System

Abstract

An object of a system according to an embodiment is to enable busy people in society to efficiently make exercise habits.SOLUTION: A system according to an embodiment includes a calendar cooperation unit, a free time detection unit, a training suggestion unit, and a history learning unit. The calendar cooperation unit acquires calendar information of a user. The vacant time slot detecting section detects a vacant time slot based on the calendar information acquired by the calendar cooperation section. The training proposal component proposes training content on the basis of the free time detected by the free time detector. The history learning unit learns a history of the training proposed by the training proposal unit, and proposes balanced training.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] With conventional technology, it has been difficult for busy working people to find the right time to train to make up for lack of exercise.

[0005] The system according to the embodiment aims to help busy working people efficiently develop exercise habits. [Means for solving the problem]

[0006] The system according to the embodiment includes a calendar linking unit, a free time detection unit, a training suggestion unit, and a history learning unit. The calendar linking unit acquires calendar information of the user. The free time detection unit detects free time based on the calendar information acquired by the calendar linking unit. The training suggestion unit suggests training content based on the free time detected by the free time detection unit. The history learning unit learns the history of training suggested by the training suggestion unit and suggests balanced training. [Effects of the Invention]

[0007] The system according to the embodiment allows busy working people to efficiently develop an exercise habit. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

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

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

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

[0017] 1, a 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

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

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0024] 2, in the data processing device 12, a specific process 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" according to the technology of the present 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 process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0027] Note that a device other than the data processing device 12 may 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 a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The personal trainer app according to an embodiment of the present invention is a system that acquires a user's calendar information, uses a generation AI to find free time, and proposes training content that matches that time period and the amount of time available. This allows the personal trainer app to find the user's free time and propose appropriate training content, thereby helping to eliminate a lack of exercise and supporting a healthy lifestyle.

[0029] A personal trainer app according to an embodiment includes a calendar linking unit, a free time detection unit, a training suggestion unit, and a history learning unit. The calendar linking unit acquires a user's calendar information. For example, the calendar linking unit can link with an external calendar service such as Google Calendar or Outlook Calendar to acquire the user's schedule information. The calendar linking unit can also acquire schedule information manually entered by the user. The free time detection unit detects free time based on the calendar information acquired by the calendar linking unit. For example, if the user's calendar includes an event such as a "meeting" or "work," the free time detection unit can find free time between or before or after the event. The free time detection unit can set a minimum unit of continuous free time and detect free time based on that unit. The training suggestion unit suggests training content based on the free time detected by the free time detection unit. For example, if the user has 30 minutes of free time, the training suggestion unit can suggest a training video that can be completed in 30 minutes. The training suggestion unit can suggest appropriate training content based on the user's training history and preferences. The history learning unit learns the training history suggested by the training suggestion unit and suggests balanced training. For example, if the user has only been training the upper body, the history learning unit can suggest lower body training next time. The history learning unit can also analyze the user's training history, identify areas that are not well-trained, and suggest balanced training. As a result, the personal trainer app according to the embodiment can identify the user's free time and suggest appropriate training content, thereby eliminating lack of exercise and supporting a healthy lifestyle.

[0030] The free time detection unit analyzes calendar information and can predict future free time from past behavioral patterns. The free time detection unit, for example, uses a generation AI to analyze the user's calendar information and learn past behavioral patterns. For example, if a user has a meeting every Monday afternoon, the time slots before and after that can be predicted as free time. The free time detection unit also analyzes past behavioral patterns based on the user's calendar and predicts future free time. For example, if a user has regular appointments, free time can be found based on that pattern. The free time detection unit also uses a generation AI to analyze the user's calendar information and learn past behavioral patterns to predict future free time. For example, if a user has free time at a specific time each week, that time can be suggested as exercise time. This allows for future free time to be predicted based on past behavioral patterns, enabling more accurate training suggestions.

[0031] The free time detection unit can suggest optimal exercise times based on weather forecasts and traffic information in addition to calendar information. For example, the generation AI in the free time detection unit analyzes the weather forecast in addition to the user's calendar information to suggest times when outdoor exercise is possible. For example, indoor training can be suggested on rainy days. Furthermore, the generation AI in the free time detection unit analyzes the user's calendar information and traffic information to suggest exercise times that take commute time into consideration. For example, exercise times can be set to avoid times when traffic congestion is expected. Furthermore, the generation AI in the free time detection unit integrates the user's calendar information, weather forecasts, and traffic information to suggest optimal exercise times. For example, on sunny days, it can suggest outdoor jogging and select times when traffic congestion is minimal. This allows more appropriate exercise times to be suggested by taking weather forecasts and traffic information into consideration.

[0032] The training suggestion unit can analyze the user's past training data and suggest training with the optimal load and intensity. In the training suggestion unit, for example, the generation AI analyzes the user's past training data and suggests training with the optimal load and intensity. For example, it can set an appropriate weight and number of repetitions based on past training history. In addition, the training suggestion unit can suggest training with the optimal load and intensity based on the user's past training data. For example, it can adjust the content of the next training session taking into account past training results. In addition, the training suggestion unit can analyze the user's past training data and suggest training with the optimal load and intensity. For example, it can recommend training to improve muscle strength and endurance based on past training data. This enables effective training by suggesting training with the optimal load and intensity based on past training data.

[0033] The training suggestion unit can work with the user's dietary data to suggest training based on their nutritional status. For example, the generation AI in the training suggestion unit analyzes the user's dietary data and suggests training that suits their nutritional status. For example, if their protein intake is low, it can recommend strength training. The training suggestion unit can also work with the user's dietary data to suggest training that suits their nutritional status. For example, if their carbohydrate intake is high, it can recommend aerobic exercise. The training suggestion unit can also analyze the user's dietary data to suggest training that suits their nutritional status. For example, it can recommend appropriate training taking into account their vitamin and mineral intake. This allows for more effective training by suggesting training that suits their nutritional status based on their dietary data.

[0034] The history learning unit can analyze the user's training history and suggest training that takes into account the degree of muscle growth and fatigue level. In the history learning unit, for example, the generation AI analyzes the user's training history and suggests training that takes into account the degree of muscle growth and fatigue level. For example, it can recommend training that focuses on areas where muscle growth is lagging. In addition, the history learning unit can suggest training that takes into account the degree of muscle growth and fatigue level based on the user's training history. For example, it can recommend lighter training for areas where fatigue has accumulated. In addition, the history learning unit can analyze the user's training history and suggest training that takes into account the degree of muscle growth and fatigue level. For example, it can suggest increased load for areas where growth is visible and rest for areas where fatigue is visible. This allows for more effective training by suggesting training that takes into account the degree of muscle growth and fatigue level.

[0035] The history learning unit works in conjunction with the user's sleep data to suggest optimal training times and content. In the history learning unit, for example, the generation AI analyzes the user's sleep data to suggest optimal training times and content. For example, it can recommend hard training on days when the quality of sleep is good. The history learning unit also works in conjunction with the user's sleep data to suggest optimal training times and content. For example, it can recommend lighter training on days when the user has insufficient sleep. The history learning unit also works in conjunction with the user's sleep data to suggest optimal training times and content. For example, it can recommend strength training on days when the user had deep sleep and relaxation exercises on days when the user had light sleep. This allows for more effective training by suggesting optimal training times and content based on sleep data.

[0036] The history learning unit can work with the user's training history to suggest training plans that allow the user to compete with friends and family. For example, the history learning unit can use the generation AI to analyze the user's training history and suggest training plans that allow the user to compete with friends and family. For example, it can recommend a challenge-style training session to be held together with friends. The history learning unit can also work with the user's training history to suggest training plans that allow the user to compete with friends and family. For example, it can recommend a fitness challenge for the whole family. The history learning unit can also use the generation AI to analyze the user's training history and suggest training plans that allow the user to compete with friends and family. For example, it can recommend a training session in which the user competes with friends for the highest score. This can increase motivation by suggesting training plans that allow the user to compete with friends and family.

[0037] The history learning unit can work with the user's training history to propose special training plans based on seasons and events. For example, the generation AI in the history learning unit analyzes the user's training history to propose special training plans tailored to seasons and events. For example, it can recommend a diet plan for the summer or a fitness challenge before Christmas. The history learning unit can also work with the user's training history to propose special training plans tailored to seasons and events. For example, it can recommend a training plan aimed at achieving New Year's goals. The history learning unit can also analyze the user's training history to propose special training plans tailored to seasons and events. For example, it can recommend a training plan for a spring marathon. This can increase the user's motivation by proposing special training plans tailored to seasons and events.

[0038] The calendar linking unit links with the user's calendar and can suggest joint training times based on the calendars of family and friends. In the calendar linking unit, for example, the generation AI links the user's calendar with the calendars of family and friends to suggest joint training times. For example, the whole family can exercise together during a time when they are free. The calendar linking unit also analyzes the user's calendar and the calendars of friends to find common free time and suggest joint training. For example, it is possible to train online with friends. In addition, the generation AI integrates the user's calendar with the calendars of family and friends to suggest joint training times. For example, it is possible to set a time for the whole family to exercise on the weekend. This can increase motivation by suggesting times to train together with family and friends.

[0039] The calendar linking unit links with the user's calendar and can suggest refreshing exercises that can be done in a short amount of time between work. For example, the calendar linking unit uses a generation AI to analyze the user's calendar information and suggest refreshing exercises that can be done in a short amount of time between work. For example, it can recommend 5 minutes of stretching or light exercise. The calendar linking unit also suggests refreshing exercises that can be done in a short amount of time between work based on the user's calendar. For example, it can suggest simple exercises that can be done in between desk work. The calendar linking unit also uses a generation AI to analyze the user's calendar information and suggest refreshing exercises that can be done in a short amount of time between work. For example, it can recommend 10 minutes of walking or light muscle training. This makes it possible to support the user's health by suggesting refreshing exercises that can be done in a short amount of time between work.

[0040] The training suggestion unit can suggest training content that can be enjoyed based on the user's hobbies and interests. For example, the generation AI in the training suggestion unit analyzes the user's hobbies and interests and suggests training content that can be enjoyed based on that. For example, dance exercises can be recommended to a user who likes dancing. The training suggestion unit also suggests training content that can be enjoyed based on the user's hobbies and interests. For example, hiking or trail running can be recommended to a user who likes the outdoors. The training suggestion unit also suggests training content that can be enjoyed based on that analysis. For example, exercises set to music can be recommended to a user who likes music. In this way, by suggesting training content based on the user's hobbies and interests, the user can continue training while having fun.

[0041] The training suggestion unit works in conjunction with the user's calendar to suggest training that can be done at the travel destination or business trip destination. For example, the generation AI analyzes the user's calendar information to suggest training that can be done at the travel destination or business trip destination. For example, it can recommend simple exercises that can be done in a hotel room. The training suggestion unit also works in conjunction with the user's calendar to suggest training that can be done at the travel destination or business trip destination. For example, it can recommend training that uses a gym or fitness facility at the business trip destination. The training suggestion unit also analyzes the user's calendar information to suggest training that can be done at the travel destination or business trip destination. For example, it can recommend walking or running around the tourist spots at the travel destination. This allows the user to continue training even at the travel destination or business trip destination.

[0042] The history learning unit can learn the user's operation history and automatically set the most user-friendly interface. For example, the history learning unit allows the generation AI to analyze the user's operation history and automatically customize the most user-friendly interface. For example, it can place frequently used functions on the home screen. The history learning unit also allows the generation AI to automatically customize the most user-friendly interface based on the user's operation history. For example, it can adjust the button layout and color to suit the user's preferences. The history learning unit also allows the generation AI to analyze the user's operation history and automatically customize the most user-friendly interface. For example, it can make frequently used functions accessible with one tap. This makes it possible to improve user convenience by learning the user's operation history and automatically customizing the most user-friendly interface.

[0043] The history learning unit can analyze the user's visual preferences and suggest optimal colors and designs. In the history learning unit, for example, the generation AI analyzes the user's visual preferences and suggests optimal colors and designs. For example, the interface can be customized based on the user's preferred colors and designs. In addition, the history learning unit can also enable the generation AI to suggest optimal colors and designs based on the user's visual preferences. For example, the interface can be adjusted based on a theme color selected by the user. In addition, the history learning unit can enable the generation AI to analyze the user's visual preferences and suggest optimal colors and designs. For example, the interface can be customized based on the user's preferred fonts and icon styles. In this way, the user's visual preferences can be analyzed and optimal colors and designs can be suggested, improving user convenience.

[0044] The history learning unit can analyze the user's interface operation history and suggest voice operations and gesture operations. In the history learning unit, for example, the generation AI analyzes the user's interface operation history and suggests voice operations. For example, it can make it possible to operate frequently used functions with voice commands. In addition, the history learning unit can have the generation AI suggest gesture operations based on the user's interface operation history. For example, it can make it possible to easily operate with swipes and taps. In addition, the history learning unit can have the generation AI analyze the user's interface operation history and suggest voice operations and gesture operations. For example, it can recommend operations that combine voice commands and gestures. In this way, it is possible to improve user convenience by suggesting voice operations and gesture operations.

[0045] The history learning unit can analyze the user's interface operation history and propose an interface using AR and VR. In the history learning unit, for example, the generation AI analyzes the user's interface operation history and proposes an interface using AR. For example, feedback can be displayed in real time during training. In addition, the history learning unit can have the generation AI propose an interface using VR based on the user's interface operation history. For example, a training experience in a virtual space can be provided. In addition, the history learning unit can have the generation AI analyze the user's interface operation history and propose an interface using AR or VR. For example, a virtual coach can provide guidance in real time during training. In this way, user convenience can be improved by proposing an interface using AR or VR.

[0046] The history learning unit can analyze the user's health data and propose optimal meal plans and rest plans. In the history learning unit, for example, the generation AI analyzes the user's health data and proposes optimal meal plans. For example, it can recommend meal menus that take nutritional balance into consideration. The history learning unit also allows the generation AI to propose optimal rest plans based on the user's health data. For example, it can adjust sleep time and rest timing. The history learning unit also allows the generation AI to analyze the user's health data and propose optimal meal plans and rest plans. For example, if a specific nutrient is lacking, it can recommend meals that supplement that nutrient. In this way, the user's health can be supported by analyzing the user's health data and proposing optimal meal plans and rest plans.

[0047] The history learning unit can analyze the user's stress level and suggest relaxation and meditation content. In the history learning unit, for example, the generation AI analyzes the user's stress level and suggests relaxation content. For example, when stress is high, relaxing music or meditation can be recommended. In addition, the history learning unit can have the generation AI suggest meditation content based on the user's stress level. For example, deep breathing or mindfulness exercises can be recommended. In addition, the history learning unit can have the generation AI analyze the user's stress level and suggest relaxation or meditation content. For example, when stress is high, yoga, which has a relaxing effect, can be recommended. In this way, the user's health can be supported by analyzing the user's stress level and suggesting relaxation or meditation content.

[0048] The history learning unit can analyze the user's health data and provide a plan to support the health of the entire family. For example, the generation AI in the history learning unit analyzes the user's health data and proposes a plan to support the health of the entire family. For example, it can recommend a fitness program that the entire family can do. The history learning unit can also use the generation AI to propose a plan to support the health of the entire family based on the user's health data. For example, it can recommend health events that the entire family can participate in. The history learning unit can also use the generation AI to analyze the user's health data and propose a plan to support the health of the entire family. For example, it can recommend a meal plan or exercise program that the entire family can work on. This allows the health of the entire family to be improved by proposing a plan to support the health of the entire family.

[0049] The history learning unit can analyze the user's health data and propose health support plans for the workplace and school. In the history learning unit, for example, the generation AI analyzes the user's health data and proposes a health support plan for the workplace. For example, it can recommend simple exercises that can be done in the office. In addition, the history learning unit can use the generation AI to propose a health support plan for school based on the user's health data. For example, it can recommend a fitness program that students can participate in. In addition, the history learning unit can use the generation AI to analyze the user's health data and propose a health support plan for the workplace or school. For example, it can recommend a stress management program for the workplace or health education at school. In this way, the user's health can be improved by proposing health support plans for the workplace or school.

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

[0051] The calendar linking unit not only acquires the user's calendar information but also adds new events to the user's calendar. For example, the training time suggested by the training suggestion unit can be automatically added to the calendar. The calendar linking unit can also set a reminder in the user's calendar to notify the user of the start time of the training. Furthermore, the calendar linking unit can record the progress of training in the user's calendar and make it easy to check past training history. This allows the user to more efficiently manage their training planning and execution.

[0052] The free time detection unit not only detects free time based on the user's calendar information, but can also analyze the user's sleep patterns and suggest optimal exercise times. For example, if the user has the habit of waking up early in the morning, it can suggest exercise at that time. The free time detection unit can also work with the user's calendar information to identify the time of day when the user is most energetic and suggest exercise at that time. Furthermore, the free time detection unit can analyze the user's calendar information and suggest refreshing exercises after long periods of sedentary work. This allows the user to exercise at the optimal time, supporting a healthy lifestyle.

[0053] The free time detection unit can analyze the user's location information in addition to calendar information and suggest optimal places to exercise. For example, if the user is in the office, it can suggest exercise at a nearby gym or park. The free time detection unit can also link the user's calendar information with location information to suggest places to exercise that take commute time into consideration. For example, it can suggest gyms or fitness facilities that can be stopped off on the way to work. Furthermore, the free time detection unit can analyze the user's location information and suggest exercises that can be done even when traveling or on a business trip. This allows users to exercise appropriately wherever they are, supporting a healthy lifestyle.

[0054] The training suggestion unit can analyze the user's past training data and suggest training with the optimal load and intensity. For example, the generation AI can analyze the user's past training data and set the appropriate weight and number of repetitions. The training suggestion unit can also suggest training with the optimal load and intensity based on the user's past training data. Furthermore, the training suggestion unit can also analyze the user's past training data and recommend training that will improve muscle strength and endurance. This allows for effective training by suggesting training with the optimal load and intensity based on past training data.

[0055] The training suggestion unit can link with the user's dietary data and suggest training based on their nutritional status. For example, if the generation AI analyzes the user's dietary data and finds that their protein intake is low, it can recommend strength training. The training suggestion unit can also link with the user's dietary data and suggest training that suits their nutritional status. Furthermore, the training suggestion unit can analyze the user's dietary data and recommend appropriate training, taking into account their vitamin and mineral intake. This allows for more effective training by suggesting training that suits their nutritional status based on their dietary data.

[0056] The history learning unit can analyze the user's training history and suggest training that takes into account the degree of muscle growth and fatigue level. For example, the generation AI can analyze the user's training history and recommend training that focuses on areas where muscle growth is lagging. The history learning unit can also suggest training that takes into account the degree of muscle growth and fatigue level based on the user's training history. Furthermore, the history learning unit can analyze the user's training history and suggest increasing the load on areas where growth is visible and resting on areas where fatigue is visible. This allows for more effective training by suggesting training that takes into account the degree of muscle growth and fatigue level.

[0057] The history learning unit can work with the user's sleep data to suggest optimal training times and content. For example, the generation AI can analyze the user's sleep data and recommend hard training on days when the quality of sleep is good. The history learning unit can also work with the user's sleep data to suggest optimal training times and content. Furthermore, the generation AI can analyze the user's sleep data and recommend strength training on days when the user had deep sleep, and relaxation exercises on days when the user had light sleep. This allows for more effective training by suggesting optimal training times and content based on sleep data.

[0058] The processing flow of the first embodiment will be briefly explained below.

[0059] Step 1: The calendar integration unit obtains the user's calendar information. For example, the calendar integration unit can integrate with external calendar services such as Google Calendar or Outlook Calendar to obtain the user's schedule information. The calendar integration unit can also obtain schedule information manually entered by the user. Step 2: The free time detection unit detects free time based on the calendar information acquired by the calendar linkage unit. For example, if the user's calendar contains events such as "meetings" or "work," the free time detection unit can find free time between or before or after those events. The free time detection unit can also set the smallest unit of continuous free time and detect free time based on that unit. Step 3: The training suggestion unit suggests training content based on the free time detected by the free time detection unit. For example, if there is 30 minutes of free time, the training suggestion unit can suggest a training video that can be completed in 30 minutes. The training suggestion unit can also suggest appropriate training content based on the user's training history and preferences. Step 4: The history learning unit learns the training history suggested by the training suggestion unit and suggests balanced training. For example, if the user has only been training the upper body, the history learning unit can suggest lower body training next time. The history learning unit can also analyze the user's training history, find areas that are not well-trained, and suggest balanced training.

[0060] (Example 2) The personal trainer app according to an embodiment of the present invention is a system that acquires a user's calendar information, uses a generation AI to find free time, and proposes training content that matches that time period and the amount of time available. This allows the personal trainer app to find the user's free time and propose appropriate training content, thereby helping to eliminate a lack of exercise and supporting a healthy lifestyle.

[0061] A personal trainer app according to an embodiment includes a calendar linking unit, a free time detection unit, a training suggestion unit, and a history learning unit. The calendar linking unit acquires a user's calendar information. For example, the calendar linking unit can link with an external calendar service such as Google Calendar or Outlook Calendar to acquire the user's schedule information. The calendar linking unit can also acquire schedule information manually entered by the user. The free time detection unit detects free time based on the calendar information acquired by the calendar linking unit. For example, if the user's calendar includes an event such as a "meeting" or "work," the free time detection unit can find free time between or before or after the event. The free time detection unit can set a minimum unit of continuous free time and detect free time based on that unit. The training suggestion unit suggests training content based on the free time detected by the free time detection unit. For example, if the user has 30 minutes of free time, the training suggestion unit can suggest a training video that can be completed in 30 minutes. The training suggestion unit can suggest appropriate training content based on the user's training history and preferences. The history learning unit learns the training history suggested by the training suggestion unit and suggests balanced training. For example, if the user has only been training the upper body, the history learning unit can suggest lower body training next time. The history learning unit can also analyze the user's training history, identify areas that are not well-trained, and suggest balanced training. As a result, the personal trainer app according to the embodiment can identify the user's free time and suggest appropriate training content, thereby eliminating lack of exercise and supporting a healthy lifestyle.

[0062] The free time detection unit analyzes calendar information and can predict future free time from past behavioral patterns. The free time detection unit, for example, uses a generation AI to analyze the user's calendar information and learn past behavioral patterns. For example, if a user has a meeting every Monday afternoon, the time slots before and after that can be predicted as free time. The free time detection unit also analyzes past behavioral patterns based on the user's calendar and predicts future free time. For example, if a user has regular appointments, free time can be found based on that pattern. The free time detection unit also uses a generation AI to analyze the user's calendar information and learn past behavioral patterns to predict future free time. For example, if a user has free time at a specific time each week, that time can be suggested as exercise time. This allows for future free time to be predicted based on past behavioral patterns, enabling more accurate training suggestions.

[0063] The free time detection unit can suggest optimal exercise times based on weather forecasts and traffic information in addition to calendar information. For example, the generation AI in the free time detection unit analyzes the weather forecast in addition to the user's calendar information to suggest times when outdoor exercise is possible. For example, indoor training can be suggested on rainy days. Furthermore, the generation AI in the free time detection unit analyzes the user's calendar information and traffic information to suggest exercise times that take commute time into consideration. For example, exercise times can be set to avoid times when traffic congestion is expected. Furthermore, the generation AI in the free time detection unit integrates the user's calendar information, weather forecasts, and traffic information to suggest optimal exercise times. For example, on sunny days, it can suggest outdoor jogging and select times when traffic congestion is minimal. This allows more appropriate exercise times to be suggested by taking weather forecasts and traffic information into consideration.

[0064] The free time detection unit can use the emotion estimation function to detect the user's stress level and suggest training during times when the user can relax. The free time detection unit can, for example, use the emotion estimation function to analyze the user's stress level in real time and suggest exercise during times when the user can relax. For example, it can set exercise times that avoid times when stress is high. The free time detection unit can also identify times when the user's stress level is low based on the user's emotion data and suggest relaxing exercises during those times. For example, it can recommend yoga or stretching. The free time detection unit can also use the emotion estimation function to detect the user's stress level and suggest exercise during times when the user can relax. For example, it can suggest exercises that have a relaxing effect during times when stress is low. This allows for more effective exercise by suggesting exercises during times when the user can relax, taking the user's stress level into consideration.

[0065] The training suggestion unit can analyze the user's past training data and suggest training with the optimal load and intensity. In the training suggestion unit, for example, the generation AI analyzes the user's past training data and suggests training with the optimal load and intensity. For example, it can set an appropriate weight and number of repetitions based on past training history. In addition, the training suggestion unit can suggest training with the optimal load and intensity based on the user's past training data. For example, it can adjust the content of the next training session taking into account past training results. In addition, the training suggestion unit can analyze the user's past training data and suggest training with the optimal load and intensity. For example, it can recommend training to improve muscle strength and endurance based on past training data. This enables effective training by suggesting training with the optimal load and intensity based on past training data.

[0066] The training suggestion unit can work with the user's dietary data to suggest training based on their nutritional status. For example, the generation AI in the training suggestion unit analyzes the user's dietary data and suggests training that suits their nutritional status. For example, if their protein intake is low, it can recommend strength training. The training suggestion unit can also work with the user's dietary data to suggest training that suits their nutritional status. For example, if their carbohydrate intake is high, it can recommend aerobic exercise. The training suggestion unit can also analyze the user's dietary data to suggest training that suits their nutritional status. For example, it can recommend appropriate training taking into account their vitamin and mineral intake. This allows for more effective training by suggesting training that suits their nutritional status based on their dietary data.

[0067] The training suggestion unit can use the emotion estimation function to suggest training content based on the user's mood. The training suggestion unit, for example, uses the emotion estimation function to suggest training content that matches the user's mood. For example, when the user is feeling down, the training suggestion unit can recommend relaxing yoga. The training suggestion unit also suggests training content that matches the user's mood based on the user's emotion data. For example, when the user is feeling high, the training suggestion unit can recommend hard strength training. The training suggestion unit also uses the emotion estimation function to suggest training content that matches the user's mood. For example, when the user is feeling high in stress, the training suggestion unit can recommend stretching that has a relaxing effect. This allows for more effective training by suggesting training content that matches the user's mood.

[0068] The history learning unit can analyze the user's training history and suggest training that takes into account the degree of muscle growth and fatigue level. In the history learning unit, for example, the generation AI analyzes the user's training history and suggests training that takes into account the degree of muscle growth and fatigue level. For example, it can recommend training that focuses on areas where muscle growth is lagging. In addition, the history learning unit can suggest training that takes into account the degree of muscle growth and fatigue level based on the user's training history. For example, it can recommend lighter training for areas where fatigue has accumulated. In addition, the history learning unit can analyze the user's training history and suggest training that takes into account the degree of muscle growth and fatigue level. For example, it can suggest increased load for areas where growth is visible and rest for areas where fatigue is visible. This allows for more effective training by suggesting training that takes into account the degree of muscle growth and fatigue level.

[0069] The history learning unit works in conjunction with the user's sleep data to suggest optimal training times and content. In the history learning unit, for example, the generation AI analyzes the user's sleep data to suggest optimal training times and content. For example, it can recommend hard training on days when the quality of sleep is good. The history learning unit also works in conjunction with the user's sleep data to suggest optimal training times and content. For example, it can recommend lighter training on days when the user has insufficient sleep. The history learning unit also works in conjunction with the user's sleep data to suggest optimal training times and content. For example, it can recommend strength training on days when the user had deep sleep and relaxation exercises on days when the user had light sleep. This allows for more effective training by suggesting optimal training times and content based on sleep data.

[0070] The history learning unit can use the emotion estimation function to provide a training plan to maintain the user's motivation. The history learning unit, for example, uses the emotion estimation function to propose a training plan to maintain the user's motivation. For example, when motivation is low, it can recommend training that will give the user a sense of accomplishment in a short amount of time. The history learning unit also proposes a training plan to maintain motivation based on the user's emotion data. For example, it can recommend challenging training when motivation is high. The history learning unit also uses the emotion estimation function to propose a training plan to maintain the user's motivation. For example, it can recommend fun training when motivation is low. In this way, by proposing a training plan to maintain the user's motivation, continuous training becomes possible.

[0071] The history learning unit can work with the user's training history to suggest training plans that allow the user to compete with friends and family. For example, the history learning unit can use the generation AI to analyze the user's training history and suggest training plans that allow the user to compete with friends and family. For example, it can recommend a challenge-style training session to be held together with friends. The history learning unit can also work with the user's training history to suggest training plans that allow the user to compete with friends and family. For example, it can recommend a fitness challenge for the whole family. The history learning unit can also use the generation AI to analyze the user's training history and suggest training plans that allow the user to compete with friends and family. For example, it can recommend a training session in which the user competes with friends for the highest score. This can increase motivation by suggesting training plans that allow the user to compete with friends and family.

[0072] The history learning unit can work with the user's training history to propose special training plans based on seasons and events. For example, the generation AI in the history learning unit analyzes the user's training history to propose special training plans tailored to seasons and events. For example, it can recommend a diet plan for the summer or a fitness challenge before Christmas. The history learning unit can also work with the user's training history to propose special training plans tailored to seasons and events. For example, it can recommend a training plan aimed at achieving New Year's goals. The history learning unit can also analyze the user's training history to propose special training plans tailored to seasons and events. For example, it can recommend a training plan for a spring marathon. This can increase the user's motivation by proposing special training plans tailored to seasons and events.

[0073] The history learning unit can use the emotion estimation function to propose a training plan that gives the user the greatest sense of accomplishment. The history learning unit, for example, uses the emotion estimation function to propose a training plan that gives the user the greatest sense of accomplishment. For example, it can recommend training that shows results in a short period of time. The history learning unit can also propose a training plan that gives the user a sense of accomplishment based on the user's emotion data. For example, it can recommend step-by-step training toward goal achievement. The history learning unit can also use the emotion estimation function to propose a training plan that gives the user the greatest sense of accomplishment. For example, it can recommend challenge-style training to enhance the sense of accomplishment. This allows the user to continue training by proposing a training plan that gives the user the greatest sense of accomplishment.

[0074] The calendar linking unit links with the user's calendar and can suggest joint training times based on the calendars of family and friends. In the calendar linking unit, for example, the generation AI links the user's calendar with the calendars of family and friends to suggest joint training times. For example, the whole family can exercise together during a time when they are free. The calendar linking unit also analyzes the user's calendar and the calendars of friends to find common free time and suggest joint training. For example, it is possible to train online with friends. In addition, the generation AI integrates the user's calendar with the calendars of family and friends to suggest joint training times. For example, it is possible to set a time for the whole family to exercise on the weekend. This can increase motivation by suggesting times to train together with family and friends.

[0075] The calendar linking unit links with the user's calendar and can suggest refreshing exercises that can be done in a short amount of time between work. For example, the calendar linking unit uses a generation AI to analyze the user's calendar information and suggest refreshing exercises that can be done in a short amount of time between work. For example, it can recommend 5 minutes of stretching or light exercise. The calendar linking unit also suggests refreshing exercises that can be done in a short amount of time between work based on the user's calendar. For example, it can suggest simple exercises that can be done in between desk work. The calendar linking unit also uses a generation AI to analyze the user's calendar information and suggest refreshing exercises that can be done in a short amount of time between work. For example, it can recommend 10 minutes of walking or light muscle training. This makes it possible to support the user's health by suggesting refreshing exercises that can be done in a short amount of time between work.

[0076] The calendar linkage unit can use the emotion estimation function to find the time period when the user is most motivated and suggest training at that time. The calendar linkage unit can, for example, use the emotion estimation function to identify the time period when the user is most motivated and suggest exercise at that time. For example, it can recommend hard training during the time period when motivation is high. The calendar linkage unit can also find the time period when motivation is high based on the user's emotion data and suggest exercise at that time. For example, it can recommend aerobic exercise during the time period when energy is high. The calendar linkage unit can also use the emotion estimation function to find the time period when the user is most motivated and suggest exercise at that time. For example, it can recommend strength training during the time period when motivation is high. This allows for effective training by suggesting exercise during the time period when the user is most motivated.

[0077] The training suggestion unit can suggest training content that can be enjoyed based on the user's hobbies and interests. For example, the generation AI in the training suggestion unit analyzes the user's hobbies and interests and suggests training content that can be enjoyed based on that. For example, dance exercises can be recommended to a user who likes dancing. The training suggestion unit also suggests training content that can be enjoyed based on the user's hobbies and interests. For example, hiking or trail running can be recommended to a user who likes the outdoors. The training suggestion unit also suggests training content that can be enjoyed based on that analysis. For example, exercises set to music can be recommended to a user who likes music. In this way, by suggesting training content based on the user's hobbies and interests, the user can continue training while having fun.

[0078] The training suggestion unit works in conjunction with the user's calendar to suggest training that can be done at the travel destination or business trip destination. For example, the generation AI analyzes the user's calendar information to suggest training that can be done at the travel destination or business trip destination. For example, it can recommend simple exercises that can be done in a hotel room. The training suggestion unit also works in conjunction with the user's calendar to suggest training that can be done at the travel destination or business trip destination. For example, it can recommend training that uses a gym or fitness facility at the business trip destination. The training suggestion unit also analyzes the user's calendar information to suggest training that can be done at the travel destination or business trip destination. For example, it can recommend walking or running around the tourist spots at the travel destination. This allows the user to continue training even at the travel destination or business trip destination.

[0079] The training suggestion unit can use the emotion estimation function to suggest training content with the user's most relaxing music and environmental sounds as a background. The training suggestion unit, for example, uses the emotion estimation function to suggest training content with the user's most relaxing music and environmental sounds as a background. For example, it can recommend yoga with the sounds of nature or relaxing music as a background. The training suggestion unit can also suggest training content with the user's most relaxing music and environmental sounds as a background based on the user's emotion data. For example, it can recommend stretching with the sounds of the ocean or forest as a background. The training suggestion unit can also use the emotion estimation function to suggest training content with the user's most relaxing music and environmental sounds as a background. For example, it can recommend meditation with the sounds of piano or rain as a background. This allows for more effective training by suggesting training content with the user's relaxing music and environmental sounds as a background.

[0080] The history learning unit can learn the user's operation history and automatically set the most user-friendly interface. For example, the history learning unit allows the generation AI to analyze the user's operation history and automatically customize the most user-friendly interface. For example, it can place frequently used functions on the home screen. The history learning unit also allows the generation AI to automatically customize the most user-friendly interface based on the user's operation history. For example, it can adjust the button layout and color to suit the user's preferences. The history learning unit also allows the generation AI to analyze the user's operation history and automatically customize the most user-friendly interface. For example, it can make frequently used functions accessible with one tap. This makes it possible to improve user convenience by learning the user's operation history and automatically customizing the most user-friendly interface.

[0081] The history learning unit can analyze the user's visual preferences and suggest optimal colors and designs. In the history learning unit, for example, the generation AI analyzes the user's visual preferences and suggests optimal colors and designs. For example, the interface can be customized based on the user's preferred colors and designs. In addition, the history learning unit can also enable the generation AI to suggest optimal colors and designs based on the user's visual preferences. For example, the interface can be adjusted based on a theme color selected by the user. In addition, the history learning unit can enable the generation AI to analyze the user's visual preferences and suggest optimal colors and designs. For example, the interface can be customized based on the user's preferred fonts and icon styles. In this way, the user's visual preferences can be analyzed and optimal colors and designs can be suggested, improving user convenience.

[0082] The history learning unit can use the emotion estimation function to suggest an interface design based on the user's mood. The history learning unit, for example, uses the emotion estimation function to suggest an interface design that matches the user's mood. For example, when the user is feeling depressed, it can recommend a brightly colored design. The history learning unit also suggests an interface design that matches the user's mood based on the user's emotion data. For example, when the user is feeling high in stress, it can recommend a relaxing design. The history learning unit also uses the emotion estimation function to suggest an interface design that matches the user's mood. For example, when the user is feeling high in energy, it can recommend a lively design. In this way, by suggesting an interface design that matches the user's mood, it is possible to improve user convenience.

[0083] The history learning unit can analyze the user's interface operation history and suggest voice operations and gesture operations. In the history learning unit, for example, the generation AI analyzes the user's interface operation history and suggests voice operations. For example, it can make it possible to operate frequently used functions with voice commands. In addition, the history learning unit can have the generation AI suggest gesture operations based on the user's interface operation history. For example, it can make it possible to easily operate with swipes and taps. In addition, the history learning unit can have the generation AI analyze the user's interface operation history and suggest voice operations and gesture operations. For example, it can recommend operations that combine voice commands and gestures. In this way, it is possible to improve user convenience by suggesting voice operations and gesture operations.

[0084] The history learning unit can analyze the user's interface operation history and propose an interface using AR and VR. In the history learning unit, for example, the generation AI analyzes the user's interface operation history and proposes an interface using AR. For example, feedback can be displayed in real time during training. In addition, the history learning unit can have the generation AI propose an interface using VR based on the user's interface operation history. For example, a training experience in a virtual space can be provided. In addition, the history learning unit can have the generation AI analyze the user's interface operation history and propose an interface using AR or VR. For example, a virtual coach can provide guidance in real time during training. In this way, user convenience can be improved by proposing an interface using AR or VR.

[0085] The history learning unit can use the emotion estimation function to provide an interface design that allows the user to feel most relaxed. The history learning unit, for example, uses the emotion estimation function to suggest an interface design that allows the user to feel most relaxed. For example, it can recommend colors and designs that have a relaxing effect. The history learning unit also suggests a relaxing interface design based on the user's emotion data. For example, it can recommend a design with calming colors when stress is high. The history learning unit also uses the emotion estimation function to suggest an interface design that allows the user to feel most relaxed. For example, it can add background sounds or animations that have a relaxing effect. This can improve user convenience by suggesting an interface design that allows the user to feel most relaxed.

[0086] The history learning unit can analyze the user's health data and propose optimal meal plans and rest plans. In the history learning unit, for example, the generation AI analyzes the user's health data and proposes optimal meal plans. For example, it can recommend meal menus that take nutritional balance into consideration. The history learning unit also allows the generation AI to propose optimal rest plans based on the user's health data. For example, it can adjust sleep time and rest timing. The history learning unit also allows the generation AI to analyze the user's health data and propose optimal meal plans and rest plans. For example, if a specific nutrient is lacking, it can recommend meals that supplement that nutrient. In this way, the user's health can be supported by analyzing the user's health data and proposing optimal meal plans and rest plans.

[0087] The history learning unit can analyze the user's stress level and suggest relaxation and meditation content. In the history learning unit, for example, the generation AI analyzes the user's stress level and suggests relaxation content. For example, when stress is high, relaxing music or meditation can be recommended. In addition, the history learning unit can have the generation AI suggest meditation content based on the user's stress level. For example, deep breathing or mindfulness exercises can be recommended. In addition, the history learning unit can have the generation AI analyze the user's stress level and suggest relaxation or meditation content. For example, when stress is high, yoga, which has a relaxing effect, can be recommended. In this way, the user's health can be supported by analyzing the user's stress level and suggesting relaxation or meditation content.

[0088] The history learning unit can use the emotion estimation function to propose a health support plan based on the user's mood. The history learning unit, for example, uses the emotion estimation function to propose a health support plan that matches the user's mood. For example, when the user is feeling depressed, it can recommend relaxing activities. The history learning unit also proposes a health support plan that matches the user's mood based on the user's emotion data. For example, it can recommend active exercise when the user is feeling high. The history learning unit also uses the emotion estimation function to propose a health support plan that matches the user's mood. For example, it can recommend relaxation or meditation when the user is feeling high stress. In this way, it is possible to support the user's health by proposing a health support plan that matches the user's mood.

[0089] The history learning unit can analyze the user's health data and provide a plan to support the health of the entire family. For example, the generation AI in the history learning unit analyzes the user's health data and proposes a plan to support the health of the entire family. For example, it can recommend a fitness program that the entire family can do. The history learning unit can also use the generation AI to propose a plan to support the health of the entire family based on the user's health data. For example, it can recommend health events that the entire family can participate in. The history learning unit can also use the generation AI to analyze the user's health data and propose a plan to support the health of the entire family. For example, it can recommend a meal plan or exercise program that the entire family can work on. This allows the health of the entire family to be improved by proposing a plan to support the health of the entire family.

[0090] The history learning unit can analyze the user's health data and propose health support plans for the workplace and school. In the history learning unit, for example, the generation AI analyzes the user's health data and proposes a health support plan for the workplace. For example, it can recommend simple exercises that can be done in the office. In addition, the history learning unit can use the generation AI to propose a health support plan for school based on the user's health data. For example, it can recommend a fitness program that students can participate in. In addition, the history learning unit can use the generation AI to analyze the user's health data and propose a health support plan for the workplace or school. For example, it can recommend a stress management program for the workplace or health education at school. In this way, the user's health can be improved by proposing health support plans for the workplace or school.

[0091] The history learning unit can use the emotion estimation function to suggest environments and activities that will help the user to relax most. The history learning unit, for example, uses the emotion estimation function to suggest environments that will help the user to relax most. For example, it can recommend taking a walk in nature or reading in a quiet place. The history learning unit can also suggest relaxing activities based on the user's emotion data. For example, it can recommend relaxation activities such as listening to music or enjoying art. The history learning unit can also use the emotion estimation function to suggest environments and activities that will help the user to relax most. For example, it can recommend relaxation in a hot spring or spa. In this way, by suggesting environments and activities that will help the user to relax most, it is possible to improve the user's health.

[0092] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0093] The calendar linking unit not only acquires the user's calendar information but also adds new events to the user's calendar. For example, the training time suggested by the training suggestion unit can be automatically added to the calendar. The calendar linking unit can also set a reminder in the user's calendar to notify the user of the start time of the training. Furthermore, the calendar linking unit can record the progress of training in the user's calendar and make it easy to check past training history. This allows the user to more efficiently manage their training planning and execution.

[0094] The free time detection unit not only detects free time based on the user's calendar information, but can also analyze the user's sleep patterns and suggest optimal exercise times. For example, if the user has the habit of waking up early in the morning, it can suggest exercise at that time. The free time detection unit can also work with the user's calendar information to identify the time of day when the user is most energetic and suggest exercise at that time. Furthermore, the free time detection unit can analyze the user's calendar information and suggest refreshing exercises after long periods of sedentary work. This allows the user to exercise at the optimal time, supporting a healthy lifestyle.

[0095] The free time detection unit can analyze the user's location information in addition to calendar information and suggest optimal places to exercise. For example, if the user is in the office, it can suggest exercise at a nearby gym or park. The free time detection unit can also link the user's calendar information with location information to suggest places to exercise that take commute time into consideration. For example, it can suggest gyms or fitness facilities that can be stopped off on the way to work. Furthermore, the free time detection unit can analyze the user's location information and suggest exercises that can be done even when traveling or on a business trip. This allows users to exercise appropriately wherever they are, supporting a healthy lifestyle.

[0096] The free time detection unit can use the emotion estimation function to detect the user's stress level and suggest training for a time period when the user can relax. For example, the emotion estimation function can be used to set exercise times that avoid time periods when the user's stress level is high. The free time detection unit can also identify time periods when the user's stress level is low based on the user's emotion data and suggest relaxing exercises for those times. Furthermore, the free time detection unit can use the emotion estimation function to detect the user's stress level and suggest exercises for a time period when the user can relax. This allows for more effective exercise by suggesting exercises for time periods when the user can relax, taking the user's stress level into consideration.

[0097] The training suggestion unit can analyze the user's past training data and suggest training with the optimal load and intensity. For example, the generation AI can analyze the user's past training data and set the appropriate weight and number of repetitions. The training suggestion unit can also suggest training with the optimal load and intensity based on the user's past training data. Furthermore, the training suggestion unit can also analyze the user's past training data and recommend training that will improve muscle strength and endurance. This allows for effective training by suggesting training with the optimal load and intensity based on past training data.

[0098] The training suggestion unit can link with the user's dietary data and suggest training based on their nutritional status. For example, if the generation AI analyzes the user's dietary data and finds that their protein intake is low, it can recommend strength training. The training suggestion unit can also link with the user's dietary data and suggest training that suits their nutritional status. Furthermore, the training suggestion unit can analyze the user's dietary data and recommend appropriate training, taking into account their vitamin and mineral intake. This allows for more effective training by suggesting training that suits their nutritional status based on their dietary data.

[0099] The training suggestion unit can use the emotion estimation function to suggest training content based on the user's mood. For example, the emotion estimation function can be used to suggest training content that matches the user's mood. The training suggestion unit can also suggest training content that matches the user's mood based on the user's emotion data. Furthermore, the training suggestion unit can use the emotion estimation function to suggest training content that matches the user's mood. This allows for more effective training by suggesting training content that matches the user's mood.

[0100] The history learning unit can analyze the user's training history and suggest training that takes into account the degree of muscle growth and fatigue level. For example, the generation AI can analyze the user's training history and recommend training that focuses on areas where muscle growth is lagging. The history learning unit can also suggest training that takes into account the degree of muscle growth and fatigue level based on the user's training history. Furthermore, the history learning unit can analyze the user's training history and suggest increasing the load on areas where growth is visible and resting on areas where fatigue is visible. This allows for more effective training by suggesting training that takes into account the degree of muscle growth and fatigue level.

[0101] The history learning unit can work with the user's sleep data to suggest optimal training times and content. For example, the generation AI can analyze the user's sleep data and recommend hard training on days when the quality of sleep is good. The history learning unit can also work with the user's sleep data to suggest optimal training times and content. Furthermore, the generation AI can analyze the user's sleep data and recommend strength training on days when the user had deep sleep, and relaxation exercises on days when the user had light sleep. This allows for more effective training by suggesting optimal training times and content based on sleep data.

[0102] The history learning unit can use the emotion estimation function to provide a training plan to maintain the user's motivation. For example, the emotion estimation function can be used to recommend training that will give the user a sense of accomplishment in a short amount of time when the user's motivation is low. The history learning unit can also suggest a training plan to maintain motivation based on the user's emotion data. Furthermore, the history learning unit can use the emotion estimation function to recommend enjoyable training when the user's motivation is low. This allows the user to continue training by suggesting a training plan to maintain their motivation.

[0103] The processing flow of the second embodiment will be briefly explained below.

[0104] Step 1: The calendar integration unit obtains the user's calendar information. For example, the calendar integration unit can integrate with external calendar services such as Google Calendar or Outlook Calendar to obtain the user's schedule information. The calendar integration unit can also obtain schedule information manually entered by the user. Step 2: The free time detection unit detects free time based on the calendar information acquired by the calendar linkage unit. For example, if the user's calendar contains events such as "meetings" or "work," the free time detection unit can find free time between or before or after those events. The free time detection unit can also set the smallest unit of continuous free time and detect free time based on that unit. Step 3: The training suggestion unit suggests training content based on the free time detected by the free time detection unit. For example, if there is 30 minutes of free time, the training suggestion unit can suggest a training video that can be completed in 30 minutes. The training suggestion unit can also suggest appropriate training content based on the user's training history and preferences. Step 4: The history learning unit learns the training history suggested by the training suggestion unit and suggests balanced training. For example, if the user has only been training the upper body, the history learning unit can suggest lower body training next time. The history learning unit can also analyze the user's training history, find areas that are not well-trained, and suggest balanced training.

[0105] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the 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.

[0106] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0107] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, 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.

[0108] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0109] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0110] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0111] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0112] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0113] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0114] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0115] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0116] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0117] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0118] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0119] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0120] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0121] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0122] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0123] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0124] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0125] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0126] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0127] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0128] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0129] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0130] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0131] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0132] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0133] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0134] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0135] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0136] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0137] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0138] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0139] 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.

[0140] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0141] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0142] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0143] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0144] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0145] The control 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0146] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0147] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0148] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0149] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0150] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0151] 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 control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0152] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0153] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0154] The emotion identification model 59 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 an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0155] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0156] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0157] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0158] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0159] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0160] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0161] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0162] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0163] 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.

[0164] It is not necessary to store all 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 all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0165] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0166] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with 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). Also, the hardware resource that executes the specific process may be a single processor.

[0167] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0168] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0169] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0170] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0171] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0172] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a calendar linking unit that acquires calendar information of a user; a free time detection unit that detects free time based on the calendar information acquired by the calendar linkage unit; a training suggestion unit that suggests training content based on the free time detected by the free time detection unit; a history learning unit that learns the history of the training suggested by the training suggestion unit and suggests balanced training. A system characterized by:

2. The free time detection unit Analyze the calendar information and predict future free time based on past behavior patterns 2. The system of claim 1.

3. The free time detection unit In addition to the calendar information, the app suggests optimal exercise times based on weather forecasts and traffic information.

2. The system of claim 1.

4. The free time detection unit Detecting the user's stress level and suggesting training times when they can relax 2. The system of claim 1.

5. The training suggestion unit Analyzing the user's past training data and suggesting optimal load and intensity training 2. The system of claim 1.

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