System

Abstract

The system according to the embodiment aims to provide a fitness program individually customized based on a health condition or a fitness goal of a user.SOLUTION: A system according to an embodiment includes a reception unit, an analysis unit, a generation unit, a tracking unit, and a feedback unit. The reception unit receives a health condition of a user and a target of fitness. The analysis unit analyzes the information input by the reception unit. The generation unit generates an exercise or stretch program based on the information analyzed by the analysis unit. The tracking unit tracks a progress status of the user based on the program generated by the generation unit. The feedback unit provides feedback based on the progress tracked by the tracking unit.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 technologies struggle to provide individually customized fitness programs and leave room for improvement in effectively supporting users' health and fitness goals.

[0005] The system according to the embodiment aims to provide a fitness program that is individually customized based on the user's health status and fitness goals. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, a generation unit, a tracking unit, and a feedback unit. The reception unit inputs the user's health status and fitness goals. The analysis unit analyzes the information input by the reception unit. The generation unit generates an exercise and stretching program based on the information analyzed by the analysis unit. The tracking unit tracks the user's progress based on the program generated by the generation unit. The feedback unit provides feedback based on the progress tracked by the tracking unit. [Effects of the Invention]

[0007] Embodiments of the system can provide a user with a personalized fitness program based on their health and fitness goals. [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 fitness support system according to an embodiment of the present invention uses a generation AI to provide users with individually customized exercise and stretching advice. In the fitness support system, a user inputs their health status and fitness goals, and the generation AI analyzes the input information to generate an optimal exercise and stretching program for the user. The generated program is updated in real time based on the user's progress. For example, in the fitness support system, a user inputs their health status and fitness goals, such as weight, height, age, exercise experience, and specific health issues. This information is input to the generation AI. The fitness support system then analyzes the input information and generates an optimal exercise and stretching program for the user using the generation AI. The generation AI creates an individually customized program based on the user's health status and fitness goals. For example, a user who wants to lose weight would receive a program centered on aerobic exercise. The generated program is updated in real time based on the user's progress. For example, when a user inputs the results of an exercise, the generation AI analyzes the information and adjusts the content of the next exercise. This allows the user to always receive the latest advice. This allows the fitness support system to provide individually customized exercise and stretching advice based on the user's health condition and fitness goals, and updates it in real time according to progress. This allows the fitness support system to provide individually customized exercise and stretching advice based on the user's health condition and fitness goals, and updates it in real time according to progress. For example, users can exercise at their own pace and receive support to achieve their fitness goals. It is also available 24 hours a day and is less expensive than a human trainer.

[0029] A fitness support system according to an embodiment includes a reception unit, an analysis unit, a generation unit, a tracking unit, and a feedback unit. The reception unit receives input of a user's health condition and fitness goals. The user's health condition may include, but is not limited to, weight, height, age, exercise experience, and specific health issues. The reception unit, for example, stores the information received by the user in a database and provides the information to the analysis unit. The analysis unit analyzes the information received by the reception unit. The analysis unit may evaluate the user's health condition and fitness goals using, for example, a data analysis algorithm. The analysis unit may also evaluate the reliability of the user's input data and filter out unreliable data. The generation unit generates an exercise and stretching program based on the information analyzed by the analysis unit. The generation unit uses, for example, a generation AI to create an optimal exercise and stretching program for the user. The generation AI may be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to, these examples. The generation unit generates an individually customized program based on the user's health condition and fitness goals. The tracking unit tracks the user's progress based on the program generated by the generation unit. For example, the tracking unit inputs the results of the user's exercise, and the generation AI analyzes the information to adjust the content of the next exercise. The tracking unit can also improve tracking accuracy by referring to the user's past exercise data. The feedback unit provides feedback based on the progress tracked by the tracking unit. For example, the feedback unit can provide advice updated in real time according to the user's progress. The feedback unit can also estimate the user's emotions and adjust the content of the feedback based on the estimated user's emotions. As a result, the fitness support system according to the embodiment provides individually customized exercise and stretching advice based on the user's health condition and fitness goals, and is updated in real time according to the user's progress.

[0030] The reception unit can input information regarding the user's weight, height, age, exercise experience, and specific health issues. The reception unit, for example, stores the information regarding the user's weight, height, age, exercise experience, and specific health issues in a database. For example, the reception unit can automatically complete the weight and height data entered by the user. The reception unit can also automatically set an appropriate exercise level based on the user's past exercise experience. Furthermore, the reception unit can reference data regarding the user's past health issues and automatically complete related input items. This allows for the generation of more accurate exercise and stretching programs by entering detailed health information about the user. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without AI. For example, the reception unit can input the data entered by the user into a generation AI and have the generation AI analyze and complete the data.

[0031] The generation unit can generate individually customized exercise and stretching programs based on the user's health condition and fitness goals. The generation unit, for example, uses a generation AI to create an exercise and stretching program optimal for the user. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. The generation unit generates an individually customized program based on the user's health condition and fitness goals. For example, the generation unit may generate a program centered on aerobic exercise for a user who wants to lose weight. The generation unit may also generate a program centered on strength training for a user who wants to increase muscle strength. Furthermore, the generation unit may generate a program centered on endurance training for a user who wants to improve endurance. This enables effective exercise and stretching by providing a program tailored to the user's health condition and fitness goals. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit may input user data into the generation AI and cause the generation AI to generate a program.

[0032] The tracking unit can input the results of a user's exercise, and the generation AI can analyze the information and adjust the content of the next exercise. For example, the tracking unit can input the results of a user's exercise, and the generation AI can analyze the information and adjust the content of the next exercise. For example, the tracking unit can store the results of the user's exercise in a database and provide the results to the generation AI. The tracking unit can also improve tracking accuracy by referencing the user's past exercise data. For example, the tracking unit can adjust the current exercise program based on the user's past exercise data. The tracking unit can also analyze the user's past exercise data and generate an optimal exercise program. This allows for a more effective fitness program by adjusting the content of the next exercise based on the user's exercise results. Some or all of the above-described processing in the tracking unit can be performed using, or without, AI. For example, the tracking unit can input the user's exercise data to the generation AI and have the generation AI adjust the content of the next exercise.

[0033] The feedback unit can provide advice updated in real time according to the user's progress. For example, the feedback unit provides advice updated in real time according to the user's progress. For example, the feedback unit stores the user's progress in a database and provides it to the generation AI. The feedback unit can also estimate the user's emotions and adjust the content of the feedback based on the estimated user's emotions. For example, the feedback unit can provide detailed feedback if the user is relaxed. The feedback unit can also provide concise feedback if the user is in a hurry. The feedback unit can also provide feedback to reduce stress if the user is feeling stressed. This makes it easier to maintain the user's motivation by providing real-time advice according to the user's progress. Some or all of the above-described processing in the feedback unit may be performed using AI, for example, or may be performed without using AI. For example, the feedback unit can input the user's progress to the generation AI and cause the generation AI to adjust the content of the feedback.

[0034] The generation unit can generate a program centered on aerobic exercise for a user who wants to lose weight. For example, the generation unit generates a program centered on aerobic exercise for a user who wants to lose weight. For example, the generation unit creates a program that includes aerobic exercise such as running, cycling, and aerobics. The generation unit can also adjust the intensity and frequency of the aerobic exercise according to the user's weight loss goal. Furthermore, the generation unit can update the content of the aerobic exercise in real time according to the user's progress. This supports goal achievement by providing a program that matches the user's goal. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input user data into the generation AI and have the generation AI generate a program.

[0035] The reception unit can automatically complete input items based on the user's past health data. The reception unit, for example, references the user's past health data and automatically completes input items. For example, the reception unit automatically completes weight and height data previously entered by the user. The reception unit can also automatically set an appropriate exercise level based on the user's past exercise experience. The reception unit can also reference data regarding the user's past health issues and automatically complete related input items. By referencing past data, this improves the efficiency of input work and reduces the burden on the user. Some or all of the above-mentioned processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past health data into the generation AI and have the generation AI automatically complete the input items.

[0036] The reception unit can evaluate the reliability of data input by the user and filter out low-reliability data. The reception unit, for example, evaluates the reliability of data input by the user and filters out low-reliability data. For example, the reception unit checks the consistency of data input by the user and displays a warning if there is an inconsistency. The reception unit can also detect and filter out abnormal values ​​by comparing the data with data previously input by the user. The reception unit can also evaluate the reliability of data input by the user and automatically exclude low-reliability data. This eliminates low-reliability data, thereby improving the accuracy of the analysis results. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's input data to a generation AI and have the generation AI evaluate the reliability of the data and perform filtering.

[0037] The reception unit can encrypt the user's input data to protect privacy. The reception unit, for example, encrypts the user's input data to protect privacy. For example, the reception unit encrypts health data entered by the user to prevent access by third parties. The reception unit can also encrypt the user's personal information and securely store it. The reception unit can also protect the data using an encryption protocol when transmitting the user's input data. This protects the user's privacy and allows the user to input data with peace of mind. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the user's input data to a generation AI and have the generation AI encrypt the data.

[0038] The reception unit can add input items related to region-specific health issues based on the user's geographical location information. The reception unit, for example, adds input items related to region-specific health issues taking into account the user's geographical location information. For example, if the user lives in a high altitude, the reception unit can add input items related to high altitude health issues. Furthermore, if the user lives in an urban area, the reception unit can add input items related to city-specific health issues. Furthermore, if the user lives by the sea, the reception unit can add input items related to seaside health issues. This enables more appropriate health management by addressing region-specific health issues. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's geographical location information to the generation AI and cause the generation AI to add input items.

[0039] The reception unit can analyze the user's social media activity and automatically input related health information. The reception unit, for example, analyzes the user's social media activity and automatically inputs related health information. For example, the reception unit automatically inputs exercise records shared by the user on social media. The reception unit can also automatically input health issues mentioned by the user on social media. The reception unit can also analyze the user's social media activity and automatically input related health information. This makes it possible to utilize social media information to streamline input work and provide more accurate data. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's social media data into a generation AI and have the generation AI automatically input health information.

[0040] The reception unit can customize the input interface by reflecting the user's past feedback. The reception unit customizes the input interface by reflecting the user's past feedback, for example. For example, the reception unit improves the input interface based on feedback provided by the user in the past. The reception unit can also adjust the arrangement of input items by reflecting the user's past feedback. The reception unit can also change the design of the input interface based on the user's past feedback. In this way, an interface that is easy for the user to use can be provided by reflecting the past feedback. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the user's feedback data to a generation AI and cause the generation AI to customize the interface.

[0041] The analysis unit can improve the accuracy of the analysis based on the user's past health data during analysis. The analysis unit, for example, improves the accuracy of the analysis by referring to the user's past health data during analysis. For example, the analysis unit can analyze current weight changes by referring to the user's past weight data. The analysis unit can also analyze the effects of current exercise by referring to the user's past exercise records. The analysis unit can also analyze the user's current health condition by referring to data related to the user's past health issues. In this way, the accuracy of the analysis is improved by referring to past 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 the user's past health data into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0042] The analysis unit can customize the analysis results based on the user's lifestyle rhythm during analysis. The analysis unit, for example, customizes the analysis results taking into account the user's lifestyle rhythm during analysis. For example, the analysis unit customizes the analysis results taking into account the user's sleep patterns. The analysis unit can also customize the analysis results taking into account the user's eating patterns. The analysis unit can also customize the analysis results taking into account the user's exercise habits. This enables more appropriate advice to be provided by providing analysis results that match the user's lifestyle rhythm. 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 user's lifestyle rhythm data into the generation AI and have the generation AI customize the analysis results.

[0043] The analysis unit can improve the analysis algorithm by reflecting user feedback during analysis. The analysis unit can improve the analysis algorithm by reflecting user feedback during analysis, for example. For example, the analysis unit improves the analysis algorithm based on feedback provided by the user. The analysis unit can also improve the accuracy of the analysis by reflecting user feedback. The analysis unit can also adjust parameters of the analysis algorithm based on user feedback. In this way, the accuracy of the analysis algorithm is improved by reflecting user feedback. Some or all of the above-mentioned processing in the analysis unit can be performed using AI, for example, or can be performed without using AI. For example, the analysis unit can input user feedback data into the generation AI and cause the generation AI to improve the analysis algorithm.

[0044] The analysis unit can customize the analysis results based on the user's geographical location information during analysis. The analysis unit, for example, customizes the analysis results by taking into account the user's geographical location information during analysis. For example, if the user lives at high altitude, the analysis unit customizes the analysis results by taking into account health issues specific to high altitudes. Furthermore, if the user lives in an urban area, the analysis unit can customize the analysis results by taking into account health issues specific to urban areas. Furthermore, if the user lives by the sea, the analysis unit can customize the analysis results by taking into account health issues specific to seaside areas. In this way, by taking into account the geographical location information, analysis results that address health issues specific to the region can be provided. 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 user's geographical location information into the generation AI and cause the generation AI to customize the analysis results.

[0045] The analysis unit can analyze the user's social media activities during the analysis and reflect related data in the analysis. For example, the analysis unit can analyze the user's social media activities during the analysis and reflect related data in the analysis. For example, the analysis unit can reflect exercise records shared by the user on social media in the analysis. The analysis unit can also reflect health issues mentioned by the user on social media in the analysis. The analysis unit can also analyze the user's social media activities and reflect related data in the analysis. This makes it possible to provide more accurate analysis results by utilizing social media information. Some or all of the above-mentioned processing in the analysis unit can be performed using AI, for example, or without AI. For example, the analysis unit can input the user's social media data into the generation AI and cause the generation AI to extract data to be reflected in the analysis.

[0046] The analysis unit can customize the analysis algorithm by reflecting the user's past feedback during analysis. The analysis unit, for example, customizes the analysis algorithm by reflecting the user's past feedback during analysis. For example, the analysis unit customizes the analysis algorithm based on feedback provided by the user. The analysis unit can also improve the accuracy of the analysis by reflecting the user's feedback. The analysis unit can also adjust parameters of the analysis algorithm based on the user's feedback. In this way, the accuracy of the analysis algorithm is improved by reflecting the past feedback. 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 feedback data into the generation AI and cause the generation AI to customize the analysis algorithm.

[0047] The generation unit can improve the accuracy of the program based on the user's past exercise data during generation. For example, the generation unit can improve the accuracy of the program by referring to the user's past exercise data during generation. For example, the generation unit can adjust the current exercise program based on the user's past exercise data. The generation unit can also generate an effective exercise program based on the user's past exercise data. The generation unit can also analyze the user's past exercise data and generate an optimal exercise program. This improves the accuracy of the program by referring to the past data. Some or all of the above-described processing in the generation unit can be performed using, for example, AI, or can be performed without AI. For example, the generation unit can input the user's past exercise data into the generation AI and have the generation AI improve the accuracy of the program.

[0048] The generation unit can customize the program based on the user's lifestyle rhythm at the time of generation. For example, the generation unit customizes the program taking the user's lifestyle rhythm into consideration at the time of generation. For example, the generation unit customizes the exercise program taking the user's sleep pattern into consideration. The generation unit can also customize the exercise program taking the user's eating pattern into consideration. The generation unit can also customize the exercise program taking the user's exercise habits into consideration. This maximizes the effect of exercise by providing a program that matches the user's lifestyle rhythm. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the user's lifestyle rhythm data into the generation AI and have the generation AI customize the program.

[0049] The generation unit can improve the program by reflecting user feedback during generation. For example, the generation unit improves the program by reflecting user feedback during generation. For example, the generation unit improves the exercise program based on feedback provided by the user. The generation unit can also improve the accuracy of the exercise program by reflecting user feedback. The generation unit can also adjust the content of the exercise program based on user feedback. In this way, the accuracy of the program is improved by reflecting user feedback. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input user feedback data into the generation AI and have the generation AI improve the program.

[0050] The generation unit can customize the program based on the user's geographical location information at the time of generation. For example, the generation unit customizes the program by taking the user's geographical location information into account at the time of generation. For example, if the user lives in a high altitude, the generation unit can generate a high altitude-specific exercise program. Also, if the user lives in an urban area, the generation unit can generate a city-specific exercise program. Also, if the user lives by the sea, the generation unit can generate a seaside-specific exercise program. In this way, by taking the geographical location information into account, it is possible to provide a region-specific exercise program. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the user's geographical location information into the generation AI and cause the generation AI to customize the program.

[0051] The generation unit can analyze the user's social media activities and reflect the relevant data in the program at the time of generation. For example, the generation unit can analyze the user's social media activities and reflect the relevant data in the program at the time of generation. For example, the generation unit can reflect exercise records shared by the user on social media in the program. The generation unit can also reflect health issues mentioned by the user on social media in the program. The generation unit can also analyze the user's social media activities and reflect the relevant data in the program. This makes it possible to provide a more accurate program by utilizing social media information. Some or all of the above-mentioned processing in the generation unit can be performed using, for example, AI, or can be performed without using AI. For example, the generation unit can input the user's social media data into the generation AI and cause the generation AI to extract data to be reflected in the program.

[0052] The generation unit can customize the program by reflecting the user's past feedback at the time of generation. The generation unit, for example, customizes the program by reflecting the user's past feedback at the time of generation. For example, the generation unit customizes the exercise program based on feedback provided by the user. The generation unit can also improve the accuracy of the exercise program by reflecting the user's feedback. The generation unit can also adjust the content of the exercise program based on the user's feedback. In this way, the accuracy of the program is improved by reflecting the past feedback. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the user's feedback data into the generation AI and have the generation AI customize the program.

[0053] The tracking unit can improve tracking accuracy based on the user's past exercise data during tracking. For example, the tracking unit can improve tracking accuracy by referring to the user's past exercise data during tracking. For example, the tracking unit can adjust current tracking based on the user's past exercise data. The tracking unit can also use an effective tracking method based on the user's past exercise data. The tracking unit can also analyze the user's past exercise data and use an optimal tracking method. This improves tracking accuracy by referring to past data. Some or all of the above-described processing in the tracking unit can be performed using, for example, AI, or can be performed without AI. For example, the tracking unit can input the user's past exercise data into the generation AI and cause the generation AI to improve tracking accuracy.

[0054] The tracking unit can customize tracking based on the user's lifestyle rhythm during tracking. For example, the tracking unit customizes tracking taking into account the user's lifestyle rhythm during tracking. For example, the tracking unit customizes tracking taking into account the user's sleep pattern. The tracking unit can also customize tracking taking into account the user's eating pattern. The tracking unit can also customize tracking taking into account the user's exercise habits. This improves tracking accuracy by providing tracking according to the user's lifestyle rhythm. Some or all of the above-described processing in the tracking unit may be performed using, for example, AI, or may be performed without using AI. For example, the tracking unit can input the user's lifestyle rhythm data into a generation AI and cause the generation AI to customize the tracking.

[0055] The tracking unit can improve the tracking method by reflecting user feedback during tracking. For example, the tracking unit improves the tracking method by reflecting user feedback during tracking. For example, the tracking unit improves the tracking method based on feedback provided by the user. The tracking unit can also improve tracking accuracy by reflecting user feedback. The tracking unit can also adjust parameters of the tracking method based on user feedback. In this way, tracking accuracy is improved by reflecting user feedback. Some or all of the above-described processing in the tracking unit may be performed using AI, for example, or may be performed without using AI. For example, the tracking unit can input user feedback data into a generation AI and cause the generation AI to improve the tracking method.

[0056] The tracking unit can customize tracking based on the user's geographical location information during tracking. For example, the tracking unit customizes tracking by taking the user's geographical location information into account during tracking. For example, if the user lives in a highland, the tracking unit can use a highland-specific tracking method. Also, if the user lives in an urban area, the tracking unit can use a city-specific tracking method. Also, if the user lives by the sea, the tracking unit can use a seaside-specific tracking method. In this way, a region-specific tracking method can be provided by taking the geographical location information into account. Some or all of the above-described processing in the tracking unit may be performed using AI, for example, or may be performed without using AI. For example, the tracking unit can input the user's geographical location information to the generation AI and cause the generation AI to customize the tracking.

[0057] The tracking unit can analyze the user's social media activities during tracking and reflect related data in the tracking. For example, the tracking unit can analyze the user's social media activities during tracking and reflect related data in the tracking. For example, the tracking unit can reflect exercise records shared by the user on social media in the tracking. The tracking unit can also reflect health issues mentioned by the user on social media in the tracking. The tracking unit can also analyze the user's social media activities and reflect related data in the tracking. This makes it possible to provide more accurate tracking by utilizing social media information. Some or all of the above-mentioned processing in the tracking unit can be performed using AI, for example, or without AI. For example, the tracking unit can input the user's social media data into a generation AI and cause the generation AI to extract data to be reflected in the tracking.

[0058] The tracking unit can customize the tracking method by reflecting the user's past feedback during tracking. The tracking unit, for example, customizes the tracking method by reflecting the user's past feedback during tracking. For example, the tracking unit customizes the tracking method based on feedback provided by the user. The tracking unit can also improve tracking accuracy by reflecting the user's feedback. The tracking unit can also adjust parameters of the tracking method based on the user's feedback. In this way, tracking accuracy is improved by reflecting the past feedback. Some or all of the above-described processing in the tracking unit may be performed using AI, for example, or may be performed without using AI. For example, the tracking unit can input user feedback data into a generation AI and cause the generation AI to customize the tracking method.

[0059] The feedback unit can improve the accuracy of the feedback based on the user's past exercise data when providing feedback. For example, the feedback unit can improve the accuracy of the feedback by referring to the user's past exercise data when providing feedback. For example, the feedback unit can adjust the current feedback based on the user's past exercise data. The feedback unit can also provide effective feedback based on the user's past exercise data. The feedback unit can also analyze the user's past exercise data and provide optimal feedback. This improves the accuracy of the feedback by referring to the past data. Some or all of the above-described processing by the feedback unit can be performed using, or without, AI. For example, the feedback unit can input the user's past exercise data into the generation AI and cause the generation AI to improve the accuracy of the feedback.

[0060] The feedback unit can customize the feedback based on the user's lifestyle rhythm when providing feedback. For example, the feedback unit customizes the feedback taking into account the user's lifestyle rhythm when providing feedback. For example, the feedback unit customizes the feedback taking into account the user's sleep pattern. The feedback unit can also customize the feedback taking into account the user's eating pattern. The feedback unit can also customize the feedback taking into account the user's exercise habits. This maximizes the effect of the feedback by providing feedback that matches the user's lifestyle rhythm. Some or all of the above-described processing in the feedback unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback unit can input the user's lifestyle rhythm data into the generation AI and cause the generation AI to customize the feedback.

[0061] The feedback unit can improve the feedback content by reflecting the user's feedback when providing feedback. For example, the feedback unit improves the feedback content by reflecting the user's feedback when providing feedback. For example, the feedback unit improves the feedback content based on feedback provided by the user. The feedback unit can also improve the accuracy of the feedback by reflecting the user's feedback. The feedback unit can also adjust parameters of the feedback content based on the user's feedback. In this way, the accuracy of the feedback is improved by reflecting the user's feedback. Some or all of the above-mentioned processing in the feedback unit may be performed using AI, for example, or may be performed without using AI. For example, the feedback unit can input the user's feedback data into a generation AI and cause the generation AI to improve the feedback content.

[0062] The feedback unit can customize the feedback based on the user's geographical location information when providing feedback. For example, the feedback unit customizes the feedback by taking the user's geographical location information into consideration when providing feedback. For example, if the user lives in a high altitude, the feedback unit can provide high altitude-specific feedback. Furthermore, if the user lives in an urban area, the feedback unit can provide city-specific feedback. Furthermore, if the user lives by the sea, the feedback unit can provide seaside-specific feedback. In this way, region-specific feedback can be provided by taking the geographical location information into consideration. Some or all of the above-described processing in the feedback unit may be performed using AI, for example, or may be performed without using AI. For example, the feedback unit can input the user's geographical location information to the generation AI and cause the generation AI to customize the feedback.

[0063] The feedback unit can analyze the user's social media activities and reflect relevant data in the feedback when providing feedback. For example, the feedback unit can analyze the user's social media activities and reflect relevant data in the feedback when providing feedback. For example, the feedback unit can reflect exercise records shared by the user on social media in the feedback. The feedback unit can also reflect health issues mentioned by the user on social media in the feedback. The feedback unit can also analyze the user's social media activities and reflect relevant data in the feedback. This makes it possible to provide more accurate feedback by utilizing social media information. Some or all of the above-described processing in the feedback unit can be performed using AI, for example, or without AI. For example, the feedback unit can input the user's social media data into a generation AI and cause the generation AI to extract data to be reflected in the feedback.

[0064] The feedback unit can customize the feedback content by reflecting the user's past feedback when providing feedback. For example, the feedback unit customizes the feedback content by reflecting the user's past feedback when providing feedback. For example, the feedback unit customizes the feedback content based on feedback provided by the user. The feedback unit can also improve the accuracy of the feedback by reflecting the user's feedback. The feedback unit can also adjust parameters of the feedback content based on the user's feedback. In this way, the accuracy of the feedback is improved by reflecting the past feedback. Some or all of the above-mentioned processing in the feedback unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback unit can input the user's feedback data into a generation AI and cause the generation AI to customize the feedback content.

[0065] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0066] During analysis, the analysis unit can improve the accuracy of the analysis based on the user's past health data. For example, the analysis unit can refer to the user's past weight data to analyze current weight changes. The analysis unit can also refer to the user's past exercise records to analyze the current exercise effect. The analysis unit can also refer to data related to the user's past health problems to analyze the user's current health condition. In this way, the accuracy of the analysis can be improved by referring to past 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 the user's past health data into the generation AI and have the generation AI improve the accuracy of the analysis.

[0067] During generation, the generation unit can improve the accuracy of the program based on the user's past exercise data. For example, the generation unit can adjust the current exercise program based on the user's past exercise data. The generation unit can also generate an effective exercise program based on the user's past exercise data. The generation unit can also analyze the user's past exercise data and generate an optimal exercise program. This improves the accuracy of the program by referencing the past data. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without AI. For example, the generation unit can input the user's past exercise data into the generation AI and have the generation AI improve the accuracy of the program.

[0068] During tracking, the tracking unit can improve tracking accuracy based on the user's past exercise data. For example, the tracking unit can adjust current tracking based on the user's past exercise data. The tracking unit can also use an effective tracking method based on the user's past exercise data. The tracking unit can also analyze the user's past exercise data and use an optimal tracking method. This improves tracking accuracy by referring to past data. Some or all of the above-described processing in the tracking unit can be performed using, for example, AI or without AI. For example, the tracking unit can input the user's past exercise data into the generation AI and cause the generation AI to improve tracking accuracy.

[0069] The feedback unit can improve the accuracy of the feedback based on the user's past exercise data when providing feedback. For example, the feedback unit can adjust the current feedback based on the user's past exercise data. The feedback unit can also provide effective feedback based on the user's past exercise data. The feedback unit can also analyze the user's past exercise data and provide optimal feedback. This improves the accuracy of the feedback by referring to the past data. Some or all of the above-described processing by the feedback unit can be performed using, or without, AI. For example, the feedback unit can input the user's past exercise data into the generation AI and cause the generation AI to improve the accuracy of the feedback.

[0070] The reception unit can automatically complete input items based on the user's past health data. For example, the reception unit references the user's past health data and automatically completes input items. For example, the reception unit automatically completes weight and height data previously entered by the user. The reception unit can also automatically set an appropriate exercise level based on the user's past exercise experience. The reception unit can also reference data regarding the user's past health issues and automatically complete related input items. By referencing past data, this improves the efficiency of input work and reduces the burden on the user. Some or all of the above-mentioned processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past health data into the generation AI and have the generation AI perform automatic completion of input items.

[0071] The processing flow of the first embodiment will be briefly explained below.

[0072] Step 1: The reception unit inputs the user's health status and fitness goals. The user's health status may include, for example, weight, height, age, exercise experience, and specific health issues. The reception unit stores the information the user inputs in a database and provides it to the analysis unit. Step 2: The analysis unit analyzes the information input by the reception unit. The analysis unit uses data analysis algorithms to evaluate the user's health status and fitness goals. The analysis unit can also evaluate the reliability of the user's input data and filter out unreliable data. Step 3: The generator generates an exercise and stretching program based on the information analyzed by the analyzer. The generator uses a generative AI to create an exercise and stretching program that is optimal for the user. The generator generates an individually customized program based on the user's health condition and fitness goals. Step 4: The tracking unit tracks the user's progress based on the program generated by the generation unit. The tracking unit inputs the user's exercise results, and the generation AI analyzes the information to adjust the content of the next exercise. The tracking unit can also refer to the user's past exercise data to improve tracking accuracy. Step 5: The feedback unit provides feedback based on the progress tracked by the tracking unit. The feedback unit provides advice updated in real time according to the user's progress. The feedback unit can also estimate the user's emotions and adjust the content of the feedback based on the estimated user's emotions.

[0073] (Example 2) A fitness support system according to an embodiment of the present invention uses a generation AI to provide users with individually customized exercise and stretching advice. In the fitness support system, a user inputs their health status and fitness goals, and the generation AI analyzes the input information to generate an optimal exercise and stretching program for the user. The generated program is updated in real time based on the user's progress. For example, in the fitness support system, a user inputs their health status and fitness goals, such as weight, height, age, exercise experience, and specific health issues. This information is input to the generation AI. The fitness support system then analyzes the input information and generates an optimal exercise and stretching program for the user using the generation AI. The generation AI creates an individually customized program based on the user's health status and fitness goals. For example, a user who wants to lose weight would receive a program centered on aerobic exercise. The generated program is updated in real time based on the user's progress. For example, when a user inputs the results of an exercise, the generation AI analyzes the information and adjusts the content of the next exercise. This allows the user to always receive the latest advice. This allows the fitness support system to provide individually customized exercise and stretching advice based on the user's health condition and fitness goals, and updates it in real time according to progress. This allows the fitness support system to provide individually customized exercise and stretching advice based on the user's health condition and fitness goals, and updates it in real time according to progress. For example, users can exercise at their own pace and receive support to achieve their fitness goals. It is also available 24 hours a day and is less expensive than a human trainer.

[0074] A fitness support system according to an embodiment includes a reception unit, an analysis unit, a generation unit, a tracking unit, and a feedback unit. The reception unit receives input of a user's health condition and fitness goals. The user's health condition may include, but is not limited to, weight, height, age, exercise experience, and specific health issues. The reception unit, for example, stores the information received by the user in a database and provides the information to the analysis unit. The analysis unit analyzes the information received by the reception unit. The analysis unit may evaluate the user's health condition and fitness goals using, for example, a data analysis algorithm. The analysis unit may also evaluate the reliability of the user's input data and filter out unreliable data. The generation unit generates an exercise and stretching program based on the information analyzed by the analysis unit. The generation unit uses, for example, a generation AI to create an optimal exercise and stretching program for the user. The generation AI may be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to, these examples. The generation unit generates an individually customized program based on the user's health condition and fitness goals. The tracking unit tracks the user's progress based on the program generated by the generation unit. For example, the tracking unit inputs the results of the user's exercise, and the generation AI analyzes the information to adjust the content of the next exercise. The tracking unit can also improve tracking accuracy by referring to the user's past exercise data. The feedback unit provides feedback based on the progress tracked by the tracking unit. For example, the feedback unit can provide advice updated in real time according to the user's progress. The feedback unit can also estimate the user's emotions and adjust the content of the feedback based on the estimated user's emotions. As a result, the fitness support system according to the embodiment provides individually customized exercise and stretching advice based on the user's health condition and fitness goals, and is updated in real time according to the user's progress.

[0075] The reception unit can input information regarding the user's weight, height, age, exercise experience, and specific health issues. The reception unit, for example, stores the information regarding the user's weight, height, age, exercise experience, and specific health issues in a database. For example, the reception unit can automatically complete the weight and height data entered by the user. The reception unit can also automatically set an appropriate exercise level based on the user's past exercise experience. Furthermore, the reception unit can reference data regarding the user's past health issues and automatically complete related input items. This allows for the generation of more accurate exercise and stretching programs by entering detailed health information about the user. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without AI. For example, the reception unit can input the data entered by the user into a generation AI and have the generation AI analyze and complete the data.

[0076] The generation unit can generate individually customized exercise and stretching programs based on the user's health condition and fitness goals. The generation unit, for example, uses a generation AI to create an exercise and stretching program optimal for the user. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. The generation unit generates an individually customized program based on the user's health condition and fitness goals. For example, the generation unit may generate a program centered on aerobic exercise for a user who wants to lose weight. The generation unit may also generate a program centered on strength training for a user who wants to increase muscle strength. Furthermore, the generation unit may generate a program centered on endurance training for a user who wants to improve endurance. This enables effective exercise and stretching by providing a program tailored to the user's health condition and fitness goals. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit may input user data into the generation AI and cause the generation AI to generate a program.

[0077] The tracking unit can input the results of a user's exercise, and the generation AI can analyze the information and adjust the content of the next exercise. For example, the tracking unit can input the results of a user's exercise, and the generation AI can analyze the information and adjust the content of the next exercise. For example, the tracking unit can store the results of the user's exercise in a database and provide the results to the generation AI. The tracking unit can also improve tracking accuracy by referencing the user's past exercise data. For example, the tracking unit can adjust the current exercise program based on the user's past exercise data. The tracking unit can also analyze the user's past exercise data and generate an optimal exercise program. This allows for a more effective fitness program by adjusting the content of the next exercise based on the user's exercise results. Some or all of the above-described processing in the tracking unit can be performed using, or without, AI. For example, the tracking unit can input the user's exercise data to the generation AI and have the generation AI adjust the content of the next exercise.

[0078] The feedback unit can provide advice updated in real time according to the user's progress. For example, the feedback unit provides advice updated in real time according to the user's progress. For example, the feedback unit stores the user's progress in a database and provides it to the generation AI. The feedback unit can also estimate the user's emotions and adjust the content of the feedback based on the estimated user's emotions. For example, the feedback unit can provide detailed feedback if the user is relaxed. The feedback unit can also provide concise feedback if the user is in a hurry. The feedback unit can also provide feedback to reduce stress if the user is feeling stressed. This makes it easier to maintain the user's motivation by providing real-time advice according to the user's progress. Some or all of the above-described processing in the feedback unit may be performed using AI, for example, or may be performed without using AI. For example, the feedback unit can input the user's progress to the generation AI and cause the generation AI to adjust the content of the feedback.

[0079] The generation unit can generate a program centered on aerobic exercise for a user who wants to lose weight. For example, the generation unit generates a program centered on aerobic exercise for a user who wants to lose weight. For example, the generation unit creates a program that includes aerobic exercise such as running, cycling, and aerobics. The generation unit can also adjust the intensity and frequency of the aerobic exercise according to the user's weight loss goal. Furthermore, the generation unit can update the content of the aerobic exercise in real time according to the user's progress. This supports goal achievement by providing a program that matches the user's goal. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input user data into the generation AI and have the generation AI generate a program.

[0080] The reception unit can estimate the user's emotion and adjust the design of the input interface based on the estimated user emotion. For example, the reception unit can estimate the user's emotion and adjust the design of the input interface based on the estimated user emotion. For example, if the user is stressed, the reception unit can provide an interface with subdued colors to reduce visual stress. Furthermore, if the user is relaxed, the reception unit can provide an interface with bright colors to make input work more enjoyable. Furthermore, if the user is tired, the reception unit can provide a simple, highly visible interface to make input work easier. By providing an interface that corresponds to the user's emotion, the user's stress is reduced and input work is more enjoyable. The 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 reception unit can be performed using, for example, an AI, or without an AI. For example, the reception unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the interface design.

[0081] The reception unit can automatically complete input items based on the user's past health data. The reception unit, for example, references the user's past health data and automatically completes input items. For example, the reception unit automatically completes weight and height data previously entered by the user. The reception unit can also automatically set an appropriate exercise level based on the user's past exercise experience. The reception unit can also reference data regarding the user's past health issues and automatically complete related input items. By referencing past data, this improves the efficiency of input work and reduces the burden on the user. Some or all of the above-mentioned processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past health data into the generation AI and have the generation AI automatically complete the input items.

[0082] The reception unit can evaluate the reliability of data input by the user and filter out low-reliability data. The reception unit, for example, evaluates the reliability of data input by the user and filters out low-reliability data. For example, the reception unit checks the consistency of data input by the user and displays a warning if there is an inconsistency. The reception unit can also detect and filter out abnormal values ​​by comparing the data with data previously input by the user. The reception unit can also evaluate the reliability of data input by the user and automatically exclude low-reliability data. This eliminates low-reliability data, thereby improving the accuracy of the analysis results. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's input data to a generation AI and have the generation AI evaluate the reliability of the data and perform filtering.

[0083] The reception unit can encrypt the user's input data to protect privacy. The reception unit, for example, encrypts the user's input data to protect privacy. For example, the reception unit encrypts health data entered by the user to prevent access by third parties. The reception unit can also encrypt the user's personal information and securely store it. The reception unit can also protect the data using an encryption protocol when transmitting the user's input data. This protects the user's privacy and allows the user to input data with peace of mind. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the user's input data to a generation AI and have the generation AI encrypt the data.

[0084] The reception unit can estimate the user's emotions and adjust the priority of input items based on the estimated user emotions. The reception unit, for example, estimates the user's emotions and adjusts the priority of input items based on the estimated user emotions. For example, when the user is in a hurry, the reception unit prioritizes displaying important input items. Furthermore, when the user is relaxed, the reception unit can display detailed input items. Furthermore, when the user is stressed, the reception unit can minimize the number of input items displayed. This adjusts the priority of input items according to the user's emotions, thereby streamlining input work. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may 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 reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit can input the user's emotion data into the generation AI and have the generation AI adjust the priority of the input items.

[0085] The reception unit can add input items related to region-specific health issues based on the user's geographical location information. The reception unit, for example, adds input items related to region-specific health issues taking into account the user's geographical location information. For example, if the user lives in a high altitude, the reception unit can add input items related to high altitude health issues. Furthermore, if the user lives in an urban area, the reception unit can add input items related to city-specific health issues. Furthermore, if the user lives by the sea, the reception unit can add input items related to seaside health issues. This enables more appropriate health management by addressing region-specific health issues. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's geographical location information to the generation AI and cause the generation AI to add input items.

[0086] The reception unit can analyze the user's social media activity and automatically input related health information. The reception unit, for example, analyzes the user's social media activity and automatically inputs related health information. For example, the reception unit automatically inputs exercise records shared by the user on social media. The reception unit can also automatically input health issues mentioned by the user on social media. The reception unit can also analyze the user's social media activity and automatically input related health information. This makes it possible to utilize social media information to streamline input work and provide more accurate data. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's social media data into a generation AI and have the generation AI automatically input health information.

[0087] The reception unit can customize the input interface by reflecting the user's past feedback. The reception unit customizes the input interface by reflecting the user's past feedback, for example. For example, the reception unit improves the input interface based on feedback provided by the user in the past. The reception unit can also adjust the arrangement of input items by reflecting the user's past feedback. The reception unit can also change the design of the input interface based on the user's past feedback. In this way, an interface that is easy for the user to use can be provided by reflecting the past feedback. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the user's feedback data to a generation AI and cause the generation AI to customize the interface.

[0088] The analysis unit can estimate the user's emotions and adjust the analysis algorithm based on the estimated user emotions. For example, the analysis unit estimates the user's emotions and adjusts the analysis algorithm based on the estimated user emotions. For example, if the user is relaxed, the analysis unit uses an algorithm that performs detailed analysis. Also, if the user is in a hurry, the analysis unit can use an algorithm that performs quick analysis. Also, if the user is stressed, the analysis unit can use an algorithm that performs simplified analysis. This allows for more appropriate analysis results to be provided by using an analysis algorithm that corresponds to 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, 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 analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the analysis algorithm.

[0089] The analysis unit can improve the accuracy of the analysis based on the user's past health data during analysis. The analysis unit, for example, improves the accuracy of the analysis by referring to the user's past health data during analysis. For example, the analysis unit can analyze current weight changes by referring to the user's past weight data. The analysis unit can also analyze the effects of current exercise by referring to the user's past exercise records. The analysis unit can also analyze the user's current health condition by referring to data related to the user's past health issues. In this way, the accuracy of the analysis is improved by referring to past 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 the user's past health data into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0090] The analysis unit can customize the analysis results based on the user's lifestyle rhythm during analysis. The analysis unit, for example, customizes the analysis results taking into account the user's lifestyle rhythm during analysis. For example, the analysis unit customizes the analysis results taking into account the user's sleep patterns. The analysis unit can also customize the analysis results taking into account the user's eating patterns. The analysis unit can also customize the analysis results taking into account the user's exercise habits. This enables more appropriate advice to be provided by providing analysis results that match the user's lifestyle rhythm. 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 user's lifestyle rhythm data into the generation AI and have the generation AI customize the analysis results.

[0091] The analysis unit can improve the analysis algorithm by reflecting user feedback during analysis. The analysis unit can improve the analysis algorithm by reflecting user feedback during analysis, for example. For example, the analysis unit improves the analysis algorithm based on feedback provided by the user. The analysis unit can also improve the accuracy of the analysis by reflecting user feedback. The analysis unit can also adjust parameters of the analysis algorithm based on user feedback. In this way, the accuracy of the analysis algorithm is improved by reflecting user feedback. Some or all of the above-mentioned processing in the analysis unit can be performed using AI, for example, or can be performed without using AI. For example, the analysis unit can input user feedback data into the generation AI and cause the generation AI to improve the analysis algorithm.

[0092] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, the analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is nervous, the analysis unit can provide a simple, highly visible display method. If the user is relaxed, the analysis unit can provide a display method that includes detailed information. If the user is in a hurry, the analysis unit can provide a display method that focuses on the main points. This helps the user understand the analysis results by providing a display method that corresponds to 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, 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 analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the display method.

[0093] The analysis unit can customize the analysis results based on the user's geographical location information during analysis. The analysis unit, for example, customizes the analysis results by taking into account the user's geographical location information during analysis. For example, if the user lives at high altitude, the analysis unit customizes the analysis results by taking into account health issues specific to high altitudes. Furthermore, if the user lives in an urban area, the analysis unit can customize the analysis results by taking into account health issues specific to urban areas. Furthermore, if the user lives by the sea, the analysis unit can customize the analysis results by taking into account health issues specific to seaside areas. In this way, by taking into account the geographical location information, analysis results that address health issues specific to the region can be provided. 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 user's geographical location information into the generation AI and cause the generation AI to customize the analysis results.

[0094] The analysis unit can analyze the user's social media activities during the analysis and reflect related data in the analysis. For example, the analysis unit can analyze the user's social media activities during the analysis and reflect related data in the analysis. For example, the analysis unit can reflect exercise records shared by the user on social media in the analysis. The analysis unit can also reflect health issues mentioned by the user on social media in the analysis. The analysis unit can also analyze the user's social media activities and reflect related data in the analysis. This makes it possible to provide more accurate analysis results by utilizing social media information. Some or all of the above-mentioned processing in the analysis unit can be performed using AI, for example, or without AI. For example, the analysis unit can input the user's social media data into the generation AI and cause the generation AI to extract data to be reflected in the analysis.

[0095] The analysis unit can customize the analysis algorithm by reflecting the user's past feedback during analysis. The analysis unit, for example, customizes the analysis algorithm by reflecting the user's past feedback during analysis. For example, the analysis unit customizes the analysis algorithm based on feedback provided by the user. The analysis unit can also improve the accuracy of the analysis by reflecting the user's feedback. The analysis unit can also adjust parameters of the analysis algorithm based on the user's feedback. In this way, the accuracy of the analysis algorithm is improved by reflecting the past feedback. 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 feedback data into the generation AI and cause the generation AI to customize the analysis algorithm.

[0096] The generation unit can estimate the user's emotions and adjust the content of the program to be generated based on the estimated user emotions. For example, the generation unit can estimate the user's emotions and adjust the content of the program to be generated based on the estimated user emotions. For example, if the user is relaxed, the generation unit can generate a leisurely exercise program. Furthermore, if the user is in a hurry, the generation unit can generate a short, effective exercise program. Furthermore, if the user is feeling stressed, the generation unit can generate an exercise program that emphasizes relaxation. This maximizes the effectiveness of the exercise by providing a program tailored to the user's emotions. The emotion estimation is achieved 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 generation unit can be performed using, for example, an AI, or without an AI. For example, the generation unit can input the user's emotion data into the generation AI and have the generation AI adjust the content of the program.

[0097] The generation unit can improve the accuracy of the program based on the user's past exercise data during generation. For example, the generation unit can improve the accuracy of the program by referring to the user's past exercise data during generation. For example, the generation unit can adjust the current exercise program based on the user's past exercise data. The generation unit can also generate an effective exercise program based on the user's past exercise data. The generation unit can also analyze the user's past exercise data and generate an optimal exercise program. This improves the accuracy of the program by referring to the past data. Some or all of the above-described processing in the generation unit can be performed using, for example, AI, or can be performed without AI. For example, the generation unit can input the user's past exercise data into the generation AI and have the generation AI improve the accuracy of the program.

[0098] The generation unit can customize the program based on the user's lifestyle rhythm at the time of generation. For example, the generation unit customizes the program taking the user's lifestyle rhythm into consideration at the time of generation. For example, the generation unit customizes the exercise program taking the user's sleep pattern into consideration. The generation unit can also customize the exercise program taking the user's eating pattern into consideration. The generation unit can also customize the exercise program taking the user's exercise habits into consideration. This maximizes the effect of exercise by providing a program that matches the user's lifestyle rhythm. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the user's lifestyle rhythm data into the generation AI and have the generation AI customize the program.

[0099] The generation unit can improve the program by reflecting user feedback during generation. For example, the generation unit improves the program by reflecting user feedback during generation. For example, the generation unit improves the exercise program based on feedback provided by the user. The generation unit can also improve the accuracy of the exercise program by reflecting user feedback. The generation unit can also adjust the content of the exercise program based on user feedback. In this way, the accuracy of the program is improved by reflecting user feedback. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input user feedback data into the generation AI and have the generation AI improve the program.

[0100] The generation unit can estimate the user's emotions and determine the priority of the programs to be generated based on the estimated user emotions. The generation unit, for example, estimates the user's emotions and determines the priority of the programs to be generated based on the estimated user emotions. For example, if the user is relaxed, the generation unit can prioritize programs that emphasize relaxation. Furthermore, if the user is in a hurry, the generation unit can prioritize programs that are quick and effective. Furthermore, if the user is stressed, the generation unit can prioritize programs that emphasize stress relief. This maximizes the effectiveness of exercise by determining the priority of programs according to the user's emotions. The emotion estimation is achieved 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 generation unit can be performed using, for example, an AI. For example, the generation unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of the programs.

[0101] The generation unit can customize the program based on the user's geographical location information at the time of generation. For example, the generation unit customizes the program by taking the user's geographical location information into account at the time of generation. For example, if the user lives in a high altitude, the generation unit can generate a high altitude-specific exercise program. Also, if the user lives in an urban area, the generation unit can generate a city-specific exercise program. Also, if the user lives by the sea, the generation unit can generate a seaside-specific exercise program. In this way, by taking the geographical location information into account, it is possible to provide a region-specific exercise program. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the user's geographical location information into the generation AI and cause the generation AI to customize the program.

[0102] The generation unit can analyze the user's social media activities and reflect the relevant data in the program at the time of generation. For example, the generation unit can analyze the user's social media activities and reflect the relevant data in the program at the time of generation. For example, the generation unit can reflect exercise records shared by the user on social media in the program. The generation unit can also reflect health issues mentioned by the user on social media in the program. The generation unit can also analyze the user's social media activities and reflect the relevant data in the program. This makes it possible to provide a more accurate program by utilizing social media information. Some or all of the above-mentioned processing in the generation unit can be performed using, for example, AI, or can be performed without using AI. For example, the generation unit can input the user's social media data into the generation AI and cause the generation AI to extract data to be reflected in the program.

[0103] The generation unit can customize the program by reflecting the user's past feedback at the time of generation. The generation unit, for example, customizes the program by reflecting the user's past feedback at the time of generation. For example, the generation unit customizes the exercise program based on feedback provided by the user. The generation unit can also improve the accuracy of the exercise program by reflecting the user's feedback. The generation unit can also adjust the content of the exercise program based on the user's feedback. In this way, the accuracy of the program is improved by reflecting the past feedback. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the user's feedback data into the generation AI and have the generation AI customize the program.

[0104] The tracking unit can estimate the user's emotions and adjust the tracking method based on the estimated user emotions. For example, the tracking unit can estimate the user's emotions and adjust the tracking method based on the estimated user emotions. For example, the tracking unit can perform detailed tracking when the user is relaxed. Furthermore, the tracking unit can also perform simplified tracking when the user is in a hurry. Furthermore, the tracking unit can use a tracking method to reduce stress when the user is feeling stressed. This improves tracking accuracy by providing a tracking method that corresponds to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using 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 tracking unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the tracking unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the tracking method.

[0105] The tracking unit can improve tracking accuracy based on the user's past exercise data during tracking. For example, the tracking unit can improve tracking accuracy by referring to the user's past exercise data during tracking. For example, the tracking unit can adjust current tracking based on the user's past exercise data. The tracking unit can also use an effective tracking method based on the user's past exercise data. The tracking unit can also analyze the user's past exercise data and use an optimal tracking method. This improves tracking accuracy by referring to past data. Some or all of the above-described processing in the tracking unit can be performed using, for example, AI, or can be performed without AI. For example, the tracking unit can input the user's past exercise data into the generation AI and cause the generation AI to improve tracking accuracy.

[0106] The tracking unit can customize tracking based on the user's lifestyle rhythm during tracking. For example, the tracking unit customizes tracking taking into account the user's lifestyle rhythm during tracking. For example, the tracking unit customizes tracking taking into account the user's sleep pattern. The tracking unit can also customize tracking taking into account the user's eating pattern. The tracking unit can also customize tracking taking into account the user's exercise habits. This improves tracking accuracy by providing tracking according to the user's lifestyle rhythm. Some or all of the above-described processing in the tracking unit may be performed using, for example, AI, or may be performed without using AI. For example, the tracking unit can input the user's lifestyle rhythm data into a generation AI and cause the generation AI to customize the tracking.

[0107] The tracking unit can improve the tracking method by reflecting user feedback during tracking. For example, the tracking unit improves the tracking method by reflecting user feedback during tracking. For example, the tracking unit improves the tracking method based on feedback provided by the user. The tracking unit can also improve tracking accuracy by reflecting user feedback. The tracking unit can also adjust parameters of the tracking method based on user feedback. In this way, tracking accuracy is improved by reflecting user feedback. Some or all of the above-described processing in the tracking unit may be performed using AI, for example, or may be performed without using AI. For example, the tracking unit can input user feedback data into a generation AI and cause the generation AI to improve the tracking method.

[0108] The tracking unit can estimate the user's emotions and determine tracking priorities based on the estimated user emotions. The tracking unit, for example, estimates the user's emotions and determines tracking priorities based on the estimated user emotions. For example, if the user is relaxed, the tracking unit can prioritize tracking that emphasizes relaxation. Also, if the user is in a hurry, the tracking unit can prioritize tracking that is quick and effective. Also, if the user is stressed, the tracking unit can prioritize tracking that emphasizes stress relief. This improves tracking accuracy by determining tracking priorities according to 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 such examples. Some or all of the above-mentioned processing in the tracking unit may be performed using an AI, for example, or without an AI. For example, the tracking unit can input the user's emotion data into the generation AI and have the generation AI determine the tracking priorities.

[0109] The tracking unit can customize tracking based on the user's geographical location information during tracking. For example, the tracking unit customizes tracking by taking the user's geographical location information into account during tracking. For example, if the user lives in a highland, the tracking unit can use a highland-specific tracking method. Also, if the user lives in an urban area, the tracking unit can use a city-specific tracking method. Also, if the user lives by the sea, the tracking unit can use a seaside-specific tracking method. In this way, a region-specific tracking method can be provided by taking the geographical location information into account. Some or all of the above-described processing in the tracking unit may be performed using AI, for example, or may be performed without using AI. For example, the tracking unit can input the user's geographical location information to the generation AI and cause the generation AI to customize the tracking.

[0110] The tracking unit can analyze the user's social media activities during tracking and reflect related data in the tracking. For example, the tracking unit can analyze the user's social media activities during tracking and reflect related data in the tracking. For example, the tracking unit can reflect exercise records shared by the user on social media in the tracking. The tracking unit can also reflect health issues mentioned by the user on social media in the tracking. The tracking unit can also analyze the user's social media activities and reflect related data in the tracking. This makes it possible to provide more accurate tracking by utilizing social media information. Some or all of the above-mentioned processing in the tracking unit can be performed using AI, for example, or without AI. For example, the tracking unit can input the user's social media data into a generation AI and cause the generation AI to extract data to be reflected in the tracking.

[0111] The tracking unit can customize the tracking method by reflecting the user's past feedback during tracking. The tracking unit, for example, customizes the tracking method by reflecting the user's past feedback during tracking. For example, the tracking unit customizes the tracking method based on feedback provided by the user. The tracking unit can also improve tracking accuracy by reflecting the user's feedback. The tracking unit can also adjust parameters of the tracking method based on the user's feedback. In this way, tracking accuracy is improved by reflecting the past feedback. Some or all of the above-described processing in the tracking unit may be performed using AI, for example, or may be performed without using AI. For example, the tracking unit can input user feedback data into a generation AI and cause the generation AI to customize the tracking method.

[0112] The feedback unit can estimate the user's emotion and adjust the content of the feedback based on the estimated user's emotion. For example, the feedback unit can estimate the user's emotion and adjust the content of the feedback based on the estimated user's emotion. For example, the feedback unit can provide detailed feedback when the user is relaxed. The feedback unit can also provide concise feedback when the user is in a hurry. The feedback unit can also provide feedback to reduce stress when the user is feeling stressed. This maximizes the effect of the feedback by providing feedback according to the user's emotion. Emotion estimation is achieved 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 feedback unit can be performed using an AI, for example, or without an AI. For example, the feedback unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the feedback content.

[0113] The feedback unit can improve the accuracy of the feedback based on the user's past exercise data when providing feedback. For example, the feedback unit can improve the accuracy of the feedback by referring to the user's past exercise data when providing feedback. For example, the feedback unit can adjust the current feedback based on the user's past exercise data. The feedback unit can also provide effective feedback based on the user's past exercise data. The feedback unit can also analyze the user's past exercise data and provide optimal feedback. This improves the accuracy of the feedback by referring to the past data. Some or all of the above-described processing by the feedback unit can be performed using, or without, AI. For example, the feedback unit can input the user's past exercise data into the generation AI and cause the generation AI to improve the accuracy of the feedback.

[0114] The feedback unit can customize the feedback based on the user's lifestyle rhythm when providing feedback. For example, the feedback unit customizes the feedback taking into account the user's lifestyle rhythm when providing feedback. For example, the feedback unit customizes the feedback taking into account the user's sleep pattern. The feedback unit can also customize the feedback taking into account the user's eating pattern. The feedback unit can also customize the feedback taking into account the user's exercise habits. This maximizes the effect of the feedback by providing feedback that matches the user's lifestyle rhythm. Some or all of the above-described processing in the feedback unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback unit can input the user's lifestyle rhythm data into the generation AI and cause the generation AI to customize the feedback.

[0115] The feedback unit can improve the feedback content by reflecting the user's feedback when providing feedback. For example, the feedback unit improves the feedback content by reflecting the user's feedback when providing feedback. For example, the feedback unit improves the feedback content based on feedback provided by the user. The feedback unit can also improve the accuracy of the feedback by reflecting the user's feedback. The feedback unit can also adjust parameters of the feedback content based on the user's feedback. In this way, the accuracy of the feedback is improved by reflecting the user's feedback. Some or all of the above-mentioned processing in the feedback unit may be performed using AI, for example, or may be performed without using AI. For example, the feedback unit can input the user's feedback data into a generation AI and cause the generation AI to improve the feedback content.

[0116] The feedback unit can estimate the user's emotions and determine the priority of feedback based on the estimated user's emotions. For example, the feedback unit can estimate the user's emotions and determine the priority of feedback based on the estimated user's emotions. For example, if the user is relaxed, the feedback unit can prioritize feedback that emphasizes relaxation. Also, if the user is in a hurry, the feedback unit can prioritize feedback that is quick and effective. Also, if the user is stressed, the feedback unit can prioritize feedback that emphasizes stress relief. This maximizes the effectiveness of feedback by determining the priority of feedback according to 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 such examples. Some or all of the above-mentioned processing in the feedback unit can be performed using, for example, an AI. For example, the feedback unit can input the user's emotion data into the generation AI and cause the generation AI to determine the priority of feedback.

[0117] The feedback unit can customize the feedback based on the user's geographical location information when providing feedback. For example, the feedback unit customizes the feedback by taking the user's geographical location information into consideration when providing feedback. For example, if the user lives in a high altitude, the feedback unit can provide high altitude-specific feedback. Furthermore, if the user lives in an urban area, the feedback unit can provide city-specific feedback. Furthermore, if the user lives by the sea, the feedback unit can provide seaside-specific feedback. In this way, region-specific feedback can be provided by taking the geographical location information into consideration. Some or all of the above-described processing in the feedback unit may be performed using AI, for example, or may be performed without using AI. For example, the feedback unit can input the user's geographical location information to the generation AI and cause the generation AI to customize the feedback.

[0118] The feedback unit can analyze the user's social media activities and reflect relevant data in the feedback when providing feedback. For example, the feedback unit can analyze the user's social media activities and reflect relevant data in the feedback when providing feedback. For example, the feedback unit can reflect exercise records shared by the user on social media in the feedback. The feedback unit can also reflect health issues mentioned by the user on social media in the feedback. The feedback unit can also analyze the user's social media activities and reflect relevant data in the feedback. This makes it possible to provide more accurate feedback by utilizing social media information. Some or all of the above-described processing in the feedback unit can be performed using AI, for example, or without AI. For example, the feedback unit can input the user's social media data into a generation AI and cause the generation AI to extract data to be reflected in the feedback.

[0119] The feedback unit can customize the feedback content by reflecting the user's past feedback when providing feedback. For example, the feedback unit customizes the feedback content by reflecting the user's past feedback when providing feedback. For example, the feedback unit customizes the feedback content based on feedback provided by the user. The feedback unit can also improve the accuracy of the feedback by reflecting the user's feedback. The feedback unit can also adjust parameters of the feedback content based on the user's feedback. In this way, the accuracy of the feedback is improved by reflecting the past feedback. Some or all of the above-mentioned processing in the feedback unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback unit can input the user's feedback data into a generation AI and cause the generation AI to customize the feedback content. === Hard Collateral 1-1 === Each of the above-described elements, including the reception unit, analysis unit, generation unit, tracking unit, and feedback unit, is implemented, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is implemented by the reception device 38 of the smart device 14 and inputs the user's health status and fitness goals. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the input information. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and generates exercise and stretching programs using a generation AI. The tracking unit is implemented, for example, by the control unit 46A of the smart device 14 and tracks the user's progress. The feedback unit is implemented, for example, by the output device 40 of the smart device 14 and provides updated advice in real time. === Hard Collateral 1-2 === Each of the multiple elements, including the above-mentioned reception unit, analysis unit, generation unit, tracking unit, and feedback unit, is implemented, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is implemented by the microphone 238 of the smart glasses 214 and inputs the user's health status and fitness goals. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the input information. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and generates exercise and stretching programs using a generation AI. The tracking unit is implemented, for example, by the control unit 46A of the smart glasses 214 and tracks the user's progress. The feedback unit is implemented, for example, by the speaker 240 of the smart glasses 214 and provides updated advice in real time. === Hard Collateral 1-3 === Each of the multiple elements, including the above-mentioned reception unit, analysis unit, generation unit, tracking unit, and feedback unit, is implemented, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit is implemented by the microphone 238 of the headset-type terminal 314 and inputs the user's health status and fitness goals. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the input information. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and generates exercise and stretching programs using a generation AI. The tracking unit is implemented, for example, by the control unit 46A of the headset-type terminal 314 and tracks the user's progress. The feedback unit is implemented, for example, by the speaker 240 of the headset-type terminal 314 and provides advice updated in real time. === Hard Collateral 1-4 === Each of the multiple elements, including the above-mentioned reception unit, analysis unit, generation unit, tracking unit, and feedback unit, is implemented, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is implemented by the microphone 238 of the robot 414 and inputs the user's health status and fitness goals. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the input information. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and generates exercise and stretching programs using a generation AI. The tracking unit is implemented, for example, by the control unit 46A of the robot 414 and tracks the user's progress. The feedback unit is implemented, for example, by the speaker 240 of the robot 414 and provides updated advice in real time.

[0120] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0121] The analysis unit can estimate the user's emotions and adjust the analysis algorithm based on the estimated user emotions. For example, if the user is relaxed, an algorithm that performs detailed analysis can be used. If the user is in a hurry, an algorithm that performs quick analysis can be used. If the user is stressed, an algorithm that performs simplified analysis can be used. This allows for more appropriate analysis results to be provided by using an analysis algorithm that corresponds to 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, 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 analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the analysis algorithm.

[0122] The generation unit can estimate the user's emotions and adjust the content of the program to be generated based on the estimated user's emotions. For example, if the user is relaxed, a leisurely exercise program can be generated. If the user is in a hurry, a short, effective exercise program can be generated. If the user is stressed, an exercise program that emphasizes relaxation can be generated. This maximizes the effectiveness of the exercise by providing a program tailored to 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, 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 generation unit can be performed using, for example, an AI, or without an AI. For example, the generation unit can input the user's emotion data into the generation AI and have the generation AI adjust the content of the program.

[0123] The tracking unit can estimate the user's emotions and adjust the tracking method based on the estimated user's emotions. For example, the tracking unit can perform detailed tracking when the user is relaxed. Alternatively, the tracking unit can perform simplified tracking when the user is in a hurry. Alternatively, the tracking unit can use a tracking method to reduce stress when the user is stressed. This improves tracking accuracy by providing a tracking method that corresponds to the user's emotions. The 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 tracking unit can be performed using, for example, an AI, or without an AI. For example, the tracking unit can input the user's emotion data into the generation AI and have the generation AI adjust the tracking method.

[0124] The feedback unit can estimate the user's emotions and adjust the content of the feedback based on the estimated user's emotions. For example, the feedback unit can provide detailed feedback when the user is relaxed. Alternatively, the feedback unit can provide brief feedback when the user is in a hurry. Alternatively, the feedback unit can provide feedback to reduce stress when the user is feeling stressed. This maximizes the effectiveness of the feedback by providing feedback according to the user's emotions. The emotion estimation is achieved 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 feedback unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the feedback unit can input the user's emotion data into the generation AI and have the generation AI adjust the feedback content.

[0125] The reception unit can estimate the user's emotions and adjust the design of the input interface based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit can provide an interface with subdued colors to reduce visual stress. Alternatively, if the user is relaxed, the reception unit can provide an interface with bright colors to make input work more enjoyable. Alternatively, if the user is tired, the reception unit can provide a simple, highly visible interface to make input work easier. By providing an interface that corresponds to the user's emotions, the user's stress is reduced and input work is more enjoyable. The 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 reception unit can be performed using, for example, an AI, or without an AI. For example, the reception unit can input the user's emotion data into the generation AI and have the generation AI adjust the interface design.

[0126] During analysis, the analysis unit can improve the accuracy of the analysis based on the user's past health data. For example, the analysis unit can refer to the user's past weight data to analyze current weight changes. The analysis unit can also refer to the user's past exercise records to analyze the current exercise effect. The analysis unit can also refer to data related to the user's past health problems to analyze the user's current health condition. In this way, the accuracy of the analysis can be improved by referring to past 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 the user's past health data into the generation AI and have the generation AI improve the accuracy of the analysis.

[0127] During generation, the generation unit can improve the accuracy of the program based on the user's past exercise data. For example, the generation unit can adjust the current exercise program based on the user's past exercise data. The generation unit can also generate an effective exercise program based on the user's past exercise data. The generation unit can also analyze the user's past exercise data and generate an optimal exercise program. This improves the accuracy of the program by referencing the past data. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without AI. For example, the generation unit can input the user's past exercise data into the generation AI and have the generation AI improve the accuracy of the program.

[0128] During tracking, the tracking unit can improve tracking accuracy based on the user's past exercise data. For example, the tracking unit can adjust current tracking based on the user's past exercise data. The tracking unit can also use an effective tracking method based on the user's past exercise data. The tracking unit can also analyze the user's past exercise data and use an optimal tracking method. This improves tracking accuracy by referring to past data. Some or all of the above-described processing in the tracking unit can be performed using, for example, AI or without AI. For example, the tracking unit can input the user's past exercise data into the generation AI and cause the generation AI to improve tracking accuracy.

[0129] The feedback unit can improve the accuracy of the feedback based on the user's past exercise data when providing feedback. For example, the feedback unit can adjust the current feedback based on the user's past exercise data. The feedback unit can also provide effective feedback based on the user's past exercise data. The feedback unit can also analyze the user's past exercise data and provide optimal feedback. This improves the accuracy of the feedback by referring to the past data. Some or all of the above-described processing by the feedback unit can be performed using, or without, AI. For example, the feedback unit can input the user's past exercise data into the generation AI and cause the generation AI to improve the accuracy of the feedback.

[0130] The reception unit can automatically complete input items based on the user's past health data. For example, the reception unit references the user's past health data and automatically completes input items. For example, the reception unit automatically completes weight and height data previously entered by the user. The reception unit can also automatically set an appropriate exercise level based on the user's past exercise experience. The reception unit can also reference data regarding the user's past health issues and automatically complete related input items. By referencing past data, this improves the efficiency of input work and reduces the burden on the user. Some or all of the above-mentioned processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past health data into the generation AI and have the generation AI perform automatic completion of input items.

[0131] The processing flow of the second embodiment will be briefly explained below.

[0132] Step 1: The reception unit inputs the user's health status and fitness goals. The user's health status may include, for example, weight, height, age, exercise experience, and specific health issues. The reception unit stores the information the user inputs in a database and provides it to the analysis unit. Step 2: The analysis unit analyzes the information input by the reception unit. The analysis unit uses data analysis algorithms to evaluate the user's health status and fitness goals. The analysis unit can also evaluate the reliability of the user's input data and filter out unreliable data. Step 3: The generator generates an exercise and stretching program based on the information analyzed by the analyzer. The generator uses a generative AI to create an exercise and stretching program that is optimal for the user. The generator generates an individually customized program based on the user's health condition and fitness goals. Step 4: The tracking unit tracks the user's progress based on the program generated by the generation unit. The tracking unit inputs the user's exercise results, and the generation AI analyzes the information to adjust the content of the next exercise. The tracking unit can also refer to the user's past exercise data to improve tracking accuracy. Step 5: The feedback unit provides feedback based on the progress tracked by the tracking unit. The feedback unit provides advice updated in real time according to the user's progress. The feedback unit can also estimate the user's emotions and adjust the content of the feedback based on the estimated user's emotions.

[0133] 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.

[0134] 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.

[0135] 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.

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

[0138] 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.

[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 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.

[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. 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.

[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 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.

[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 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.

[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 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.

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

[0154] 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.

[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 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.

[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 (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).

[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] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0169] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0170] 7, a 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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).

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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).

[0190] 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.

[0191] 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."

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] [Explanation of symbols]

[0205] 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 reception unit for inputting a user's health status and fitness goals; an analysis unit that analyzes the information input by the reception unit; a generating unit that generates an exercise or stretching program based on the information analyzed by the analyzing unit; a tracking unit that tracks a user's progress based on the program generated by the generation unit; a feedback unit that provides feedback based on the progress tracked by the tracking unit. A system characterized by:

2. The reception unit Enter information about the user's weight, height, age, exercise experience, and specific health issues 2. The system of claim 1.

3. The generation unit Generate personalized exercise and stretching programs based on the user's health and fitness goals 2. The system of claim 1.

4. The tracking unit The user inputs the results of their exercise, and the AI ​​analyzes that information to adjust the content of the next exercise.

2. The system of claim 1.

5. The feedback unit Provides real-time updated advice based on the user's progress 2. The system of claim 1.

6. The generation unit For users who want to lose weight, a program focused on aerobic exercise is generated.

2. The system of claim 1.

7. The reception unit Estimate user emotions and adjust the design of the input interface based on the estimated user emotions.

2. The system of claim 1.

8. The reception unit Auto-complete input fields based on the user's past health data 2. The system of claim 1.

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