System

Abstract

An object of a system according to an embodiment is to provide a training method in accordance with a physical state and a life pattern of a user.SOLUTION: A system includes a collection unit, an analysis unit, a proposal unit, and an adjustment unit. The collection unit collects information on a physical condition, a chronic disease, and a daily life pattern of a user. The analysis unit analyzes the information collected by the collection unit. The proposal unit proposes an appropriate training method based on the result obtained by the analysis unit. The adjustment unit adjusts the training method proposed by the proposal unit in accordance with a free time of the user.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] Conventional techniques have not adequately provided training methods that are tailored to the user's physical condition and lifestyle patterns, and there is room for improvement.

[0005] The system according to the embodiment aims to provide a training method that is suited to the user's physical condition and lifestyle pattern. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a proposal unit, and an adjustment unit. The collection unit collects information on the user's physical condition, chronic illnesses, and daily life patterns. The analysis unit analyzes the information collected by the collection unit. The proposal unit proposes an appropriate training method based on the results obtained by the analysis unit. The adjustment unit adjusts the training method proposed by the proposal unit to suit the user's free time. [Effects of the Invention]

[0007] The system according to the embodiment can provide a training method that is suited to the user's physical condition and lifestyle pattern. [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) A training provision system according to an embodiment of the present invention provides training methods tailored to individual circumstances and chronic illnesses to people with physical problems, people who want to work out steadily at home in their spare time, and especially the elderly. The training provision system collects information about the user's physical condition, chronic illnesses, daily lifestyle patterns, and other factors, and AI proposes optimal training methods and adjusts them to suit the user's spare time. For example, the training provision system uses information entered by the user and data acquired from a smart device to collect information about the user's physical condition, chronic illnesses, and daily lifestyle patterns. Based on the collected information, the training provision system then proposes optimal training methods using AI. For example, the AI ​​suggests light aerobic exercise to stabilize blood pressure for a user with high blood pressure. The training provision system also adjusts the proposed training methods to suit the user's spare time. For example, if a user has about 10 minutes of spare time each day, the AI ​​suggests a training method tailored to that time. This allows people with physical problems or the elderly to continue training without straining themselves. This helps the training provision system maintain and improve the user's health. For example, a user with high blood pressure can expect to stabilize their blood pressure by continuing the suggested light aerobic exercise. Also, training in the morning can help you start your day off right.

[0029] A training provision system according to an embodiment includes a collection unit, an analysis unit, a suggestion unit, and an adjustment unit. The collection unit collects information on a user's physical condition, chronic illnesses, and daily lifestyle patterns. The collection unit collects, for example, information input by the user and data acquired from a smart device. For example, if the user has high blood pressure as a chronic illness, the collection unit collects such information. The collection unit also collects information on the user's daily lifestyle patterns, if the user has free time in the morning. The analysis unit analyzes the information collected by the collection unit. For example, the analysis unit analyzes the user's physical condition, chronic illnesses, and daily lifestyle patterns based on the collected information. For example, the analysis unit performs analysis using statistical analysis or a machine learning algorithm. The suggestion unit suggests an appropriate training method based on the results obtained by the analysis unit. For example, the suggestion unit suggests a reasonable training method based on the analysis results. For example, the suggestion unit suggests light aerobic exercise to stabilize blood pressure to a user with high blood pressure. The suggestion unit also suggests a training method that can be performed in the morning to a user who has free time in the morning. The adjustment unit adjusts the training method proposed by the suggestion unit to suit the user's free time. For example, the adjustment unit adjusts the proposed training method to suit the user's free time. For example, if the user has about 10 minutes of free time each day, the adjustment unit suggests a training method that suits that time. In this way, the training provision system according to the embodiment provides the optimal training method based on the user's physical condition, chronic illness, and daily life pattern, allowing the user to continue training without straining themselves.

[0030] The collection unit can collect information input by a user or data acquired from a smart device. The collection unit, for example, collects information input by a user. For example, if the user has high blood pressure as a chronic condition, the collection unit collects that information. The collection unit can also collect data acquired from a smart device. For example, the collection unit collects data acquired from devices such as smartphones, smart watches, and fitness trackers. By collecting user input information and data from the smart device, a training method can be suggested based on more accurate information. Some or all of the above-described processing in the collection unit may be performed using, or without, AI, for example. For example, the collection unit can input data acquired from a smart device to a generation AI and have the generation AI analyze the data.

[0031] The analysis unit can analyze the user's physical condition, chronic illnesses, and daily lifestyle patterns based on the collected information. The analysis unit, for example, analyzes the user's physical condition based on the collected information. For example, the analysis unit analyzes data such as the user's heart rate, blood pressure, and weight. The analysis unit can also analyze the user's chronic illnesses based on the collected information. For example, the analysis unit analyzes the user's chronic illnesses such as diabetes, high blood pressure, and heart disease. The analysis unit can also analyze the user's daily lifestyle patterns based on the collected information. For example, the analysis unit analyzes the user's sleep time, meal frequency, exercise habits, etc. By analyzing the collected information, the analysis unit can suggest an optimal training method for the user. Some or all of the above-described processing by the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the collected data into a generation AI and have the generation AI analyze the data.

[0032] The suggestion unit can suggest an appropriate training method based on the analysis results. The suggestion unit, for example, suggests a reasonable training method based on the analysis results. For example, the suggestion unit suggests light aerobic exercise to stabilize blood pressure to a user with high blood pressure. The suggestion unit can also suggest a training method that can be performed in the morning to a user who has time in the morning. In this way, by suggesting a reasonable training method based on the analysis results, the user can continue training without straining themselves. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the analysis results to a generation AI and have the generation AI execute a suggestion of an optimal training method.

[0033] The adjustment unit can adjust the proposed training method to suit the user's free time. For example, the adjustment unit adjusts the proposed training method to suit the user's spare time. For example, if the user has about 10 minutes of spare time in a day, the adjustment unit can suggest a training method that suits that time. The adjustment unit can also adjust the training method based on the user's schedule. For example, the adjustment unit can manage the user's schedule and use a reminder function to encourage the user to train. By adjusting the proposed training method to suit the user's spare time, the user can continue training without strain. Some or all of the above-mentioned processing in the adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the adjustment unit can input the user's schedule data into the generation AI and cause the generation AI to adjust the training method.

[0034] The collection unit can analyze the user's past health data and select an appropriate information collection method. For example, the collection unit can analyze the user's past blood pressure data and collect information during time periods when blood pressure is stable. For example, the collection unit can identify time periods when blood pressure is stable based on the user's past blood pressure data and collect information during those time periods. The collection unit can also analyze the user's past exercise history and collect information during time periods when the user is relaxing after exercise. For example, the collection unit can identify time periods when the user is relaxing after exercise based on the user's past exercise history and collect information during those time periods. The collection unit can also analyze the user's past sleep data and collect information during time periods when the user's sleep quality is good. For example, the collection unit can identify time periods when the user's sleep quality is good based on the user's past sleep data and collect information during those time periods. This allows the optimal information collection method to be selected by analyzing the user's past health data. Some or all of the above-described processing by the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input the user's past health data into the generation AI and cause the generation AI to select an information collection method.

[0035] When collecting information, the collection unit can perform filtering based on the user's current activity status and environment. For example, when the user is exercising, the collection unit collects only data related to exercise. For example, the collection unit detects that the user is exercising and prioritizes collecting data related to exercise. Furthermore, when the user is resting, the collection unit can also prioritize collecting data related to relaxation. For example, the collection unit detects that the user is resting and collects data related to relaxation. Furthermore, when the user is out, the collection unit can also collect data related to the environment of the user's destination. For example, the collection unit detects that the user is out and collects data related to the environment of the user's destination. In this way, highly relevant data can be collected by filtering based on the user's current activity status and environment. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's activity status and environmental data into a generation AI and have the generation AI perform data filtering.

[0036] When collecting information, the collection unit can select an appropriate collection means depending on the user's input method. For example, when the user uses voice input, the collection unit prioritizes collecting voice data. For example, the collection unit detects that the user is using voice input and collects voice data. Furthermore, when the user uses text input, the collection unit can also prioritize collecting text data. For example, the collection unit detects that the user is using text input and collects text data. Furthermore, when the user uses image input, the collection unit can also prioritize collecting image data. For example, the collection unit detects that the user is using image input and collects image data. This enables efficient information collection by selecting the optimal collection means depending on the user's input method. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input user input method data to a generation AI and cause the generation AI to select a collection means.

[0037] When collecting information, the collection unit can prioritize collecting highly relevant data based on the user's geographical location information. For example, when the user is at home, the collection unit prioritizes collecting data related to home training. For example, the collection unit detects that the user is at home and collects data related to home training. Furthermore, when the user is at a gym, the collection unit can prioritize collecting data related to gym training. For example, the collection unit detects that the user is at the gym and collects data related to gym training. Furthermore, when the user is in a park, the collection unit can prioritize collecting data related to park training. For example, the collection unit detects that the user is in the park and collects data related to park training. In this way, highly relevant data can be collected preferentially by taking the user's geographical location information into consideration. Some or all of the above-described processing by the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the user's geographical location information data to the generation AI and cause the generation AI to collect highly relevant data.

[0038] When collecting information, the collection unit can analyze the user's social media activities and collect related data. The collection unit, for example, collects training content shared by the user on social media. For example, the collection unit analyzes the training content shared by the user on social media and collects related data. The collection unit can also analyze the user's feedback on social media and collect related data. For example, the collection unit analyzes the user's feedback on social media and collects related data. The collection unit can also collect related data by referring to the activities of the user's friends on social media. For example, the collection unit analyzes the activities of the user's friends on social media and collects related data. In this way, related data can be collected by analyzing the user's social media activities. Some or all of the above-mentioned processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's social media data into the generation AI and cause the generation AI to collect related data.

[0039] When collecting information, the collection unit can customize the collection method by reflecting the user's past feedback. The collection unit, for example, adjusts the collection method based on feedback provided by the user in the past. For example, the collection unit analyzes feedback provided by the user in the past and adjusts the collection method. The collection unit can also preferentially collect specific data from the user's past feedback. For example, the collection unit preferentially collects specific data based on the user's past feedback. The collection unit can also analyze the user's past feedback and optimize the collection method. For example, the collection unit optimizes the collection method based on the user's past feedback. In this way, the collection method can be customized by reflecting the user's past feedback. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the user's past feedback data to the generation AI and cause the generation AI to customize the collection method.

[0040] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the collected data. For example, the analysis unit performs a detailed analysis on data with high importance. For example, the analysis unit evaluates the importance of the collected data and performs a detailed analysis on the data with high importance. The analysis unit can also perform a brief analysis on data with low importance. For example, the analysis unit evaluates the importance of the collected data and performs a brief analysis on the data with low importance. The analysis unit can also determine the priority of the analysis based on the importance of the data. For example, the analysis unit evaluates the importance of the collected data and prioritizes analysis of data with high importance. This enables efficient analysis by adjusting the level of detail of the analysis based on the importance of the collected data. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the importance of the collected data to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0041] During analysis, the analysis unit can apply different analysis algorithms depending on the data category. For example, the analysis unit applies a health analysis algorithm to health data. For example, the analysis unit detects that the collected data is health data and applies a health analysis algorithm. The analysis unit can also apply an exercise analysis algorithm to exercise data. For example, the analysis unit detects that the collected data is exercise data and applies an exercise analysis algorithm. The analysis unit can also apply a sleep analysis algorithm to sleep data. For example, the analysis unit detects that the collected data is sleep data and applies a sleep analysis algorithm. This enables more accurate analysis by applying different analysis algorithms depending on the data category. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the category of the collected data to the generation AI and cause the generation AI to apply the analysis algorithm.

[0042] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit, for example, corrects the current analysis result based on the user's past analysis results. For example, the analysis unit analyzes the user's past analysis results and corrects the current analysis result. The analysis unit can also adjust the analysis algorithm by referring to the user's past analysis results. For example, the analysis unit adjusts the analysis algorithm based on the user's past analysis results. The analysis unit can also analyze the user's past analysis results and improve the accuracy of the analysis. For example, the analysis unit improves the accuracy of the analysis based on the user's past analysis results. In this way, the accuracy of the analysis can be improved by referring to the user's past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's past analysis result data into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0043] During analysis, the analysis unit can determine the analysis priority based on the time when the data was collected. The analysis unit, for example, prioritizes analyzing the most recent data. For example, the analysis unit evaluates the time when the collected data was collected and prioritizes analyzing the most recent data. The analysis unit can also analyze the most recent data while referring to past data. For example, the analysis unit evaluates the time when the collected data was collected and analyzes the most recent data while referring to the past data. The analysis unit can also adjust the analysis priority according to the time when the data was collected. For example, the analysis unit evaluates the time when the collected data was collected and adjusts the analysis priority according to the time when the data was collected. In this way, the most recent data can be prioritized for analysis by determining the analysis priority based on the time when the data was collected. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on the time when the collected data was collected to the generation AI and have the generation AI determine the analysis priority.

[0044] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the data. The analysis unit, for example, prioritizes analysis of highly relevant data. For example, the analysis unit evaluates the relevance of the collected data and prioritizes analysis of highly relevant data. The analysis unit can also postpone analysis of less relevant data. For example, the analysis unit evaluates the relevance of the collected data and analyzes less relevant data later. The analysis unit can also adjust the order of analysis based on the relevance of the data. For example, the analysis unit evaluates the relevance of the collected data and adjusts the order of analysis based on the relevance. This enables efficient analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input relevance data of the collected data to the generation AI and cause the generation AI to adjust the order of analysis.

[0045] During analysis, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. For example, if the user has technical expertise, the analysis unit provides analysis results that use a lot of technical terms. For example, the analysis unit can detect that the user has technical expertise and provide analysis results that use a lot of technical terms. Furthermore, if the user does not have technical expertise, the analysis unit can provide analysis results in simple language. For example, the analysis unit can detect that the user does not have technical expertise and provide analysis results in simple language. Furthermore, the analysis unit can adjust the way the analysis results are presented according to the user's level of expertise. For example, the analysis unit can evaluate the user's level of expertise and adjust the way the analysis results are presented according to the level of expertise. This allows for the provision of analysis results that are easy for the user to understand by adjusting the use of technical terms in the analysis according to the user's level of expertise. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input the user's level of expertise data into the generation AI and cause the generation AI to adjust the way the analysis results are presented.

[0046] When making a proposal, the suggestion unit can adjust the level of detail of the proposal based on the importance of the training method. For example, the suggestion unit makes a detailed proposal for a training method with a high importance. For example, the suggestion unit evaluates the importance of the training method and makes a detailed proposal for the training method with a high importance. The suggestion unit can also make a concise proposal for a training method with a low importance. For example, the suggestion unit evaluates the importance of the training method and makes a concise proposal for the training method with a low importance. The suggestion unit can also determine the priority of the proposal according to the importance of the training method. For example, the suggestion unit evaluates the importance of the training method and preferentially suggests a training method with a high importance. This enables efficient proposals by adjusting the level of detail of the proposal based on the importance of the training method. Some or all of the above-described processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit may input importance data of the training method to a generation AI and cause the generation AI to adjust the level of detail of the proposal.

[0047] When making a suggestion, the suggestion unit can apply different suggestion algorithms depending on the category of the training method. For example, the suggestion unit applies a suggestion algorithm specialized for aerobic exercise to aerobic exercise. For example, the suggestion unit detects that the collected data is related to aerobic exercise and applies a suggestion algorithm specialized for aerobic exercise. The suggestion unit can also apply a suggestion algorithm specialized for strength training to strength training. For example, the suggestion unit detects that the collected data is related to strength training and applies a suggestion algorithm specialized for strength training. The suggestion unit can also apply a suggestion algorithm specialized for flexibility exercise to flexibility exercise. For example, the suggestion unit detects that the collected data is related to flexibility exercise and applies a suggestion algorithm specialized for flexibility exercise. This enables more accurate suggestions by applying different suggestion algorithms depending on the category of the training method. Some or all of the above-mentioned processing by the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can input the category of the collected data to a generation AI and cause the generation AI to apply a suggestion algorithm.

[0048] When making a proposal, the suggestion unit can improve the accuracy of the proposal by referring to the user's past proposal results. The suggestion unit, for example, corrects the current proposal based on the user's past proposal results. For example, the suggestion unit analyzes the user's past proposal results and corrects the current proposal. The suggestion unit can also adjust the proposal algorithm by referring to the user's past proposal results. For example, the suggestion unit adjusts the proposal algorithm based on the user's past proposal results. The suggestion unit can also analyze the user's past proposal results and improve the accuracy of the proposal. For example, the suggestion unit improves the accuracy of the proposal based on the user's past proposal results. In this way, the accuracy of the proposal can be improved by referring to the user's past proposal results. Some or all of the above-mentioned processing in the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can input the user's past proposal result data into the generation AI and cause the generation AI to improve the accuracy of the proposal.

[0049] When making a proposal, the proposal unit can determine the priority of the proposals based on the submission dates of the training methods. The proposal unit, for example, prioritizes the most recent training methods. For example, the proposal unit evaluates the submission dates of the training methods and prioritizes the most recent training methods. The proposal unit can also propose the most recent training methods while referring to past training methods. For example, the proposal unit evaluates the submission dates of the training methods and proposes the most recent training methods while referring to past training methods. The proposal unit can also adjust the priority of the proposals according to the submission dates of the training methods. For example, the proposal unit evaluates the submission dates of the training methods and adjusts the priority of the proposals according to the submission dates. In this way, the most recent training methods can be prioritized by determining the priority of the proposals based on the submission dates of the training methods. Some or all of the above-described processing in the proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the proposal unit can input training method submission date data to the generation AI and cause the generation AI to determine the priority of the proposals.

[0050] When making a proposal, the proposal unit can adjust the order of proposals based on the relevance of the training methods. The proposal unit, for example, prioritizes proposing highly relevant training methods. For example, the proposal unit evaluates the relevance of the collected data and prioritizes proposing highly relevant training methods. The proposal unit can also postpone proposing less relevant training methods. For example, the proposal unit evaluates the relevance of the collected data and postpones proposing less relevant training methods. The proposal unit can also adjust the order of proposals based on the relevance of the training methods. For example, the proposal unit evaluates the relevance of the collected data and adjusts the order of proposals based on the relevance. This enables efficient proposals. Some or all of the above-described processing in the proposal unit may be performed using AI, or may be performed without using AI. For example, the proposal unit can input relevance data of the collected data to a generation AI and cause the generation AI to adjust the order of proposals.

[0051] When making a proposal, the suggestion unit can adjust the use of technical terminology in the proposal according to the user's level of expertise. For example, if the user has technical expertise, the suggestion unit makes a proposal that uses a lot of technical terminology. For example, the suggestion unit detects that the user has technical expertise and makes a proposal that uses a lot of technical terminology. Furthermore, if the user does not have technical expertise, the suggestion unit can make a proposal in simpler language. For example, the suggestion unit detects that the user does not have technical expertise and makes a proposal in simpler language. Furthermore, the suggestion unit can adjust the way the proposal is expressed according to the user's level of expertise. For example, the suggestion unit evaluates the user's level of expertise and adjusts the way the proposal is expressed according to the level of expertise. In this way, by adjusting the use of technical terminology in the proposal according to the user's level of expertise, it is possible to provide a proposal that is easy for the user to understand. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the user's level of expertise data into a generation AI and cause the generation AI to adjust the way the proposal is expressed.

[0052] During adjustment, the adjustment unit can analyze the user's past training history and select the optimal adjustment method. The adjustment unit, for example, adjusts the current training method based on the user's past training history. For example, the adjustment unit analyzes the user's past training history and adjusts the current training method. The adjustment unit can also adjust the training intensity by referring to the user's past training history. For example, the adjustment unit adjusts the training intensity based on the user's past training history. The adjustment unit can also analyze the user's past training history and select the optimal training method. For example, the adjustment unit selects the optimal training method based on the user's past training history. In this way, the optimal adjustment method can be selected by analyzing the user's past training history. Some or all of the above-described processing in the adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the adjustment unit can input the user's past training history data into the generation AI and cause the generation AI to select the adjustment method.

[0053] During adjustment, the adjustment unit can customize the training method based on the user's current living situation. For example, if the user is busy, the adjustment unit suggests a short and effective training method. For example, the adjustment unit detects that the user is busy and suggests a short and effective training method. The adjustment unit can also suggest a long-term training method if the user is relaxed. For example, the adjustment unit detects that the user is relaxed and suggests a long-term training method. The adjustment unit can also customize the training method according to the user's living situation. For example, the adjustment unit evaluates the user's living situation and customizes the training method according to the living situation. In this way, the training method can be customized based on the user's current living situation, thereby providing an optimal training method for the user. Some or all of the above-described processing in the adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the adjustment unit can input the user's living situation data into the generation AI and cause the generation AI to customize the training method.

[0054] During adjustment, the adjustment unit can improve the training method by reflecting user feedback. The adjustment unit, for example, adjusts the training method based on user feedback. For example, the adjustment unit analyzes the user feedback and adjusts the training method. The adjustment unit can also adjust the training intensity by referring to the user feedback. For example, the adjustment unit adjusts the training intensity based on the user feedback. The adjustment unit can also analyze the user feedback and improve the training method. For example, the adjustment unit improves the training method based on the user feedback. In this way, the training method can be improved by reflecting the user feedback. Some or all of the above-described processing in the adjustment unit may be performed using AI, for example, or may be performed without using AI. For example, the adjustment unit can input user feedback data into the generation AI and cause the generation AI to improve the training method.

[0055] During adjustment, the adjustment unit can select an optimal training method by taking into account the user's geographical location information. For example, if the user is at home, the adjustment unit suggests a training method that can be done at home. For example, the adjustment unit detects that the user is at home and suggests a training method that can be done at home. Furthermore, if the user is at a gym, the adjustment unit can also suggest a training method that can be done at the gym. For example, the adjustment unit detects that the user is at the gym and suggests a training method that can be done at the gym. Furthermore, if the user is in a park, the adjustment unit can also suggest a training method that can be done in the park. For example, the adjustment unit detects that the user is in the park and suggests a training method that can be done in the park. In this way, the optimal training method can be selected by taking into account the user's geographical location information. Some or all of the above-described processing in the adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the adjustment unit can input the user's geographical location information data into the generation AI and cause the generation AI to select a training method.

[0056] During adjustment, the adjustment unit can analyze the user's social media activities to suggest a training method. The adjustment unit can suggest a training method based on, for example, training content shared by the user on social media. For example, the adjustment unit can analyze the training content shared by the user on social media and suggest a training method. The adjustment unit can also analyze the user's feedback on social media and suggest a training method. For example, the adjustment unit can analyze the user's feedback on social media and suggest a training method. The adjustment unit can also suggest a training method by referring to the activities of the user's friends on social media. For example, the adjustment unit can analyze the activities of the user's friends on social media and suggest a training method. In this way, the optimal training method can be suggested by analyzing the user's social media activities. Some or all of the above-mentioned processing in the adjustment unit can be performed using, for example, AI, or can be performed without using AI. For example, the adjustment unit can input the user's social media data into the generation AI and cause the generation AI to suggest a training method.

[0057] During adjustment, the adjustment unit can customize the training method by reflecting the user's past feedback. The adjustment unit adjusts the training method based on, for example, the user's past feedback. For example, the adjustment unit analyzes the user's past feedback and adjusts the training method. The adjustment unit can also adjust the training intensity by referring to the user's past feedback. For example, the adjustment unit adjusts the training intensity based on the user's past feedback. The adjustment unit can also analyze the user's past feedback and customize the training method. For example, the adjustment unit customizes the training method based on the user's past feedback. In this way, the training method can be customized by reflecting the user's past feedback. Some or all of the above-described processing in the adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the adjustment unit can input the user's past feedback data into the generation AI and cause the generation AI to customize the training method.

[0058] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0059] The suggestion unit can analyze the user's past training history and optimize the suggested training method. For example, the suggestion unit suggests a current training method based on the user's past training history. The suggestion unit analyzes the user's past training history and suggests a current training method. The suggestion unit can also adjust the intensity of the training by referring to the user's past training history. The suggestion unit adjusts the intensity of the training based on the user's past training history. Furthermore, the suggestion unit can analyze the user's past training history and select an optimal training method. The suggestion unit selects an optimal training method based on the user's past training history. In this way, optimal suggestions can be made by analyzing the user's past training history.

[0060] The collection unit can analyze the user's social media activity and collect information to increase motivation for training. For example, the collection unit collects information to increase motivation based on training content shared by the user on social media. The collection unit analyzes the training content shared by the user on social media and collects information to increase motivation. The collection unit can also analyze the user's feedback on social media and collect information to increase motivation. The collection unit can also collect information to increase motivation by referring to the activities of the user's friends on social media. The collection unit analyzes the activities of the user's friends on social media and collects information to increase motivation. In this way, information to increase motivation can be collected by analyzing the user's social media activity.

[0061] The adjustment unit can analyze the user's past training history and adjust the training frequency. For example, the adjustment unit adjusts the current training frequency based on the user's past training history. The adjustment unit analyzes the user's past training history and adjusts the current training frequency. The adjustment unit can also adjust the training intensity by referring to the user's past training history. The adjustment unit adjusts the training intensity based on the user's past training history. Furthermore, the adjustment unit can analyze the user's past training history and select an optimal training frequency. The adjustment unit selects an optimal training frequency based on the user's past training history. In this way, an optimal training frequency can be provided by analyzing the user's past training history.

[0062] The analysis unit can analyze the user's past health data and predict the effectiveness of training. For example, the analysis unit predicts the effectiveness of current training based on the user's past health data. The analysis unit analyzes the user's past health data and predicts the effectiveness of current training. The analysis unit can also evaluate the effectiveness of training by referring to the user's past health data. The analysis unit evaluates the effectiveness of training based on the user's past health data. Furthermore, the analysis unit can analyze the user's past health data and select an optimal training method. The analysis unit selects an optimal training method based on the user's past health data. In this way, by analyzing the user's past health data, the effectiveness of training can be predicted and an optimal training method can be provided.

[0063] The adjustment unit can adjust the training intensity taking into account the user's geographical location information. For example, the adjustment unit can lower the training intensity when the user is at high altitude. The adjustment unit can detect that the user is at high altitude and lower the training intensity. Also, the adjustment unit can increase the training intensity when the user is at low altitude. The adjustment unit can detect that the user is at low altitude and increase the training intensity. Furthermore, when the user is at the seaside, the adjustment unit can suggest a training method suitable for a specific environment. The adjustment unit detects that the user is at the seaside and suggests a training method suitable for a specific environment. In this way, the optimal training intensity can be provided by taking into account the user's geographical location information.

[0064] The processing flow of the first embodiment will be briefly explained below.

[0065] Step 1: The collection unit collects information on the user's physical condition, chronic illnesses, and daily life patterns. The collection unit collects, for example, information entered by the user and data acquired from a smart device. For example, if the user has high blood pressure as a chronic illness, the collection unit collects information about that. In addition, if the user has free time in the morning as part of their daily life patterns, the collection unit also collects information about that. Step 2: The analysis unit analyzes the information collected by the collection unit. For example, the analysis unit analyzes the user's physical condition, chronic illnesses, and daily life patterns based on the collected information. For example, the analysis unit performs analysis using statistical analysis or machine learning algorithms. Step 3: The suggestion unit suggests an appropriate training method based on the results obtained by the analysis unit. For example, the suggestion unit suggests a reasonable training method based on the analysis results. For example, the suggestion unit suggests light aerobic exercise to stabilize blood pressure to a user with high blood pressure. Furthermore, the suggestion unit suggests a training method that can be performed in the morning to a user who has time to spare in the morning. Step 4: The adjustment unit adjusts the training method proposed by the proposal unit to suit the user's free time. The adjustment unit, for example, adjusts the proposed training method to suit the user's free time. For example, if the user has about 10 minutes of free time in a day, the adjustment unit proposes a training method that suits that time.

[0066] (Example 2) A training provision system according to an embodiment of the present invention provides training methods tailored to individual circumstances and chronic illnesses to people with physical problems, people who want to work out steadily at home in their spare time, and especially the elderly. The training provision system collects information about the user's physical condition, chronic illnesses, daily lifestyle patterns, and other factors, and AI proposes optimal training methods and adjusts them to suit the user's spare time. For example, the training provision system uses information entered by the user and data acquired from a smart device to collect information about the user's physical condition, chronic illnesses, and daily lifestyle patterns. Based on the collected information, the training provision system then proposes optimal training methods using AI. For example, the AI ​​suggests light aerobic exercise to stabilize blood pressure for a user with high blood pressure. The training provision system also adjusts the proposed training methods to suit the user's spare time. For example, if a user has about 10 minutes of spare time each day, the AI ​​suggests a training method tailored to that time. This allows people with physical problems or the elderly to continue training without straining themselves. This helps the training provision system maintain and improve the user's health. For example, a user with high blood pressure can expect to stabilize their blood pressure by continuing the suggested light aerobic exercise. Also, training in the morning can help you start your day off right.

[0067] A training provision system according to an embodiment includes a collection unit, an analysis unit, a suggestion unit, and an adjustment unit. The collection unit collects information on a user's physical condition, chronic illnesses, and daily lifestyle patterns. The collection unit collects, for example, information input by the user and data acquired from a smart device. For example, if the user has high blood pressure as a chronic illness, the collection unit collects such information. The collection unit also collects information on the user's daily lifestyle patterns, if the user has free time in the morning. The analysis unit analyzes the information collected by the collection unit. For example, the analysis unit analyzes the user's physical condition, chronic illnesses, and daily lifestyle patterns based on the collected information. For example, the analysis unit performs analysis using statistical analysis or a machine learning algorithm. The suggestion unit suggests an appropriate training method based on the results obtained by the analysis unit. For example, the suggestion unit suggests a reasonable training method based on the analysis results. For example, the suggestion unit suggests light aerobic exercise to stabilize blood pressure to a user with high blood pressure. The suggestion unit also suggests a training method that can be performed in the morning to a user who has free time in the morning. The adjustment unit adjusts the training method proposed by the suggestion unit to suit the user's free time. For example, the adjustment unit adjusts the proposed training method to suit the user's free time. For example, if the user has about 10 minutes of free time each day, the adjustment unit suggests a training method that suits that time. In this way, the training provision system according to the embodiment provides the optimal training method based on the user's physical condition, chronic illness, and daily life pattern, allowing the user to continue training without straining themselves.

[0068] The collection unit can collect information input by a user or data acquired from a smart device. The collection unit, for example, collects information input by a user. For example, if the user has high blood pressure as a chronic condition, the collection unit collects that information. The collection unit can also collect data acquired from a smart device. For example, the collection unit collects data acquired from devices such as smartphones, smart watches, and fitness trackers. By collecting user input information and data from the smart device, a training method can be suggested based on more accurate information. Some or all of the above-described processing in the collection unit may be performed using, or without, AI, for example. For example, the collection unit can input data acquired from a smart device to a generation AI and have the generation AI analyze the data.

[0069] The analysis unit can analyze the user's physical condition, chronic illnesses, and daily lifestyle patterns based on the collected information. The analysis unit, for example, analyzes the user's physical condition based on the collected information. For example, the analysis unit analyzes data such as the user's heart rate, blood pressure, and weight. The analysis unit can also analyze the user's chronic illnesses based on the collected information. For example, the analysis unit analyzes the user's chronic illnesses such as diabetes, high blood pressure, and heart disease. The analysis unit can also analyze the user's daily lifestyle patterns based on the collected information. For example, the analysis unit analyzes the user's sleep time, meal frequency, exercise habits, etc. By analyzing the collected information, the analysis unit can suggest an optimal training method for the user. Some or all of the above-described processing by the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the collected data into a generation AI and have the generation AI analyze the data.

[0070] The suggestion unit can suggest an appropriate training method based on the analysis results. The suggestion unit, for example, suggests a reasonable training method based on the analysis results. For example, the suggestion unit suggests light aerobic exercise to stabilize blood pressure to a user with high blood pressure. The suggestion unit can also suggest a training method that can be performed in the morning to a user who has time in the morning. In this way, by suggesting a reasonable training method based on the analysis results, the user can continue training without straining themselves. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the analysis results to a generation AI and have the generation AI execute a suggestion of an optimal training method.

[0071] The adjustment unit can adjust the proposed training method to suit the user's free time. For example, the adjustment unit adjusts the proposed training method to suit the user's spare time. For example, if the user has about 10 minutes of spare time in a day, the adjustment unit can suggest a training method that suits that time. The adjustment unit can also adjust the training method based on the user's schedule. For example, the adjustment unit can manage the user's schedule and use a reminder function to encourage the user to train. By adjusting the proposed training method to suit the user's spare time, the user can continue training without strain. Some or all of the above-mentioned processing in the adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the adjustment unit can input the user's schedule data into the generation AI and cause the generation AI to adjust the training method.

[0072] The collection unit can estimate the user's emotions and adjust the timing of information collection based on the estimated user emotions. For example, when the user is relaxed, the collection unit selects the timing to collect information to avoid stress. For example, the collection unit collects information during a time period when the user is relaxed. Furthermore, when the user is feeling stressed, the collection unit can temporarily delay information collection and resume collection when the user is relaxed. For example, when the user is feeling stressed, the collection unit temporarily suspends information collection and resumes it when the user is relaxed. Furthermore, when the user is in a hurry, the collection unit can quickly collect information and acquire necessary data in a short time. For example, when the user is in a hurry, the collection unit quickly collects information and acquires the minimum amount of data necessary. This allows information collection to avoid stress by adjusting the timing of information collection based on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may input user emotion data to the generation AI and cause the generation AI to adjust the timing of information collection.

[0073] The collection unit can analyze the user's past health data and select an appropriate information collection method. For example, the collection unit can analyze the user's past blood pressure data and collect information during time periods when blood pressure is stable. For example, the collection unit can identify time periods when blood pressure is stable based on the user's past blood pressure data and collect information during those time periods. The collection unit can also analyze the user's past exercise history and collect information during time periods when the user is relaxing after exercise. For example, the collection unit can identify time periods when the user is relaxing after exercise based on the user's past exercise history and collect information during those time periods. The collection unit can also analyze the user's past sleep data and collect information during time periods when the user's sleep quality is good. For example, the collection unit can identify time periods when the user's sleep quality is good based on the user's past sleep data and collect information during those time periods. This allows the optimal information collection method to be selected by analyzing the user's past health data. Some or all of the above-described processing by the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input the user's past health data into the generation AI and cause the generation AI to select an information collection method.

[0074] When collecting information, the collection unit can perform filtering based on the user's current activity status and environment. For example, when the user is exercising, the collection unit collects only data related to exercise. For example, the collection unit detects that the user is exercising and prioritizes collecting data related to exercise. Furthermore, when the user is resting, the collection unit can also prioritize collecting data related to relaxation. For example, the collection unit detects that the user is resting and collects data related to relaxation. Furthermore, when the user is out, the collection unit can also collect data related to the environment of the user's destination. For example, the collection unit detects that the user is out and collects data related to the environment of the user's destination. In this way, highly relevant data can be collected by filtering based on the user's current activity status and environment. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's activity status and environmental data into a generation AI and have the generation AI perform data filtering.

[0075] When collecting information, the collection unit can select an appropriate collection means depending on the user's input method. For example, when the user uses voice input, the collection unit prioritizes collecting voice data. For example, the collection unit detects that the user is using voice input and collects voice data. Furthermore, when the user uses text input, the collection unit can also prioritize collecting text data. For example, the collection unit detects that the user is using text input and collects text data. Furthermore, when the user uses image input, the collection unit can also prioritize collecting image data. For example, the collection unit detects that the user is using image input and collects image data. This enables efficient information collection by selecting the optimal collection means depending on the user's input method. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input user input method data to a generation AI and cause the generation AI to select a collection means.

[0076] The collection unit can estimate the user's emotions and determine the priority of information to be collected based on the estimated user's emotions. For example, when the user is relaxed, the collection unit prioritizes collecting health data. For example, the collection unit detects that the user is relaxed and prioritizes collecting health data. Furthermore, when the user is feeling stressed, the collection unit can prioritize collecting data related to stress reduction. For example, the collection unit detects that the user is feeling stressed and collects data related to stress reduction. Furthermore, when the user is in a hurry, the collection unit can prioritize collecting the minimum amount of data necessary. For example, the collection unit detects that the user is in a hurry and collects the minimum amount of data necessary. This enables more appropriate information collection by determining the priority of information to be collected based on the user's emotions. The emotion estimation is realized using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit can be performed using, for example, an AI, or without an AI. For example, the collection unit can input the user's emotional data into the generation AI and have the generation AI determine the priority of the information.

[0077] When collecting information, the collection unit can prioritize collecting highly relevant data based on the user's geographical location information. For example, when the user is at home, the collection unit prioritizes collecting data related to home training. For example, the collection unit detects that the user is at home and collects data related to home training. Furthermore, when the user is at a gym, the collection unit can prioritize collecting data related to gym training. For example, the collection unit detects that the user is at the gym and collects data related to gym training. Furthermore, when the user is in a park, the collection unit can prioritize collecting data related to park training. For example, the collection unit detects that the user is in the park and collects data related to park training. In this way, highly relevant data can be collected preferentially by taking the user's geographical location information into consideration. Some or all of the above-described processing by the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the user's geographical location information data to the generation AI and cause the generation AI to collect highly relevant data.

[0078] When collecting information, the collection unit can analyze the user's social media activities and collect related data. The collection unit, for example, collects training content shared by the user on social media. For example, the collection unit analyzes the training content shared by the user on social media and collects related data. The collection unit can also analyze the user's feedback on social media and collect related data. For example, the collection unit analyzes the user's feedback on social media and collects related data. The collection unit can also collect related data by referring to the activities of the user's friends on social media. For example, the collection unit analyzes the activities of the user's friends on social media and collects related data. In this way, related data can be collected by analyzing the user's social media activities. Some or all of the above-mentioned processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's social media data into the generation AI and cause the generation AI to collect related data.

[0079] When collecting information, the collection unit can customize the collection method by reflecting the user's past feedback. The collection unit, for example, adjusts the collection method based on feedback provided by the user in the past. For example, the collection unit analyzes feedback provided by the user in the past and adjusts the collection method. The collection unit can also preferentially collect specific data from the user's past feedback. For example, the collection unit preferentially collects specific data based on the user's past feedback. The collection unit can also analyze the user's past feedback and optimize the collection method. For example, the collection unit optimizes the collection method based on the user's past feedback. In this way, the collection method can be customized by reflecting the user's past feedback. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the user's past feedback data to the generation AI and cause the generation AI to customize the collection method.

[0080] The analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the estimated user's emotions. For example, if the user is relaxed, the analysis unit provides detailed analysis results. For example, the analysis unit detects that the user is relaxed and provides detailed analysis results. Furthermore, if the user is feeling stressed, the analysis unit can provide concise and to-the-point analysis results. For example, the analysis unit detects that the user is feeling stressed and provides concise and to-the-point analysis results. Furthermore, if the user is in a hurry, the analysis unit can provide analysis results in a format that can be quickly understood. For example, the analysis unit detects that the user is in a hurry and provides analysis results in a format that can be quickly understood. This allows the analysis results to be easily understood by adjusting the way the analysis is presented based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the user's emotional data into the generation AI and have the generation AI adjust the way the analysis is expressed.

[0081] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the collected data. For example, the analysis unit performs a detailed analysis on data with high importance. For example, the analysis unit evaluates the importance of the collected data and performs a detailed analysis on the data with high importance. The analysis unit can also perform a brief analysis on data with low importance. For example, the analysis unit evaluates the importance of the collected data and performs a brief analysis on the data with low importance. The analysis unit can also determine the priority of the analysis based on the importance of the data. For example, the analysis unit evaluates the importance of the collected data and prioritizes analysis of data with high importance. This enables efficient analysis by adjusting the level of detail of the analysis based on the importance of the collected data. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the importance of the collected data to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0082] During analysis, the analysis unit can apply different analysis algorithms depending on the data category. For example, the analysis unit applies a health analysis algorithm to health data. For example, the analysis unit detects that the collected data is health data and applies a health analysis algorithm. The analysis unit can also apply an exercise analysis algorithm to exercise data. For example, the analysis unit detects that the collected data is exercise data and applies an exercise analysis algorithm. The analysis unit can also apply a sleep analysis algorithm to sleep data. For example, the analysis unit detects that the collected data is sleep data and applies a sleep analysis algorithm. This enables more accurate analysis by applying different analysis algorithms depending on the data category. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the category of the collected data to the generation AI and cause the generation AI to apply the analysis algorithm.

[0083] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit, for example, corrects the current analysis result based on the user's past analysis results. For example, the analysis unit analyzes the user's past analysis results and corrects the current analysis result. The analysis unit can also adjust the analysis algorithm by referring to the user's past analysis results. For example, the analysis unit adjusts the analysis algorithm based on the user's past analysis results. The analysis unit can also analyze the user's past analysis results and improve the accuracy of the analysis. For example, the analysis unit improves the accuracy of the analysis based on the user's past analysis results. In this way, the accuracy of the analysis can be improved by referring to the user's past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's past analysis result data into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0084] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user's emotions. For example, if the user is relaxed, the analysis unit performs a detailed analysis. For example, the analysis unit detects that the user is relaxed and performs a detailed analysis. The analysis unit can also perform a concise analysis if the user is stressed. For example, the analysis unit detects that the user is stressed and performs a concise analysis. The analysis unit can also perform an analysis that can be understood in a short time if the user is in a hurry. For example, the analysis unit detects that the user is in a hurry and performs an analysis that can be understood in a short time. This allows the length of the analysis to be adjusted based on the user's emotions, thereby providing an optimal analysis result for the user. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the user's emotional data into the generation AI and have the generation AI adjust the length of the analysis.

[0085] During analysis, the analysis unit can determine the analysis priority based on the time when the data was collected. The analysis unit, for example, prioritizes analyzing the most recent data. For example, the analysis unit evaluates the time when the collected data was collected and prioritizes analyzing the most recent data. The analysis unit can also analyze the most recent data while referring to past data. For example, the analysis unit evaluates the time when the collected data was collected and analyzes the most recent data while referring to the past data. The analysis unit can also adjust the analysis priority according to the time when the data was collected. For example, the analysis unit evaluates the time when the collected data was collected and adjusts the analysis priority according to the time when the data was collected. In this way, the most recent data can be prioritized for analysis by determining the analysis priority based on the time when the data was collected. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on the time when the collected data was collected to the generation AI and have the generation AI determine the analysis priority.

[0086] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the data. The analysis unit, for example, prioritizes analysis of highly relevant data. For example, the analysis unit evaluates the relevance of the collected data and prioritizes analysis of highly relevant data. The analysis unit can also postpone analysis of less relevant data. For example, the analysis unit evaluates the relevance of the collected data and analyzes less relevant data later. The analysis unit can also adjust the order of analysis based on the relevance of the data. For example, the analysis unit evaluates the relevance of the collected data and adjusts the order of analysis based on the relevance. This enables efficient analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input relevance data of the collected data to the generation AI and cause the generation AI to adjust the order of analysis.

[0087] During analysis, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. For example, if the user has technical expertise, the analysis unit provides analysis results that use a lot of technical terms. For example, the analysis unit can detect that the user has technical expertise and provide analysis results that use a lot of technical terms. Furthermore, if the user does not have technical expertise, the analysis unit can provide analysis results in simple language. For example, the analysis unit can detect that the user does not have technical expertise and provide analysis results in simple language. Furthermore, the analysis unit can adjust the way the analysis results are presented according to the user's level of expertise. For example, the analysis unit can evaluate the user's level of expertise and adjust the way the analysis results are presented according to the level of expertise. This allows for the provision of analysis results that are easy for the user to understand by adjusting the use of technical terms in the analysis according to the user's level of expertise. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input the user's level of expertise data into the generation AI and cause the generation AI to adjust the way the analysis results are presented.

[0088] The suggestion unit can estimate the user's emotions and adjust the way the suggestions are expressed based on the estimated user's emotions. For example, if the user is relaxed, the suggestion unit makes a detailed suggestion. For example, the suggestion unit detects that the user is relaxed and makes a detailed suggestion. Furthermore, if the user is feeling stressed, the suggestion unit can make a concise and to-the-point suggestion. For example, the suggestion unit detects that the user is feeling stressed and makes a concise and to-the-point suggestion. Furthermore, if the user is in a hurry, the suggestion unit can make a suggestion in a format that can be quickly understood. For example, the suggestion unit detects that the user is in a hurry and makes a suggestion in a format that can be quickly understood. In this way, by adjusting the way the suggestions are expressed based on the user's emotions, it is possible to provide suggestions that are easy for the user to understand. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the suggestion unit can input the user's emotional data into the generation AI and have the generation AI adjust the way the suggestion is expressed.

[0089] When making a proposal, the suggestion unit can adjust the level of detail of the proposal based on the importance of the training method. For example, the suggestion unit makes a detailed proposal for a training method with a high importance. For example, the suggestion unit evaluates the importance of the training method and makes a detailed proposal for the training method with a high importance. The suggestion unit can also make a concise proposal for a training method with a low importance. For example, the suggestion unit evaluates the importance of the training method and makes a concise proposal for the training method with a low importance. The suggestion unit can also determine the priority of the proposal according to the importance of the training method. For example, the suggestion unit evaluates the importance of the training method and preferentially suggests a training method with a high importance. This enables efficient proposals by adjusting the level of detail of the proposal based on the importance of the training method. Some or all of the above-described processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit may input importance data of the training method to a generation AI and cause the generation AI to adjust the level of detail of the proposal.

[0090] When making a suggestion, the suggestion unit can apply different suggestion algorithms depending on the category of the training method. For example, the suggestion unit applies a suggestion algorithm specialized for aerobic exercise to aerobic exercise. For example, the suggestion unit detects that the collected data is related to aerobic exercise and applies a suggestion algorithm specialized for aerobic exercise. The suggestion unit can also apply a suggestion algorithm specialized for strength training to strength training. For example, the suggestion unit detects that the collected data is related to strength training and applies a suggestion algorithm specialized for strength training. The suggestion unit can also apply a suggestion algorithm specialized for flexibility exercise to flexibility exercise. For example, the suggestion unit detects that the collected data is related to flexibility exercise and applies a suggestion algorithm specialized for flexibility exercise. This enables more accurate suggestions by applying different suggestion algorithms depending on the category of the training method. Some or all of the above-mentioned processing by the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can input the category of the collected data to a generation AI and cause the generation AI to apply a suggestion algorithm.

[0091] When making a proposal, the suggestion unit can improve the accuracy of the proposal by referring to the user's past proposal results. The suggestion unit, for example, corrects the current proposal based on the user's past proposal results. For example, the suggestion unit analyzes the user's past proposal results and corrects the current proposal. The suggestion unit can also adjust the proposal algorithm by referring to the user's past proposal results. For example, the suggestion unit adjusts the proposal algorithm based on the user's past proposal results. The suggestion unit can also analyze the user's past proposal results and improve the accuracy of the proposal. For example, the suggestion unit improves the accuracy of the proposal based on the user's past proposal results. In this way, the accuracy of the proposal can be improved by referring to the user's past proposal results. Some or all of the above-mentioned processing in the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can input the user's past proposal result data into the generation AI and cause the generation AI to improve the accuracy of the proposal.

[0092] The suggestion unit can estimate the user's emotions and adjust the length of the suggestions based on the estimated user emotions. For example, if the user is relaxed, the suggestion unit makes detailed suggestions. For example, the suggestion unit detects that the user is relaxed and makes detailed suggestions. The suggestion unit can also make concise suggestions if the user is stressed. For example, the suggestion unit detects that the user is stressed and makes concise suggestions. The suggestion unit can also make suggestions that can be understood in a short time if the user is in a hurry. For example, the suggestion unit detects that the user is in a hurry and makes suggestions that can be understood in a short time. In this way, by adjusting the length of the suggestions based on the user's emotions, it is possible to provide optimal suggestions for the user. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the suggestion unit can input the user's emotional data into the generation AI and cause the generation AI to adjust the length of the suggestion.

[0093] When making a proposal, the proposal unit can determine the priority of the proposals based on the submission dates of the training methods. The proposal unit, for example, prioritizes the most recent training methods. For example, the proposal unit evaluates the submission dates of the training methods and prioritizes the most recent training methods. The proposal unit can also propose the most recent training methods while referring to past training methods. For example, the proposal unit evaluates the submission dates of the training methods and proposes the most recent training methods while referring to past training methods. The proposal unit can also adjust the priority of the proposals according to the submission dates of the training methods. For example, the proposal unit evaluates the submission dates of the training methods and adjusts the priority of the proposals according to the submission dates. In this way, the most recent training methods can be prioritized by determining the priority of the proposals based on the submission dates of the training methods. Some or all of the above-described processing in the proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the proposal unit can input training method submission date data to the generation AI and cause the generation AI to determine the priority of the proposals.

[0094] When making a proposal, the proposal unit can adjust the order of proposals based on the relevance of the training methods. The proposal unit, for example, prioritizes proposing highly relevant training methods. For example, the proposal unit evaluates the relevance of the collected data and prioritizes proposing highly relevant training methods. The proposal unit can also postpone proposing less relevant training methods. For example, the proposal unit evaluates the relevance of the collected data and postpones proposing less relevant training methods. The proposal unit can also adjust the order of proposals based on the relevance of the training methods. For example, the proposal unit evaluates the relevance of the collected data and adjusts the order of proposals based on the relevance. This enables efficient proposals. Some or all of the above-described processing in the proposal unit may be performed using AI, or may be performed without using AI. For example, the proposal unit can input relevance data of the collected data to a generation AI and cause the generation AI to adjust the order of proposals.

[0095] When making a proposal, the suggestion unit can adjust the use of technical terminology in the proposal according to the user's level of expertise. For example, if the user has technical expertise, the suggestion unit makes a proposal that uses a lot of technical terminology. For example, the suggestion unit detects that the user has technical expertise and makes a proposal that uses a lot of technical terminology. Furthermore, if the user does not have technical expertise, the suggestion unit can make a proposal in simpler language. For example, the suggestion unit detects that the user does not have technical expertise and makes a proposal in simpler language. Furthermore, the suggestion unit can adjust the way the proposal is expressed according to the user's level of expertise. For example, the suggestion unit evaluates the user's level of expertise and adjusts the way the proposal is expressed according to the level of expertise. In this way, by adjusting the use of technical terminology in the proposal according to the user's level of expertise, it is possible to provide a proposal that is easy for the user to understand. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the user's level of expertise data into a generation AI and cause the generation AI to adjust the way the proposal is expressed.

[0096] The adjustment unit can estimate the user's emotions and adjust the training method based on the estimated user's emotions. For example, the adjustment unit increases the intensity of the training when the user is relaxed. For example, the adjustment unit detects that the user is relaxed and increases the intensity of the training. The adjustment unit can also decrease the intensity of the training when the user is stressed. For example, the adjustment unit detects that the user is stressed and decreases the intensity of the training. The adjustment unit can also suggest a short and effective training method when the user is in a hurry. For example, the adjustment unit detects that the user is in a hurry and suggests a short and effective training method. In this way, by adjusting the training method based on the user's emotions, it is possible to provide an optimal training method for the user. Emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the adjustment unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the adjustment unit can input the user's emotional data into the generation AI and cause the generation AI to adjust the training method.

[0097] During adjustment, the adjustment unit can analyze the user's past training history and select the optimal adjustment method. The adjustment unit, for example, adjusts the current training method based on the user's past training history. For example, the adjustment unit analyzes the user's past training history and adjusts the current training method. The adjustment unit can also adjust the training intensity by referring to the user's past training history. For example, the adjustment unit adjusts the training intensity based on the user's past training history. The adjustment unit can also analyze the user's past training history and select the optimal training method. For example, the adjustment unit selects the optimal training method based on the user's past training history. In this way, the optimal adjustment method can be selected by analyzing the user's past training history. Some or all of the above-described processing in the adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the adjustment unit can input the user's past training history data into the generation AI and cause the generation AI to select the adjustment method.

[0098] During adjustment, the adjustment unit can customize the training method based on the user's current living situation. For example, if the user is busy, the adjustment unit suggests a short and effective training method. For example, the adjustment unit detects that the user is busy and suggests a short and effective training method. The adjustment unit can also suggest a long-term training method if the user is relaxed. For example, the adjustment unit detects that the user is relaxed and suggests a long-term training method. The adjustment unit can also customize the training method according to the user's living situation. For example, the adjustment unit evaluates the user's living situation and customizes the training method according to the living situation. In this way, the training method can be customized based on the user's current living situation, thereby providing an optimal training method for the user. Some or all of the above-described processing in the adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the adjustment unit can input the user's living situation data into the generation AI and cause the generation AI to customize the training method.

[0099] During adjustment, the adjustment unit can improve the training method by reflecting user feedback. The adjustment unit, for example, adjusts the training method based on user feedback. For example, the adjustment unit analyzes the user feedback and adjusts the training method. The adjustment unit can also adjust the training intensity by referring to the user feedback. For example, the adjustment unit adjusts the training intensity based on the user feedback. The adjustment unit can also analyze the user feedback and improve the training method. For example, the adjustment unit improves the training method based on the user feedback. In this way, the training method can be improved by reflecting the user feedback. Some or all of the above-described processing in the adjustment unit may be performed using AI, for example, or may be performed without using AI. For example, the adjustment unit can input user feedback data into the generation AI and cause the generation AI to improve the training method.

[0100] The adjustment unit can estimate the user's emotions and prioritize training methods based on the estimated user emotions. For example, if the user is relaxed, the adjustment unit preferentially suggests training methods with a relaxing effect. For example, the adjustment unit detects that the user is relaxed and preferentially suggests training methods with a relaxing effect. Furthermore, if the user is feeling stressed, the adjustment unit can preferentially suggest training methods with a stress-reducing effect. For example, the adjustment unit detects that the user is feeling stressed and preferentially suggests training methods with a stress-reducing effect. Furthermore, if the user is in a hurry, the adjustment unit can preferentially suggest training methods that are effective in a short time. For example, the adjustment unit detects that the user is in a hurry and preferentially suggests training methods that are effective in a short time. In this way, by prioritizing training methods based on the user's emotions, it is possible to provide an optimal training method for the user. Emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the adjustment unit may input user emotion data to the generation AI and have the generation AI determine the priority of the training methods.

[0101] During adjustment, the adjustment unit can select an optimal training method by taking into account the user's geographical location information. For example, if the user is at home, the adjustment unit suggests a training method that can be done at home. For example, the adjustment unit detects that the user is at home and suggests a training method that can be done at home. Furthermore, if the user is at a gym, the adjustment unit can also suggest a training method that can be done at the gym. For example, the adjustment unit detects that the user is at the gym and suggests a training method that can be done at the gym. Furthermore, if the user is in a park, the adjustment unit can also suggest a training method that can be done in the park. For example, the adjustment unit detects that the user is in the park and suggests a training method that can be done in the park. In this way, the optimal training method can be selected by taking into account the user's geographical location information. Some or all of the above-described processing in the adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the adjustment unit can input the user's geographical location information data into the generation AI and cause the generation AI to select a training method.

[0102] During adjustment, the adjustment unit can analyze the user's social media activities to suggest a training method. The adjustment unit can suggest a training method based on, for example, training content shared by the user on social media. For example, the adjustment unit can analyze the training content shared by the user on social media and suggest a training method. The adjustment unit can also analyze the user's feedback on social media and suggest a training method. For example, the adjustment unit can analyze the user's feedback on social media and suggest a training method. The adjustment unit can also suggest a training method by referring to the activities of the user's friends on social media. For example, the adjustment unit can analyze the activities of the user's friends on social media and suggest a training method. In this way, the optimal training method can be suggested by analyzing the user's social media activities. Some or all of the above-mentioned processing in the adjustment unit can be performed using, for example, AI, or can be performed without using AI. For example, the adjustment unit can input the user's social media data into the generation AI and cause the generation AI to suggest a training method.

[0103] During adjustment, the adjustment unit can customize the training method by reflecting the user's past feedback. The adjustment unit adjusts the training method based on, for example, the user's past feedback. For example, the adjustment unit analyzes the user's past feedback and adjusts the training method. The adjustment unit can also adjust the training intensity by referring to the user's past feedback. For example, the adjustment unit adjusts the training intensity based on the user's past feedback. The adjustment unit can also analyze the user's past feedback and customize the training method. For example, the adjustment unit customizes the training method based on the user's past feedback. In this way, the training method can be customized by reflecting the user's past feedback. Some or all of the above-described processing in the adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the adjustment unit can input the user's past feedback data into the generation AI and cause the generation AI to customize the training method. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, analysis unit, suggestion unit, and adjustment unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit can collect information on the user's physical condition, chronic illnesses, and daily life patterns using the camera 42 and microphone 38B of the smart device 14. The collection unit can also be realized by the specific processing unit 290 of the data processing device 12 and can collect information input by the user. The analysis unit, for example, is realized by the specific processing unit 290 of the data processing device 12 and analyzes the user's physical condition, chronic illnesses, and daily life patterns based on the collected information. The suggestion unit, for example, is realized by the specific processing unit 290 of the data processing device 12 and suggests an appropriate training method based on the analysis results. The adjustment unit, for example, is realized by the control unit 46A of the smart device 14 and adjusts the suggested training method to suit the user's spare time. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, suggestion unit, and adjustment unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit can collect information on the user's physical condition, chronic illnesses, and daily life patterns using the camera 42 and microphone 238 of the smart glasses 214. The collection unit can also be realized by the specific processing unit 290 of the data processing device 12 and can collect information input by the user. The analysis unit, for example, is realized by the specific processing unit 290 of the data processing device 12 and analyzes the user's physical condition, chronic illnesses, and daily life patterns based on the collected information. The suggestion unit, for example, is realized by the specific processing unit 290 of the data processing device 12 and suggests an appropriate training method based on the analysis results. The adjustment unit, for example, is realized by the control unit 46A of the smart glasses 214 and adjusts the suggested training method to suit the user's spare time. === Hard Collateral 1-3 === Each of the multiple elements, including the collection unit, analysis unit, suggestion unit, and adjustment unit, described above, is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the collection unit can collect information on the user's physical condition, chronic illnesses, and daily life patterns using the camera 42 and microphone 238 of the headset-type terminal 314. The collection unit can also be realized by the specific processing unit 290 of the data processing device 12 and can collect information input by the user. The analysis unit, for example, is realized by the specific processing unit 290 of the data processing device 12 and analyzes the user's physical condition, chronic illnesses, and daily life patterns based on the collected information. The suggestion unit, for example, is realized by the specific processing unit 290 of the data processing device 12 and suggests an appropriate training method based on the analysis results. The adjustment unit, for example, is realized by the control unit 46A of the headset-type terminal 314 and adjusts the suggested training method to suit the user's spare time. === Hard Collateral 1-4 === Each of the multiple elements, including the collection unit, analysis unit, suggestion unit, and adjustment unit, described above, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit can collect information on the user's physical condition, chronic illnesses, and daily life patterns using the camera 42 and microphone 238 of the robot 414. The collection unit can also be realized by the specific processing unit 290 of the data processing device 12 and can collect information input by the user. The analysis unit, for example, is realized by the specific processing unit 290 of the data processing device 12 and analyzes the user's physical condition, chronic illnesses, and daily life patterns based on the collected information. The suggestion unit, for example, is realized by the specific processing unit 290 of the data processing device 12 and suggests an appropriate training method based on the analysis results. The adjustment unit, for example, is realized by the control unit 46A of the robot 414 and adjusts the suggested training method to suit the user's spare time.

[0104] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0105] The analysis unit can estimate the user's emotions and determine the priority of analysis based on the estimated user's emotions. For example, if the user is relaxed, a detailed analysis can be given priority. The analysis unit detects that the user is relaxed and gives priority to a detailed analysis. Also, if the user is feeling stressed, a concise analysis can be given priority. The analysis unit detects that the user is feeling stressed and gives priority to a concise analysis. Furthermore, if the user is in a hurry, the analysis can be performed in a format that can be quickly understood. The analysis unit detects that the user is in a hurry and gives priority to the analysis in a format that can be quickly understood. In this way, by determining the priority of analysis based on the user's emotions, it is possible to provide the user with the optimal analysis results.

[0106] The suggestion unit can analyze the user's past training history and optimize the suggested training method. For example, the suggestion unit suggests a current training method based on the user's past training history. The suggestion unit analyzes the user's past training history and suggests a current training method. The suggestion unit can also adjust the intensity of the training by referring to the user's past training history. The suggestion unit adjusts the intensity of the training based on the user's past training history. Furthermore, the suggestion unit can analyze the user's past training history and select an optimal training method. The suggestion unit selects an optimal training method based on the user's past training history. In this way, optimal suggestions can be made by analyzing the user's past training history.

[0107] The adjustment unit can estimate the user's emotions and adjust the timing of training based on the estimated user's emotions. For example, if the user is relaxed, the timing of training can be set to a relaxing time period. The adjustment unit detects that the user is relaxed and sets the timing of training to a relaxing time period. Furthermore, if the user is feeling stressed, the timing of training can also be set to a less stressful time period. The adjustment unit detects that the user is feeling stressed and sets the timing of training to a less stressful time period. Furthermore, if the user is in a hurry, it can also suggest short and effective training. The adjustment unit detects that the user is in a hurry and suggests short and effective training. In this way, by adjusting the timing of training based on the user's emotions, it is possible to provide the user with optimal training.

[0108] The collection unit can analyze the user's social media activity and collect information to increase motivation for training. For example, the collection unit collects information to increase motivation based on training content shared by the user on social media. The collection unit analyzes the training content shared by the user on social media and collects information to increase motivation. The collection unit can also analyze the user's feedback on social media and collect information to increase motivation. The collection unit can also collect information to increase motivation by referring to the activities of the user's friends on social media. The collection unit analyzes the activities of the user's friends on social media and collects information to increase motivation. In this way, information to increase motivation can be collected by analyzing the user's social media activity.

[0109] The suggestion unit can estimate the user's emotions and suggest a type of training based on the estimated user's emotions. For example, if the user is relaxed, it can suggest training that has a relaxing effect. The suggestion unit detects that the user is relaxed and suggests training that has a relaxing effect. Furthermore, if the user is feeling stressed, it can also suggest training that has a stress-reducing effect. The suggestion unit can detect that the user is feeling stressed and suggest training that has a stress-reducing effect. Furthermore, if the user is in a hurry, it can also suggest short, effective training. The suggestion unit detects that the user is in a hurry and suggests short, effective training. In this way, by suggesting a type of training based on the user's emotions, it is possible to provide the user with the optimal training.

[0110] The adjustment unit can analyze the user's past training history and adjust the training frequency. For example, the adjustment unit adjusts the current training frequency based on the user's past training history. The adjustment unit analyzes the user's past training history and adjusts the current training frequency. The adjustment unit can also adjust the training intensity by referring to the user's past training history. The adjustment unit adjusts the training intensity based on the user's past training history. Furthermore, the adjustment unit can analyze the user's past training history and select an optimal training frequency. The adjustment unit selects an optimal training frequency based on the user's past training history. In this way, an optimal training frequency can be provided by analyzing the user's past training history.

[0111] The collection unit can estimate the user's emotions and adjust the method of information collection based on the estimated user's emotions. For example, if the user is relaxed, detailed information can be collected. The collection unit detects that the user is relaxed and collects detailed information. Also, if the user is feeling stressed, concise information can be collected. The collection unit detects that the user is feeling stressed and collects concise information. Furthermore, if the user is in a hurry, information can be collected quickly. The collection unit detects that the user is in a hurry and collects information quickly. In this way, by adjusting the method of information collection based on the user's emotions, it is possible to collect information that is optimal for the user.

[0112] The analysis unit can analyze the user's past health data and predict the effectiveness of training. For example, the analysis unit predicts the effectiveness of current training based on the user's past health data. The analysis unit analyzes the user's past health data and predicts the effectiveness of current training. The analysis unit can also evaluate the effectiveness of training by referring to the user's past health data. The analysis unit evaluates the effectiveness of training based on the user's past health data. Furthermore, the analysis unit can analyze the user's past health data and select an optimal training method. The analysis unit selects an optimal training method based on the user's past health data. In this way, by analyzing the user's past health data, the effectiveness of training can be predicted and an optimal training method can be provided.

[0113] The suggestion unit can estimate the user's emotions and set training goals based on the estimated user's emotions. For example, if the user is relaxed, it can set training goals that have a relaxing effect. The suggestion unit detects that the user is relaxed and sets training goals that have a relaxing effect. Furthermore, if the user is feeling stressed, it can set training goals that have a stress-reducing effect. The suggestion unit detects that the user is feeling stressed and sets training goals that have a stress-reducing effect. Furthermore, if the user is in a hurry, it can set training goals that can be achieved in a short time. The suggestion unit detects that the user is in a hurry and sets training goals that can be achieved in a short time. In this way, by setting training goals based on the user's emotions, it is possible to provide the user with optimal training goals.

[0114] The adjustment unit can adjust the training intensity taking into account the user's geographical location information. For example, the adjustment unit can lower the training intensity when the user is at high altitude. The adjustment unit can detect that the user is at high altitude and lower the training intensity. Also, the adjustment unit can increase the training intensity when the user is at low altitude. The adjustment unit can detect that the user is at low altitude and increase the training intensity. Furthermore, when the user is at the seaside, the adjustment unit can suggest a training method suitable for a specific environment. The adjustment unit detects that the user is at the seaside and suggests a training method suitable for a specific environment. In this way, the optimal training intensity can be provided by taking into account the user's geographical location information.

[0115] The processing flow of the second embodiment will be briefly explained below.

[0116] Step 1: The collection unit collects information on the user's physical condition, chronic illnesses, and daily life patterns. The collection unit collects, for example, information entered by the user and data acquired from a smart device. For example, if the user has high blood pressure as a chronic illness, the collection unit collects information about that. In addition, if the user has free time in the morning as part of their daily life patterns, the collection unit also collects information about that. Step 2: The analysis unit analyzes the information collected by the collection unit. For example, the analysis unit analyzes the user's physical condition, chronic illnesses, and daily life patterns based on the collected information. For example, the analysis unit performs analysis using statistical analysis or machine learning algorithms. Step 3: The suggestion unit suggests an appropriate training method based on the results obtained by the analysis unit. For example, the suggestion unit suggests a reasonable training method based on the analysis results. For example, the suggestion unit suggests light aerobic exercise to stabilize blood pressure to a user with high blood pressure. Furthermore, the suggestion unit suggests a training method that can be performed in the morning to a user who has time to spare in the morning. Step 4: The adjustment unit adjusts the training method proposed by the proposal unit to suit the user's free time. The adjustment unit, for example, adjusts the proposed training method to suit the user's free time. For example, if the user has about 10 minutes of free time in a day, the adjustment unit proposes a training method that suits that time.

[0117] 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.

[0118] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<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.

[0119] 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.

[0120] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0121] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0122] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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).

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0137] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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).

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification 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 identification processing unit 290 using these models.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0153] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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).

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also 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 perform the same process as the identification processing unit 290 using these models.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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).

[0174] 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.

[0175] 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."

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] [Explanation of symbols]

[0189] 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 collection unit that collects information on the user's physical condition, chronic illnesses, and daily life patterns; an analysis unit that analyzes the information collected by the collection unit; a suggestion unit that suggests an appropriate training method based on the results obtained by the analysis unit; an adjustment unit that adjusts the training method proposed by the suggestion unit to suit the user's free time; Equipped with A system characterized by:

2. The collecting unit Collecting information entered by the user or data obtained from a smart device 2. The system of claim 1.

3. The analysis unit Analyze the user's physical condition, chronic illnesses, and daily life patterns based on the collected information 2. The system of claim 1.

4. The proposal unit Proposes appropriate training methods based on analysis results 2. The system of claim 1.

5. The adjustment unit Adapting suggested training methods to suit the user's availability 2. The system of claim 1.

6. The collecting unit Estimate the user's emotions and adjust the timing of information collection based on the estimated user emotions.

2. The system of claim 1.

7. The collecting unit Analyze the user's past health data and select the appropriate information collection method 2. The system of claim 1.

8. The collecting unit When collecting information, it filters it based on the user's current activity and environment.

2. The system of claim 1.

9. The collecting unit When collecting information, select the appropriate collection method depending on the user's input method.

2. The system of claim 1.

10. The collecting unit Estimate the user's emotions and prioritize the information to be collected based on the estimated user emotions.

2. The system of claim 1.

Images