system

Abstract

The system according to this embodiment aims to analyze user data and propose and execute an optimal yoga program tailored to the user's goals. [Solution] The system according to the embodiment comprises a data collection unit, an analysis unit, and an execution unit. The data collection unit collects user data. The analysis unit analyzes the data collected by the data collection unit and proposes an optimal yoga program tailored to the user's goals. The execution unit executes the yoga program proposed by the analysis unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that it is difficult to propose an optimal yoga program according to the health goals of individual users.

[0005] The system according to the embodiment aims to analyze user data and propose and execute an optimal yoga program according to the goal.

Means for Solving the Problems

[0006] The system according to the embodiment includes a collection unit, an analysis unit, and an execution unit. The collection unit collects user data. The analysis unit analyzes the data collected by the collection unit and proposes an optimal yoga program according to the user's goal. The execution unit executes the yoga program proposed by the analysis unit. [Effects of the Invention]

[0007] The system according to this embodiment can analyze user data and propose and execute an optimal yoga program tailored to the user's 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 signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

[0017] As shown in FIG. 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. Also, the reception device 38, the output device 40, and the camera 42 are connected to the bus 52.

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The personal healthcare system according to an embodiment of the present invention is a system for extending healthy life expectancy by utilizing generative AI. This personal healthcare system can extend healthy life expectancy and improve the quality of life of an individual by collecting and analyzing user data, proposing and executing an optimal yoga program. For example, the personal healthcare system collects user data using dedicated yoga wear, a yoga mat, and a wearable device. The yoga wear collects physical data such as height, weight, and muscle mass, and the yoga mat digitizes the center of gravity and movement. The wearable device collects data such as heart rate. This data is input into the generative AI. Next, the generative AI analyzes the collected data. Based on the user's goals and physical condition, the generative AI proposes an optimal yoga program. For example, for a user aiming for longevity, it proposes a program aimed at improving physical fitness and reducing stress, and for a user aiming to improve their golf score, it proposes a program that emphasizes flexibility and balance. Furthermore, the generative AI approaches mental health not only through physical exercise but also by incorporating elements of Zen. For example, it proposes a program that incorporates meditation and breathing techniques to balance the user's mind and body. By implementing the suggested programs, users can extend their healthy lifespan and improve their quality of life. This contributes to reducing the burden of medical and long-term care expenses and enhancing the sustainability of the social security system. This system integrates generative AI, yoga mats, and wearable devices to comprehensively support individual health. Through avatar functions and autonomous conversations within the app, it also helps prevent dementia and frailty, providing each user with an optimal health maintenance plan. In this way, the personal healthcare system can extend users' healthy lifespan and improve their quality of life.

[0029] The personal healthcare system according to this embodiment comprises a data collection unit, an analysis unit, and an execution unit. The data collection unit collects user data. The data collection unit collects user data using, for example, dedicated yoga wear, a yoga mat, and a wearable device. The yoga wear collects physical data such as height, weight, and muscle mass. The yoga mat digitizes the center of gravity and movement. The wearable device collects data such as heart rate. This data is input into a generating AI. The analysis unit analyzes the data collected by the data collection unit and proposes an optimal yoga program tailored to the user's goals. The analysis unit analyzes the collected data using, for example, a generating AI. The generating AI proposes an optimal yoga program based on the user's goals and physical condition. For example, for a user aiming for longevity, it proposes a program aimed at improving physical fitness and reducing stress. For a user aiming to improve their golf score, it proposes a program that emphasizes flexibility and balance. Furthermore, the analysis unit approaches mental health by incorporating elements of Zen in addition to physical exercise. For example, it proposes a program that incorporates meditation and breathing techniques to balance the user's mind and body. The execution unit executes the yoga program proposed by the analysis unit. For example, the execution unit executes the proposed yoga program to extend the user's healthy lifespan. The execution unit also contributes to dementia prevention and frailty prevention through avatar functions and autonomous conversation. As a result, the personal healthcare system according to this embodiment can extend the user's healthy lifespan and improve their quality of life.

[0030] The data collection unit collects user data. For example, it uses specialized yoga wear, yoga mats, and wearable devices to collect user data. The yoga wear collects physical data such as height, weight, and muscle mass. Specifically, the yoga wear has multiple built-in sensors that measure the dimensions of various parts of the user's body and muscle tension in real time. This allows for a detailed understanding of the user's body type and muscle condition. The yoga mat digitizes the center of gravity and movement. The yoga mat incorporates pressure sensors and acceleration sensors to detect changes in the user's movements and posture on the mat with high accuracy. This allows for evaluation of the user's balance and the accuracy of their movements. The wearable device collects data such as heart rate. The wearable device is equipped with a heart rate sensor and an oxygen saturation sensor to monitor the user's heart rate and blood oxygen concentration in real time. This data is input into the generating AI. Based on the collected data, the generating AI evaluates the user's health status and fitness level. For example, the user's fatigue level and stress level can be estimated from heart rate variability and muscle tension. This allows the data collection unit to gather highly accurate data to provide to the analysis and execution units, giving a detailed understanding of the user's physical and physiological state. Furthermore, the data collection unit protects the data using encryption technology to ensure data privacy and security. This minimizes the risk of unauthorized access to the user's personal information.

[0031] The analysis unit analyzes the data collected by the data collection unit and proposes an optimal yoga program tailored to the user's goals. For example, the analysis unit uses generative AI to analyze the collected data. Based on the user's goals and physical condition, the generative AI proposes the optimal yoga program. Specifically, the generative AI analyzes the collected data multidimensionally, comprehensively evaluating the user's physical fitness, flexibility, balance, heart rate, stress level, etc. For example, for a user aiming for longevity, it proposes a program aimed at improving physical fitness and reducing stress. The generative AI refers to past data and data from similar users to generate the optimal combination of exercises. For a user aiming to improve their golf score, it proposes a program that emphasizes flexibility and balance. The generative AI selects and provides exercises to strengthen specific muscle groups and movements necessary for golf. Furthermore, the analysis unit approaches mental health not only through physical exercise but also by incorporating elements of Zen. For example, it proposes a program that incorporates meditation and breathing techniques to balance the user's mind and body. The generative AI analyzes the user's stress level and heart rate fluctuations to select the optimal meditation and breathing techniques. This allows the analysis unit to comprehensively support the user's physical and mental health. Furthermore, the analysis unit collects user feedback and evaluates the program's effectiveness. Based on the user feedback, the generating AI can continuously improve the program content and continue to provide the user with the most suitable exercises.

[0032] The execution unit executes the yoga program proposed by the analysis unit. For example, the execution unit executes the proposed yoga program to extend the user's healthy lifespan. Specifically, the execution unit explains each step of the yoga program in detail to the user and instructs them on correct posture and movement. The execution unit also contributes to dementia prevention and frailty prevention through avatar functionality and autonomous conversation. The avatar function provides real-time feedback to the user as a virtual instructor, guiding them to maintain correct posture and movement. The autonomous conversation function immediately answers the user's questions and doubts, helping them understand the effects and purpose of the exercises. This allows the user to effectively perform the yoga program and extend their healthy lifespan. Furthermore, the execution unit monitors the user's progress and adjusts the program content as needed. For example, if the user's physical strength and flexibility improve, more advanced exercises are added to encourage continued growth. The execution unit also visualizes progress toward achieving goals and provides a sense of accomplishment to maintain the user's motivation. This allows the user to continuously perform the yoga program and extend their healthy lifespan. Furthermore, the execution unit collects user feedback and evaluates the program's effectiveness. Based on the user feedback, the execution unit can continuously improve the program content and provide users with the most suitable exercises. This allows the execution unit to extend users' healthy lifespan and improve their quality of life.

[0033] The data collection unit collects user data using specialized yoga wear, a yoga mat, and a wearable device. For example, the data collection unit collects physical data such as height, weight, and muscle mass using the specialized yoga wear. The data collection unit digitizes the center of gravity and movement using the yoga mat. The data collection unit collects data such as heart rate using the wearable device. This allows for accurate data collection by using specialized devices. 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 data acquired with the specialized yoga wear into a generating AI and have the generating AI perform data analysis.

[0034] The analysis unit proposes an optimal yoga program tailored to the user's goals based on the collected data. For example, based on the collected data, the analysis unit proposes a program aimed at improving physical fitness and reducing stress for users aiming for longevity. For users aiming to improve their golf score, the analysis unit proposes a program that emphasizes flexibility and balance. For users aiming to improve their stamina, the analysis unit proposes a program aimed at improving endurance. In this way, by proposing an optimal yoga program tailored to the user's goals, effective health management becomes possible. Some or all of the above processing in the analysis unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the analysis unit can input the collected data into a generation AI and have the generation AI execute the task of proposing an optimal yoga program tailored to the user's goals.

[0035] The analysis unit proposes programs that incorporate not only physical exercise but also elements of Zen. For example, the analysis unit can propose programs that incorporate meditation and breathing exercises to balance the user's mind and body. By proposing programs that incorporate meditation, the analysis unit also addresses the user's mental health. By proposing programs that incorporate breathing exercises, the analysis unit can enhance the user's relaxation effect. In this way, by incorporating elements of Zen, it can also address mental health. Some or all of the above processing in the analysis unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the analysis unit can input collected data into a generative AI and have the generative AI execute a program proposal that incorporates elements of Zen.

[0036] The execution unit executes the proposed yoga program to extend the user's healthy lifespan. For example, the execution unit executes the proposed yoga program to improve the user's physical fitness. The execution unit executes the proposed yoga program to reduce the user's stress. The execution unit executes the proposed yoga program to increase the user's flexibility. In this way, by executing the proposed program, the user's healthy lifespan can be extended. Some or all of the above processes in the execution unit may be performed using AI or not. For example, the execution unit can input the proposed yoga program into a generating AI and have the generating AI execute the program.

[0037] The execution unit can also be used to help prevent dementia and frailty through avatar functions and autonomous conversation. For example, the execution unit can use avatar functions to provide exercise guidance to the user. The execution unit can monitor the user's health status through autonomous conversation. The execution unit can use avatar functions to send encouraging messages to the user. In this way, it can be used to help prevent dementia and frailty through avatar functions and autonomous conversation. Some or all of the above processes in the execution unit may be performed using AI or not. For example, the execution unit can input avatar functions into a generating AI and have the generating AI execute the avatar's actions.

[0038] The data collection unit analyzes the user's past health data and selects the optimal data collection method. For example, the data collection unit analyzes the user's past heart rate data and collects data during periods when the heart rate is stable. Based on the user's past exercise history, the data collection unit prioritizes data collection after exercise. Based on the user's past sleep data, the data collection unit collects data after sleep. In this way, the optimal data collection method can be selected by analyzing past health data. 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 past health data into a generating AI and have the generating AI select the optimal data collection method.

[0039] The data collection unit filters data based on the user's current lifestyle and activity level. For example, if the user is at work, the data collection unit refrains from collecting data and collects data during breaks. If the user is exercising, the data collection unit collects data according to the exercise intensity. If the user is relaxed, the data collection unit collects detailed data. This allows for the collection of more relevant data by filtering the data based on the user's lifestyle and activity level. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input data on the user's lifestyle and activity level into a generating AI and have the generating AI perform the data filtering.

[0040] The data collection unit prioritizes the collection of highly relevant data, taking into account the user's geographical location information during data collection. For example, if the user is exercising outdoors, the data collection unit collects environmental data (temperature, humidity, etc.). If the user is relaxing at home, the data collection unit collects indoor environmental data (temperature, humidity, etc.). If the user is training at a gym, the data collection unit collects data on the use of training equipment. By considering geographical location information, the data collection unit can prioritize the collection of highly relevant data. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's geographical location information into a generating AI and have the generating AI perform the collection of highly relevant data.

[0041] The data collection unit analyzes the user's social media activity and collects relevant data during data collection. For example, if a user posts about exercise on social media, the data collection unit collects data based on that content. If a user posts about stress, the data collection unit collects data on stress levels. If a user posts about health, the data collection unit collects data based on that content. In this way, relevant data can be collected by analyzing social media activity. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's social media activity into a generating AI and have the generating AI perform the collection of relevant data.

[0042] The analysis unit adjusts the level of detail in the program based on the user's health condition during analysis. For example, if the user is in good health, the analysis unit suggests a detailed yoga program. If the user is in poor health, the analysis unit suggests a simplified yoga program. If the user has a specific health problem, the analysis unit suggests a yoga program tailored to that problem. In this way, by adjusting the level of detail in the program based on the user's health condition, a program suitable for the user can be suggested. Some or all of the above processing in the analysis unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the analysis unit can input the user's health data into a generative AI and have the generative AI perform the adjustment of the level of detail in the program.

[0043] The analysis unit applies different analysis algorithms depending on the user's goals during the analysis. For example, for a user aiming for longevity, the analysis unit applies an analysis algorithm that emphasizes physical fitness improvement and stress reduction. For a user aiming to improve their golf score, the analysis unit applies an analysis algorithm that emphasizes flexibility and balance. For a user aiming to improve their stamina, the analysis unit applies an analysis algorithm that emphasizes endurance improvement. By applying an analysis algorithm tailored to the user's goals, a more effective program can be proposed. Some or all of the above processing in the analysis unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the analysis unit can input the user's goal data into a generative AI and have the generative AI execute the application of different analysis algorithms.

[0044] The analysis unit determines program priorities based on the user's data collection timing during analysis. For example, if the user collects data in the morning, the analysis unit will prioritize suggesting morning yoga programs. If the user collects data at night, the analysis unit will prioritize suggesting evening relaxation programs. If the user collects data on the weekend, the analysis unit will prioritize suggesting special weekend programs. By prioritizing programs based on data collection timing, programs can be provided at the appropriate time. Some or all of the above processing in the analysis unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the analysis unit can input the user's data collection timing into a generative AI and have the generative AI determine the program priorities.

[0045] The analysis unit adjusts the program sequence based on the user's relevant data during analysis. For example, based on the user's heart rate data, the analysis unit suggests a program sequence that stabilizes the heart rate. Based on the user's muscle tension data, the analysis unit suggests a program sequence that relieves tension. Based on the user's flexibility data, the analysis unit suggests a program sequence that increases flexibility. By adjusting the program sequence based on relevant data, an effective program can be provided. Some or all of the above processing in the analysis unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the analysis unit can input the user's relevant data into a generative AI and have the generative AI perform the adjustment of the program sequence.

[0046] The execution unit analyzes the user's past execution history to select the optimal execution method when a program is executed. For example, the execution unit may suggest a similar execution method based on programs the user has successfully executed in the past. The execution unit may also suggest alternative execution methods, avoiding programs the user has failed at in the past. Finally, the execution unit selects the most effective execution method from the user's past execution history. In this way, the optimal execution method can be selected by analyzing past execution history. Some or all of the above processes in the execution unit may be performed using AI, or they may not. For example, the execution unit can input the user's past execution history into a generating AI and have the generating AI select the optimal execution method.

[0047] The execution unit customizes the program's content based on the user's current physical condition during program execution. For example, if the user is tired, the execution unit suggests light exercise. If the user is energetic, the execution unit suggests high-intensity exercise. If the user is unwell, the execution unit suggests a program with a high relaxation effect. In this way, by customizing the content based on the user's current physical condition, a program suitable for the user can be provided. Some or all of the above processing in the execution unit may be performed using AI or not. For example, the execution unit can input the user's physical condition data into a generating AI and have the generating AI perform the customization of the content.

[0048] The execution unit selects the optimal execution method when executing a program, taking into account the user's geographical location information. For example, if the user is exercising outdoors, the execution unit suggests exercises suitable for the environment. If the user is exercising at home, the execution unit suggests exercises suitable for the indoor environment. If the user is exercising at a gym, the execution unit suggests exercises that utilize the gym's equipment. In this way, the optimal execution method can be selected by considering geographical location information. Some or all of the above processing in the execution unit may be performed using AI, or it may be performed without AI. For example, the execution unit can input the user's geographical location information into a generating AI and have the generating AI select the optimal execution method.

[0049] The execution unit analyzes the user's social media activity and proposes actions to take when the program is executed. For example, the execution unit may propose similar exercises based on exercises the user has shared on social media. The execution unit may propose exercises from fitness influencers the user follows on social media. The execution unit may propose actions based on exercises the user has shown interest in on social media. In this way, relevant actions can be proposed by analyzing social media activity. Some or all of the above processing in the execution unit may be performed using AI or not. For example, the execution unit can input the user's social media activity into a generating AI and have the generating AI execute the suggestions for actions.

[0050] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0051] The data collection unit collects the user's dietary data, and the analysis unit can propose a yoga program that takes nutritional balance into consideration based on the collected dietary data. For example, the data collection unit collects data on the calories and nutrients consumed by the user. The analysis unit then proposes a yoga program that optimizes energy expenditure based on the collected dietary data. Furthermore, if there is a deficiency in a particular nutrient, the analysis unit can also provide dietary advice to supplement that nutrient. This enables health management that considers the balance between diet and exercise.

[0052] The analysis unit collects user sleep data and, based on this data, can also suggest yoga programs to improve sleep quality. For example, the data collection unit digitizes the user's sleep duration and sleep depth. Based on the collected sleep data, the analysis unit suggests yoga programs with high relaxation effects. Furthermore, if sleep quality is poor, the analysis unit can identify the cause and suggest solutions. This enables a comprehensive approach to improving sleep quality.

[0053] The data collection unit monitors the user's activity level in real time, and the analysis unit can suggest a yoga program tailored to the activity level based on the collected activity data. For example, the data collection unit digitizes the user's steps and exercise volume. Based on the collected activity data, the analysis unit suggests light exercises if the activity level is low, and high-intensity exercises if the activity level is high. This allows for the provision of an optimal yoga program tailored to the user's activity level.

[0054] The data collection unit collects the user's geographical location information, and the analysis unit can then suggest yoga programs suitable for the local climate and environment based on this collected geographical location data. For example, the data collection unit digitizes the temperature and humidity of the area where the user lives. Based on the collected geographical location data, the analysis unit suggests exercises to warm the body for users living in cold regions and exercises with a cooling effect for users living in hot regions. This allows for the provision of optimal yoga programs tailored to the local climate and environment.

[0055] The data collection unit analyzes users' social media activity, and the analysis unit can then suggest yoga programs tailored to the user's interests based on the collected social media data. For example, the data collection unit digitizes fitness-related posts shared by users on social media. The analysis unit then suggests exercises that the user is interested in based on the collected social media data. This allows for the provision of optimal yoga programs tailored to the user's interests.

[0056] The data collection unit analyzes the user's past health data, and the analysis unit can then suggest a yoga program tailored to the user's health condition based on the collected data. For example, the data collection unit collects the user's past heart rate and blood pressure data. Based on the collected data, the analysis unit suggests high-intensity exercises if the heart rate is stable, and exercises with a high relaxation effect if the blood pressure is high. This allows for the provision of an optimal yoga program based on past health data.

[0057] The following briefly describes the processing flow for example form 1.

[0058] Step 1: The data collection unit collects user data. The data collection unit uses specialized yoga wear, a yoga mat, and a wearable device to collect user data. The yoga wear collects physical data such as height, weight, and muscle mass; the yoga mat digitizes center of gravity and movement data; and the wearable device collects data such as heart rate. This data is then input into the generating AI. Step 2: The analysis unit analyzes the data collected by the data collection unit and proposes the optimal yoga program tailored to the user's goals. The analysis unit uses generative AI to analyze the collected data and proposes the optimal yoga program based on the user's goals and physical condition. For example, it proposes a program aimed at improving physical fitness and reducing stress for users who aim to live longer, and a program that emphasizes flexibility and balance for users who aim to improve their golf score. Furthermore, it proposes programs that incorporate elements of Zen in addition to physical exercise to balance the user's mind and body. Step 3: The execution unit executes the yoga program proposed by the analysis unit. The execution unit executes the proposed yoga program to extend the user's healthy lifespan. The execution unit also contributes to dementia prevention and frailty prevention through avatar functions and autonomous conversation.

[0059] (Example of form 2) The personal healthcare system according to an embodiment of the present invention is a system for extending healthy life expectancy by utilizing generative AI. This personal healthcare system can extend healthy life expectancy and improve the quality of life of an individual by collecting and analyzing user data, proposing and executing an optimal yoga program. For example, the personal healthcare system collects user data using dedicated yoga wear, a yoga mat, and a wearable device. The yoga wear collects physical data such as height, weight, and muscle mass, and the yoga mat digitizes the center of gravity and movement. The wearable device collects data such as heart rate. This data is input into the generative AI. Next, the generative AI analyzes the collected data. Based on the user's goals and physical condition, the generative AI proposes an optimal yoga program. For example, for a user aiming for longevity, it proposes a program aimed at improving physical fitness and reducing stress, and for a user aiming to improve their golf score, it proposes a program that emphasizes flexibility and balance. Furthermore, the generative AI approaches mental health not only through physical exercise but also by incorporating elements of Zen. For example, it proposes a program that incorporates meditation and breathing techniques to balance the user's mind and body. By implementing the suggested programs, users can extend their healthy lifespan and improve their quality of life. This contributes to reducing the burden of medical and long-term care expenses and enhancing the sustainability of the social security system. This system integrates generative AI, yoga mats, and wearable devices to comprehensively support individual health. Through avatar functions and autonomous conversations within the app, it also helps prevent dementia and frailty, providing each user with an optimal health maintenance plan. In this way, the personal healthcare system can extend users' healthy lifespan and improve their quality of life.

[0060] The personal healthcare system according to this embodiment comprises a data collection unit, an analysis unit, and an execution unit. The data collection unit collects user data. The data collection unit collects user data using, for example, dedicated yoga wear, a yoga mat, and a wearable device. The yoga wear collects physical data such as height, weight, and muscle mass. The yoga mat digitizes the center of gravity and movement. The wearable device collects data such as heart rate. This data is input into a generating AI. The analysis unit analyzes the data collected by the data collection unit and proposes an optimal yoga program tailored to the user's goals. The analysis unit analyzes the collected data using, for example, a generating AI. The generating AI proposes an optimal yoga program based on the user's goals and physical condition. For example, for a user aiming for longevity, it proposes a program aimed at improving physical fitness and reducing stress. For a user aiming to improve their golf score, it proposes a program that emphasizes flexibility and balance. Furthermore, the analysis unit approaches mental health by incorporating elements of Zen in addition to physical exercise. For example, it proposes a program that incorporates meditation and breathing techniques to balance the user's mind and body. The execution unit executes the yoga program proposed by the analysis unit. For example, the execution unit executes the proposed yoga program to extend the user's healthy lifespan. The execution unit also contributes to dementia prevention and frailty prevention through avatar functions and autonomous conversation. As a result, the personal healthcare system according to this embodiment can extend the user's healthy lifespan and improve their quality of life.

[0061] The data collection unit collects user data. For example, it uses specialized yoga wear, yoga mats, and wearable devices to collect user data. The yoga wear collects physical data such as height, weight, and muscle mass. Specifically, the yoga wear has multiple built-in sensors that measure the dimensions of various parts of the user's body and muscle tension in real time. This allows for a detailed understanding of the user's body type and muscle condition. The yoga mat digitizes the center of gravity and movement. The yoga mat incorporates pressure sensors and acceleration sensors to detect changes in the user's movements and posture on the mat with high accuracy. This allows for evaluation of the user's balance and the accuracy of their movements. The wearable device collects data such as heart rate. The wearable device is equipped with a heart rate sensor and an oxygen saturation sensor to monitor the user's heart rate and blood oxygen concentration in real time. This data is input into the generating AI. Based on the collected data, the generating AI evaluates the user's health status and fitness level. For example, the user's fatigue level and stress level can be estimated from heart rate variability and muscle tension. This allows the data collection unit to gather highly accurate data to provide to the analysis and execution units, giving a detailed understanding of the user's physical and physiological state. Furthermore, the data collection unit protects the data using encryption technology to ensure data privacy and security. This minimizes the risk of unauthorized access to the user's personal information.

[0062] The analysis unit analyzes the data collected by the data collection unit and proposes an optimal yoga program tailored to the user's goals. For example, the analysis unit uses generative AI to analyze the collected data. Based on the user's goals and physical condition, the generative AI proposes the optimal yoga program. Specifically, the generative AI analyzes the collected data multidimensionally, comprehensively evaluating the user's physical fitness, flexibility, balance, heart rate, stress level, etc. For example, for a user aiming for longevity, it proposes a program aimed at improving physical fitness and reducing stress. The generative AI refers to past data and data from similar users to generate the optimal combination of exercises. For a user aiming to improve their golf score, it proposes a program that emphasizes flexibility and balance. The generative AI selects and provides exercises to strengthen specific muscle groups and movements necessary for golf. Furthermore, the analysis unit approaches mental health not only through physical exercise but also by incorporating elements of Zen. For example, it proposes a program that incorporates meditation and breathing techniques to balance the user's mind and body. The generative AI analyzes the user's stress level and heart rate fluctuations to select the optimal meditation and breathing techniques. This allows the analysis unit to comprehensively support the user's physical and mental health. Furthermore, the analysis unit collects user feedback and evaluates the program's effectiveness. Based on the user feedback, the generating AI can continuously improve the program content and continue to provide the user with the most suitable exercises.

[0063] The execution unit executes the yoga program proposed by the analysis unit. For example, the execution unit executes the proposed yoga program to extend the user's healthy lifespan. Specifically, the execution unit explains each step of the yoga program in detail to the user and instructs them on correct posture and movement. The execution unit also contributes to dementia prevention and frailty prevention through avatar functionality and autonomous conversation. The avatar function provides real-time feedback to the user as a virtual instructor, guiding them to maintain correct posture and movement. The autonomous conversation function immediately answers the user's questions and doubts, helping them understand the effects and purpose of the exercises. This allows the user to effectively perform the yoga program and extend their healthy lifespan. Furthermore, the execution unit monitors the user's progress and adjusts the program content as needed. For example, if the user's physical strength and flexibility improve, more advanced exercises are added to encourage continued growth. The execution unit also visualizes progress toward achieving goals and provides a sense of accomplishment to maintain the user's motivation. This allows the user to continuously perform the yoga program and extend their healthy lifespan. Furthermore, the execution unit collects user feedback and evaluates the program's effectiveness. Based on the user feedback, the execution unit can continuously improve the program content and provide users with the most suitable exercises. This allows the execution unit to extend users' healthy lifespan and improve their quality of life.

[0064] The data collection unit collects user data using specialized yoga wear, a yoga mat, and a wearable device. For example, the data collection unit collects physical data such as height, weight, and muscle mass using the specialized yoga wear. The data collection unit digitizes the center of gravity and movement using the yoga mat. The data collection unit collects data such as heart rate using the wearable device. This allows for accurate data collection by using specialized devices. 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 data acquired with the specialized yoga wear into a generating AI and have the generating AI perform data analysis.

[0065] The analysis unit proposes an optimal yoga program tailored to the user's goals based on the collected data. For example, based on the collected data, the analysis unit proposes a program aimed at improving physical fitness and reducing stress for users aiming for longevity. For users aiming to improve their golf score, the analysis unit proposes a program that emphasizes flexibility and balance. For users aiming to improve their stamina, the analysis unit proposes a program aimed at improving endurance. In this way, by proposing an optimal yoga program tailored to the user's goals, effective health management becomes possible. Some or all of the above processing in the analysis unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the analysis unit can input the collected data into a generation AI and have the generation AI execute the task of proposing an optimal yoga program tailored to the user's goals.

[0066] The analysis unit proposes programs that incorporate not only physical exercise but also elements of Zen. For example, the analysis unit can propose programs that incorporate meditation and breathing exercises to balance the user's mind and body. By proposing programs that incorporate meditation, the analysis unit also addresses the user's mental health. By proposing programs that incorporate breathing exercises, the analysis unit can enhance the user's relaxation effect. In this way, by incorporating elements of Zen, it can also address mental health. Some or all of the above processing in the analysis unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the analysis unit can input collected data into a generative AI and have the generative AI execute a program proposal that incorporates elements of Zen.

[0067] The execution unit executes the proposed yoga program to extend the user's healthy lifespan. For example, the execution unit executes the proposed yoga program to improve the user's physical fitness. The execution unit executes the proposed yoga program to reduce the user's stress. The execution unit executes the proposed yoga program to increase the user's flexibility. In this way, by executing the proposed program, the user's healthy lifespan can be extended. Some or all of the above processes in the execution unit may be performed using AI or not. For example, the execution unit can input the proposed yoga program into a generating AI and have the generating AI execute the program.

[0068] The execution unit can also be used to help prevent dementia and frailty through avatar functions and autonomous conversation. For example, the execution unit can use avatar functions to provide exercise guidance to the user. The execution unit can monitor the user's health status through autonomous conversation. The execution unit can use avatar functions to send encouraging messages to the user. In this way, it can be used to help prevent dementia and frailty through avatar functions and autonomous conversation. Some or all of the above processes in the execution unit may be performed using AI or not. For example, the execution unit can input avatar functions into a generating AI and have the generating AI execute the avatar's actions.

[0069] The data collection unit estimates the user's emotions and adjusts the timing of data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit collects data after a yoga session to capture data in a relaxed state. If the user is relaxed, the data collection unit collects data in real time during the yoga session. If the user is tired, the data collection unit collects data after rest to obtain accurate data. This allows for the collection of more accurate data by adjusting the timing of data collection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI adjust the timing of data collection.

[0070] The data collection unit analyzes the user's past health data and selects the optimal data collection method. For example, the data collection unit analyzes the user's past heart rate data and collects data during periods when the heart rate is stable. Based on the user's past exercise history, the data collection unit prioritizes data collection after exercise. Based on the user's past sleep data, the data collection unit collects data after sleep. In this way, the optimal data collection method can be selected by analyzing past health data. 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 past health data into a generating AI and have the generating AI select the optimal data collection method.

[0071] The data collection unit filters data based on the user's current lifestyle and activity level. For example, if the user is at work, the data collection unit refrains from collecting data and collects data during breaks. If the user is exercising, the data collection unit collects data according to the exercise intensity. If the user is relaxed, the data collection unit collects detailed data. This allows for the collection of more relevant data by filtering the data based on the user's lifestyle and activity level. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input data on the user's lifestyle and activity level into a generating AI and have the generating AI perform the data filtering.

[0072] The data collection unit estimates the user's emotions and determines the priority of data to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit prioritizes collecting heart rate and respiratory rate data. If the user is relaxed, the data collection unit prioritizes collecting muscle tension and flexibility data. If the user is tired, the data collection unit prioritizes collecting sleep quality and rest data. This allows for the priority collection of important data by prioritizing data based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI determine the data prioritization.

[0073] The data collection unit prioritizes the collection of highly relevant data, taking into account the user's geographical location information during data collection. For example, if the user is exercising outdoors, the data collection unit collects environmental data (temperature, humidity, etc.). If the user is relaxing at home, the data collection unit collects indoor environmental data (temperature, humidity, etc.). If the user is training at a gym, the data collection unit collects data on the use of training equipment. By considering geographical location information, the data collection unit can prioritize the collection of highly relevant data. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's geographical location information into a generating AI and have the generating AI perform the collection of highly relevant data.

[0074] The data collection unit analyzes the user's social media activity and collects relevant data during data collection. For example, if a user posts about exercise on social media, the data collection unit collects data based on that content. If a user posts about stress, the data collection unit collects data on stress levels. If a user posts about health, the data collection unit collects data based on that content. In this way, relevant data can be collected by analyzing social media activity. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's social media activity into a generating AI and have the generating AI perform the collection of relevant data.

[0075] The analysis unit estimates the user's emotions and adjusts the yoga program suggestion method based on the estimated emotions. For example, if the user is stressed, the analysis unit suggests a yoga program with a high relaxation effect. If the user is relaxed, the analysis unit suggests a yoga program that increases flexibility. If the user is tired, the analysis unit suggests a yoga program that promotes recovery. In this way, by adjusting the suggestion method based on the user's emotions, a more effective yoga program can be suggested. 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 or without a generative AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI perform the adjustment of the yoga program suggestion method.

[0076] The analysis unit adjusts the level of detail in the program based on the user's health condition during analysis. For example, if the user is in good health, the analysis unit suggests a detailed yoga program. If the user is in poor health, the analysis unit suggests a simplified yoga program. If the user has a specific health problem, the analysis unit suggests a yoga program tailored to that problem. In this way, by adjusting the level of detail in the program based on the user's health condition, a program suitable for the user can be suggested. Some or all of the above processing in the analysis unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the analysis unit can input the user's health data into a generative AI and have the generative AI perform the adjustment of the level of detail in the program.

[0077] The analysis unit applies different analysis algorithms depending on the user's goals during the analysis. For example, for a user aiming for longevity, the analysis unit applies an analysis algorithm that emphasizes physical fitness improvement and stress reduction. For a user aiming to improve their golf score, the analysis unit applies an analysis algorithm that emphasizes flexibility and balance. For a user aiming to improve their stamina, the analysis unit applies an analysis algorithm that emphasizes endurance improvement. By applying an analysis algorithm tailored to the user's goals, a more effective program can be proposed. Some or all of the above processing in the analysis unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the analysis unit can input the user's goal data into a generative AI and have the generative AI execute the application of different analysis algorithms.

[0078] The analysis unit estimates the user's emotions and adjusts the program length based on the estimated emotions. For example, if the user is stressed, the analysis unit suggests a short, highly relaxing program. If the user is relaxed, the analysis unit suggests a longer yoga program. If the user is tired, the analysis unit suggests a short, highly restorative program. In this way, by adjusting the program length based on emotions, a program suitable for the user can be provided. 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 or without a generative AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI adjust the program length.

[0079] The analysis unit determines program priorities based on the user's data collection timing during analysis. For example, if the user collects data in the morning, the analysis unit will prioritize suggesting morning yoga programs. If the user collects data at night, the analysis unit will prioritize suggesting evening relaxation programs. If the user collects data on the weekend, the analysis unit will prioritize suggesting special weekend programs. By prioritizing programs based on data collection timing, programs can be provided at the appropriate time. Some or all of the above processing in the analysis unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the analysis unit can input the user's data collection timing into a generative AI and have the generative AI determine the program priorities.

[0080] The analysis unit adjusts the program sequence based on the user's relevant data during analysis. For example, based on the user's heart rate data, the analysis unit suggests a program sequence that stabilizes the heart rate. Based on the user's muscle tension data, the analysis unit suggests a program sequence that relieves tension. Based on the user's flexibility data, the analysis unit suggests a program sequence that increases flexibility. By adjusting the program sequence based on relevant data, an effective program can be provided. Some or all of the above processing in the analysis unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the analysis unit can input the user's relevant data into a generative AI and have the generative AI perform the adjustment of the program sequence.

[0081] The execution unit estimates the user's emotions and adjusts the program execution method based on the estimated user emotions. For example, if the user is stressed, the execution unit will run the program while playing relaxing music. If the user is relaxed, the execution unit will run the program in a quiet environment. If the user is tired, the execution unit will run a short, effective program. By adjusting the execution method based on the user's emotions, more effective program execution becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the execution unit may be performed using AI or not. For example, the execution unit can input user emotion data into a generative AI and have the generative AI adjust the program execution method.

[0082] The execution unit analyzes the user's past execution history to select the optimal execution method when a program is executed. For example, the execution unit may suggest a similar execution method based on programs the user has successfully executed in the past. The execution unit may also suggest alternative execution methods, avoiding programs the user has failed at in the past. Finally, the execution unit selects the most effective execution method from the user's past execution history. In this way, the optimal execution method can be selected by analyzing past execution history. Some or all of the above processes in the execution unit may be performed using AI, or they may not. For example, the execution unit can input the user's past execution history into a generating AI and have the generating AI select the optimal execution method.

[0083] The execution unit customizes the program's content based on the user's current physical condition during program execution. For example, if the user is tired, the execution unit suggests light exercise. If the user is energetic, the execution unit suggests high-intensity exercise. If the user is unwell, the execution unit suggests a program with a high relaxation effect. In this way, by customizing the content based on the user's current physical condition, a program suitable for the user can be provided. Some or all of the above processing in the execution unit may be performed using AI or not. For example, the execution unit can input the user's physical condition data into a generating AI and have the generating AI perform the customization of the content.

[0084] The execution unit estimates the user's emotions and determines the program execution order based on the estimated emotions. For example, if the user is stressed, the execution unit will first execute exercises that promote relaxation. If the user is relaxed, the execution unit will first execute exercises that increase flexibility. If the user is tired, the execution unit will first execute exercises that promote recovery. This allows for more effective program execution by determining the execution order based on 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 execution unit may be performed using AI or not. For example, the execution unit can input user emotion data into a generative AI and have the generative AI determine the program execution order.

[0085] The execution unit selects the optimal execution method when executing a program, taking into account the user's geographical location information. For example, if the user is exercising outdoors, the execution unit suggests exercises suitable for the environment. If the user is exercising at home, the execution unit suggests exercises suitable for the indoor environment. If the user is exercising at a gym, the execution unit suggests exercises that utilize the gym's equipment. In this way, the optimal execution method can be selected by considering geographical location information. Some or all of the above processing in the execution unit may be performed using AI, or it may be performed without AI. For example, the execution unit can input the user's geographical location information into a generating AI and have the generating AI select the optimal execution method.

[0086] The execution unit analyzes the user's social media activity and proposes actions to take when the program is executed. For example, the execution unit may propose similar exercises based on exercises the user has shared on social media. The execution unit may propose exercises from fitness influencers the user follows on social media. The execution unit may propose actions based on exercises the user has shown interest in on social media. In this way, relevant actions can be proposed by analyzing social media activity. Some or all of the above processing in the execution unit may be performed using AI or not. For example, the execution unit can input the user's social media activity into a generating AI and have the generating AI execute the suggestions for actions.

[0087] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0088] The data collection unit collects the user's dietary data, and the analysis unit can propose a yoga program that takes nutritional balance into consideration based on the collected dietary data. For example, the data collection unit collects data on the calories and nutrients consumed by the user. The analysis unit then proposes a yoga program that optimizes energy expenditure based on the collected dietary data. Furthermore, if there is a deficiency in a particular nutrient, the analysis unit can also provide dietary advice to supplement that nutrient. This enables health management that considers the balance between diet and exercise.

[0089] The analysis unit collects user sleep data and, based on this data, can also suggest yoga programs to improve sleep quality. For example, the data collection unit digitizes the user's sleep duration and sleep depth. Based on the collected sleep data, the analysis unit suggests yoga programs with high relaxation effects. Furthermore, if sleep quality is poor, the analysis unit can identify the cause and suggest solutions. This enables a comprehensive approach to improving sleep quality.

[0090] The analysis unit can also estimate the user's emotions and adjust the difficulty level of the yoga program based on those emotions. For example, if the user is stressed, it will suggest a low-difficulty, highly relaxing program. If the user is relaxed, it will suggest a high-difficulty, challenging program. If the user is tired, it will suggest light exercises to promote recovery. This allows the system to provide a program of optimal difficulty level according to the user's emotions.

[0091] The data collection unit monitors the user's activity level in real time, and the analysis unit can suggest a yoga program tailored to the activity level based on the collected activity data. For example, the data collection unit digitizes the user's steps and exercise volume. Based on the collected activity data, the analysis unit suggests light exercises if the activity level is low, and high-intensity exercises if the activity level is high. This allows for the provision of an optimal yoga program tailored to the user's activity level.

[0092] The analysis unit can estimate the user's emotions and select music for the yoga program based on those emotions. For example, if the user is stressed, it will select music with a high relaxation effect. If the user is relaxed, it will select music that enhances concentration. If the user is tired, it will select music that restores energy. By providing optimal music tailored to the user's emotions, the effectiveness of the yoga program can be enhanced.

[0093] The data collection unit collects the user's geographical location information, and the analysis unit can then suggest yoga programs suitable for the local climate and environment based on this collected geographical location data. For example, the data collection unit digitizes the temperature and humidity of the area where the user lives. Based on the collected geographical location data, the analysis unit suggests exercises to warm the body for users living in cold regions and exercises with a cooling effect for users living in hot regions. This allows for the provision of optimal yoga programs tailored to the local climate and environment.

[0094] The analysis unit can estimate the user's emotions and adjust the timing of the yoga program based on those estimates. For example, if the user is stressed, it might suggest a relaxing program in the evening. If the user is relaxed, it might suggest a program to improve concentration in the morning. If the user is tired, it might suggest a program to promote recovery during the day. This allows the system to provide yoga programs at the optimal time according to the user's emotions.

[0095] The data collection unit analyzes users' social media activity, and the analysis unit can then suggest yoga programs tailored to the user's interests based on the collected social media data. For example, the data collection unit digitizes fitness-related posts shared by users on social media. The analysis unit then suggests exercises that the user is interested in based on the collected social media data. This allows for the provision of optimal yoga programs tailored to the user's interests.

[0096] The analysis unit can estimate the user's emotions and, based on those estimates, select a yoga instructor. For example, if the user is stressed, it will select an instructor who promotes relaxation. If the user is relaxed, it will select an instructor who enhances concentration. If the user is tired, it will select an instructor who helps restore energy. By providing the optimal instructor tailored to the user's emotions, the effectiveness of the yoga program can be enhanced.

[0097] The data collection unit analyzes the user's past health data, and the analysis unit can then suggest a yoga program tailored to the user's health condition based on the collected data. For example, the data collection unit collects the user's past heart rate and blood pressure data. Based on the collected data, the analysis unit suggests high-intensity exercises if the heart rate is stable, and exercises with a high relaxation effect if the blood pressure is high. This allows for the provision of an optimal yoga program based on past health data.

[0098] The following briefly describes the processing flow for example form 2.

[0099] Step 1: The data collection unit collects user data. The data collection unit uses specialized yoga wear, a yoga mat, and a wearable device to collect user data. The yoga wear collects physical data such as height, weight, and muscle mass; the yoga mat digitizes center of gravity and movement data; and the wearable device collects data such as heart rate. This data is then input into the generating AI. Step 2: The analysis unit analyzes the data collected by the data collection unit and proposes the optimal yoga program tailored to the user's goals. The analysis unit uses generative AI to analyze the collected data and proposes the optimal yoga program based on the user's goals and physical condition. For example, it proposes a program aimed at improving physical fitness and reducing stress for users who aim to live longer, and a program that emphasizes flexibility and balance for users who aim to improve their golf score. Furthermore, it proposes programs that incorporate elements of Zen in addition to physical exercise to balance the user's mind and body. Step 3: The execution unit executes the yoga program proposed by the analysis unit. The execution unit executes the proposed yoga program to extend the user's healthy lifespan. The execution unit also contributes to dementia prevention and frailty prevention through avatar functions and autonomous conversation.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] Each of the multiple elements described above, including the data collection unit, analysis unit, and execution unit, is implemented in at least one of the smart device 14 and the data processing device 12. For example, the data collection unit collects user data using yoga wear, yoga mats, and wearable devices on the smart device 14. The analysis unit is implemented in the specific processing unit 290 of the data processing device 12, which analyzes the collected data using generating AI and proposes an optimal yoga program. The execution unit is implemented in the control unit 46A of the smart device 14, which executes the proposed yoga program to extend the user's healthy lifespan. 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.

[0104] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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).

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.).

[0116] 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.

[0117] 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.

[0118] 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.

[0119] Each of the multiple elements described above, including the data collection unit, analysis unit, and execution 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 data using the yoga wear, yoga mat, and wearable device of the smart glasses 214. The analysis unit is implemented in the identification processing unit 290 of the data processing unit 12, which analyzes the collected data using generating AI and proposes an optimal yoga program. The execution unit is implemented in the control unit 46A of the smart glasses 214, which executes the proposed yoga program to extend the user's healthy lifespan. 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.

[0120] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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).

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.).

[0132] 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.

[0133] 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.

[0134] 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.

[0135] Each of the multiple elements described above, including the data collection unit, analysis unit, and execution unit, is implemented in at least one of the following: the headset terminal 314 and the data processing unit 12. For example, the data collection unit collects user data using the yoga wear, yoga mat, and wearable device of the headset terminal 314. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, which analyzes the collected data using a generation AI and proposes an optimal yoga program. The execution unit is implemented, for example, by the control unit 46A of the headset terminal 314, which executes the proposed yoga program to extend the user's healthy lifespan. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0136] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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).

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.).

[0149] 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.

[0150] 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.

[0151] 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.

[0152] Each of the multiple elements described above, including the data collection unit, analysis unit, and execution unit, is implemented in, for example, at least one of the robot 414 and the data processing unit 12. For example, the data collection unit collects user data using the yoga wear, yoga mat, and wearable device of the robot 414. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, which analyzes the collected data using a generation AI and proposes an optimal yoga program. The execution unit is implemented, for example, by the control unit 46A of the robot 414, which executes the proposed yoga program to extend the user's healthy lifespan. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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."

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] (Note 1) A data collection unit that collects user data, The data collected by the aforementioned collection unit is analyzed by the analysis unit, and an analysis unit proposes an optimal yoga program tailored to the user's goals. The system comprises an execution unit that executes the yoga program proposed by the analysis unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is We collect user data using specialized yoga wear, yoga mats, and wearable devices. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, Based on the collected data, we propose the optimal yoga program tailored to the user's goals. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned analysis unit, We propose a program that incorporates not only physical exercise but also elements of Zen. The system described in Appendix 1, characterized by the features described herein. (Note 5) The execution unit is, By implementing the proposed yoga program, users can extend their healthy lifespan. The system described in Appendix 1, characterized by the features described herein. (Note 6) The execution unit is, Through avatar functions and autonomous conversations, it can also be used to help prevent dementia and frailty. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is Analyze the user's past health data and select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is During data collection, filtering is performed based on the user's current lifestyle and activity level. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is During data collection, the system prioritizes the collection of highly relevant data, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is During data collection, the system analyzes users' social media activity and collects relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, The system estimates the user's emotions and adjusts how yoga programs are suggested based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, the program's level of detail is adjusted based on the user's health status. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the user's goals. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, The program estimates the user's emotions and adjusts the program length based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, program priorities are determined based on when the user collected the data. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, the program sequence is adjusted based on the user's relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 19) The execution unit is, It estimates the user's emotions and adjusts how the program is executed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The execution unit is, When a program is executed, the system analyzes the user's past execution history to select the optimal execution method. The system described in Appendix 1, characterized by the features described herein. (Note 21) The execution unit is, When the program is executed, the execution content is customized based on the user's current physical condition. The system described in Appendix 1, characterized by the features described herein. (Note 22) The execution unit is, It estimates the user's emotions and determines the execution order of the program based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The execution unit is, When the program is executed, the optimal execution method is selected considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 24) The execution unit is, When the program is executed, it analyzes the user's social media activity and suggests actions to take. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0172] 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 data, The data collected by the aforementioned collection unit is analyzed by the analysis unit, and an analysis unit proposes an optimal yoga program tailored to the user's goals. The system comprises an execution unit that executes the yoga program proposed by the analysis unit. A system characterized by the following features.

2. The aforementioned collection unit is We collect user data using specialized yoga wear, yoga mats, and wearable devices. The system according to feature 1.

3. The aforementioned analysis unit, Based on the collected data, we propose the optimal yoga program tailored to the user's goals. The system according to feature 1.

4. The aforementioned analysis unit, We propose a program that incorporates not only physical exercise but also elements of Zen. The system according to feature 1.

5. The execution unit is, By implementing the proposed yoga program, users can extend their healthy lifespan. The system according to feature 1.

6. The execution unit is, Through avatar functions and autonomous conversations, it can also be used to help prevent dementia and frailty. The system according to feature 1.

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

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

9. The aforementioned collection unit is During data collection, filtering is performed based on the user's current lifestyle and activity level. The system according to feature 1.

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

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