the user's lifestyle data, supporting their daily training.

[0030] The data collection unit collects user lifestyle data. This data includes, but is not limited to, sleep patterns, meal records, fitness levels, and exercise history. For example, the unit collects data such as how many hours of sleep a user gets each day, what they eat, and how much exercise they do. The data collection unit can also collect user lifestyle data through sensors and applications. For example, it can acquire data from the user's smartphone or wearable device. Smartphone applications can provide an interface for users to record their meals, take photos of their food using the camera, and automatically analyze the meal content using image recognition technology. Wearable devices collect data such as the user's heart rate, steps taken, and calories burned in real time and transmit it to the smartphone via Bluetooth® or Wi-Fi. The data collection unit can also collect data manually entered by the user. For example, if a user enters their weight and body fat percentage into the application, this data is also collected. The data collection unit centrally manages this data and stores it on a cloud server. The cloud server efficiently stores large amounts of data and makes it accessible to the analysis and creation units. Furthermore, the data collection unit can flexibly respond to specific situations and conditions by adjusting the frequency and accuracy of data collection. For example, if a user sets a specific fitness goal, the data collection frequency can be increased accordingly. This allows the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0031] The analysis unit analyzes data collected by the data collection unit to understand the user's health status and fitness goals. The analysis unit analyzes data using methods such as statistical analysis and machine learning algorithms. Specifically, it analyzes the user's sleep patterns and meal records to assess their health status. For example, sleep pattern analysis assesses how many hours the user sleeps each night, the ratio of deep to light sleep, and the quality of sleep. Meal record analysis assesses the user's calorie intake, nutrient balance, and meal timing. Furthermore, the analysis unit can analyze the user's exercise history to assess their fitness level. For example, it assesses how often the user exercises, the intensity and type of exercise, and their recovery after exercise. The analysis unit integrates this data to gain a comprehensive understanding of the user's health status and fitness goals. The analysis unit uses AI to process data in real time to grasp the user's health status and fitness goals. For example, machine learning algorithms can be used to predict health trends from the user's past data and assess future risks. Furthermore, by using anomaly detection algorithms, it is possible to detect unusual patterns and abnormal data, and issue warnings early. This allows the analysis unit to not only grasp the situation in real time, but also to handle long-term risk management and anomaly detection, thereby improving the reliability and safety of the entire system.

[0032] The creation unit creates a personalized training plan based on the analysis results obtained by the analysis unit. For example, the creation unit adjusts the type, frequency, and intensity of exercise according to the user's health condition and fitness goals. Specifically, if a user wants to lose weight, it creates a training plan to increase calorie expenditure. For example, it creates a plan centered on aerobic exercise and provides specific instructions on how many times a week and for how long to exercise. If a user wants to increase muscle strength, it can also create a plan that includes strength training. For example, it incorporates exercises targeting specific muscle groups and provides detailed instructions on the number of sets, reps, and rest times. The creation unit can periodically update the training plan according to the user's fitness goals. For example, it monitors the user's progress and adjusts the plan according to the degree of goal achievement. Furthermore, the creation unit can collect user feedback and continuously improve the accuracy and effectiveness of the training plan. For example, if a user finds a particular exercise too difficult, it adjusts the exercise or suggests an alternative exercise. In this way, the creation unit can provide the user with the optimal training plan and support the user in achieving their fitness goals.

[0033] The support team provides daily training support based on the training plans created by the development team. For example, the support team provides encouraging messages to users. Specifically, they send motivational messages before users start training and track their progress in real time during training, providing feedback. For example, when a user has achieved half of their goal, they send a message such as, "Great progress! Keep it up!" The support team also displays the user's training progress in graphs and charts, visually showing the user's progress. This allows users to see their progress at a glance and maintain their motivation. Furthermore, the support team can also provide a competitive feature with other users to increase user motivation. For example, they can display rankings where users compete with each other on their training results, and offer rewards to users who rank highly. The support team also compares the user's past training data with current data to evaluate the user's progress. For example, based on training data from the past month, they evaluate the user's improvement in fitness level and weight loss, and provide feedback with specific numerical values. In this way, the support team can provide appropriate support to users and help them achieve their fitness goals.

[0034] The support unit can provide encouraging messages to users. For example, the support unit can provide encouraging messages to help users continue their training. For example, the support unit can send messages such as "Keep going!" or "You're almost there!" The support unit can also provide encouraging messages at appropriate times depending on the user's training progress. For example, the support unit can send an encouraging message before the user starts training to boost their motivation. The support unit can also send an encouraging message after the user finishes training to enhance their sense of accomplishment. In this way, providing encouraging messages to users can increase their motivation. Some or all of the above processes in the support unit may be performed using AI, for example, or not using AI. For example, the support unit can input the user's training progress into a generating AI and have the generating AI generate encouraging messages.

[0035] The support unit can track training progress in real time. For example, the support unit tracks the user's training progress in real time through sensors and applications. For example, the support unit acquires data from the user's smartphone or wearable device to understand the training progress in real time. The support unit can also track data manually entered by the user in real time. For example, the support unit updates the data each time the user enters their training progress into the application. This makes it easier to understand the user's progress by tracking training progress in real time. Some or all of the above processes in the support unit may be performed using AI, for example, or not using AI. For example, the support unit can input the user's training progress data into a generating AI and have the generating AI perform real-time analysis of the progress.

[0036] The support unit can automate the comparison of the user's current performance to their past performance and visualize their progress. For example, the support unit can compare the user's past training data with their current data to visualize their progress. For example, the support unit can display the user's past training data in graphs or charts to visually show their progress. The support unit can also compare the user's past training data with their current data and evaluate their progress. For example, the support unit can evaluate the effectiveness of their current training based on the user's past training data. This automates the comparison of the user's current performance to their past performance and visualizes their progress, thereby increasing their motivation. 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 training data and current data into a generating AI and have the generating AI perform the visualization of progress.

[0037] The support unit can provide a competitive function with other users. For example, the support unit can provide a function for users to compete with other users. For example, the support unit can enable users to compete with other users in terms of training results. The support unit can also enable users to compare their training progress with other users. For example, the support unit can provide a function for users to compare their training progress with other users in real time. This can increase user motivation by providing a competitive function with other users. 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 training progress data into a generating AI and have the generating AI execute the competitive function with other users.

[0038] The data collection unit can analyze the user's past lifestyle data and select the optimal data collection method. For example, the data collection unit can suggest the optimal data collection method based on the devices the user has used in the past. For example, the data collection unit can analyze data from devices the user has used in the past and suggest the optimal device. The data collection unit can also analyze the user's past data collection frequency and set the optimal collection interval. For example, the data collection unit can suggest the optimal collection interval based on the user's past data collection frequency. The data collection unit can also suggest the optimal collection timing based on the user's past data collection time slots. For example, the data collection unit can suggest the optimal collection timing based on the user's past data collection time slots. In this way, by analyzing the user's past lifestyle data, the optimal data collection method can be selected. 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 lifestyle data into a generating AI and have the generating AI select the optimal data collection method.

[0039] The data collection unit can filter lifestyle data based on the user's current health status and fitness goals. For example, if a user wants to lose weight, the data collection unit can prioritize collecting data related to calorie consumption. For example, it can prioritize collecting data related to calorie consumption based on the user's food diary and exercise history. Similarly, if a user wants to increase muscle mass, the data collection unit can prioritize collecting data related to strength training. For example, it can prioritize collecting data related to strength training based on the user's exercise history. Furthermore, if a user aims to maintain their health, the data collection unit can collect data related to their overall health status. For example, it can collect data related to their overall health status based on their sleep patterns and food diary. This allows for the collection of more relevant data by filtering it based on the user's health status and fitness goals. 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 have a generating AI perform the filtering of data to be collected based on the user's health status and fitness goals.

[0040] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location when collecting lifestyle data. For example, if the user is at a gym, the data collection unit can prioritize the collection of exercise data from the gym. For example, the data collection unit can prioritize the collection of exercise data from the gym based on the user's geographical location. The data collection unit can also prioritize the collection of lifestyle data from the home if the user is at home. For example, the data collection unit can prioritize the collection of lifestyle data from the home based on the user's geographical location. The data collection unit can also prioritize the collection of activity data from outside if the user is out. For example, the data collection unit can prioritize the collection of activity data from outside based on the user's geographical location. In this way, by considering the user's geographical location, highly relevant data can be prioritized. 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 into a generating AI and have the generating AI perform the priority collection of highly relevant data.

[0041] The data collection unit can analyze the user's social media activity and collect relevant data when collecting lifestyle data. For example, the data collection unit can collect meal records shared by the user on social media. For example, the data collection unit can collect meal records based on the user's social media activity. The data collection unit can also collect exercise records shared by the user on social media. For example, the data collection unit can collect exercise records based on the user's social media activity. The data collection unit can also collect sleep patterns shared by the user on social media. For example, the data collection unit can collect sleep patterns based on the user's social media activity. In this way, relevant data can be collected by analyzing the user's social media activity. 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 social media activity data into a generating AI and have the generating AI collect the relevant data.

[0042] 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 important data and a simplified analysis on less important data. For example, the analysis unit can perform a detailed analysis on data directly related to the user's fitness goals. The analysis unit can also perform a detailed analysis on data that significantly impacts the user's health. For example, the analysis unit can perform a detailed analysis on data that significantly impacts the user's health. In this way, by adjusting the level of detail of the analysis based on the importance of the data, a detailed analysis can be performed on important 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 adjust the level of detail of the analysis.

[0043] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply an algorithm to evaluate sleep quality to sleep data. The analysis unit can also apply an algorithm to evaluate nutritional balance to diet data. The analysis unit can also apply an algorithm to evaluate the effects of exercise to exercise data. By applying different analysis algorithms depending on the data category, more appropriate analysis results can be provided. 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.

[0044] The analysis unit can determine the priority of analysis based on the data collection timing during analysis. For example, the analysis unit may prioritize the analysis of the most recent data. The analysis unit may also prioritize the analysis of data directly related to the user's fitness goals. The analysis unit may also prioritize the analysis of data that significantly impacts the user's health status. By determining the priority of analysis based on the data collection timing, the analysis unit can prioritize the analysis of the most recent 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 data collection timing into a generating AI and have the generating AI determine the priority of analysis.

[0045] The analysis unit can adjust the order of analysis based on the relevance of the data during analysis. For example, the analysis unit can prioritize the analysis of highly relevant data. For example, the analysis unit can prioritize the analysis of data related to the user's fitness goals. For example, the analysis unit can prioritize the analysis of data related to the user's health status. For example, the analysis unit can prioritize the analysis of data related to the user's health status. By adjusting the order of analysis based on the relevance of the data, it is possible to prioritize the analysis of highly relevant 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 relevance of the data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0046] The creation unit can analyze the user's past training history to select the optimal plan when creating a training plan. For example, the creation unit can create a new plan based on a training plan that the user has successfully completed in the past. The creation unit can also select effective training based on the user's past training history. For example, the creation unit can select effective training based on the user's past training history. The creation unit can also analyze the user's past training history and propose the optimal training plan. For example, the creation unit can propose the optimal training plan based on the user's past training history. In this way, the optimal training plan can be provided by analyzing the user's past training history. Some or all of the above processes in the creation unit may be performed using AI, for example, or without AI. For example, the creation unit can input the user's past training history into a generation AI and have the generation AI select the optimal plan.

[0047] The creation unit can customize the training plan based on the user's current health condition. For example, if the user is unwell, the creation unit can create a lighter training plan. For example, if the user is unwell, the creation unit can create a lighter training plan. For example, if the user is healthy, the creation unit can create a challenging training plan. For example, if the user is tired, the creation unit can create a recovery training plan. For example, if the user is tired, the creation unit can create a recovery training plan. This allows the creation unit to provide the user with the optimal plan by customizing the training plan based on their current health condition. Some or all of the above processes in the creation unit may be performed using AI, for example, or not using AI. For example, the creation unit can input the user's current health condition into a generating AI and have the generating AI perform the plan customization.

[0048] The creation unit can select the optimal training plan by considering the user's geographical location information when creating a training plan. For example, if the user is at a gym, the creation unit can create a training plan that can be done at the gym. For example, if the user is at a gym, the creation unit can create a training plan that can be done at home. For example, if the user is at home, the creation unit can create a training plan that can be done at a location where the user is out. For example, if the user is out, the creation unit can create a training plan that can be done at a location where the user is out. In this way, by considering the user's geographical location information, the optimal training plan can be provided. Some or all of the above processing in the creation unit may be performed using AI, for example, or without AI. For example, the creation unit can input the user's geographical location information into a generation AI and have the generation AI select the optimal plan.

[0049] The creation unit can analyze the user's social media activity and propose a training plan when creating one. For example, the creation unit can create a plan based on training records shared by the user on social media. The creation unit can also create a plan based on fitness goals shared by the user on social media. The creation unit can also create a plan based on health status shared by the user on social media. By analyzing the user's social media activity, the creation unit can provide the user with the optimal training plan. Some or all of the above processing in the creation unit may be performed using AI, for example, or without AI. For example, the creation unit can input the user's social media activity data into a generating AI and have the generating AI propose a plan.

[0050] The support unit can provide optimal support by referring to the user's past training history when tracking the progress of training. For example, the support unit can support the current training based on the user's past successful training. The support unit can also suggest effective support methods based on the user's past training history. For example, the support unit can suggest effective support methods based on the user's past training history. The support unit can also analyze the user's past training history and provide optimal support. For example, the support unit can provide optimal support based on the user's past training history. This allows the support unit to provide optimal support by referring to the user's past training history. Some or all of the above processes in the support unit may be performed using AI, for example, or not using AI. For example, the support unit can input the user's past training history into a generating AI and have the generating AI perform the task of providing optimal support.

[0051] The support unit can take into account the user's current health condition when automating the comparison of the user to their past self and visualizing their progress. For example, if the user is unwell, the support unit can perform a more restrained comparison to their past self. For example, if the user is healthy, the support unit can perform a more active comparison to their past self. For example, if the user is healthy, the support unit can perform a more active comparison to their past self. For example, if the user is tired, the support unit can simplify the visualization of progress. For example, if the user is tired, the support unit can simplify the visualization of progress. This allows for appropriate visualization of progress by taking the user's current health condition into account. Some or all of the above processing in the support unit may be performed using AI, for example, or not using AI. For example, the support unit can input the user's current health condition into a generating AI and have the generating AI adjust the visualization of progress.

[0052] The support unit can provide optimal support by considering the user's geographical location when tracking training progress. For example, if the user is at the gym, the support unit can provide training support at the gym. For example, if the user is at the gym, the support unit can provide training support at home. For example, if the user is at home, the support unit can provide training support at their destination if they are out. In this way, by considering the user's geographical location, the support unit can provide optimal training support. 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 perform the task of providing optimal support.

[0053] The support unit can analyze and compare the user's social media activity when automating the comparison of the user's current state with their past self and visualizing their progress. For example, the support unit can compare based on training records shared by the user on social media. The support unit can also compare based on fitness goals shared by the user on social media. The support unit can also compare based on health status shared by the user on social media. By analyzing the user's social media activity, appropriate progress can be visualized. 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 perform the comparison for visualization of progress.

[0054] The support unit, when providing a competitive function with other users, can select the optimal competitor by referring to the user's past competition history. For example, the support unit can select the optimal competitor based on the opponents the user has competed against in the past. The support unit can also suggest effective competitors based on the user's past competition history. The support unit can also analyze the user's past competition history and select the optimal competitor. For example, the support unit can select the optimal competitor based on the user's past competition history. This allows the support unit to select the optimal competitor by referring to the user's past competition history. 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 past competition history into a generating AI and have the generating AI select the optimal competitor.

[0055] The support unit can customize the content of the competition based on the user's current fitness goals when providing a competitive function with other users. For example, if the user wants to lose weight, the support unit can customize the content to be a competition based on calorie consumption. For example, if the user wants to increase muscle strength, the support unit can customize the content to be a competition based on strength training. For example, if the user wants to increase muscle strength, the support unit can customize the content to be a competition based on strength training. For example, if the user aims to maintain their health, the support unit can customize the content to be a competition based on overall health. By customizing the content of the competition based on the user's current fitness goals, the support unit can provide the user with the most suitable competition. Some or all of the above processing in the support unit may be performed using AI, for example, or not using AI. For example, the support unit can input the user's current fitness goals into a generating AI and have the generating AI perform the customization of the competition content.

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

[0057] The data collection unit can adjust the timing of data collection, taking into account the user's geographical location. For example, if the user is at a gym, it can prioritize collecting exercise data from the gym. If the user is at home, it can prioritize collecting lifestyle data from home, and if the user is out, it can prioritize collecting activity data from outside. This allows for the priority collection of highly relevant data by considering the user's geographical location. Some or all of the above processing in the data collection unit may be performed using AI, or not. For example, the data collection unit can input the user's geographical location into a generating AI and have the generating AI adjust the timing of data collection.

[0058] The analysis unit can determine the priority of analysis based on the data collection timing. For example, it can prioritize the analysis of the most recent data, or prioritize the analysis of data directly related to the user's fitness goals. It can also prioritize the analysis of data that significantly impacts the user's health status. By determining the priority of analysis based on the data collection timing, the most recent data can be prioritized. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input the data collection timing into a generating AI and have the generating AI perform the determination of the analysis priority.

[0059] The creation unit can analyze the user's past training history to create an optimal training plan. For example, it can create a new plan based on a training plan that the user has successfully completed in the past. It can also select effective training from the user's past training history and propose an optimal training plan by analyzing the user's past training history. In this way, it can provide an optimal training plan by analyzing the user's past training history. Some or all of the above processes in the creation unit may be performed using AI or not. For example, the creation unit can input the user's past training history into a generation AI and have the generation AI select the optimal plan.

[0060] The support unit can provide optimal support by referring to the user's past training history. For example, it can support the user's current training based on their past successful training. It can suggest effective support methods based on the user's past training history and provide optimal support by analyzing the user's past training history. In this way, optimal support can be provided by referring to the user's past training history. Some or all of the above processes in the support unit may be performed using AI or not. For example, the support unit can input the user's past training history into a generating AI and have the generating AI perform the task of providing optimal support.

[0061] The support unit can adjust the feedback on training progress, taking into account the user's current health condition. For example, if the user is unwell, positive feedback will be prioritized. If the user is healthy, detailed feedback will be provided, and if the user is tired, simplified feedback will be provided. This ensures that appropriate feedback is provided by considering the user's current health condition. Some or all of the above processing in the support unit may be performed using AI or not. For example, the support unit can input the user's current health condition into a generating AI and have the generating AI perform the feedback adjustments.

[0062] The following briefly describes the processing flow for example form 1.

[0063] Step 1: The data collection unit collects the user's lifestyle data. This lifestyle data includes sleep patterns, meal records, fitness levels, and exercise history. The data collection unit acquires data from the user's smartphone or wearable device through sensors and applications. It can also collect data manually entered by the user. Step 2: The analysis unit analyzes the data collected by the data collection unit to understand the user's health status and fitness goals. The analysis unit uses statistical analysis and machine learning algorithms to analyze the data and evaluate the user's sleep patterns, dietary records, and exercise history. It can also integrate and analyze multiple data sources. Step 3: The creation unit creates a personalized training plan based on the analysis results obtained by the analysis unit. The creation unit adjusts the type, frequency, and intensity of exercise according to the user's health condition and fitness goals. For example, if the user wants to lose weight, it will create a plan to increase calorie expenditure; if the user wants to increase muscle strength, it will create a plan that includes strength training. The training plan is updated regularly. Step 4: The support team provides daily training support based on the training plan created by the creation team. The support team provides encouraging messages to users, tracks training progress in real time, and visualizes progress. It also provides a competitive feature with other users to boost motivation.

[0064] (Example of form 2) An AI agent system according to an embodiment of the present invention is a system that provides an optimal training plan based on the user's individual health status and fitness goals. This AI agent system creates a personalized fitness plan based on the user's lifestyle data (e.g., sleep patterns, food records), fitness level, and exercise history, and supports daily training. Furthermore, the AI ​​agent system provides encouragement, automates comparisons of the user with their past self, and increases motivation through visualization of progress and competition with other users. First, the AI ​​agent system collects the user's lifestyle data. In this process, it collects detailed data such as the user's sleep patterns, food records, fitness level, and exercise history. For example, the AI ​​agent system collects data such as how many hours the user sleeps each day, what they eat, and how much exercise they do. Next, the AI ​​agent system analyzes the collected data. Based on the collected data, the AI ​​understands the user's health status and fitness goals and creates an optimal training plan. For example, if the user wants to lose weight, the AI ​​analyzes the user's food records and exercise history and creates a training plan to increase calorie consumption. Furthermore, the AI ​​agent system supports daily training. Specifically, the AI ​​agent system provides encouraging messages to the user. Furthermore, the AI ​​agent system automates comparisons between the user's past performance and current performance, boosting motivation through visualization of progress and competition with other users. For example, the AI ​​agent system tracks the user's training progress in real time, visualizing their progress. It also provides a competitive feature with other users to increase user motivation. As a result, the AI ​​agent system can provide personalized training plans based on the user's lifestyle data, supporting their daily training.

[0065] The AI ​​agent system according to this embodiment comprises a collection unit, an analysis unit, a creation unit, and a support unit. The collection unit collects user lifestyle data. User lifestyle data includes, but is not limited to, sleep patterns, meal records, fitness levels, and exercise history. For example, the collection unit collects data such as how many hours the user sleeps each day, what they eat, and how much exercise they do. The collection unit can collect user lifestyle data through sensors or applications. For example, the collection unit acquires data from the user's smartphone or wearable device. The collection unit can also collect data manually entered by the user. The analysis unit analyzes the data collected by the collection unit to understand the user's health status and fitness goals. The analysis unit analyzes the data using, for example, statistical analysis or machine learning algorithms. For example, the analysis unit analyzes the user's sleep patterns and meal records to evaluate the user's health status. The analysis unit can also analyze the user's exercise history to evaluate the user's fitness level. The analysis unit can integrate and analyze multiple data sources to understand the user's health status and fitness goals. The creation unit creates a personalized training plan based on the analysis results obtained by the analysis unit. For example, the creation unit adjusts the type, frequency, and intensity of exercise according to the user's health status and fitness goals. For example, if the user wants to lose weight, the creation unit will create a training plan to increase calorie expenditure. The creation unit can also create a plan that includes strength training if the user wants to increase muscle strength. The creation unit can periodically update the training plan according to the user's fitness goals. The support unit supports daily training based on the training plan created by the creation unit. For example, the support unit provides encouraging messages to the user. The support unit tracks the user's training progress in real time and visualizes the user's progress. The support unit can also provide a competitive function with other users to increase user motivation.For example, the support unit displays the user's training progress using graphs and charts, visually showing the user's progress. The support unit also compares the user's past training data with current data to evaluate the user's progress. As a result, the AI ​​agent system according to this embodiment can provide a personalized training plan based on the user's lifestyle data, supporting their daily training.

[0066] The data collection unit collects user lifestyle data. This data includes, but is not limited to, sleep patterns, meal records, fitness levels, and exercise history. For example, the unit collects data such as how many hours of sleep a user gets each day, what they eat, and how much exercise they do. The data collection unit can also collect user lifestyle data through sensors and applications. For example, it can acquire data from the user's smartphone or wearable device. Smartphone applications can provide an interface for users to record their meals, take photos of their food using the camera, and automatically analyze the meal content using image recognition technology. Wearable devices collect data such as the user's heart rate, steps taken, and calories burned in real time and transmit it to the smartphone via Bluetooth or Wi-Fi. The data collection unit can also collect data manually entered by the user. For example, if a user enters their weight and body fat percentage into the application, this data is also collected. The data collection unit centrally manages this data and stores it on a cloud server. The cloud server efficiently stores large amounts of data and makes it accessible to the analysis and creation units. Furthermore, the data collection unit can flexibly respond to specific situations and conditions by adjusting the frequency and accuracy of data collection. For example, if a user sets a specific fitness goal, the data collection frequency can be increased accordingly. This allows the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0067] The analysis unit analyzes data collected by the data collection unit to understand the user's health status and fitness goals. The analysis unit analyzes data using methods such as statistical analysis and machine learning algorithms. Specifically, it analyzes the user's sleep patterns and meal records to assess their health status. For example, sleep pattern analysis assesses how many hours the user sleeps each night, the ratio of deep to light sleep, and the quality of sleep. Meal record analysis assesses the user's calorie intake, nutrient balance, and meal timing. Furthermore, the analysis unit can analyze the user's exercise history to assess their fitness level. For example, it assesses how often the user exercises, the intensity and type of exercise, and their recovery after exercise. The analysis unit integrates this data to gain a comprehensive understanding of the user's health status and fitness goals. The analysis unit uses AI to process data in real time to grasp the user's health status and fitness goals. For example, machine learning algorithms can be used to predict health trends from the user's past data and assess future risks. Furthermore, by using anomaly detection algorithms, it is possible to detect unusual patterns and abnormal data, and issue warnings early. This allows the analysis unit to not only grasp the situation in real time, but also to handle long-term risk management and anomaly detection, thereby improving the reliability and safety of the entire system.

[0068] The creation unit creates a personalized training plan based on the analysis results obtained by the analysis unit. For example, the creation unit adjusts the type, frequency, and intensity of exercise according to the user's health condition and fitness goals. Specifically, if a user wants to lose weight, it creates a training plan to increase calorie expenditure. For example, it creates a plan centered on aerobic exercise and provides specific instructions on how many times a week and for how long to exercise. If a user wants to increase muscle strength, it can also create a plan that includes strength training. For example, it incorporates exercises targeting specific muscle groups and provides detailed instructions on the number of sets, reps, and rest times. The creation unit can periodically update the training plan according to the user's fitness goals. For example, it monitors the user's progress and adjusts the plan according to the degree of goal achievement. Furthermore, the creation unit can collect user feedback and continuously improve the accuracy and effectiveness of the training plan. For example, if a user finds a particular exercise too difficult, it adjusts the exercise or suggests an alternative exercise. In this way, the creation unit can provide the user with the optimal training plan and support the user in achieving their fitness goals.

[0069] The support team provides daily training support based on the training plans created by the development team. For example, the support team provides encouraging messages to users. Specifically, they send motivational messages before users start training and track their progress in real time during training, providing feedback. For example, when a user has achieved half of their goal, they send a message such as, "Great progress! Keep it up!" The support team also displays the user's training progress in graphs and charts, visually showing the user's progress. This allows users to see their progress at a glance and maintain their motivation. Furthermore, the support team can also provide a competitive feature with other users to increase user motivation. For example, they can display rankings where users compete with each other on their training results, and offer rewards to users who rank highly. The support team also compares the user's past training data with current data to evaluate the user's progress. For example, based on training data from the past month, they evaluate the user's improvement in fitness level and weight loss, and provide feedback with specific numerical values. In this way, the support team can provide appropriate support to users and help them achieve their fitness goals.

[0070] The support unit can provide encouraging messages to users. For example, the support unit can provide encouraging messages to help users continue their training. For example, the support unit can send messages such as "Keep going!" or "You're almost there!" The support unit can also provide encouraging messages at appropriate times depending on the user's training progress. For example, the support unit can send an encouraging message before the user starts training to boost their motivation. The support unit can also send an encouraging message after the user finishes training to enhance their sense of accomplishment. In this way, providing encouraging messages to users can increase their motivation. Some or all of the above processes in the support unit may be performed using AI, for example, or not using AI. For example, the support unit can input the user's training progress into a generating AI and have the generating AI generate encouraging messages.

[0071] The support unit can track training progress in real time. For example, the support unit tracks the user's training progress in real time through sensors and applications. For example, the support unit acquires data from the user's smartphone or wearable device to understand the training progress in real time. The support unit can also track data manually entered by the user in real time. For example, the support unit updates the data each time the user enters their training progress into the application. This makes it easier to understand the user's progress by tracking training progress in real time. Some or all of the above processes in the support unit may be performed using AI, for example, or not using AI. For example, the support unit can input the user's training progress data into a generating AI and have the generating AI perform real-time analysis of the progress.

[0072] The support unit can automate the comparison of the user's current performance to their past performance and visualize their progress. For example, the support unit can compare the user's past training data with their current data to visualize their progress. For example, the support unit can display the user's past training data in graphs or charts to visually show their progress. The support unit can also compare the user's past training data with their current data and evaluate their progress. For example, the support unit can evaluate the effectiveness of their current training based on the user's past training data. This automates the comparison of the user's current performance to their past performance and visualizes their progress, thereby increasing their motivation. 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 training data and current data into a generating AI and have the generating AI perform the visualization of progress.

[0073] The support unit can provide a competitive function with other users. For example, the support unit can provide a function for users to compete with other users. For example, the support unit can enable users to compete with other users in terms of training results. The support unit can also enable users to compare their training progress with other users. For example, the support unit can provide a function for users to compare their training progress with other users in real time. This can increase user motivation by providing a competitive function with other users. 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 training progress data into a generating AI and have the generating AI execute the competitive function with other users.

[0074] The data collection unit can estimate the user's emotions and adjust the timing of lifestyle data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can collect data during times when the user is relaxed. For example, by collecting data during times when the user is relaxed, the data collection unit can obtain more accurate data. Also, if the user is tired, the data collection unit can collect data after rest. For example, by collecting data after rest, the data collection unit can prevent the user's fatigue from affecting the data. Also, if the user is energetic, the data collection unit can collect data during times when the user is active. For example, by collecting data during times when the user is active, the data collection unit can accurately understand the user's activity level. This allows for more accurate data collection by adjusting the timing of data collection 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 data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input user emotion data into a generating AI and have the generating AI adjust the timing of data collection.

[0075] The data collection unit can analyze the user's past lifestyle data and select the optimal data collection method. For example, the data collection unit can suggest the optimal data collection method based on the devices the user has used in the past. For example, the data collection unit can analyze data from devices the user has used in the past and suggest the optimal device. The data collection unit can also analyze the user's past data collection frequency and set the optimal collection interval. For example, the data collection unit can suggest the optimal collection interval based on the user's past data collection frequency. The data collection unit can also suggest the optimal collection timing based on the user's past data collection time slots. For example, the data collection unit can suggest the optimal collection timing based on the user's past data collection time slots. In this way, by analyzing the user's past lifestyle data, the optimal data collection method can be selected. 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 lifestyle data into a generating AI and have the generating AI select the optimal data collection method.

[0076] The data collection unit can filter lifestyle data based on the user's current health status and fitness goals. For example, if a user wants to lose weight, the data collection unit can prioritize collecting data related to calorie consumption. For example, it can prioritize collecting data related to calorie consumption based on the user's food diary and exercise history. Similarly, if a user wants to increase muscle mass, the data collection unit can prioritize collecting data related to strength training. For example, it can prioritize collecting data related to strength training based on the user's exercise history. Furthermore, if a user aims to maintain their health, the data collection unit can collect data related to their overall health status. For example, it can collect data related to their overall health status based on their sleep patterns and food diary. This allows for the collection of more relevant data by filtering it based on the user's health status and fitness goals. 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 have a generating AI perform the filtering of data to be collected based on the user's health status and fitness goals.

[0077] 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 may prioritize collecting data related to stress management. For example, based on the user's stress level, the data collection unit may prioritize collecting data related to stress management. Also, if the user is relaxed, the data collection unit may prioritize collecting data to maintain that relaxed state. For example, based on the user's relaxed state, the data collection unit may prioritize collecting data to maintain that relaxed state. Also, if the user is energetic, the data collection unit may prioritize collecting active data. For example, based on the user's activity level, the data collection unit may prioritize collecting active data. In this way, by prioritizing data based on the user's emotions, more important data can be collected preferentially. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input user emotion data into a generating AI and have the generating AI determine the priority of the data.

[0078] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location when collecting lifestyle data. For example, if the user is at a gym, the data collection unit can prioritize the collection of exercise data from the gym. For example, the data collection unit can prioritize the collection of exercise data from the gym based on the user's geographical location. The data collection unit can also prioritize the collection of lifestyle data from the home if the user is at home. For example, the data collection unit can prioritize the collection of lifestyle data from the home based on the user's geographical location. The data collection unit can also prioritize the collection of activity data from outside if the user is out. For example, the data collection unit can prioritize the collection of activity data from outside based on the user's geographical location. In this way, by considering the user's geographical location, highly relevant data can be prioritized. 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 into a generating AI and have the generating AI perform the priority collection of highly relevant data.

[0079] The data collection unit can analyze the user's social media activity and collect relevant data when collecting lifestyle data. For example, the data collection unit can collect meal records shared by the user on social media. For example, the data collection unit can collect meal records based on the user's social media activity. The data collection unit can also collect exercise records shared by the user on social media. For example, the data collection unit can collect exercise records based on the user's social media activity. The data collection unit can also collect sleep patterns shared by the user on social media. For example, the data collection unit can collect sleep patterns based on the user's social media activity. In this way, relevant data can be collected by analyzing the user's social media activity. 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 social media activity data into a generating AI and have the generating AI collect the relevant data.

[0080] 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 easy-to-understand analysis results. For example, if the user is stressed, the analysis unit can provide analysis results using simple graphs or charts. The analysis unit can also provide detailed analysis results if the user is relaxed. For example, if the user is relaxed, the analysis unit can provide a detailed report. The analysis unit can also provide visually appealing analysis results if the user is energetic. For example, if the user is energetic, the analysis unit can provide analysis results using colorful graphs or charts. In this way, by adjusting the presentation of the analysis based on the user's emotions, it is possible to provide analysis results that are easy for the user to understand. 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 analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into the generating AI and have the generating AI adjust the way the analysis is expressed.

[0081] 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 important data and a simplified analysis on less important data. For example, the analysis unit can perform a detailed analysis on data directly related to the user's fitness goals. The analysis unit can also perform a detailed analysis on data that significantly impacts the user's health. For example, the analysis unit can perform a detailed analysis on data that significantly impacts the user's health. In this way, by adjusting the level of detail of the analysis based on the importance of the data, a detailed analysis can be performed on important 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 adjust the level of detail of the analysis.

[0082] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply an algorithm to evaluate sleep quality to sleep data. The analysis unit can also apply an algorithm to evaluate nutritional balance to diet data. The analysis unit can also apply an algorithm to evaluate the effects of exercise to exercise data. By applying different analysis algorithms depending on the data category, more appropriate analysis results can be provided. 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.

[0083] 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 in a hurry, the analysis unit can provide a short, concise analysis result. The analysis unit can also provide a detailed analysis result if the user is relaxed. For example, if the user is relaxed, the analysis unit can provide a detailed analysis result. The analysis unit can also provide a visually stimulating analysis result if the user is excited. For example, if the user is excited, the analysis unit can provide a visually stimulating analysis result. By adjusting the length of the analysis based on the user's emotions, the analysis unit can provide an analysis result of an appropriate length for the user. 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, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described 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 generating AI and have the generating AI adjust the length of the analysis.

[0084] The analysis unit can determine the priority of analysis based on the data collection timing during analysis. For example, the analysis unit may prioritize the analysis of the most recent data. The analysis unit may also prioritize the analysis of data directly related to the user's fitness goals. The analysis unit may also prioritize the analysis of data that significantly impacts the user's health status. By determining the priority of analysis based on the data collection timing, the analysis unit can prioritize the analysis of the most recent 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 data collection timing into a generating AI and have the generating AI determine the priority of analysis.

[0085] The analysis unit can adjust the order of analysis based on the relevance of the data during analysis. For example, the analysis unit can prioritize the analysis of highly relevant data. For example, the analysis unit can prioritize the analysis of data related to the user's fitness goals. For example, the analysis unit can prioritize the analysis of data related to the user's health status. For example, the analysis unit can prioritize the analysis of data related to the user's health status. By adjusting the order of analysis based on the relevance of the data, it is possible to prioritize the analysis of highly relevant 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 relevance of the data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0086] The creation unit can estimate the user's emotions and adjust how the training plan is created based on the estimated emotions. For example, if the user is stressed, the creation unit can create a relaxing training plan. For example, if the user is relaxed, the creation unit can create a challenging training plan. For example, if the user is relaxed, the creation unit can create a challenging training plan. For example, if the user is energetic, the creation unit can create an active training plan. For example, if the user is energetic, the creation unit can create an active training plan. In this way, by adjusting how the training plan is created based on the user's emotions, the optimal training plan for the user can be provided. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the creation unit may be performed using AI, for example, or without AI. For example, the creation unit can input user emotion data into the generating AI and have the generating AI adjust how the training plan is created.

[0087] The creation unit can analyze the user's past training history to select the optimal plan when creating a training plan. For example, the creation unit can create a new plan based on a training plan that the user has successfully completed in the past. The creation unit can also select effective training based on the user's past training history. For example, the creation unit can select effective training based on the user's past training history. The creation unit can also analyze the user's past training history and propose the optimal training plan. For example, the creation unit can propose the optimal training plan based on the user's past training history. In this way, the optimal training plan can be provided by analyzing the user's past training history. Some or all of the above processes in the creation unit may be performed using AI, for example, or without AI. For example, the creation unit can input the user's past training history into a generation AI and have the generation AI select the optimal plan.

[0088] The creation unit can customize the training plan based on the user's current health condition. For example, if the user is unwell, the creation unit can create a lighter training plan. For example, if the user is unwell, the creation unit can create a lighter training plan. For example, if the user is healthy, the creation unit can create a challenging training plan. For example, if the user is tired, the creation unit can create a recovery training plan. For example, if the user is tired, the creation unit can create a recovery training plan. This allows the creation unit to provide the user with the optimal plan by customizing the training plan based on their current health condition. Some or all of the above processes in the creation unit may be performed using AI, for example, or not using AI. For example, the creation unit can input the user's current health condition into a generating AI and have the generating AI perform the plan customization.

[0089] The creation unit can estimate the user's emotions and determine the priority of the training plan based on the estimated emotions. For example, if the user is feeling stressed, the creation unit will prioritize relaxing training. For example, if the user is feeling stressed, the creation unit will prioritize relaxing training. For example, if the user is relaxed, the creation unit will prioritize challenging training. For example, if the user is relaxed, the creation unit will prioritize challenging training. For example, if the user is energetic, the creation unit will prioritize active training. For example, if the user is energetic, the creation unit will prioritize active training. In this way, by determining the priority of the training plan based on the user's emotions, the optimal training for the user can be provided. 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, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the creation unit may be performed using AI, for example, or without AI. For example, the creation unit can input user emotion data into the generating AI and have the generating AI determine the priorities of the training plan.

[0090] The creation unit can select the optimal training plan by considering the user's geographical location information when creating a training plan. For example, if the user is at a gym, the creation unit can create a training plan that can be done at the gym. For example, if the user is at a gym, the creation unit can create a training plan that can be done at home. For example, if the user is at home, the creation unit can create a training plan that can be done at a location where the user is out. For example, if the user is out, the creation unit can create a training plan that can be done at a location where the user is out. In this way, by considering the user's geographical location information, the optimal training plan can be provided. Some or all of the above processing in the creation unit may be performed using AI, for example, or without AI. For example, the creation unit can input the user's geographical location information into a generation AI and have the generation AI select the optimal plan.

[0091] The creation unit can analyze the user's social media activity and propose a training plan when creating one. For example, the creation unit can create a plan based on training records shared by the user on social media. The creation unit can also create a plan based on fitness goals shared by the user on social media. The creation unit can also create a plan based on health status shared by the user on social media. By analyzing the user's social media activity, the creation unit can provide the user with the optimal training plan. Some or all of the above processing in the creation unit may be performed using AI, for example, or without AI. For example, the creation unit can input the user's social media activity data into a generating AI and have the generating AI propose a plan.

[0092] The support unit can estimate the user's emotions and adjust encouraging messages based on those emotions. For example, if the user is feeling stressed, the support unit can provide a relaxing message. For example, if the user is feeling stressed, the support unit can provide a relaxing message. The support unit can also provide a challenging message if the user is relaxed. For example, if the user is relaxed, the support unit can provide a challenging message. The support unit can also provide an active message if the user is feeling energetic. For example, if the user is feeling energetic, the support unit can provide an active message. By adjusting encouraging messages based on the user's emotions, the user's motivation can be increased. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a 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, for example, or without AI. For example, the support department can input user emotion data into a generating AI and have the AI ​​adjust the encouraging messages.

[0093] The support unit can provide optimal support by referring to the user's past training history when tracking the progress of training. For example, the support unit can support the current training based on the user's past successful training. The support unit can also suggest effective support methods based on the user's past training history. For example, the support unit can suggest effective support methods based on the user's past training history. The support unit can also analyze the user's past training history and provide optimal support. For example, the support unit can provide optimal support based on the user's past training history. This allows the support unit to provide optimal support by referring to the user's past training history. Some or all of the above processes in the support unit may be performed using AI, for example, or not using AI. For example, the support unit can input the user's past training history into a generating AI and have the generating AI perform the task of providing optimal support.

[0094] The support unit can take into account the user's current health condition when automating the comparison of the user to their past self and visualizing their progress. For example, if the user is unwell, the support unit can perform a more restrained comparison to their past self. For example, if the user is healthy, the support unit can perform a more active comparison to their past self. For example, if the user is healthy, the support unit can perform a more active comparison to their past self. For example, if the user is tired, the support unit can simplify the visualization of progress. For example, if the user is tired, the support unit can simplify the visualization of progress. This allows for appropriate visualization of progress by taking the user's current health condition into account. Some or all of the above processing in the support unit may be performed using AI, for example, or not using AI. For example, the support unit can input the user's current health condition into a generating AI and have the generating AI adjust the visualization of progress.

[0095] The support unit can estimate the user's emotions and prioritize encouraging messages based on those emotions. For example, if the user is stressed, the support unit will prioritize providing relaxing messages. The support unit can also prioritize providing challenging messages if the user is relaxed. Furthermore, if the user is energetic, the support unit can prioritize providing active messages. This allows for increased user motivation by prioritizing encouraging messages 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 includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the support unit may be performed using AI, or not. For example, the support department can input user emotion data into a generating AI and have the AI ​​determine the priority of encouraging messages.

[0096] The support unit can provide optimal support by considering the user's geographical location when tracking training progress. For example, if the user is at the gym, the support unit can provide training support at the gym. For example, if the user is at the gym, the support unit can provide training support at home. For example, if the user is at home, the support unit can provide training support at their destination if they are out. In this way, by considering the user's geographical location, the support unit can provide optimal training support. 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 perform the task of providing optimal support.

[0097] The support unit can analyze and compare the user's social media activity when automating the comparison of the user's current state with their past self and visualizing their progress. For example, the support unit can compare based on training records shared by the user on social media. The support unit can also compare based on fitness goals shared by the user on social media. The support unit can also compare based on health status shared by the user on social media. By analyzing the user's social media activity, appropriate progress can be visualized. 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 perform the comparison for visualization of progress.

[0098] The support unit, when providing a competitive function with other users, can select the optimal competitor by referring to the user's past competition history. For example, the support unit can select the optimal competitor based on the opponents the user has competed against in the past. The support unit can also suggest effective competitors based on the user's past competition history. The support unit can also analyze the user's past competition history and select the optimal competitor. For example, the support unit can select the optimal competitor based on the user's past competition history. This allows the support unit to select the optimal competitor by referring to the user's past competition history. 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 past competition history into a generating AI and have the generating AI select the optimal competitor.

[0099] The support unit can customize the content of the competition based on the user's current fitness goals when providing a competitive function with other users. For example, if the user wants to lose weight, the support unit can customize the content to be a competition based on calorie consumption. For example, if the user wants to increase muscle strength, the support unit can customize the content to be a competition based on strength training. For example, if the user wants to increase muscle strength, the support unit can customize the content to be a competition based on strength training. For example, if the user aims to maintain their health, the support unit can customize the content to be a competition based on overall health. By customizing the content of the competition based on the user's current fitness goals, the support unit can provide the user with the most suitable competition. Some or all of the above processing in the support unit may be performed using AI, for example, or not using AI. For example, the support unit can input the user's current fitness goals into a generating AI and have the generating AI perform the customization of the competition content.

[0100] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0101] The analysis unit can estimate the user's emotions and determine the priority of analysis based on the estimated emotions. For example, if the user is stressed, it can prioritize the analysis of data related to stress management. If the user is relaxed, it can perform a detailed analysis, and if the user is energetic, it can prioritize the analysis of active data. By prioritizing analysis based on the user's emotions, it is possible to provide more appropriate analysis results. 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 analysis unit may be performed using AI or not. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI determine the priority of analysis.

[0102] The creation unit can estimate the user's emotions and adjust the difficulty level of the training plan based on the estimated emotions. For example, if the user is stressed, it can create a lighter, more relaxing training plan. If the user is relaxed, it can create a more challenging training plan, and if the user is energetic, it can create an active training plan. By adjusting the difficulty level of the training plan based on the user's emotions, the system can provide the user with the optimal training plan. 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 creation unit may be performed using AI or not. For example, the creation unit can input user emotion data into a generative AI and have the generative AI adjust the difficulty level of the training plan.

[0103] The support unit can estimate the user's emotions and adjust the training progress feedback based on the estimated emotions. For example, if the user is stressed, positive feedback can be prioritized. If the user is relaxed, detailed feedback can be provided, and if the user is energetic, challenging feedback can be provided. This allows for increased user motivation by adjusting the training progress feedback 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 support unit may be performed using AI or not. For example, the support unit can input user emotion data into a generative AI and have the generative AI perform the feedback adjustment.

[0104] The data collection unit can estimate the user's emotions and adjust the frequency of data collection based on the estimated emotions. For example, if the user is stressed, the frequency of data collection can be reduced, and if the user is relaxed, the frequency can be increased. If the user is energetic, data can be collected during active times. By adjusting the frequency of data collection based on the user's emotions, more accurate data can be collected. 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. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI adjust the frequency of data collection.

[0105] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated emotions. For example, if the user is stressed, a simple and easy-to-understand display method can be adopted. If the user is relaxed, detailed analysis results can be displayed, and if the user is energetic, a visually appealing display method can be adopted. In this way, by adjusting the display method of the analysis results based on the user's emotions, it is possible to provide analysis results that are easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the analysis unit may be performed using AI or not using AI. For example, the analysis unit can input user emotion data into the generative AI and have the generative AI perform the adjustment of the display method of the analysis results.

[0106] The data collection unit can adjust the timing of data collection, taking into account the user's geographical location. For example, if the user is at a gym, it can prioritize collecting exercise data from the gym. If the user is at home, it can prioritize collecting lifestyle data from home, and if the user is out, it can prioritize collecting activity data from outside. This allows for the priority collection of highly relevant data by considering the user's geographical location. Some or all of the above processing in the data collection unit may be performed using AI, or not. For example, the data collection unit can input the user's geographical location into a generating AI and have the generating AI adjust the timing of data collection.

[0107] The analysis unit can determine the priority of analysis based on the data collection timing. For example, it can prioritize the analysis of the most recent data, or prioritize the analysis of data directly related to the user's fitness goals. It can also prioritize the analysis of data that significantly impacts the user's health status. By determining the priority of analysis based on the data collection timing, the most recent data can be prioritized. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input the data collection timing into a generating AI and have the generating AI perform the determination of the analysis priority.

[0108] The creation unit can analyze the user's past training history to create an optimal training plan. For example, it can create a new plan based on a training plan that the user has successfully completed in the past. It can also select effective training from the user's past training history and propose an optimal training plan by analyzing the user's past training history. In this way, it can provide an optimal training plan by analyzing the user's past training history. Some or all of the above processes in the creation unit may be performed using AI or not. For example, the creation unit can input the user's past training history into a generation AI and have the generation AI select the optimal plan.

[0109] The support unit can provide optimal support by referring to the user's past training history. For example, it can support the user's current training based on their past successful training. It can suggest effective support methods based on the user's past training history and provide optimal support by analyzing the user's past training history. In this way, optimal support can be provided by referring to the user's past training history. Some or all of the above processes in the support unit may be performed using AI or not. For example, the support unit can input the user's past training history into a generating AI and have the generating AI perform the task of providing optimal support.

[0110] The support unit can adjust the feedback on training progress, taking into account the user's current health condition. For example, if the user is unwell, positive feedback will be prioritized. If the user is healthy, detailed feedback will be provided, and if the user is tired, simplified feedback will be provided. This ensures that appropriate feedback is provided by considering the user's current health condition. Some or all of the above processing in the support unit may be performed using AI or not. For example, the support unit can input the user's current health condition into a generating AI and have the generating AI perform the feedback adjustments.

[0111] The following briefly describes the processing flow for example form 2.

[0112] Step 1: The data collection unit collects the user's lifestyle data. This lifestyle data includes sleep patterns, meal records, fitness levels, and exercise history. The data collection unit acquires data from the user's smartphone or wearable device through sensors and applications. It can also collect data manually entered by the user. Step 2: The analysis unit analyzes the data collected by the data collection unit to understand the user's health status and fitness goals. The analysis unit uses statistical analysis and machine learning algorithms to analyze the data and evaluate the user's sleep patterns, dietary records, and exercise history. It can also integrate and analyze multiple data sources. Step 3: The creation unit creates a personalized training plan based on the analysis results obtained by the analysis unit. The creation unit adjusts the type, frequency, and intensity of exercise according to the user's health condition and fitness goals. For example, if the user wants to lose weight, it will create a plan to increase calorie expenditure; if the user wants to increase muscle strength, it will create a plan that includes strength training. The training plan is updated regularly. Step 4: The support team provides daily training support based on the training plan created by the creation team. The support team provides encouraging messages to users, tracks training progress in real time, and visualizes progress. It also provides a competitive feature with other users to boost motivation.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] Each of the multiple elements described above, including the collection unit, analysis unit, creation unit, and support unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects user lifestyle data through the sensors and applications of the smart device 14. The analysis unit analyzes the collected data by the specific processing unit 290 of the data processing unit 12. The creation unit creates a personalized training plan by the specific processing unit 290 of the data processing unit 12. The support unit supports daily training by the control unit 46A of the smart device 14. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0117] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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).

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.).

[0129] 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.

[0130] 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.

[0131] 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.

[0132] Each of the multiple elements described above, including the data collection unit, analysis unit, creation 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 user lifestyle data through the sensors and applications of the smart glasses 214. The analysis unit analyzes the collected data by the specific processing unit 290 of the data processing unit 12. The creation unit creates a personalized training plan by the specific processing unit 290 of the data processing unit 12. The support unit supports daily training by the control unit 46A of the smart glasses 214. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0133] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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).

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.).

[0145] 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.

[0146] 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.

[0147] 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.

[0148] Each of the multiple elements described above, including the data collection unit, analysis unit, creation 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 user lifestyle data through the sensors and applications of the headset terminal 314. The analysis unit analyzes the collected data by the specific processing unit 290 of the data processing unit 12. The creation unit creates a personalized training plan by the specific processing unit 290 of the data processing unit 12. The support unit supports daily training by the control unit 46A of the headset terminal 314. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0149] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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).

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.).

[0162] 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.

[0163] 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.

[0164] 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.

[0165] Each of the multiple elements described above, including the data collection unit, analysis unit, creation unit, and support unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the data collection unit collects user lifestyle data through the robot 414's sensors and applications. The analysis unit analyzes the collected data by the specific processing unit 290 of the data processing unit 12. The creation unit creates a personalized training plan by the specific processing unit 290 of the data processing unit 12. The support unit supports daily training by the control unit 46A of the robot 414. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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."

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] (Note 1) A data collection unit that collects user lifestyle data, The data collected by the aforementioned collection unit is analyzed by an analysis unit to understand the user's health status and fitness goals, A creation unit that creates a personalized training plan based on the analysis results obtained by the aforementioned analysis unit, The system includes a support unit that supports daily training based on the training plan created by the creation unit. A system characterized by the following features. (Note 2) The aforementioned support unit is Provide encouraging messages to users. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned support unit is Track training progress in real time. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned support unit is Automate the comparison of the user's current performance to their past self and visualize their progress. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned support unit is Provides a competitive feature against other users. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned collection unit is The system estimates the user's emotions and adjusts the timing of lifestyle data collection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is We analyze the user's past lifestyle data and select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is When collecting lifestyle data, filtering is performed based on the user's current health status and fitness goals. The system described in Appendix 1, characterized by the features described herein. (Note 9) 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 10) The aforementioned collection unit is When collecting lifestyle data, the system prioritizes collecting highly relevant data by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting lifestyle data, analyze users' social media activity and collect relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 12) 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 13) 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 14) 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 15) 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 16) 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 17) 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 18) The aforementioned creation unit, It estimates the user's emotions and adjusts how the training plan is created based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned creation unit, When creating a training plan, the system analyzes the user's past training history to select the optimal plan. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned creation unit, When creating a training plan, customize the plan based on the user's current health status. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned creation unit, It estimates the user's emotions and prioritizes training plans based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned creation unit, When creating a training plan, the optimal plan is selected by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned creation unit, When creating a training plan, we analyze the user's social media activity and propose a plan accordingly. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned support unit is It estimates the user's emotions and adjusts encouraging messages based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned support unit is When tracking training progress, we refer to the user's past training history to provide optimal support. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned support unit is When automating the comparison of a user's current state with their past self and visualizing their progress, the system takes the user's current health status into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned support unit is It estimates the user's emotions and prioritizes encouraging messages based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned support unit is When tracking training progress, we take the user's geographical location into consideration to provide optimal support. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned support unit is To automate the comparison of a user's current state with their past self and visualize their progress, the system analyzes and compares the user's social media activity. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned support unit is When providing a competitive feature against other users, the system selects the most suitable competitor by referring to the user's past competition history. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned support unit is When providing a competitive feature against other users, customize the competition based on the user's current fitness goals. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0185] 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 user lifestyle data, The data collected by the aforementioned collection unit is analyzed by an analysis unit to understand the user's health status and fitness goals, A creation unit that creates a personalized training plan based on the analysis results obtained by the aforementioned analysis unit, The system includes a support unit that supports daily training based on the training plan created by the creation unit. A system characterized by the following features.

2. The aforementioned support unit is Provide encouraging messages to users. The system according to feature 1.

3. The aforementioned support unit is Track training progress in real time. The system according to feature 1.

4. The aforementioned support unit is Automate the comparison of the user's current performance to their past self and visualize their progress. The system according to feature 1.

5. The aforementioned creation unit, It estimates the user's emotions and adjusts how the training plan is created based on those estimated emotions. The system according to feature 1.

6. The aforementioned creation unit, When creating a training plan, the optimal plan is selected by considering the user's geographical location. The system according to feature 1.

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