system

Abstract

The system according to this embodiment aims to provide a personalized workout plan that takes into account the user's lifestyle, health condition, and environmental factors. [Solution] The system according to the embodiment comprises an analysis unit, a provision unit, and an interactive unit. The analysis unit analyzes the user's lifestyle patterns, health status, and environmental factors in real time. The provision unit provides a personalized workout plan based on the information obtained by the analysis unit. The interactive unit provides interactive functions based on the workout plan provided by the provision 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 that responds to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there is a problem that it is difficult to provide a personalized workout plan considering the user's lifestyle pattern, health condition, and environmental factors.

[0005] The system according to the embodiment aims to provide a personalized workout plan considering the user's lifestyle pattern, health condition, and environmental factors.

Means for Solving the Problems

[0006] The system according to this embodiment comprises an analysis unit, a provision unit, and an interactive unit. The analysis unit analyzes the user's lifestyle patterns, health status, and environmental factors in real time. The provision unit provides a personalized workout plan based on the information obtained by the analysis unit. The interactive unit provides interactive functions based on the workout plan provided by the provision unit. [Effects of the Invention]

[0007] The system according to this embodiment can provide a personalized workout plan that takes into account the user's lifestyle, health condition, and environmental factors. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

[0019] The smart device 14 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The fitness assistant system according to an embodiment of the present invention is an AI-based fitness assistant integrated into the user's daily life. This fitness assistant system analyzes the user's lifestyle patterns, health status, and environmental factors in real time and promotes exercise at the optimal time. Furthermore, it provides a personalized workout plan tailored to the user's preferences and goals and includes interactive features to maintain motivation. For example, the fitness assistant system analyzes the user's lifestyle patterns, health status, and environmental factors in real time. Next, the fitness assistant system provides a personalized workout plan tailored to the user's preferences and goals. Furthermore, the fitness assistant system includes interactive features to maintain the user's motivation. This allows the user to exercise at the optimal time and achieve a healthy lifestyle. In this way, the fitness assistant system can support the user's healthy lifestyle.

[0029] The fitness assistant system according to this embodiment comprises an analysis unit, a provision unit, and an interactive unit. The analysis unit analyzes the user's lifestyle patterns, health status, and environmental factors in real time. For example, the analysis unit collects daily activities, eating habits, sleep patterns, etc., to analyze the user's lifestyle patterns. The analysis unit can collect data such as heart rate, blood pressure, and weight to assess the health status. The analysis unit can collect data such as temperature, humidity, and air quality to consider environmental factors. The provision unit provides a personalized workout plan based on the information obtained by the analysis unit. For example, the provision unit creates a customized workout plan based on the user's health status and goals. The provision unit can adjust the type and intensity of exercise to suit the user's preferences. The provision unit can suggest the timing of exercise according to the user's schedule. The interactive unit provides interactive functions based on the workout plan provided by the provision unit. For example, the interactive unit checks the user's exercise form in real time and provides correction instructions. The interactive unit can provide encouraging messages and exercise tips to maintain the user's motivation. The interactive unit features a smart scheduling function that works with the user's calendar to suggest the optimal timing for exercise. The interactive unit also features a biofeedback function that works with a wearable device to adjust exercise intensity according to the user's physical condition and fatigue level. Furthermore, the interactive unit includes a predictive analytics function that analyzes the user's past data and behavioral patterns to predict future exercise habits and the likelihood of achieving goals. As a result, the fitness assistant system according to this embodiment can support the user's healthy lifestyle.

[0030] The analytics department analyzes users' lifestyle patterns, health status, and environmental factors in real time. Specifically, it monitors users' daily activities in detail, collecting data such as steps taken, exercise time, and sitting time. Regarding eating habits, it records the types of meals, calories, and nutrient balance of the user's intake, and also considers the timing and frequency of meals. Regarding sleep patterns, it tracks the user's bedtime, wake-up time, and the quality and depth of their sleep. This data is collected via smartphones and wearable devices and stored in a cloud-based database. To assess health status, vital signs such as heart rate, blood pressure, weight, body fat percentage, and blood glucose levels are measured regularly to comprehensively evaluate the user's health. This data is automatically acquired from the user's smartwatch or fitness tracker. For collecting data on environmental factors, it utilizes publicly available online databases and local sensors to obtain information such as temperature, humidity, air quality, and UV radiation levels in the user's residential area. This allows the analytics department to consider the impact of the user's living environment on their health and fitness. Furthermore, the analytics department integrates this data to track changes in users' health status and lifestyle patterns in real time, detecting anomalies and shifts in trends. This allows for early warnings of health risks to users and the suggestion of appropriate countermeasures.

[0031] The service provider offers personalized workout plans based on information obtained by the analytics department. Specifically, it creates individually customized workout plans according to the user's health status and fitness goals. For example, a user who wants to lose weight will be provided with a plan centered on aerobic exercise, while a user who wants to increase muscle strength will be provided with a plan centered on strength training. The service provider can adjust the type and intensity of exercise to suit the user's preferences. For example, a user who likes running will be provided with a plan centered on running, and a user who likes yoga will be provided with a plan centered on yoga. The service provider can also suggest the timing of exercise according to the user's schedule. For example, a busy user will be suggested to do short, effective workouts, while a user with more time will be suggested to do longer workouts. Furthermore, the service provider can monitor the user's progress and adjust the workout plan as needed. For example, if a user achieves a goal, a new goal will be set and a plan will be provided accordingly. Rewards and badges can also be provided for achieved goals to help users maintain their motivation to continue exercising. In this way, the service provider can support users in maintaining a healthy lifestyle and achieving their fitness goals.

[0032] The interactive section provides interactive functions based on the workout plan provided by the service provider. Specifically, it checks the user's exercise form in real time and provides correction instructions. For example, when a user is performing squats, it monitors whether the knee position and back posture are correct using a camera and provides correction instructions via voice or on-screen display as needed. This allows the user to exercise with correct form and reduce the risk of injury. The interactive section can also provide encouraging messages and exercise tips to maintain the user's motivation. For example, it can display messages such as "Just a little more!" or "Great form!" during exercise to motivate the user. After exercise, it can also suggest advice for the next workout and stretching methods for recovery. The interactive section has a smart scheduling function that works with the user's calendar to suggest the optimal timing for exercise. For example, by considering the user's work and personal schedule and suggesting the optimal exercise time, it makes it easier to make exercise a habit. Furthermore, the interactive section has a biofeedback function that works with wearable devices to adjust the exercise intensity according to the user's physical condition and fatigue level. For example, by monitoring heart rate and fatigue levels in real time and adjusting exercise intensity as needed, users can continue exercising without overexerting themselves. The interactive section features predictive analytics that analyze the user's past data and behavioral patterns to predict future exercise habits and the likelihood of achieving goals. This allows users to understand their own progress and create concrete action plans to achieve their goals. In this way, the interactive section can provide support to users in maintaining a healthy lifestyle and achieving their fitness goals.

[0033] The analysis unit can refer to the user's past health data and predict changes in their current health status. For example, the analysis unit can predict the user's current fitness level based on their past exercise history. The analysis unit can refer to the user's past dietary data and predict changes in their nutritional status. The analysis unit can analyze the user's past sleep data and predict the quality of their current sleep. This allows the system to predict changes in the user's current health status by referring to their past health data and take appropriate action. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's past health data into a generating AI and have the generating AI perform predictions of changes in health status.

[0034] The analysis unit can improve the accuracy of its analysis of environmental factors by considering seasonal and weather changes. For example, the analysis unit can analyze the appropriate timing for exercise by considering seasonal temperature changes. The analysis unit can refer to weather data and suggest appropriate days for outdoor exercise. The analysis unit can adjust exercise recommendations by considering seasonal allergy information. In this way, the accuracy of the analysis of environmental factors can be improved by considering seasonal and weather changes. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input seasonal and weather data into a generating AI and have the generating AI perform the analysis of environmental factors.

[0035] The analysis unit can assess the user's overall health status by considering their eating and sleeping patterns during analysis. For example, the analysis unit can assess nutritional balance based on the user's eating data. The analysis unit can refer to the user's sleep data and assess sleep quality. The analysis unit can integrate the eating and sleeping data to assess the overall health status. This allows for an assessment of the user's overall health status by considering their eating and sleeping patterns. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's eating and sleeping data into a generating AI and have the generating AI perform an assessment of the overall health status.

[0036] The analysis unit can analyze a user's social media activity during analysis and evaluate their stress level and psychological state. For example, the analysis unit can analyze the content of a user's posts and evaluate their stress level. The analysis unit can refer to the user's frequency of social media use and evaluate their psychological state. The analysis unit can analyze a user's social media interaction patterns and evaluate their psychological state. In this way, stress levels and psychological state can be evaluated by analyzing a user's social media activity. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's social media data into a generating AI and have the generating AI perform the evaluation of stress levels and psychological state.

[0037] The service provider can select the optimal workout plan by referring to the user's past exercise history at the time of delivery. For example, the service provider can select the optimal workout plan based on the type and frequency of exercise the user has performed in the past. The service provider can prioritize suggesting effective exercises based on the user's past exercise history. The service provider can analyze the user's past exercise history and provide a balanced workout plan. In this way, the service provider can provide the optimal workout plan by referring to the user's past exercise history. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the user's past exercise history data into a generating AI and have the generating AI select the optimal workout plan.

[0038] The service provider can adjust the intensity of the workout plan based on the user's current physical condition and fatigue level at the time of delivery. For example, if the user is tired, the service provider can suggest light exercise. If the user is in good physical condition, the service provider can suggest high-intensity exercise. The service provider can evaluate the user's fatigue level in real time and adjust the intensity of the workout plan. This allows the service provider to provide appropriate exercise by adjusting the intensity of the workout plan based on the user's current physical condition and fatigue level. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's physical condition and fatigue level data into a generating AI and have the generating AI adjust the intensity of the workout plan.

[0039] The service provider can suggest the optimal exercise location by considering the user's geographical location information at the time of service provision. For example, if the user is at home, the service provider can suggest exercises that can be done at home. If the user is near a park, the service provider can suggest exercises in the park. If the user is near a gym, the service provider can suggest exercises in the gym. In this way, the service provider can suggest the optimal exercise location by considering the user's geographical location information. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's geographical location information into a generating AI and have the generating AI perform the task of suggesting the optimal exercise location.

[0040] The service provider can provide an optimal workout plan by considering the user's device information at the time of delivery. For example, if the user is using a smartphone, the service provider can suggest exercises that can be done on the smartphone. If the user is using a tablet, the service provider can suggest exercises that can be done on a larger screen. If the user is using a smartwatch, the service provider can suggest exercises that can be done on the smartwatch. In this way, an optimal workout plan can be provided by considering the user's device information. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's device information into a generating AI and have the generating AI execute the provision of an optimal workout plan.

[0041] The interactive unit can provide optimal feedback by referring to the user's past feedback when providing interactive functions. For example, the interactive unit can provide similar feedback based on positive feedback the user has received in the past. The interactive unit can analyze the user's past feedback history and provide optimal feedback. The interactive unit can provide customized feedback by referring to the content of feedback the user has received in the past. In this way, optimal feedback can be provided by referring to the user's past feedback. Some or all of the above processing in the interactive unit may be performed using AI, for example, or without using AI. For example, the interactive unit can input the user's past feedback data into a generating AI and have the generating AI perform the task of providing optimal feedback.

[0042] The interactive unit can check the user's current exercise form in real time and provide correction instructions when providing interactive functions. For example, the interactive unit can check the user's exercise form in real time using a camera and provide correction instructions. The interactive unit can check the user's exercise form in real time using sensors and provide correction instructions. The interactive unit can have AI analyze the user's exercise form in real time and provide correction instructions. This allows the user to maintain correct exercise form by checking their current exercise form in real time and providing correction instructions. Some or all of the above processing in the interactive unit may be performed using AI, for example, or without AI. For example, the interactive unit can input the user's exercise form data into a generating AI and have the generating AI perform exercise form checks and provide correction instructions.

[0043] The interactive unit can suggest the optimal exercise timing by considering the user's calendar information when providing interactive functions. For example, the interactive unit suggests the optimal exercise timing based on the user's calendar. The interactive unit can adjust the exercise timing to match the user's schedule. The interactive unit can set exercise reminders based on the user's calendar information. This allows the system to suggest the optimal exercise timing by considering the user's calendar information. Some or all of the above processing in the interactive unit may be performed using AI, for example, or without AI. For example, the interactive unit can input the user's calendar information into a generating AI and have the generating AI suggest the optimal exercise timing.

[0044] The interactive unit can adjust exercise intensity by referring to data from the user's wearable device when providing interactive functions. For example, the interactive unit can adjust exercise intensity based on the user's heart rate data. The interactive unit can adjust exercise intensity by referring to the user's step count data. The interactive unit can adjust exercise intensity based on the user's calorie consumption data. In this way, exercise intensity can be appropriately adjusted by referring to data from the user's wearable device. Some or all of the above processing in the interactive unit may be performed using AI, for example, or without AI. For example, the interactive unit can input data from the user's wearable device into a generating AI and have the generating AI perform the adjustment of exercise intensity.

[0045] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0046] The fitness assistant system can also include a hobby analysis unit that customizes workout plans by considering the user's hobbies and interests. The hobby analysis unit analyzes the user's favorite sports and activities and proposes an exercise plan based on that. For example, if the user enjoys dancing, the hobby analysis unit will suggest exercises that incorporate dance. If the user prefers outdoor activities, the hobby analysis unit can suggest hiking or running. If the user is interested in yoga, the hobby analysis unit can suggest yoga sessions. This allows for the provision of more enjoyable and sustainable workout plans by taking the user's hobbies and interests into account.

[0047] The fitness assistant system can also include a sleep analysis unit that analyzes the user's sleep data and provides advice to improve sleep quality. The sleep analysis unit analyzes the user's sleep patterns and assesses sleep quality. For example, if the user is not getting enough sleep, the sleep analysis unit suggests a relaxing evening routine. If the user wakes up multiple times during the night, the sleep analysis unit can provide advice on improving the environment or managing stress. If the user is getting deep sleep, the sleep analysis unit can advise continuing with the same routine. In this way, by analyzing the user's sleep data, it can support better sleep.

[0048] The fitness assistant system can also include a nutrition analysis unit that analyzes the user's dietary data and provides advice to improve nutritional balance. The nutrition analysis unit analyzes the user's diet and evaluates its nutritional balance. For example, if the user is not consuming enough vegetables, the nutrition analysis unit can suggest recipes that include more vegetables. If the user is consuming too many calories, the nutrition analysis unit can suggest a low-calorie meal plan. If the user is consuming enough protein, the nutrition analysis unit can advise continuing with the same meal plan. In this way, by analyzing the user's dietary data, it can support a healthier lifestyle.

[0049] The fitness assistant system can also include an exercise effectiveness analysis unit that analyzes the user's past exercise data and evaluates the effects of exercise. The exercise effectiveness analysis unit evaluates the effects of the user's past exercises and reflects this in future exercise plans. For example, if the user lost weight through past exercise, the exercise effectiveness analysis unit will suggest similar exercises. If the user's muscle strength improved through past exercise, the exercise effectiveness analysis unit can suggest strength training. If the user's stress was reduced through past exercise, the exercise effectiveness analysis unit can suggest relaxation exercises. In this way, by analyzing the user's past exercise data, a more effective exercise plan can be provided.

[0050] The fitness assistant system may also include a geographic information analysis unit that considers the user's geographic location to suggest the optimal exercise location. The geographic information analysis unit analyzes the user's current location and suggests the best exercise location. For example, if the user is at home, the geographic information analysis unit suggests exercises that can be done at home. If the user is near a park, the geographic information analysis unit can suggest exercises in the park. If the user is near a gym, the geographic information analysis unit can suggest exercises in the gym. In this way, the system can suggest the optimal exercise location by considering the user's geographic location.

[0051] The fitness assistant system may also include a device analysis unit that considers the user's device information to provide an optimal workout plan. The device analysis unit analyzes the device the user is using and proposes an exercise plan based on that analysis. For example, if the user is using a smartphone, the device analysis unit suggests exercises that can be done on the smartphone. If the user is using a tablet, the device analysis unit can suggest exercises that can be done on a larger screen. If the user is using a smartwatch, the device analysis unit can suggest exercises that can be done on the smartwatch. This allows the system to provide an optimal workout plan by considering the user's device information.

[0052] The following briefly describes the processing flow for example form 1.

[0053] Step 1: The analysis department analyzes the user's lifestyle patterns, health status, and environmental factors in real time. Specifically, it collects and analyzes lifestyle pattern data such as daily activities, eating habits, and sleep patterns; health status data such as heart rate, blood pressure, and weight; and environmental factor data such as temperature, humidity, and air quality. Step 2: The service provider provides a personalized workout plan based on the information obtained by the analysis department. Specifically, it creates a customized workout plan based on the user's health status and goals, adjusts the type and intensity of exercise, and suggests the timing of exercise to fit the user's schedule. Step 3: The interactive section provides interactive functions based on the workout plan provided by the service provider. Specifically, it includes a function to check the user's exercise form in real time and provide correction instructions, a function to provide encouraging messages and exercise tips, a smart scheduling function that suggests the optimal timing for exercise in conjunction with the user's calendar, a biofeedback function that adjusts exercise intensity according to the user's physical condition and fatigue level in conjunction with a wearable device, and a predictive analytics function that analyzes past data and behavioral patterns to predict future exercise habits and the likelihood of achieving goals.

[0054] (Example of form 2) The fitness assistant system according to an embodiment of the present invention is an AI-based fitness assistant integrated into the user's daily life. This fitness assistant system analyzes the user's lifestyle patterns, health status, and environmental factors in real time and promotes exercise at the optimal time. Furthermore, it provides a personalized workout plan tailored to the user's preferences and goals and includes interactive features to maintain motivation. For example, the fitness assistant system analyzes the user's lifestyle patterns, health status, and environmental factors in real time. Next, the fitness assistant system provides a personalized workout plan tailored to the user's preferences and goals. Furthermore, the fitness assistant system includes interactive features to maintain the user's motivation. This allows the user to exercise at the optimal time and achieve a healthy lifestyle. In this way, the fitness assistant system can support the user's healthy lifestyle.

[0055] The fitness assistant system according to this embodiment comprises an analysis unit, a provision unit, and an interactive unit. The analysis unit analyzes the user's lifestyle patterns, health status, and environmental factors in real time. For example, the analysis unit collects daily activities, eating habits, sleep patterns, etc., to analyze the user's lifestyle patterns. The analysis unit can collect data such as heart rate, blood pressure, and weight to assess the health status. The analysis unit can collect data such as temperature, humidity, and air quality to consider environmental factors. The provision unit provides a personalized workout plan based on the information obtained by the analysis unit. For example, the provision unit creates a customized workout plan based on the user's health status and goals. The provision unit can adjust the type and intensity of exercise to suit the user's preferences. The provision unit can suggest the timing of exercise according to the user's schedule. The interactive unit provides interactive functions based on the workout plan provided by the provision unit. For example, the interactive unit checks the user's exercise form in real time and provides correction instructions. The interactive unit can provide encouraging messages and exercise tips to maintain the user's motivation. The interactive unit features a smart scheduling function that works with the user's calendar to suggest the optimal timing for exercise. The interactive unit also features a biofeedback function that works with a wearable device to adjust exercise intensity according to the user's physical condition and fatigue level. Furthermore, the interactive unit includes a predictive analytics function that analyzes the user's past data and behavioral patterns to predict future exercise habits and the likelihood of achieving goals. As a result, the fitness assistant system according to this embodiment can support the user's healthy lifestyle.

[0056] The analytics department analyzes users' lifestyle patterns, health status, and environmental factors in real time. Specifically, it monitors users' daily activities in detail, collecting data such as steps taken, exercise time, and sitting time. Regarding eating habits, it records the types of meals, calories, and nutrient balance of the user's intake, and also considers the timing and frequency of meals. Regarding sleep patterns, it tracks the user's bedtime, wake-up time, and the quality and depth of their sleep. This data is collected via smartphones and wearable devices and stored in a cloud-based database. To assess health status, vital signs such as heart rate, blood pressure, weight, body fat percentage, and blood glucose levels are measured regularly to comprehensively evaluate the user's health. This data is automatically acquired from the user's smartwatch or fitness tracker. For collecting data on environmental factors, it utilizes publicly available online databases and local sensors to obtain information such as temperature, humidity, air quality, and UV radiation levels in the user's residential area. This allows the analytics department to consider the impact of the user's living environment on their health and fitness. Furthermore, the analytics department integrates this data to track changes in users' health status and lifestyle patterns in real time, detecting anomalies and shifts in trends. This allows for early warnings of health risks to users and the suggestion of appropriate countermeasures.

[0057] The service provider offers personalized workout plans based on information obtained by the analytics department. Specifically, it creates individually customized workout plans according to the user's health status and fitness goals. For example, a user who wants to lose weight will be provided with a plan centered on aerobic exercise, while a user who wants to increase muscle strength will be provided with a plan centered on strength training. The service provider can adjust the type and intensity of exercise to suit the user's preferences. For example, a user who likes running will be provided with a plan centered on running, and a user who likes yoga will be provided with a plan centered on yoga. The service provider can also suggest the timing of exercise according to the user's schedule. For example, a busy user will be suggested to do short, effective workouts, while a user with more time will be suggested to do longer workouts. Furthermore, the service provider can monitor the user's progress and adjust the workout plan as needed. For example, if a user achieves a goal, a new goal will be set and a plan will be provided accordingly. Rewards and badges can also be provided for achieved goals to help users maintain their motivation to continue exercising. In this way, the service provider can support users in maintaining a healthy lifestyle and achieving their fitness goals.

[0058] The interactive section provides interactive functions based on the workout plan provided by the service provider. Specifically, it checks the user's exercise form in real time and provides correction instructions. For example, when a user is performing squats, it monitors whether the knee position and back posture are correct using a camera and provides correction instructions via voice or on-screen display as needed. This allows the user to exercise with correct form and reduce the risk of injury. The interactive section can also provide encouraging messages and exercise tips to maintain the user's motivation. For example, it can display messages such as "Just a little more!" or "Great form!" during exercise to motivate the user. After exercise, it can also suggest advice for the next workout and stretching methods for recovery. The interactive section has a smart scheduling function that works with the user's calendar to suggest the optimal timing for exercise. For example, by considering the user's work and personal schedule and suggesting the optimal exercise time, it makes it easier to make exercise a habit. Furthermore, the interactive section has a biofeedback function that works with wearable devices to adjust the exercise intensity according to the user's physical condition and fatigue level. For example, by monitoring heart rate and fatigue levels in real time and adjusting exercise intensity as needed, users can continue exercising without overexerting themselves. The interactive section features predictive analytics that analyze the user's past data and behavioral patterns to predict future exercise habits and the likelihood of achieving goals. This allows users to understand their own progress and create concrete action plans to achieve their goals. In this way, the interactive section can provide support to users in maintaining a healthy lifestyle and achieving their fitness goals.

[0059] The analysis unit can estimate the user's emotions and adjust the lifestyle pattern analysis method based on the estimated user emotions. For example, if the user is feeling stressed, the analysis unit can prioritize activities for relaxation in its analysis. If the user is feeling happy, the analysis unit can emphasize positive activities in its analysis. If the user is tired, the analysis unit can adjust the analysis method to recommend rest or light exercise. By adjusting the lifestyle pattern analysis method based on the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0060] The analysis unit can refer to the user's past health data and predict changes in their current health status. For example, the analysis unit can predict the user's current fitness level based on their past exercise history. The analysis unit can refer to the user's past dietary data and predict changes in their nutritional status. The analysis unit can analyze the user's past sleep data and predict the quality of their current sleep. This allows the system to predict changes in the user's current health status by referring to their past health data and take appropriate action. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's past health data into a generating AI and have the generating AI perform predictions of changes in health status.

[0061] The analysis unit can improve the accuracy of its analysis of environmental factors by considering seasonal and weather changes. For example, the analysis unit can analyze the appropriate timing for exercise by considering seasonal temperature changes. The analysis unit can refer to weather data and suggest appropriate days for outdoor exercise. The analysis unit can adjust exercise recommendations by considering seasonal allergy information. In this way, the accuracy of the analysis of environmental factors can be improved by considering seasonal and weather changes. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input seasonal and weather data into a generating AI and have the generating AI perform the analysis of environmental factors.

[0062] The analysis unit can estimate the user's emotions and adjust the order in which health analysis results are displayed based on the estimated user emotions. For example, if the user is feeling stressed, the analysis unit can display information related to relaxation first. If the user is feeling happy, the analysis unit can display positive health information first. If the user is tired, the analysis unit can display information related to rest first. In this way, by adjusting the order in which health analysis results are displayed based on the user's emotions, the system can provide the user with the most appropriate information. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0063] The analysis unit can assess the user's overall health status by considering their eating and sleeping patterns during analysis. For example, the analysis unit can assess nutritional balance based on the user's eating data. The analysis unit can refer to the user's sleep data and assess sleep quality. The analysis unit can integrate the eating and sleeping data to assess the overall health status. This allows for an assessment of the user's overall health status by considering their eating and sleeping patterns. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's eating and sleeping data into a generating AI and have the generating AI perform an assessment of the overall health status.

[0064] The analysis unit can analyze a user's social media activity during analysis and evaluate their stress level and psychological state. For example, the analysis unit can analyze the content of a user's posts and evaluate their stress level. The analysis unit can refer to the user's frequency of social media use and evaluate their psychological state. The analysis unit can analyze a user's social media interaction patterns and evaluate their psychological state. In this way, stress levels and psychological state can be evaluated by analyzing a user's social media activity. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's social media data into a generating AI and have the generating AI perform the evaluation of stress levels and psychological state.

[0065] The service provider can estimate the user's emotions and adjust the workout plan based on those emotions. For example, if the user is feeling stressed, the service provider can suggest yoga or stretching to help them relax. If the user is feeling happy, the service provider can suggest energetic exercises. If the user is tired, the service provider can suggest light exercise or rest. By adjusting the workout plan based on the user's emotions, the service provider can provide more effective exercise. 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 service provider may be performed using AI or not. For example, the service provider can input user emotion data into a generative AI and have the generative AI adjust the workout plan.

[0066] The service provider can select the optimal workout plan by referring to the user's past exercise history at the time of delivery. For example, the service provider can select the optimal workout plan based on the type and frequency of exercise the user has performed in the past. The service provider can prioritize suggesting effective exercises based on the user's past exercise history. The service provider can analyze the user's past exercise history and provide a balanced workout plan. In this way, the service provider can provide the optimal workout plan by referring to the user's past exercise history. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the user's past exercise history data into a generating AI and have the generating AI select the optimal workout plan.

[0067] The service provider can adjust the intensity of the workout plan based on the user's current physical condition and fatigue level at the time of delivery. For example, if the user is tired, the service provider can suggest light exercise. If the user is in good physical condition, the service provider can suggest high-intensity exercise. The service provider can evaluate the user's fatigue level in real time and adjust the intensity of the workout plan. This allows the service provider to provide appropriate exercise by adjusting the intensity of the workout plan based on the user's current physical condition and fatigue level. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's physical condition and fatigue level data into a generating AI and have the generating AI adjust the intensity of the workout plan.

[0068] The service provider can estimate the user's emotions and prioritize workout plans based on those emotions. For example, if the user is stressed, the service provider might prioritize relaxing exercises. If the user is feeling happy, the service provider might prioritize energetic exercises. If the user is tired, the service provider might prioritize light exercises. This allows the service provider to provide the user with the most suitable workout by prioritizing workout plans based on their 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 service provider may be performed using AI or not. For example, the service provider can input user emotion data into a generative AI and have the generative AI determine the priority of workout plans.

[0069] The service provider can suggest the optimal exercise location by considering the user's geographical location information at the time of service provision. For example, if the user is at home, the service provider can suggest exercises that can be done at home. If the user is near a park, the service provider can suggest exercises in the park. If the user is near a gym, the service provider can suggest exercises in the gym. In this way, the service provider can suggest the optimal exercise location by considering the user's geographical location information. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's geographical location information into a generating AI and have the generating AI perform the task of suggesting the optimal exercise location.

[0070] The service provider can provide an optimal workout plan by considering the user's device information at the time of delivery. For example, if the user is using a smartphone, the service provider can suggest exercises that can be done on the smartphone. If the user is using a tablet, the service provider can suggest exercises that can be done on a larger screen. If the user is using a smartwatch, the service provider can suggest exercises that can be done on the smartwatch. In this way, an optimal workout plan can be provided by considering the user's device information. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's device information into a generating AI and have the generating AI execute the provision of an optimal workout plan.

[0071] The interactive unit can estimate the user's emotions and adjust the content of interactive functions based on the estimated emotions. For example, if the user is feeling stressed, the interactive unit can provide a message to help them relax. If the user is feeling happy, the interactive unit can provide a positive message. If the user is tired, the interactive unit can provide a message encouraging them to rest. This allows the user's motivation to be maintained by adjusting the content of interactive functions based on their emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the interactive unit may be performed using AI, for example, or without AI. For example, the interactive unit can input user emotion data into a generative AI and have the generative AI adjust the content of the interactive functions.

[0072] The interactive unit can provide optimal feedback by referring to the user's past feedback when providing interactive functions. For example, the interactive unit can provide similar feedback based on positive feedback the user has received in the past. The interactive unit can analyze the user's past feedback history and provide optimal feedback. The interactive unit can provide customized feedback by referring to the content of feedback the user has received in the past. In this way, optimal feedback can be provided by referring to the user's past feedback. Some or all of the above processing in the interactive unit may be performed using AI, for example, or without using AI. For example, the interactive unit can input the user's past feedback data into a generating AI and have the generating AI perform the task of providing optimal feedback.

[0073] The interactive unit can check the user's current exercise form in real time and provide correction instructions when providing interactive functions. For example, the interactive unit can check the user's exercise form in real time using a camera and provide correction instructions. The interactive unit can check the user's exercise form in real time using sensors and provide correction instructions. The interactive unit can have AI analyze the user's exercise form in real time and provide correction instructions. This allows the user to maintain correct exercise form by checking their current exercise form in real time and providing correction instructions. Some or all of the above processing in the interactive unit may be performed using AI, for example, or without AI. For example, the interactive unit can input the user's exercise form data into a generating AI and have the generating AI perform exercise form checks and provide correction instructions.

[0074] The interactive unit can estimate the user's emotions and prioritize interactive features based on those emotions. For example, if the user is stressed, the interactive unit may prioritize features that promote relaxation. If the user is feeling happy, the interactive unit may prioritize positive features. If the user is tired, the interactive unit may prioritize features that encourage rest. By prioritizing interactive features based on the user's emotions, the system can provide the user with the most suitable features. 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-described processing in the interactive unit may be performed using AI or not. For example, the interactive unit can input user emotion data into a generative AI and have the generative AI determine the priority of interactive features.

[0075] The interactive unit can suggest the optimal exercise timing by considering the user's calendar information when providing interactive functions. For example, the interactive unit suggests the optimal exercise timing based on the user's calendar. The interactive unit can adjust the exercise timing to match the user's schedule. The interactive unit can set exercise reminders based on the user's calendar information. This allows the system to suggest the optimal exercise timing by considering the user's calendar information. Some or all of the above processing in the interactive unit may be performed using AI, for example, or without AI. For example, the interactive unit can input the user's calendar information into a generating AI and have the generating AI suggest the optimal exercise timing.

[0076] The interactive unit can adjust exercise intensity by referring to data from the user's wearable device when providing interactive functions. For example, the interactive unit can adjust exercise intensity based on the user's heart rate data. The interactive unit can adjust exercise intensity by referring to the user's step count data. The interactive unit can adjust exercise intensity based on the user's calorie consumption data. In this way, exercise intensity can be appropriately adjusted by referring to data from the user's wearable device. Some or all of the above processing in the interactive unit may be performed using AI, for example, or without AI. For example, the interactive unit can input data from the user's wearable device into a generating AI and have the generating AI perform the adjustment of exercise intensity.

[0077] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0078] The fitness assistant system can also be equipped with a voice analysis unit that analyzes the user's voice data. The voice analysis unit can estimate the user's emotions from their voice tone and speaking style, and provide feedback based on the user's emotional state. For example, if the user speaks with a tired voice, the voice analysis unit can offer advice on how to relax. If the user speaks with an excited voice, the voice analysis unit can suggest energetic exercises. If the user speaks with a calm voice, the voice analysis unit can suggest exercises to improve concentration. This allows for more personalized feedback by analyzing the user's voice data.

[0079] The fitness assistant system can also include a hobby analysis unit that customizes workout plans by considering the user's hobbies and interests. The hobby analysis unit analyzes the user's favorite sports and activities and proposes an exercise plan based on that. For example, if the user enjoys dancing, the hobby analysis unit will suggest exercises that incorporate dance. If the user prefers outdoor activities, the hobby analysis unit can suggest hiking or running. If the user is interested in yoga, the hobby analysis unit can suggest yoga sessions. This allows for the provision of more enjoyable and sustainable workout plans by taking the user's hobbies and interests into account.

[0080] The fitness assistant system can also include a sleep analysis unit that analyzes the user's sleep data and provides advice to improve sleep quality. The sleep analysis unit analyzes the user's sleep patterns and assesses sleep quality. For example, if the user is not getting enough sleep, the sleep analysis unit suggests a relaxing evening routine. If the user wakes up multiple times during the night, the sleep analysis unit can provide advice on improving the environment or managing stress. If the user is getting deep sleep, the sleep analysis unit can advise continuing with the same routine. In this way, by analyzing the user's sleep data, it can support better sleep.

[0081] The fitness assistant system can also include a nutrition analysis unit that analyzes the user's dietary data and provides advice to improve nutritional balance. The nutrition analysis unit analyzes the user's diet and evaluates its nutritional balance. For example, if the user is not consuming enough vegetables, the nutrition analysis unit can suggest recipes that include more vegetables. If the user is consuming too many calories, the nutrition analysis unit can suggest a low-calorie meal plan. If the user is consuming enough protein, the nutrition analysis unit can advise continuing with the same meal plan. In this way, by analyzing the user's dietary data, it can support a healthier lifestyle.

[0082] The fitness assistant system can further estimate the user's emotions and suggest types of exercise based on those emotions. For example, if the user is feeling stressed, the emotion estimation unit can suggest yoga or stretching to help them relax. If the user is feeling happy, the emotion estimation unit can suggest energetic exercises. If the user is tired, the emotion estimation unit can suggest light exercise or rest. By suggesting exercise types based on the user's emotions, the system can provide more effective workouts.

[0083] The fitness assistant system can also include an exercise effectiveness analysis unit that analyzes the user's past exercise data and evaluates the effects of exercise. The exercise effectiveness analysis unit evaluates the effects of the user's past exercises and reflects this in future exercise plans. For example, if the user lost weight through past exercise, the exercise effectiveness analysis unit will suggest similar exercises. If the user's muscle strength improved through past exercise, the exercise effectiveness analysis unit can suggest strength training. If the user's stress was reduced through past exercise, the exercise effectiveness analysis unit can suggest relaxation exercises. In this way, by analyzing the user's past exercise data, a more effective exercise plan can be provided.

[0084] The fitness assistant system can further estimate the user's emotions and suggest exercise timing based on those emotions. For example, if the user is feeling stressed, the emotion estimation unit may suggest relaxing exercise in the evening. If the user is feeling happy, the emotion estimation unit may suggest energetic exercise in the morning. If the user is tired, the emotion estimation unit may suggest light exercise during the day. By suggesting exercise timing based on the user's emotions, the system can provide more effective workouts.

[0085] The fitness assistant system may also include a geographic information analysis unit that considers the user's geographic location to suggest the optimal exercise location. The geographic information analysis unit analyzes the user's current location and suggests the best exercise location. For example, if the user is at home, the geographic information analysis unit suggests exercises that can be done at home. If the user is near a park, the geographic information analysis unit can suggest exercises in the park. If the user is near a gym, the geographic information analysis unit can suggest exercises in the gym. In this way, the system can suggest the optimal exercise location by considering the user's geographic location.

[0086] The fitness assistant system can further estimate the user's emotions and adjust the exercise intensity based on those emotions. For example, if the user is feeling stressed, the emotion estimation unit can suggest light exercise. If the user is feeling happy, the emotion estimation unit can suggest high-intensity exercise. If the user is tired, the emotion estimation unit can suggest rest. By adjusting the exercise intensity based on the user's emotions, the system can provide a more effective workout.

[0087] The fitness assistant system may also include a device analysis unit that considers the user's device information to provide an optimal workout plan. The device analysis unit analyzes the device the user is using and proposes an exercise plan based on that analysis. For example, if the user is using a smartphone, the device analysis unit suggests exercises that can be done on the smartphone. If the user is using a tablet, the device analysis unit can suggest exercises that can be done on a larger screen. If the user is using a smartwatch, the device analysis unit can suggest exercises that can be done on the smartwatch. This allows the system to provide an optimal workout plan by considering the user's device information.

[0088] The following briefly describes the processing flow for example form 2.

[0089] Step 1: The analysis department analyzes the user's lifestyle patterns, health status, and environmental factors in real time. Specifically, it collects and analyzes lifestyle pattern data such as daily activities, eating habits, and sleep patterns; health status data such as heart rate, blood pressure, and weight; and environmental factor data such as temperature, humidity, and air quality. Step 2: The service provider provides a personalized workout plan based on the information obtained by the analysis department. Specifically, it creates a customized workout plan based on the user's health status and goals, adjusts the type and intensity of exercise, and suggests the timing of exercise to fit the user's schedule. Step 3: The interactive section provides interactive functions based on the workout plan provided by the service provider. Specifically, it includes a function to check the user's exercise form in real time and provide correction instructions, a function to provide encouraging messages and exercise tips, a smart scheduling function that suggests the optimal timing for exercise in conjunction with the user's calendar, a biofeedback function that adjusts exercise intensity according to the user's physical condition and fatigue level in conjunction with a wearable device, and a predictive analytics function that analyzes past data and behavioral patterns to predict future exercise habits and the likelihood of achieving goals.

[0090] 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.

[0091] 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.

[0092] 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.

[0093] Each of the multiple elements described above, including the analysis unit, provision unit, and interactive unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the analysis unit is implemented by the control unit 46A of the smart device 14 and analyzes the user's lifestyle patterns, health status, and environmental factors in real time. The provision unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides a personalized workout plan. The interactive unit is implemented by the control unit 46A of the smart device 14 and provides interactive functions to maintain the user's motivation. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0094] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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).

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.).

[0106] 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.

[0107] 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.

[0108] 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.

[0109] Each of the multiple elements described above, including the analysis unit, the provision unit, and the interactive unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the analysis unit is implemented by the control unit 46A of the smart glasses 214 and analyzes the user's lifestyle patterns, health status, and environmental factors in real time. The provision unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides a personalized workout plan. The interactive unit is implemented by the control unit 46A of the smart glasses 214 and provides interactive functions to maintain the user's motivation. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0110] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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).

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.).

[0122] 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.

[0123] 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.

[0124] 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.

[0125] Each of the multiple elements described above, including the analysis unit, provision unit, and interactive unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the analysis unit is implemented by the control unit 46A of the headset terminal 314 and analyzes the user's lifestyle patterns, health status, and environmental factors in real time. The provision unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides a personalized workout plan. The interactive unit is implemented by the control unit 46A of the headset terminal 314 and provides interactive functions to maintain the user's motivation. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0126] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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).

[0132] 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.

[0133] 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.

[0134] 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.

[0135] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0136] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0137] In 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.

[0138] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0139] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0140] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0141] The data processing system 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.

[0142] Each of the multiple elements described above, including the analysis unit, the provision unit, and the interactive unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the analysis unit is implemented by the control unit 46A of the robot 414 and analyzes the user's lifestyle patterns, health status, and environmental factors in real time. The provision unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides a personalized workout plan. The interactive unit is implemented by the control unit 46A of the robot 414 and provides interactive functions to maintain the user's motivation. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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."

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] (Note 1) The analysis department analyzes users' lifestyle patterns, health status, and environmental factors in real time, A provisioning unit provides a personalized workout plan based on the information obtained by the aforementioned analysis unit, The system includes an interactive unit that provides interactive functions based on the workout plan provided by the aforementioned provisioning unit. A system characterized by the following features. (Note 2) The aforementioned analysis unit is We estimate the user's emotions and adjust the analysis method of lifestyle patterns based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit is It refers to the user's past health data and predicts changes in their current health status. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned analysis unit is When analyzing environmental factors, consider seasonal and weather changes to improve analytical accuracy. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned analysis unit is It estimates the user's emotions and adjusts the order in which health analysis results are displayed based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned analysis unit is During the analysis, the user's diet and sleep patterns are taken into consideration to assess their overall health status. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned analysis unit is During the analysis, we analyze users' social media activity and assess their stress levels and psychological state. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned supply unit is, It estimates the user's emotions and adjusts the workout plan based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned supply unit is, When providing the service, the system selects the optimal workout plan by referring to the user's past exercise history. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned supply unit is, When providing the workout plan, the intensity will be adjusted based on the user's current physical condition and fatigue level. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned supply unit is, It estimates the user's emotions and prioritizes workout plans based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned supply unit is, When providing the service, the system will suggest the optimal exercise location considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned supply unit is, When providing the service, we will offer the optimal workout plan, taking into account the user's device information. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned interactive unit is It estimates the user's emotions and adjusts the content of interactive features based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned interactive unit is When providing interactive features, refer to the user's past feedback to provide the most appropriate feedback. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned interactive unit is When providing interactive features, the system checks the user's current exercise form in real time and provides correction instructions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned interactive unit is It estimates the user's emotions and prioritizes interactive features based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned interactive unit is When providing interactive features, the system will suggest the optimal exercise timing considering the user's calendar information. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned interactive unit is When providing interactive features, the system adjusts exercise intensity by referencing data from the user's wearable device. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0162] 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. The analysis department analyzes users' lifestyle patterns, health status, and environmental factors in real time, A provisioning unit provides a personalized workout plan based on the information obtained by the aforementioned analysis unit, The system includes an interactive unit that provides interactive functions based on the workout plan provided by the aforementioned provisioning unit. A system characterized by the following features.

2. The aforementioned analysis unit is We estimate the user's emotions and adjust the analysis method of lifestyle patterns based on the estimated user emotions. The system according to feature 1.

3. The aforementioned analysis unit is It refers to the user's past health data and predicts changes in their current health status. The system according to feature 1.

4. The aforementioned analysis unit is When analyzing environmental factors, consider seasonal and weather changes to improve analytical accuracy. The system according to feature 1.

5. The aforementioned analysis unit is It estimates the user's emotions and adjusts the order in which health analysis results are displayed based on the estimated user emotions. The system according to feature 1.

6. The aforementioned analysis unit is During the analysis, the user's diet and sleep patterns are taken into consideration to assess their overall health status. The system according to feature 1.

7. The aforementioned analysis unit is During the analysis, we analyze users' social media activity and assess their stress levels and psychological state. The system according to feature 1.

8. The aforementioned supply unit is, It estimates the user's emotions and adjusts the workout plan based on those emotions. The system according to feature 1.

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