system

Abstract

The system according to the embodiment aims to propose optimal training and dietary content based on body composition information. [Solution] A system according to an embodiment includes a collection unit, a transmission unit, an analysis unit, a suggestion unit, and a provision unit. The collection unit collects body composition information. The transmission unit transmits the information collected by the collection unit to a generation AI. The analysis unit analyzes the information transmitted by the transmission unit. The suggestion unit proposes training or meal content based on the results of the analysis by the analysis unit. The provision unit provides the content proposed by the suggestion unit to a user.

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 technology has had the problem of making it difficult to suggest appropriate training and dietary content for the most efficient body shaping.

[0005] The system according to the embodiment aims to propose optimal training and dietary content based on body composition information. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, a transmission unit, an analysis unit, a suggestion unit, and a provision unit. The collection unit collects body composition information. The transmission unit transmits the information collected by the collection unit to the generation AI. The analysis unit analyzes the information transmitted by the transmission unit. The suggestion unit proposes training content or meal content based on the results of the analysis by the analysis unit. The provision unit provides the content proposed by the suggestion unit to the user. [Effects of the Invention]

[0007] The system according to the embodiment can suggest optimal training and dietary content based on body composition information. [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 body shaping support system according to an embodiment of the present invention uses a wearable device to collect body composition information and transmit it to an app equipped with a generation AI API, thereby suggesting optimal training and dietary content at the optimal time. In this body shaping support system, a body shaping user wears a wearable device and measures body composition information, such as body fat percentage, muscle mass, and water content, in real time. The measured information is automatically transmitted to the app equipped with the generation AI API. The generation AI then analyzes the transmitted body composition information and, based on past data and current body composition information, suggests optimal training and dietary content for the body shaping user in real time. For example, if the body fat percentage is high, aerobic training is suggested, and if muscle mass is insufficient, strength training is suggested. Furthermore, dietary content is suggested, including necessary nutrients. This system enables body shaping users to achieve body shaping at low cost and with maximum efficiency. For example, if the goal is weight loss, training and dietary content to effectively reduce body fat percentage are suggested, and if the goal is training, training and dietary content to increase muscle mass are suggested. Furthermore, the generation AI monitors the body shaping user's progress in real time and adjusts the suggestions as needed. This allows body-makeup practitioners to always perform optimal training and dietary plans. This service supports body-makeup practitioners in performing body-makeup most efficiently, providing maximum results at low cost. This allows the body-makeup support system to efficiently collect, analyze, suggest, and provide user body composition information.

[0029] A body shaping support system according to an embodiment includes a collection unit, a transmission unit, an analysis unit, a suggestion unit, and a provision unit. The collection unit collects body composition information of a user. The collection unit measures information such as body fat percentage, muscle mass, and water content in real time. The collection unit measures body fat percentage using, for example, a wearable device. The collection unit can also use an electromyograph to measure muscle mass. The collection unit can also use a bioimpedance method to measure water content. The transmission unit transmits the information collected by the collection unit to a generation AI. The transmission unit transmits the information using, for example, wireless communication technology such as Bluetooth (registered trademark) or Wi-Fi. The transmission unit transmits the collected information to the generation AI in real time. The transmission unit can also transmit information in batches at regular time intervals. The analysis unit analyzes the information transmitted by the transmission unit. The analysis unit uses the generation AI to perform analysis based on past data and current body composition information. The analysis unit analyzes the current body fat percentage based on, for example, past body fat percentage data. The analysis unit can also analyze changes in muscle mass based on muscle mass data. The analysis unit can also analyze fluctuations in water content based on water content data. The suggestion unit suggests training and dietary content based on the results of the analysis by the analysis unit. The suggestion unit uses a generation AI to suggest optimal training and dietary content for the user. For example, if the body fat percentage is high, the suggestion unit can suggest training centered on aerobic exercise. Furthermore, if the muscle mass is insufficient, the suggestion unit can also suggest strength training. Furthermore, the suggestion unit can also suggest a menu containing necessary nutrients. The provision unit provides the content suggested by the suggestion unit to the user. The provision unit notifies the user of the suggested content through, for example, an app. Furthermore, the provision unit can send the suggested content to the user by email or SMS. Furthermore, the provision unit can provide the suggested content to the user through a web portal. This allows the body shaper support system according to the embodiment to efficiently collect, analyze, suggest, and provide body composition information of the user.

[0030] The collection unit can measure body composition information such as body fat percentage, muscle mass, and water content in real time. The collection unit, for example, uses bioimpedance to measure body fat percentage. For example, the collection unit measures electrical resistance in the body using electrodes to measure body fat percentage. The collection unit can also use an electromyograph to measure muscle mass. For example, the collection unit measures electrical activity of muscles to measure muscle mass. The collection unit can also use bioimpedance to measure water content. For example, the collection unit measures electrical resistance in the body to measure water content. This allows for real-time measurement of body composition information, enabling suggestions based on the most recent data. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit can input measurement data of body fat percentage to a generation AI and cause the generation AI to analyze the body fat percentage.

[0031] The analysis unit can perform analysis based on past data or current body composition information. For example, the analysis unit analyzes the current body fat percentage based on past body fat percentage data. For example, the analysis unit analyzes fluctuations in the current body fat percentage based on body fat percentage data from the past year. The analysis unit can also analyze changes in muscle mass based on muscle mass data. For example, the analysis unit analyzes fluctuations in the current muscle mass based on past muscle mass data. The analysis unit can also analyze fluctuations in water content based on water content data. For example, the analysis unit analyzes fluctuations in the current water content based on past water content data. This enables more accurate recommendations by analyzing based on past data and current data. Some or all of the above-described processing in the analysis unit can be performed using, or without, the generation AI. For example, the analysis unit can input past body fat percentage data into the generation AI and have the generation AI perform an analysis of fluctuations in the body fat percentage.

[0032] The suggestion unit can suggest training centered on aerobic exercise when the body fat percentage is above a certain level. For example, the suggestion unit suggests training centered on aerobic exercise when the body fat percentage is 20% or higher. For example, the suggestion unit suggests aerobic exercise such as jogging or cycling. Furthermore, the suggestion unit can suggest low-intensity aerobic exercise such as walking or swimming when the body fat percentage is 25% or higher. Furthermore, the suggestion unit can suggest medium-intensity aerobic exercise such as aerobics or dancing when the body fat percentage is 30% or higher. This enables effective body shaping by suggesting optimal training content according to the body fat percentage. Some or all of the above-mentioned processing in the suggestion unit may be performed using, or without, a generation AI. For example, the suggestion unit can input body fat percentage data into the generation AI and cause the generation AI to suggest optimal aerobic exercise.

[0033] The suggestion unit can suggest strength training when the muscle mass is below a certain level. For example, the suggestion unit suggests strength training when the muscle mass is 30 kg or less. For example, the suggestion unit suggests strength training such as weightlifting or resistance training. The suggestion unit can also suggest bodyweight training such as push-ups and squats when the muscle mass is 25 kg or less. Furthermore, the suggestion unit can also suggest training using barbells or dumbbells when the muscle mass is 20 kg or less. This enables effective body shaping by suggesting optimal training content according to muscle mass. Some or all of the above-mentioned processing in the suggestion unit may be performed using, or without, a generation AI. For example, the suggestion unit can input muscle mass data into the generation AI and have the generation AI suggest optimal strength training.

[0034] The suggestion unit can suggest menus containing specific nutrients. The suggestion unit can suggest, for example, menus that are high in protein. For example, the suggestion unit can suggest menus that include high-protein foods such as chicken breast and tofu. The suggestion unit can also suggest menus that are high in vitamin D. For example, the suggestion unit can suggest menus that include foods that are high in vitamin D, such as salmon and egg yolk. The suggestion unit can also suggest menus that are high in calcium. For example, the suggestion unit can suggest menus that include foods that are high in calcium, such as milk and yogurt. This enables effective dietary management by suggesting menus that include necessary nutrients. Some or all of the above-mentioned processing in the suggestion unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the suggestion unit can input nutrient data into the generation AI and cause the generation AI to suggest optimal menus.

[0035] The providing unit can provide the suggested content to the user. The providing unit notifies the user of the suggested content, for example, through an app. For example, the providing unit notifies the user of the suggested content in real time using a push notification function of the app. The providing unit can also send the suggested content to the user via email or SMS. For example, the providing unit can send the suggested content to the user by email so that the user can check the suggested content. The providing unit can also provide the suggested content to the user through a web portal. For example, the providing unit displays the suggested content to a user who logs in to the web portal. This enables effective body shaping by providing the suggested content to the user. Some or all of the above-mentioned processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the suggested content to a generation AI and cause the generation AI to execute an optimal method to provide the suggested content to the user.

[0036] The body makeover support system further includes a monitoring unit that monitors the progress of the body makeover user in real time and adjusts the suggestions based on specific conditions. The monitoring unit, for example, monitors the weight gain or loss of the body makeover user in real time. For example, the monitoring unit measures weight fluctuations using a scale and collects data. The monitoring unit can also monitor training progress. For example, the monitoring unit records the number of training sessions and the duration to understand the progress. The monitoring unit can also monitor dietary content. For example, the monitoring unit collects dietary records and analyzes ingested nutrients. This allows the progress to be monitored in real time and the suggestions to be adjusted, thereby always providing optimal training and dietary content. Some or all of the above-mentioned processing in the monitoring unit may be performed using, for example, AI, or may be performed without AI. For example, the monitoring unit can input weight data into the generation AI and have the generation AI analyze the progress.

[0037] The collection unit can improve the collection method by referring to the user's past body composition data during collection. For example, the collection unit sets the most effective collection timing based on the user's past data. For example, the collection unit analyzes the past data and sets the optimal collection timing to twice a day. The collection unit can also analyze the user's past data and concentrate collection during specific time periods. For example, the collection unit concentrates collection in the morning and evening based on the past data. Furthermore, the collection unit can also customize the collection method by referring to the user's past data. For example, the collection unit performs collection using a specific sensor based on the past data. This enables more effective data collection by optimizing the collection method based on the past data. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit can input past data into a generation AI and cause the generation AI to optimize the collection method.

[0038] The collection unit can filter collected data based on the user's lifestyle habits or activity level during collection. For example, the collection unit prioritizes filtering of data collected after the user exercises. For example, the collection unit prioritizes collecting post-exercise data to consider the effects of exercise. The collection unit can also filter data collected after the user eats to consider the effects of meals. For example, the collection unit prioritizes collecting post-meal data to analyze the effects of meals. The collection unit can also filter data collected while the user is sleeping to consider the effects of rest. For example, the collection unit prioritizes collecting data during sleep to analyze the effects of rest. This allows for more relevant data to be collected by filtering data based on lifestyle habits and activity level. Some or all of the above-described processing in the collection unit may be performed using, or without, AI. For example, the collection unit can input lifestyle habit data to a generation AI and have the generation AI perform data filtering.

[0039] During collection, the collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information. For example, when the user is at the gym, the collection unit prioritizes collecting muscle mass data. For example, the collection unit prioritizes collecting muscle mass data based on the gym's location information. The collection unit can also prioritize collecting body fat percentage data when the user is at home. For example, the collection unit prioritizes collecting body fat percentage data based on the home's location information. Furthermore, the collection unit can also prioritize collecting water content data when the user is out. For example, the collection unit prioritizes collecting water content data based on the location information of the user's destination. In this way, by collecting data by taking the geographical location information into account, more relevant data can be collected. Some or all of the above-described processing by the collection unit may be performed using AI, for example, or without AI. For example, the collection unit may input geographical location information to the generation AI and cause the generation AI to collect data.

[0040] The collection unit can analyze the user's social media activity during collection and collect specific body composition information. For example, if the user posts about exercise on social media, the collection unit collects muscle mass data. For example, the collection unit analyzes the content of the social media post and prioritizes collecting muscle mass data when there is a post about exercise. The collection unit can also collect body fat percentage data when the user posts about diet. For example, the collection unit prioritizes collecting body fat percentage data when there is a post about diet. Furthermore, the collection unit can also collect hydration data when the user posts about rest. For example, the collection unit prioritizes collecting hydration data when there is a post about rest. In this way, by collecting data based on social media activity, more relevant data can be collected. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may input social media data into a generation AI and cause the generation AI to collect data.

[0041] The transmitting unit can determine the order of transmission based on the importance of the data at the time of transmission. For example, the transmitting unit transmits the body fat percentage data with the highest priority. For example, the transmitting unit transmits the body fat percentage data with the highest priority, followed by the muscle mass data. The transmitting unit can also transmit the water content data last. For example, the transmitting unit transmits the water content data last, and postpones data of lower importance. In this way, by determining the priority of transmission based on the importance of the data, important data can be transmitted preferentially. Some or all of the above-described processing in the transmitting unit may be performed using, or without, AI. For example, the transmitting unit can input the importance of the data to the generating AI and have the generating AI determine the transmission order.

[0042] The transmitting unit can apply a specific transmission protocol depending on the type of data when transmitting. For example, the transmitting unit transmits data on body fat percentage using a high-priority protocol. For example, the transmitting unit transmits data on body fat percentage using a high-priority protocol, and then transmits data on muscle mass using a medium-priority protocol. The transmitting unit can also transmit data on water content using a low-priority protocol. For example, the transmitting unit transmits data on water content using a low-priority protocol, and postpones data of lower importance. This enables efficient data transmission by applying a transmission protocol depending on the type of data. Some or all of the above-described processing in the transmitting unit may be performed using, for example, AI, or may be performed without using AI. For example, the transmitting unit can input the type of data to a generating AI and cause the generating AI to apply a transmission protocol.

[0043] The transmitting unit can improve the transmission method based on the destination of the data when transmitting. For example, when data is transmitted to a cloud server, the transmitting unit uses a high-speed Internet connection. For example, when transmitting data to the cloud server, the transmitting unit uses a high-speed Internet connection to shorten the transmission time. The transmitting unit can also use Bluetooth when transmitting data to a local device. For example, when transmitting data to a local device, the transmitting unit uses Bluetooth to efficiently transmit the data. Furthermore, the transmitting unit can also use a secure protocol when transmitting data to another user. For example, when transmitting data to another user, the transmitting unit uses a secure protocol to ensure the security of the data. This enables efficient data transmission by optimizing the transmission method based on the destination. Some or all of the above-described processing in the transmitting unit may be performed using AI, for example, or may be performed without using AI. For example, the transmitting unit can input information about the destination to the generating AI and cause the generating AI to optimize the transmission method.

[0044] The transmitting unit can change the transmission order based on the relevance of the data when transmitting. For example, the transmitting unit transmits the body fat percentage data first. For example, the transmitting unit transmits the body fat percentage data first, and then transmits the muscle mass data. The transmitting unit can also transmit the water content data last. For example, the transmitting unit transmits the water content data last, and prioritizes the transmission of highly relevant data. By adjusting the transmission order based on the relevance of the data, important data can be transmitted with priority. Some or all of the above-described processing in the transmitting unit may be performed using, for example, AI, or may be performed without using AI. For example, the transmitting unit can input the relevance of the data to the generating AI and have the generating AI adjust the transmission order.

[0045] During analysis, the analysis unit can improve the accuracy of the analysis by referring to past data. The analysis unit, for example, optimizes the analysis algorithm based on the user's past data. For example, the analysis unit improves the analysis accuracy of the current body fat percentage based on past body fat percentage data. The analysis unit can also improve the accuracy of the analysis results by referring to the user's past data. For example, the analysis unit improves the analysis accuracy of the current muscle mass based on past muscle mass data. The analysis unit can also analyze the user's past data and adjust the analysis algorithm. For example, the analysis unit improves the analysis accuracy of the current water content based on past water content data. In this way, by referring to the past data, the analysis accuracy is improved. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit can input past data into the generation AI and cause the generation AI to improve the analysis accuracy.

[0046] During analysis, the analysis unit can apply a specific analysis method depending on the data category. For example, the analysis unit applies a specific analysis method to body fat percentage data. For example, the analysis unit applies regression analysis to the body fat percentage data to analyze fluctuations in body fat percentage. The analysis unit can also apply a different analysis method to muscle mass data. For example, the analysis unit applies clustering to the muscle mass data to analyze fluctuations in muscle mass. The analysis unit can also apply a different analysis method to water content data. For example, the analysis unit applies time series analysis to the water content data to analyze fluctuations in water content. This makes it possible to apply an optimal analysis method depending on the data category. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input the data category to the generation AI and have the generation AI apply the analysis method.

[0047] During analysis, the analysis unit can determine the order of analysis based on the time of data submission. The analysis unit, for example, gives the most recent data the highest priority. For example, the analysis unit may analyze the most recent body fat percentage data first, followed by older data. The analysis unit can also analyze older data last. For example, the analysis unit may analyze older muscle mass data last, followed by the most recent data. This allows the most recent data to be analyzed first by determining the analysis priority based on the time of data submission. Some or all of the above-described processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit may input data on the time of submission into the generation AI and have the generation AI determine the analysis order.

[0048] During analysis, the analysis unit can change the order of analysis based on the relevance of the data. For example, the analysis unit may first analyze the data on body fat percentage. For example, the analysis unit may first analyze the data on body fat percentage, and then analyze the data on muscle mass. The analysis unit may also analyze the data on water content last. For example, the analysis unit may analyze the data on water content last, giving priority to analyzing highly relevant data. By adjusting the order of analysis based on the relevance of the data, important data can be analyzed with priority. Some or all of the above-described processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit may input the relevance of the data into the generation AI and cause the generation AI to adjust the analysis order.

[0049] When making a suggestion, the suggestion unit can improve the suggestion content by referring to the user's past training history. The suggestion unit, for example, suggests optimal training content based on the user's past training history. For example, the suggestion unit analyzes the past training history and suggests optimal training content. The suggestion unit can also adjust the intensity of the training by referring to the user's past training history. For example, the suggestion unit adjusts the intensity of the training based on the past training history. The suggestion unit can also analyze the user's past training history and suggest a type of training. For example, the suggestion unit suggests an optimal type of training based on the past training history. In this way, by referring to the past training history, more effective suggestion content can be provided. Some or all of the above-mentioned processing in the suggestion unit may be performed using, or without, a generation AI. For example, the suggestion unit can input the past training history into the generation AI and cause the generation AI to improve the suggestion content.

[0050] When making a suggestion, the suggestion unit can change the suggestion content based on the user's current body composition information. The suggestion unit, for example, suggests optimal training content based on the user's body fat percentage. For example, the suggestion unit suggests optimal aerobic exercise based on body fat percentage data. The suggestion unit can also adjust the training intensity based on the user's muscle mass. For example, the suggestion unit adjusts the training intensity based on muscle mass data. The suggestion unit can also suggest a type of training based on the user's water content. For example, the suggestion unit suggests an optimal type of training based on water content data. This enables more effective suggestions to be made by customizing the suggestion content based on the current body composition information. Some or all of the above-described processing in the suggestion unit may be performed using, or without, a generation AI. For example, the suggestion unit can input current body composition information into the generation AI and cause the generation AI to customize the suggestion content.

[0051] When making a suggestion, the suggestion unit can provide the suggestion content taking into consideration the user's geographical location information. For example, if the user is at a gym, the suggestion unit can suggest workouts that can be done at the gym. For example, the suggestion unit can suggest weightlifting or machine training that can be done at the gym based on the gym's location information. Furthermore, if the user is at home, the suggestion unit can suggest workouts that can be done at home. For example, the suggestion unit can suggest push-ups or squats that can be done at home based on the home's location information. Furthermore, if the user is in a park, the suggestion unit can suggest workouts that can be done in the park. For example, the suggestion unit can suggest jogging or circuit training that can be done in the park based on the park's location information. This allows for more effective suggestions by providing suggestion content taking into consideration the geographical location information. Some or all of the above-described processing by the suggestion unit may be performed using, or without, a generation AI. For example, the suggestion unit can input geographical location information to the generation AI and cause the generation AI to provide the suggestion content.

[0052] When making a suggestion, the suggestion unit can analyze the user's social media activity and change the suggestion content. The suggestion unit, for example, suggests related training based on training content shared by the user on social media. For example, the suggestion unit analyzes social media posts and suggests related training when there is a post about training. The suggestion unit can also suggest related meal menus based on meal content shared by the user on social media. For example, the suggestion unit suggests related meal menus when there is a post about meals. Furthermore, the suggestion unit can also suggest training toward achieving goals shared by the user on social media. For example, the suggestion unit suggests training toward achieving goals when there is a post about goals. This enables more relevant suggestions to be made by adjusting the suggestion content based on social media activity. Some or all of the above-mentioned processing by the suggestion unit may be performed using, or without, a generation AI. For example, the suggestion unit can input social media data into the generation AI and have the generation AI adjust the suggestion content.

[0053] The providing unit can improve the content to be provided by referring to the user's past feedback when providing the content. The providing unit, for example, proposes optimal content to be provided based on the user's past feedback. For example, the providing unit analyzes the past feedback and proposes optimal content to be provided. The providing unit can also adjust the provision method by referring to the user's past feedback. For example, the providing unit adjusts the provision method based on the past feedback. The providing unit can also analyze the user's past feedback and customize the content to be provided. For example, the providing unit customizes the content to be provided based on the past feedback. In this way, more effective content can be provided by referring to the past feedback. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input past feedback into a generation AI and cause the generation AI to improve the content to be provided.

[0054] The providing unit can change the providing method based on the user's device information when providing the information. For example, if the user is using a smartphone, the providing unit provides a providing method tailored to the screen size. For example, the providing unit provides a display method optimized for the smartphone screen size. Furthermore, if the user is using a tablet, the providing unit can also provide a providing method optimized for a larger screen. For example, the providing unit provides a display method optimized for the tablet screen size. Furthermore, if the user is using a smartwatch, the providing unit can also provide a simple and highly visible providing method. For example, the providing unit provides a display method optimized for the smartwatch screen size. This enables more effective provision by customizing the providing method based on the device information. Some or all of the above-described processing by the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input device information to the generation AI and cause the generation AI to customize the providing method.

[0055] The providing unit can select a provision method by taking into consideration the user's geographical location information when providing the information. For example, if the user is at a gym, the providing unit provides training that can be done at the gym. For example, the providing unit provides weightlifting or machine training that can be done at the gym based on the gym's location information. The providing unit can also provide training that can be done at home if the user is at home. For example, the providing unit provides push-ups or squats that can be done at home based on the home's location information. Furthermore, if the user is in a park, the providing unit can also provide training that can be done in the park. For example, the providing unit provides jogging or circuit training that can be done in the park based on the park's location information. This enables more effective provision by selecting a provision method by taking into consideration the geographical location information. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input geographical location information to a generation AI and cause the generation AI to select a provision method.

[0056] The providing unit can analyze the user's social media activity and change the content of the content provided at the time of providing. The providing unit, for example, provides related training based on training content shared by the user on social media. For example, the providing unit analyzes social media posts and provides related training if there is a post about training. The providing unit can also provide related meal menus based on meal content shared by the user on social media. For example, the providing unit provides related meal menus if there is a post about meals. Furthermore, the providing unit can also provide training aimed at achieving goals shared by the user on social media. For example, the providing unit provides training aimed at achieving goals if there is a post about goals. This enables more relevant content to be provided by adjusting the content provided based on social media activity. Some or all of the above-mentioned processing by the providing unit may be performed using AI, for example, or without AI. For example, the providing unit can input social media data into a generation AI and cause the generation AI to adjust the content provided.

[0057] During monitoring, the monitoring unit can improve the monitoring method by referring to the user's past progress data. For example, the monitoring unit can set the most effective monitoring timing based on the user's past progress data. For example, the monitoring unit can analyze the past progress data and set the optimal monitoring timing to twice a day. The monitoring unit can also analyze the user's past progress data and concentrate monitoring during specific time periods. For example, the monitoring unit can concentrate monitoring in the morning and evening based on the past progress data. Furthermore, the monitoring unit can also customize the monitoring method by referring to the user's past progress data. For example, the monitoring unit can perform monitoring using a specific sensor based on the past progress data. This enables more effective data collection by optimizing the monitoring method based on the past progress data. Some or all of the above-described processing in the monitoring unit can be performed using, for example, AI, or without AI. For example, the monitoring unit can input past progress data into a generation AI and cause the generation AI to optimize the monitoring method.

[0058] During monitoring, the monitoring unit can filter the monitoring data based on the user's lifestyle habits and activity level. For example, the monitoring unit prioritizes filtering of data collected after the user exercises. For example, the monitoring unit prioritizes collecting post-exercise data to consider the effects of exercise. The monitoring unit can also filter data collected after the user eats to consider the effects of meals. For example, the monitoring unit prioritizes collecting post-meal data to analyze the effects of meals. The monitoring unit can also filter data collected while the user is sleeping to consider the effects of rest. For example, the monitoring unit prioritizes collecting data during sleep to analyze the effects of rest. This allows for more relevant data to be collected by filtering data based on the lifestyle habits and activity level. Some or all of the above-described processing in the monitoring unit may be performed using, or without, AI. For example, the monitoring unit can input lifestyle habit data to a generation AI and have the generation AI perform data filtering.

[0059] During monitoring, the monitoring unit can prioritize monitoring highly relevant data by taking into account the user's geographical location information. For example, when the user is at the gym, the monitoring unit prioritizes monitoring muscle mass data. For example, the monitoring unit prioritizes monitoring muscle mass data based on the gym's location information. The monitoring unit can also prioritize monitoring body fat percentage data when the user is at home. For example, the monitoring unit prioritizes monitoring body fat percentage data based on the home's location information. Furthermore, the monitoring unit can also prioritize monitoring water content data when the user is out. For example, the monitoring unit prioritizes monitoring water content data based on the location information of the user's destination. In this way, by monitoring data while taking geographical location information into account, more relevant data can be collected. Some or all of the above-described processing by the monitoring unit may be performed using, or without, AI. For example, the monitoring unit may input geographical location information to the generation AI and cause the generation AI to monitor the data.

[0060] During monitoring, the monitoring unit can analyze the user's social media activity and collect specific monitoring data. For example, if the user posts about exercise on social media, the monitoring unit collects muscle mass data. For example, the monitoring unit analyzes the content of the social media post and prioritizes collecting muscle mass data when there is a post about exercise. The monitoring unit can also collect body fat percentage data when the user posts about diet. For example, the monitoring unit prioritizes collecting body fat percentage data when there is a post about diet. Furthermore, the monitoring unit can also collect hydration data when the user posts about rest. For example, the monitoring unit prioritizes collecting hydration data when there is a post about rest. In this way, by collecting data based on social media activity, more relevant data can be collected. Some or all of the above-described processing by the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input social media data into a generation AI and cause the generation AI to collect data.

[0061] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0062] The collection unit can prioritize collecting highly relevant data by taking into account the geographical location information of the user. For example, when the user is at the gym, it prioritizes collecting muscle mass data. For example, the collection unit prioritizes collecting muscle mass data based on the location information of the gym. The collection unit can also prioritize collecting body fat percentage data when the user is at home. For example, the collection unit prioritizes collecting body fat percentage data based on the location information of the home. Furthermore, the collection unit can also prioritize collecting hydration data when the user is out. For example, the collection unit prioritizes collecting hydration data based on the location information of the user's destination. In this way, by collecting data by taking into account the geographical location information, more relevant data can be collected.

[0063] At the time of transmission, the transmitting unit can determine the order of transmission based on the importance of the data. For example, the transmitting unit transmits the body fat percentage data with the highest priority. For example, the transmitting unit transmits the body fat percentage data with the highest priority, followed by the muscle mass data. The transmitting unit can also transmit the water content data last. For example, the transmitting unit transmits the water content data last, and postpones data of lower importance. In this way, by determining the priority of transmission based on the importance of the data, important data can be transmitted preferentially.

[0064] When making a suggestion, the suggestion unit can improve the suggestion content by referring to the user's past training history. For example, the suggestion unit suggests optimal training content based on the user's past training history. For example, the suggestion unit analyzes the past training history and suggests optimal training content. The suggestion unit can also adjust the intensity of the training by referring to the user's past training history. For example, the suggestion unit adjusts the intensity of the training based on the past training history. Furthermore, the suggestion unit can analyze the user's past training history and suggest the type of training. For example, the suggestion unit suggests the optimal type of training based on the past training history. In this way, by referring to the past training history, more effective suggestions can be provided.

[0065] During monitoring, the monitoring unit can filter the monitoring data based on the user's lifestyle habits and activity level. For example, the monitoring unit prioritizes filtering of data collected after the user exercises. For example, the monitoring unit prioritizes collecting data after exercise to consider the effects of exercise. The monitoring unit can also filter data collected after the user eats to consider the effects of meals. For example, the monitoring unit prioritizes collecting data after meals to analyze the effects of meals. Furthermore, the monitoring unit can filter data collected while the user is sleeping to consider the effects of rest. For example, the monitoring unit prioritizes collecting data during sleep to analyze the effects of rest. In this way, by filtering data based on the user's lifestyle habits and activity level, more relevant data can be collected.

[0066] The providing unit can change the providing method based on the user's device information when providing information. For example, if the user is using a smartphone, the providing unit can provide a providing method that matches the screen size. For example, the providing unit can provide a display method optimized for the smartphone screen size. Furthermore, if the user is using a tablet, the providing unit can also provide a providing method that is optimized for a large screen. For example, the providing unit can provide a display method optimized for the tablet screen size. Furthermore, if the user is using a smartwatch, the providing unit can also provide a providing method that is concise and highly visible. For example, the providing unit can provide a display method optimized for the smartwatch screen size. This enables more effective provision by customizing the providing method based on the device information.

[0067] The processing flow of the first embodiment will be briefly explained below.

[0068] Step 1: The collection unit collects the user's body composition information. The collection unit measures information such as body fat percentage, muscle mass, and water content in real time. The collection unit can measure body fat percentage using a wearable device, muscle mass using an electromyograph, and water content using a bioimpedance method. Step 2: The transmitter transmits the information collected by the collector to the generator AI. The transmitter transmits the information using wireless communication technologies such as Bluetooth or Wi-Fi, and transmits the collected information to the generator AI in real time. It can also transmit information in batches at regular intervals. Step 3: The analysis unit analyzes the information sent by the transmission unit. Using the generation AI, the analysis unit performs analysis based on past data and current body composition information. For example, it analyzes the current body fat percentage based on past body fat percentage data, analyzes changes in muscle mass based on muscle mass data, and analyzes changes in water content based on water content data. Step 4: The suggestion unit proposes training and meal plans based on the results of the analysis by the analysis unit. Using generative AI, the suggestion unit proposes optimal training and meal plans for the user. For example, if the body fat percentage is high, the suggestion unit proposes training focused on aerobic exercise, and if muscle mass is insufficient, the suggestion unit proposes strength training and a menu containing the necessary nutrients. Step 5: The providing unit provides the content suggested by the suggesting unit to the user. The providing unit can notify the user of the content suggested by the suggesting unit through the app, send the content suggested by email or SMS, or provide the content suggested by the suggesting unit through a web portal.

[0069] (Example 2) A body shaping support system according to an embodiment of the present invention uses a wearable device to collect body composition information and transmit it to an app equipped with a generation AI API, thereby suggesting optimal training and dietary content at the optimal time. In this body shaping support system, a body shaping user wears a wearable device and measures body composition information, such as body fat percentage, muscle mass, and water content, in real time. The measured information is automatically transmitted to the app equipped with the generation AI API. The generation AI then analyzes the transmitted body composition information and, based on past data and current body composition information, suggests optimal training and dietary content for the body shaping user in real time. For example, if the body fat percentage is high, aerobic training is suggested, and if muscle mass is insufficient, strength training is suggested. Furthermore, dietary content is suggested, including necessary nutrients. This system enables body shaping users to achieve body shaping at low cost and with maximum efficiency. For example, if the goal is weight loss, training and dietary content to effectively reduce body fat percentage are suggested, and if the goal is training, training and dietary content to increase muscle mass are suggested. Furthermore, the generation AI monitors the body shaping user's progress in real time and adjusts the suggestions as needed. This allows body-makeup practitioners to always perform optimal training and dietary content. This service supports body-makeup practitioners in performing body-makeup most efficiently, providing maximum results at low cost. This allows the body-makeup support system to efficiently collect, analyze, suggest, and provide user's body composition information.

[0070] A body shaping support system according to an embodiment includes a collection unit, a transmission unit, an analysis unit, a suggestion unit, and a provision unit. The collection unit collects body composition information of a user. The collection unit measures information such as body fat percentage, muscle mass, and water content in real time. The collection unit measures body fat percentage using, for example, a wearable device. The collection unit can also use an electromyograph to measure muscle mass. The collection unit can also use a bioimpedance method to measure water content. The transmission unit transmits the information collected by the collection unit to a generation AI. The transmission unit transmits the information using, for example, wireless communication technology such as Bluetooth or Wi-Fi. The transmission unit transmits the collected information to the generation AI in real time. The transmission unit can also transmit information in batches at regular time intervals. The analysis unit analyzes the information transmitted by the transmission unit. The analysis unit uses the generation AI to perform analysis based on past data and current body composition information. The analysis unit analyzes the current body fat percentage based on, for example, past body fat percentage data. The analysis unit can also analyze changes in muscle mass based on muscle mass data. The analysis unit can also analyze fluctuations in water content based on water content data. The suggestion unit suggests training and dietary content based on the results of the analysis by the analysis unit. The suggestion unit uses a generation AI to suggest optimal training and dietary content for the user. For example, if the body fat percentage is high, the suggestion unit can suggest training centered on aerobic exercise. Furthermore, if the muscle mass is insufficient, the suggestion unit can also suggest strength training. Furthermore, the suggestion unit can also suggest a menu containing necessary nutrients. The provision unit provides the content suggested by the suggestion unit to the user. The provision unit notifies the user of the suggested content through, for example, an app. Furthermore, the provision unit can send the suggested content to the user by email or SMS. Furthermore, the provision unit can provide the suggested content to the user through a web portal. This allows the body shaper support system according to the embodiment to efficiently collect, analyze, suggest, and provide body composition information of the user.

[0071] The collection unit can measure body composition information such as body fat percentage, muscle mass, and water content in real time. The collection unit, for example, uses bioimpedance to measure body fat percentage. For example, the collection unit measures electrical resistance in the body using electrodes to measure body fat percentage. The collection unit can also use an electromyograph to measure muscle mass. For example, the collection unit measures electrical activity of muscles to measure muscle mass. The collection unit can also use bioimpedance to measure water content. For example, the collection unit measures electrical resistance in the body to measure water content. This allows for real-time measurement of body composition information, enabling suggestions based on the most recent data. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit can input measurement data of body fat percentage to a generation AI and cause the generation AI to analyze the body fat percentage.

[0072] The analysis unit can perform analysis based on past data or current body composition information. For example, the analysis unit analyzes the current body fat percentage based on past body fat percentage data. For example, the analysis unit analyzes fluctuations in the current body fat percentage based on body fat percentage data from the past year. The analysis unit can also analyze changes in muscle mass based on muscle mass data. For example, the analysis unit analyzes fluctuations in the current muscle mass based on past muscle mass data. The analysis unit can also analyze fluctuations in water content based on water content data. For example, the analysis unit analyzes fluctuations in the current water content based on past water content data. This enables more accurate recommendations by analyzing based on past data and current data. Some or all of the above-described processing in the analysis unit can be performed using, or without, the generation AI. For example, the analysis unit can input past body fat percentage data into the generation AI and have the generation AI perform an analysis of fluctuations in the body fat percentage.

[0073] The suggestion unit can suggest training centered on aerobic exercise when the body fat percentage is above a certain level. For example, the suggestion unit suggests training centered on aerobic exercise when the body fat percentage is 20% or higher. For example, the suggestion unit suggests aerobic exercise such as jogging or cycling. Furthermore, the suggestion unit can suggest low-intensity aerobic exercise such as walking or swimming when the body fat percentage is 25% or higher. Furthermore, the suggestion unit can suggest medium-intensity aerobic exercise such as aerobics or dancing when the body fat percentage is 30% or higher. This enables effective body shaping by suggesting optimal training content according to the body fat percentage. Some or all of the above-mentioned processing in the suggestion unit may be performed using, or without, a generation AI. For example, the suggestion unit can input body fat percentage data into the generation AI and cause the generation AI to suggest optimal aerobic exercise.

[0074] The suggestion unit can suggest strength training when the muscle mass is below a certain level. For example, the suggestion unit suggests strength training when the muscle mass is 30 kg or less. For example, the suggestion unit suggests strength training such as weightlifting or resistance training. The suggestion unit can also suggest bodyweight training such as push-ups and squats when the muscle mass is 25 kg or less. Furthermore, the suggestion unit can also suggest training using barbells or dumbbells when the muscle mass is 20 kg or less. This enables effective body shaping by suggesting optimal training content according to muscle mass. Some or all of the above-mentioned processing in the suggestion unit may be performed using, or without, a generation AI. For example, the suggestion unit can input muscle mass data into the generation AI and have the generation AI suggest optimal strength training.

[0075] The suggestion unit can suggest menus containing specific nutrients. The suggestion unit can suggest, for example, menus that are high in protein. For example, the suggestion unit can suggest menus that include high-protein foods such as chicken breast and tofu. The suggestion unit can also suggest menus that are high in vitamin D. For example, the suggestion unit can suggest menus that include foods that are high in vitamin D, such as salmon and egg yolk. The suggestion unit can also suggest menus that are high in calcium. For example, the suggestion unit can suggest menus that include foods that are high in calcium, such as milk and yogurt. This enables effective dietary management by suggesting menus that include necessary nutrients. Some or all of the above-mentioned processing in the suggestion unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the suggestion unit can input nutrient data into the generation AI and cause the generation AI to suggest optimal menus.

[0076] The providing unit can provide the suggested content to the user. The providing unit notifies the user of the suggested content, for example, through an app. For example, the providing unit notifies the user of the suggested content in real time using a push notification function of the app. The providing unit can also send the suggested content to the user via email or SMS. For example, the providing unit can send the suggested content to the user by email so that the user can check the suggested content. The providing unit can also provide the suggested content to the user through a web portal. For example, the providing unit displays the suggested content to a user who logs in to the web portal. This enables effective body shaping by providing the suggested content to the user. Some or all of the above-mentioned processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the suggested content to a generation AI and cause the generation AI to execute an optimal method to provide the suggested content to the user.

[0077] The body makeover support system further includes a monitoring unit that monitors the progress of the body makeover user in real time and adjusts the suggestions based on specific conditions. The monitoring unit, for example, monitors the weight gain or loss of the body makeover user in real time. For example, the monitoring unit measures weight fluctuations using a scale and collects data. The monitoring unit can also monitor training progress. For example, the monitoring unit records the number of training sessions and the duration to understand the progress. The monitoring unit can also monitor dietary content. For example, the monitoring unit collects dietary records and analyzes ingested nutrients. This allows the progress to be monitored in real time and the suggestions to be adjusted, thereby always providing optimal training and dietary content. Some or all of the above-mentioned processing in the monitoring unit may be performed using, for example, AI, or may be performed without AI. For example, the monitoring unit can input weight data into the generation AI and have the generation AI analyze the progress.

[0078] The collection unit can estimate the user's emotions and adjust the frequency of body composition information collection based on the estimated user emotions. For example, if the user is feeling stressed, the collection unit reduces the collection frequency to reduce the burden on the user. For example, the collection unit measures the user's stress level and, if the stress level is high, reduces the collection frequency to once a day. The collection unit can also increase the collection frequency and collect more detailed data if the user is relaxed. For example, the collection unit measures the user's state of relaxation and, if the user is relaxed, increases the collection frequency to three times a day. Furthermore, if the user is in a hurry, the collection unit can minimize the collection frequency and quickly acquire data. For example, the collection unit checks the user's schedule and, if the user is in a hurry, reduces the collection frequency to once a day. In this way, by adjusting the collection frequency according to the user's emotions, it is possible to collect more detailed data while reducing the burden on the user. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input emotion data to a generation AI and have the generation AI adjust the collection frequency.

[0079] The collection unit can improve the collection method by referring to the user's past body composition data during collection. For example, the collection unit sets the most effective collection timing based on the user's past data. For example, the collection unit analyzes the past data and sets the optimal collection timing to twice a day. The collection unit can also analyze the user's past data and concentrate collection during specific time periods. For example, the collection unit concentrates collection in the morning and evening based on the past data. Furthermore, the collection unit can also customize the collection method by referring to the user's past data. For example, the collection unit performs collection using a specific sensor based on the past data. This enables more effective data collection by optimizing the collection method based on the past data. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit can input past data into a generation AI and cause the generation AI to optimize the collection method.

[0080] The collection unit can filter collected data based on the user's lifestyle habits or activity level during collection. For example, the collection unit prioritizes filtering of data collected after the user exercises. For example, the collection unit prioritizes collecting post-exercise data to consider the effects of exercise. The collection unit can also filter data collected after the user eats to consider the effects of meals. For example, the collection unit prioritizes collecting post-meal data to analyze the effects of meals. The collection unit can also filter data collected while the user is sleeping to consider the effects of rest. For example, the collection unit prioritizes collecting data during sleep to analyze the effects of rest. This allows for more relevant data to be collected by filtering data based on lifestyle habits and activity level. Some or all of the above-described processing in the collection unit may be performed using, or without, AI. For example, the collection unit can input lifestyle habit data to a generation AI and have the generation AI perform data filtering.

[0081] The collection unit can estimate the user's emotions and determine the priority of body composition information to be collected based on the estimated user's emotions. For example, if the user is feeling stressed, the collection unit prioritizes collecting body fat percentage data. For example, the collection unit measures the stress level and prioritizes collecting body fat percentage data when the stress level is high. The collection unit can also prioritize collecting muscle mass data when the user is relaxed. For example, the collection unit measures the user's relaxed state and prioritizes collecting muscle mass data when the user is relaxed. Furthermore, the collection unit can prioritize collecting hydration data when the user is in a hurry. For example, the collection unit checks the user's schedule and prioritizes collecting hydration data when the user is in a hurry. In this way, by determining the priority of information to be collected according to the user's emotions, more important data can be collected preferentially. Some or all of the above-described processing by the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input emotion data to a generation AI and have the generation AI determine the priority of the information to be collected.

[0082] During collection, the collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information. For example, when the user is at the gym, the collection unit prioritizes collecting muscle mass data. For example, the collection unit prioritizes collecting muscle mass data based on the gym's location information. The collection unit can also prioritize collecting body fat percentage data when the user is at home. For example, the collection unit prioritizes collecting body fat percentage data based on the home's location information. Furthermore, the collection unit can also prioritize collecting water content data when the user is out. For example, the collection unit prioritizes collecting water content data based on the location information of the user's destination. In this way, by collecting data by taking the geographical location information into account, more relevant data can be collected. Some or all of the above-described processing by the collection unit may be performed using AI, for example, or without AI. For example, the collection unit may input geographical location information to the generation AI and cause the generation AI to collect data.

[0083] The collection unit can analyze the user's social media activity during collection and collect specific body composition information. For example, if the user posts about exercise on social media, the collection unit collects muscle mass data. For example, the collection unit analyzes the content of the social media post and prioritizes collecting muscle mass data when there is a post about exercise. The collection unit can also collect body fat percentage data when the user posts about diet. For example, the collection unit prioritizes collecting body fat percentage data when there is a post about diet. Furthermore, the collection unit can also collect hydration data when the user posts about rest. For example, the collection unit prioritizes collecting hydration data when there is a post about rest. In this way, by collecting data based on social media activity, more relevant data can be collected. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may input social media data into a generation AI and cause the generation AI to collect data.

[0084] The transmission unit can estimate the user's emotions and change the timing of data transmission based on the estimated user emotions. For example, the transmission unit reduces the frequency of data transmission when the user is feeling stressed. For example, the transmission unit measures the user's stress level and reduces the frequency of data transmission to once a day when stress is high. The transmission unit can also increase the frequency of data transmission when the user is relaxed. For example, the transmission unit measures the user's state of relaxation and increases the frequency of data transmission to three times a day when the user is relaxed. Furthermore, the transmission unit can quickly transmit data when the user is in a hurry. For example, the transmission unit checks the user's schedule and immediately transmits data when the user is in a hurry. This allows data transmission while reducing the burden on the user by adjusting the transmission timing according to the user's emotions. Some or all of the above-described processing in the transmission unit may be performed using, for example, AI, or may be performed without using AI. For example, the transmission unit can input emotion data to a generation AI and have the generation AI adjust the transmission timing.

[0085] The transmitting unit can determine the order of transmission based on the importance of the data at the time of transmission. For example, the transmitting unit transmits the body fat percentage data with the highest priority. For example, the transmitting unit transmits the body fat percentage data with the highest priority, followed by the muscle mass data. The transmitting unit can also transmit the water content data last. For example, the transmitting unit transmits the water content data last, and postpones data of lower importance. In this way, by determining the priority of transmission based on the importance of the data, important data can be transmitted preferentially. Some or all of the above-described processing in the transmitting unit may be performed using, or without, AI. For example, the transmitting unit can input the importance of the data to the generating AI and have the generating AI determine the transmission order.

[0086] The transmitting unit can apply a specific transmission protocol depending on the type of data when transmitting. For example, the transmitting unit transmits data on body fat percentage using a high-priority protocol. For example, the transmitting unit transmits data on body fat percentage using a high-priority protocol, and then transmits data on muscle mass using a medium-priority protocol. The transmitting unit can also transmit data on water content using a low-priority protocol. For example, the transmitting unit transmits data on water content using a low-priority protocol, and postpones data of lower importance. This enables efficient data transmission by applying a transmission protocol depending on the type of data. Some or all of the above-described processing in the transmitting unit may be performed using, for example, AI, or may be performed without using AI. For example, the transmitting unit can input the type of data to a generating AI and cause the generating AI to apply a transmission protocol.

[0087] The transmitting unit can estimate the user's emotions and change the compression rate of the transmitted data based on the estimated user emotions. For example, if the user is feeling stressed, the transmitting unit increases the data compression rate to shorten the transmission time. For example, the transmitting unit measures the user's stress level and, if the stress level is high, increases the data compression rate to shorten the transmission time. The transmitting unit can also lower the data compression rate and transmit more detailed data if the user is relaxed. For example, the transmitting unit measures the user's state of relaxation and, if the user is relaxed, decreases the data compression rate and transmits more detailed data. Furthermore, if the user is in a hurry, the transmitting unit can maximize the data compression rate and transmit data quickly. For example, the transmitting unit checks the user's schedule and, if the user is in a hurry, maximizes the data compression rate and transmits data quickly. This enables fast and efficient data transmission by adjusting the compression rate according to the user's emotions. Some or all of the above-described processing in the transmitting unit may be performed using, for example, AI, or may be performed without AI. For example, the transmitting unit can input emotion data to a generating AI and have the generating AI adjust the compression rate.

[0088] The transmitting unit can improve the transmission method based on the destination of the data when transmitting. For example, when data is transmitted to a cloud server, the transmitting unit uses a high-speed Internet connection. For example, when transmitting data to the cloud server, the transmitting unit uses a high-speed Internet connection to shorten the transmission time. The transmitting unit can also use Bluetooth when transmitting data to a local device. For example, when transmitting data to a local device, the transmitting unit uses Bluetooth to efficiently transmit the data. Furthermore, the transmitting unit can also use a secure protocol when transmitting data to another user. For example, when transmitting data to another user, the transmitting unit uses a secure protocol to ensure the security of the data. This enables efficient data transmission by optimizing the transmission method based on the destination. Some or all of the above-described processing in the transmitting unit may be performed using AI, for example, or may be performed without using AI. For example, the transmitting unit can input information about the destination to the generating AI and cause the generating AI to optimize the transmission method.

[0089] The transmitting unit can change the transmission order based on the relevance of the data when transmitting. For example, the transmitting unit transmits the body fat percentage data first. For example, the transmitting unit transmits the body fat percentage data first, and then transmits the muscle mass data. The transmitting unit can also transmit the water content data last. For example, the transmitting unit transmits the water content data last, and prioritizes the transmission of highly relevant data. By adjusting the transmission order based on the relevance of the data, important data can be transmitted with priority. Some or all of the above-described processing in the transmitting unit may be performed using, for example, AI, or may be performed without using AI. For example, the transmitting unit can input the relevance of the data to the generating AI and have the generating AI adjust the transmission order.

[0090] The analysis unit can estimate the user's emotions and change the analysis algorithm based on the estimated user emotions. For example, if the user is feeling stressed, the analysis unit simplifies the analysis algorithm and provides results quickly. For example, the analysis unit measures the user's stress level and simplifies the analysis algorithm and provides results quickly if the stress level is high. The analysis unit can also perform detailed analysis and provide highly accurate results if the user is relaxed. For example, the analysis unit measures the user's state of relaxation and performs detailed analysis and provides highly accurate results if the user is relaxed. Furthermore, the analysis unit can speed up the analysis algorithm and provide results quickly if the user is in a hurry. For example, the analysis unit checks the user's schedule and, if the user is in a hurry, speeds up the analysis algorithm and provides results quickly. This enables fast and accurate analysis by adjusting the analysis algorithm according to the user's emotions. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input emotion data into the generation AI and have the generation AI adjust the analysis algorithm.

[0091] During analysis, the analysis unit can improve the accuracy of the analysis by referring to past data. The analysis unit, for example, optimizes the analysis algorithm based on the user's past data. For example, the analysis unit improves the analysis accuracy of the current body fat percentage based on past body fat percentage data. The analysis unit can also improve the accuracy of the analysis results by referring to the user's past data. For example, the analysis unit improves the analysis accuracy of the current muscle mass based on past muscle mass data. The analysis unit can also analyze the user's past data and adjust the analysis algorithm. For example, the analysis unit improves the analysis accuracy of the current water content based on past water content data. In this way, by referring to the past data, the analysis accuracy is improved. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit can input past data into the generation AI and cause the generation AI to improve the analysis accuracy.

[0092] During analysis, the analysis unit can apply a specific analysis method depending on the data category. For example, the analysis unit applies a specific analysis method to body fat percentage data. For example, the analysis unit applies regression analysis to the body fat percentage data to analyze fluctuations in body fat percentage. The analysis unit can also apply a different analysis method to muscle mass data. For example, the analysis unit applies clustering to the muscle mass data to analyze fluctuations in muscle mass. The analysis unit can also apply a different analysis method to water content data. For example, the analysis unit applies time series analysis to the water content data to analyze fluctuations in water content. This makes it possible to apply an optimal analysis method depending on the data category. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input the data category to the generation AI and have the generation AI apply the analysis method.

[0093] The analysis unit can estimate the user's emotions and change the display method of the analysis results based on the estimated user emotions. For example, if the user is feeling stressed, the analysis unit provides a simple, highly visible display method. For example, the analysis unit measures the stress level and provides a simple graph display if the stress level is high. The analysis unit can also provide a display method including detailed information if the user is relaxed. For example, the analysis unit measures the user's relaxation state and provides a detailed text display if the user is relaxed. Furthermore, the analysis unit can also provide a display method that focuses on the main points if the user is in a hurry. For example, the analysis unit checks the user's schedule and provides a dashboard display that focuses on the main points if the user is in a hurry. This allows the display method to be adjusted according to the user's emotions, thereby providing highly visible analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input emotion data into the generation AI and have the generation AI adjust the display method.

[0094] During analysis, the analysis unit can determine the order of analysis based on the time of data submission. The analysis unit, for example, gives the most recent data the highest priority. For example, the analysis unit may analyze the most recent body fat percentage data first, followed by older data. The analysis unit can also analyze older data last. For example, the analysis unit may analyze older muscle mass data last, followed by the most recent data. This allows the most recent data to be analyzed first by determining the analysis priority based on the time of data submission. Some or all of the above-described processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit may input data on the time of submission into the generation AI and have the generation AI determine the analysis order.

[0095] During analysis, the analysis unit can change the order of analysis based on the relevance of the data. For example, the analysis unit may first analyze the data on body fat percentage. For example, the analysis unit may first analyze the data on body fat percentage, and then analyze the data on muscle mass. The analysis unit may also analyze the data on water content last. For example, the analysis unit may analyze the data on water content last, giving priority to analyzing highly relevant data. By adjusting the order of analysis based on the relevance of the data, important data can be analyzed with priority. Some or all of the above-described processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit may input the relevance of the data into the generation AI and cause the generation AI to adjust the analysis order.

[0096] The suggestion unit can estimate the user's emotions and change the way the suggestions are expressed based on the estimated user emotions. For example, if the user is feeling stressed, the suggestion unit can provide simple, highly visible suggestions. For example, the suggestion unit can measure the user's stress level and provide a simple text display if the stress level is high. The suggestion unit can also provide suggestions including detailed information if the user is relaxed. For example, the suggestion unit can measure the user's relaxation state and provide a detailed graph display if the user is relaxed. Furthermore, the suggestion unit can provide suggestions that focus on the main points if the user is in a hurry. For example, the suggestion unit can check the user's schedule and provide a dashboard display that focuses on the main points if the user is in a hurry. This allows the suggestion unit to adjust the way the suggestions are expressed according to the user's emotions, thereby providing highly visible suggestions. Some or all of the above-described processing in the suggestion unit may be performed using, or without, a generation AI. For example, the suggestion unit can input emotion data into the generation AI and cause the generation AI to adjust the way the suggestions are expressed.

[0097] When making a suggestion, the suggestion unit can improve the suggestion content by referring to the user's past training history. The suggestion unit, for example, suggests optimal training content based on the user's past training history. For example, the suggestion unit analyzes the past training history and suggests optimal training content. The suggestion unit can also adjust the intensity of the training by referring to the user's past training history. For example, the suggestion unit adjusts the intensity of the training based on the past training history. The suggestion unit can also analyze the user's past training history and suggest a type of training. For example, the suggestion unit suggests an optimal type of training based on the past training history. In this way, by referring to the past training history, more effective suggestion content can be provided. Some or all of the above-mentioned processing in the suggestion unit may be performed using, or without, a generation AI. For example, the suggestion unit can input the past training history into the generation AI and cause the generation AI to improve the suggestion content.

[0098] When making a suggestion, the suggestion unit can change the suggestion content based on the user's current body composition information. The suggestion unit, for example, suggests optimal training content based on the user's body fat percentage. For example, the suggestion unit suggests optimal aerobic exercise based on body fat percentage data. The suggestion unit can also adjust the training intensity based on the user's muscle mass. For example, the suggestion unit adjusts the training intensity based on muscle mass data. The suggestion unit can also suggest a type of training based on the user's water content. For example, the suggestion unit suggests an optimal type of training based on water content data. This enables more effective suggestions to be made by customizing the suggestion content based on the current body composition information. Some or all of the above-described processing in the suggestion unit may be performed using, or without, a generation AI. For example, the suggestion unit can input current body composition information into the generation AI and cause the generation AI to customize the suggestion content.

[0099] The suggestion unit can estimate the user's emotions and determine the order of suggested content based on the estimated user emotions. For example, if the user is feeling stressed, the suggestion unit prioritizes suggesting relaxation-enhancing workouts. For example, the suggestion unit measures the user's stress level and prioritizes relaxing yoga or stretching if the stress level is high. The suggestion unit can also prioritize strength training if the user is relaxed. For example, the suggestion unit measures the user's relaxation state and prioritizes strength training if the user is relaxed. Furthermore, the suggestion unit can prioritize short, effective workouts if the user is in a hurry. For example, the suggestion unit checks the user's schedule and prioritizes high-intensity interval training (HIIT) if the user is in a hurry. This enables more effective suggestions by prioritizing the suggested content according to the user's emotions. Some or all of the above-described processing by the suggestion unit may be performed using, or without, a generation AI. For example, the suggestion unit can input emotion data to the generation AI and have the generation AI determine the priority of the suggested content.

[0100] When making a suggestion, the suggestion unit can provide the suggestion content taking into consideration the user's geographical location information. For example, if the user is at a gym, the suggestion unit can suggest workouts that can be done at the gym. For example, the suggestion unit can suggest weightlifting or machine training that can be done at the gym based on the gym's location information. Furthermore, if the user is at home, the suggestion unit can suggest workouts that can be done at home. For example, the suggestion unit can suggest push-ups or squats that can be done at home based on the home's location information. Furthermore, if the user is in a park, the suggestion unit can suggest workouts that can be done in the park. For example, the suggestion unit can suggest jogging or circuit training that can be done in the park based on the park's location information. This allows for more effective suggestions by providing suggestion content taking into consideration the geographical location information. Some or all of the above-described processing by the suggestion unit may be performed using, or without, a generation AI. For example, the suggestion unit can input geographical location information to the generation AI and cause the generation AI to provide the suggestion content.

[0101] When making a suggestion, the suggestion unit can analyze the user's social media activity and change the suggestion content. The suggestion unit, for example, suggests related training based on training content shared by the user on social media. For example, the suggestion unit analyzes social media posts and suggests related training when there is a post about training. The suggestion unit can also suggest related meal menus based on meal content shared by the user on social media. For example, the suggestion unit suggests related meal menus when there is a post about meals. Furthermore, the suggestion unit can also suggest training toward achieving goals shared by the user on social media. For example, the suggestion unit suggests training toward achieving goals when there is a post about goals. This enables more relevant suggestions to be made by adjusting the suggestion content based on social media activity. Some or all of the above-mentioned processing by the suggestion unit may be performed using, or without, a generation AI. For example, the suggestion unit can input social media data into the generation AI and have the generation AI adjust the suggestion content.

[0102] The providing unit can estimate the user's emotions and change the presentation method based on the estimated user emotions. For example, if the user is feeling stressed, the providing unit provides a simple, highly visible presentation method. For example, the providing unit measures the user's stress level and provides a simple text notification if the stress level is high. The providing unit can also provide a presentation method including detailed information if the user is relaxed. For example, the providing unit measures the user's relaxation state and provides a detailed graph display if the user is relaxed. Furthermore, the providing unit can also provide a presentation method that focuses on the main points if the user is in a hurry. For example, the providing unit checks the user's schedule and provides a dashboard display that focuses on the main points if the user is in a hurry. This allows the presentation method to be adjusted according to the user's emotions, thereby providing a highly visible presentation method. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input emotion data to a generation AI and cause the generation AI to adjust the presentation method.

[0103] The providing unit can improve the content to be provided by referring to the user's past feedback when providing the content. The providing unit, for example, proposes optimal content to be provided based on the user's past feedback. For example, the providing unit analyzes the past feedback and proposes optimal content to be provided. The providing unit can also adjust the provision method by referring to the user's past feedback. For example, the providing unit adjusts the provision method based on the past feedback. The providing unit can also analyze the user's past feedback and customize the content to be provided. For example, the providing unit customizes the content to be provided based on the past feedback. In this way, more effective content can be provided by referring to the past feedback. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input past feedback into a generation AI and cause the generation AI to improve the content to be provided.

[0104] The providing unit can change the providing method based on the user's device information when providing the information. For example, if the user is using a smartphone, the providing unit provides a providing method tailored to the screen size. For example, the providing unit provides a display method optimized for the smartphone screen size. Furthermore, if the user is using a tablet, the providing unit can also provide a providing method optimized for a larger screen. For example, the providing unit provides a display method optimized for the tablet screen size. Furthermore, if the user is using a smartwatch, the providing unit can also provide a simple and highly visible providing method. For example, the providing unit provides a display method optimized for the smartwatch screen size. This enables more effective provision by customizing the providing method based on the device information. Some or all of the above-described processing by the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input device information to the generation AI and cause the generation AI to customize the providing method.

[0105] The providing unit can estimate the user's emotions and determine the order of content to be provided based on the estimated user's emotions. For example, if the user is feeling stressed, the providing unit can prioritize providing content that has a relaxing effect. For example, the providing unit can measure the user's stress level and prioritize providing yoga or stretching content that has a relaxing effect when the stress level is high. The providing unit can also prioritize providing strength training content when the user is relaxed. For example, the providing unit can measure the user's relaxation state and prioritize providing strength training content when the user is relaxed. Furthermore, the providing unit can prioritize providing content that is effective in a short period of time when the user is in a hurry. For example, the providing unit can check the user's schedule and prioritize providing high-intensity interval training (HIIT) content that is effective in a short period of time when the user is in a hurry. This enables more effective content to be provided by prioritizing content to be provided according to the user's emotions. Some or all of the above-described processing by the providing unit can be performed using, for example, AI, or without AI. For example, the providing unit can input emotion data to a generation AI and cause the generation AI to determine the priority of content to be provided.

[0106] The providing unit can select a provision method by taking into consideration the user's geographical location information when providing the information. For example, if the user is at a gym, the providing unit provides training that can be done at the gym. For example, the providing unit provides weightlifting or machine training that can be done at the gym based on the gym's location information. The providing unit can also provide training that can be done at home if the user is at home. For example, the providing unit provides push-ups or squats that can be done at home based on the home's location information. Furthermore, if the user is in a park, the providing unit can also provide training that can be done in the park. For example, the providing unit provides jogging or circuit training that can be done in the park based on the park's location information. This enables more effective provision by selecting a provision method by taking into consideration the geographical location information. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input geographical location information to a generation AI and cause the generation AI to select a provision method.

[0107] The providing unit can analyze the user's social media activity and change the content of the content provided at the time of providing. The providing unit, for example, provides related training based on training content shared by the user on social media. For example, the providing unit analyzes social media posts and provides related training when there is a post about training. The providing unit can also provide related meal menus based on meal content shared by the user on social media. For example, the providing unit provides related meal menus when there is a post about meals. Furthermore, the providing unit can also provide training aimed at achieving goals shared by the user on social media. For example, the providing unit provides training aimed at achieving goals when there is a post about goals. This enables more relevant content to be provided by adjusting the content provided based on social media activity. Some or all of the above-mentioned processing by the providing unit may be performed using AI, for example, or without AI. For example, the providing unit can input social media data into a generation AI and cause the generation AI to adjust the content provided.

[0108] The monitoring unit can estimate the user's emotions and change the monitoring frequency based on the estimated user emotions. For example, if the user is feeling stressed, the monitoring unit can reduce the monitoring frequency to reduce the user's burden. For example, the monitoring unit can measure the user's stress level and, if the stress level is high, reduce the monitoring frequency to once a day. The monitoring unit can also increase the monitoring frequency to collect detailed data if the user is relaxed. For example, the monitoring unit can measure the user's relaxation state and, if the user is relaxed, increase the monitoring frequency to three times a day. Furthermore, if the user is in a hurry, the monitoring unit can minimize the monitoring frequency to quickly acquire data. For example, the monitoring unit can check the user's schedule and, if the user is in a hurry, reduce the monitoring frequency to once a day. This allows the monitoring frequency to be adjusted according to the user's emotions, thereby reducing the user's burden and collecting detailed data. Some or all of the above-described processing in the monitoring unit may be performed using, or without, AI. For example, the monitoring unit can input emotion data to a generation AI and cause the generation AI to adjust the monitoring frequency.

[0109] During monitoring, the monitoring unit can improve the monitoring method by referring to the user's past progress data. For example, the monitoring unit can set the most effective monitoring timing based on the user's past progress data. For example, the monitoring unit can analyze the past progress data and set the optimal monitoring timing to twice a day. The monitoring unit can also analyze the user's past progress data and concentrate monitoring during specific time periods. For example, the monitoring unit can concentrate monitoring in the morning and evening based on the past progress data. Furthermore, the monitoring unit can also customize the monitoring method by referring to the user's past progress data. For example, the monitoring unit can perform monitoring using a specific sensor based on the past progress data. This enables more effective data collection by optimizing the monitoring method based on the past progress data. Some or all of the above-described processing in the monitoring unit can be performed using, for example, AI, or without AI. For example, the monitoring unit can input past progress data into a generation AI and cause the generation AI to optimize the monitoring method.

[0110] During monitoring, the monitoring unit can filter the monitoring data based on the user's lifestyle habits and activity level. For example, the monitoring unit prioritizes filtering of data collected after the user exercises. For example, the monitoring unit prioritizes collecting post-exercise data to consider the effects of exercise. The monitoring unit can also filter data collected after the user eats to consider the effects of meals. For example, the monitoring unit prioritizes collecting post-meal data to analyze the effects of meals. The monitoring unit can also filter data collected while the user is sleeping to consider the effects of rest. For example, the monitoring unit prioritizes collecting data during sleep to analyze the effects of rest. This allows for more relevant data to be collected by filtering data based on the lifestyle habits and activity level. Some or all of the above-described processing in the monitoring unit may be performed using, or without, AI. For example, the monitoring unit can input lifestyle habit data to a generation AI and have the generation AI perform data filtering.

[0111] The monitoring unit can estimate the user's emotions and change the display method of the monitoring results based on the estimated user emotions. For example, if the user is feeling stressed, the monitoring unit provides a simple, highly visible display method. For example, the monitoring unit measures the stress level and provides a simple graph display if the stress level is high. The monitoring unit can also provide a display method including detailed information if the user is relaxed. For example, the monitoring unit measures the relaxation state and provides a detailed text display if the user is relaxed. Furthermore, the monitoring unit can also provide a display method that focuses on the main points if the user is in a hurry. For example, the monitoring unit checks the user's schedule and provides a dashboard display that focuses on the main points if the user is in a hurry. This allows the display method to be adjusted according to the user's emotions, thereby providing highly visible monitoring results. Some or all of the above-mentioned processing in the monitoring unit may be performed using, or without, a generation AI. For example, the monitoring unit can input emotion data to the generation AI and have the generation AI adjust the display method.

[0112] During monitoring, the monitoring unit can prioritize monitoring highly relevant data by taking into account the user's geographical location information. For example, when the user is at the gym, the monitoring unit prioritizes monitoring muscle mass data. For example, the monitoring unit prioritizes monitoring muscle mass data based on the gym's location information. The monitoring unit can also prioritize monitoring body fat percentage data when the user is at home. For example, the monitoring unit prioritizes monitoring body fat percentage data based on the home's location information. Furthermore, the monitoring unit can also prioritize monitoring water content data when the user is out. For example, the monitoring unit prioritizes monitoring water content data based on the location information of the user's destination. In this way, by monitoring data while taking geographical location information into account, more relevant data can be collected. Some or all of the above-described processing by the monitoring unit may be performed using, or without, AI. For example, the monitoring unit may input geographical location information to the generation AI and cause the generation AI to monitor the data.

[0113] During monitoring, the monitoring unit can analyze the user's social media activity and collect specific monitoring data. For example, if the user posts about exercise on social media, the monitoring unit collects muscle mass data. For example, the monitoring unit analyzes the content of the social media post and prioritizes collecting muscle mass data when there is a post about exercise. The monitoring unit can also collect body fat percentage data when the user posts about diet. For example, the monitoring unit prioritizes collecting body fat percentage data when there is a post about diet. Furthermore, the monitoring unit can also collect hydration data when the user posts about rest. For example, the monitoring unit prioritizes collecting hydration data when there is a post about rest. In this way, by collecting data based on social media activity, more relevant data can be collected. Some or all of the above-described processing by the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input social media data into a generation AI and cause the generation AI to collect data. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, transmission unit, analysis unit, suggestion unit, provision unit, and monitoring unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit measures body composition information such as body fat percentage, muscle mass, and water content using a sensor in the smart device 14. The transmission unit transmits the collected information to the generation AI using the communication I / F 44 in the smart device 14. The analysis unit analyzes the information transmitted by the specific processing unit 290 in the data processing device 12. The suggestion unit proposes training and meal plans based on the analysis results by the specific processing unit 290 in the data processing device 12. The provision unit notifies the user of the suggestions using the output device 40 of the smart device 14. The monitoring unit monitors the progress of the body makeover in real time using a sensor in the smart device 14. The collection unit estimates the user's emotions and adjusts the frequency of collecting body composition information based on the estimated user emotions. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, transmission unit, analysis unit, suggestion unit, provision unit, and monitoring unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit measures body composition information such as body fat percentage, muscle mass, and water content using sensors in the smart glasses 214. The transmission unit transmits the collected information to the generation AI using the communication I / F 44 of the smart glasses 214. The analysis unit analyzes the information transmitted by the specific processing unit 290 of the data processing device 12. The suggestion unit suggests training content and meal content based on the analysis results by the specific processing unit 290 of the data processing device 12. The provision unit notifies the user of the suggestions using an output device of the smart glasses 214. The monitoring unit monitors the progress of the body makeover user in real time using sensors in the smart glasses 214. The collection unit estimates the user's emotions and adjusts the frequency of collecting body composition information based on the estimated user's emotions. === Hard Collateral 1-3 === Each of the multiple elements, including the collection unit, transmission unit, analysis unit, suggestion unit, provision unit, and monitoring unit, is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the collection unit measures body composition information such as body fat percentage, muscle mass, and water content using a sensor in the headset-type terminal 314. The transmission unit transmits the collected information to the generation AI using the communication I / F 44 in the headset-type terminal 314. The analysis unit analyzes the information transmitted by the specific processing unit 290 in the data processing device 12. The suggestion unit proposes training and meal plans based on the analysis results by the specific processing unit 290 in the data processing device 12. The provision unit notifies the user of the suggestions using an output device in the headset-type terminal 314. The monitoring unit monitors the progress of the body makeover in real time using a sensor in the headset-type terminal 314. The collection unit estimates the user's emotions and adjusts the frequency of collecting body composition information based on the estimated user emotions. === Hard Collateral 1-4 === Each of the multiple elements, including the collection unit, transmission unit, analysis unit, suggestion unit, provision unit, and monitoring unit, described above, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit measures body composition information such as body fat percentage, muscle mass, and water content using sensors in the robot 414. The transmission unit transmits the collected information to the generation AI using the communication I / F 44 of the robot 414. The analysis unit analyzes the information transmitted by the specific processing unit 290 of the data processing device 12. The suggestion unit proposes training and meal contents based on the analysis results by the specific processing unit 290 of the data processing device 12. The provision unit notifies the user of the suggestions using an output device of the robot 414. The monitoring unit monitors the progress of the body makeover user in real time using sensors in the robot 414. The collection unit estimates the user's emotions and adjusts the frequency of collecting body composition information based on the estimated user emotions.

[0114] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0115] The analysis unit can estimate the user's emotions and change the display method of the analysis results based on the estimated user's emotions. For example, if the user is feeling stressed, a simple, highly visible display method is provided. For example, the analysis unit measures the stress level and provides a simple graph display if the stress level is high. The analysis unit can also provide a display method including detailed information if the user is relaxed. For example, the analysis unit measures the relaxation state and provides a detailed text display if the user is relaxed. Furthermore, the analysis unit can also provide a display method that focuses on the main points if the user is in a hurry. For example, the analysis unit checks the user's schedule and provides a dashboard display that focuses on the main points if the user is in a hurry. In this way, by adjusting the display method according to the user's emotions, it is possible to provide highly visible analysis results.

[0116] The suggestion unit can estimate the user's emotions and change the way the suggestions are presented based on the estimated user's emotions. For example, if the user is feeling stressed, the suggestion unit can provide simple, highly visible suggestions. For example, the suggestion unit can measure the stress level and provide a simple text display if the stress level is high. The suggestion unit can also provide suggestions including detailed information if the user is relaxed. For example, the suggestion unit can measure the relaxation state and provide a detailed graph display if the user is relaxed. Furthermore, the suggestion unit can also provide suggestions that focus on the main points if the user is in a hurry. For example, the suggestion unit can check the user's schedule and provide a dashboard display that focuses on the main points if the user is in a hurry. In this way, by adjusting the way the suggestions are presented according to the user's emotions, highly visible suggestions can be provided.

[0117] The providing unit can estimate the user's emotions and change the providing method based on the estimated user's emotions. For example, if the user is feeling stressed, a simple and highly visible providing method is provided. For example, the providing unit measures the stress level and provides a simple text notification when the stress level is high. The providing unit can also provide a providing method including detailed information when the user is relaxed. For example, the providing unit measures the relaxation state and provides a detailed graph display when the user is relaxed. Furthermore, the providing unit can also provide a providing method that focuses on the main points when the user is in a hurry. For example, the providing unit checks the user's schedule and provides a dashboard display that focuses on the main points when the user is in a hurry. In this way, a highly visible providing method can be provided by adjusting the providing method according to the user's emotions.

[0118] The monitoring unit can estimate the user's emotions and change the monitoring frequency based on the estimated user emotions. For example, if the user is feeling stressed, the monitoring frequency can be reduced to ease the burden on the user. For example, the monitoring unit can measure the stress level, and if the stress level is high, the monitoring frequency can be reduced to once a day. The monitoring unit can also increase the monitoring frequency to collect detailed data if the user is relaxed. For example, the monitoring unit can measure the relaxation state, and if the user is relaxed, the monitoring frequency can be increased to three times a day. Furthermore, if the user is in a hurry, the monitoring unit can minimize the monitoring frequency to quickly obtain data. For example, the monitoring unit can check the user's schedule, and if the user is in a hurry, the monitoring frequency can be reduced to once a day. In this way, by adjusting the monitoring frequency according to the user's emotions, detailed data can be collected while reducing the burden on the user.

[0119] The analysis unit can estimate the user's emotions and change the analysis algorithm based on the estimated user emotions. For example, if the user is feeling stressed, the analysis algorithm can be simplified to provide results quickly. For example, the analysis unit can measure the stress level and, if the stress is high, simplify the analysis algorithm to provide results quickly. The analysis unit can also perform a detailed analysis to provide highly accurate results if the user is relaxed. For example, the analysis unit can measure the user's relaxation state and, if the user is relaxed, perform a detailed analysis to provide highly accurate results. Furthermore, the analysis unit can speed up the analysis algorithm to provide results quickly if the user is in a hurry. For example, the analysis unit can check the user's schedule and, if the user is in a hurry, speed up the analysis algorithm to provide results quickly. This allows for fast and highly accurate analysis by adjusting the analysis algorithm according to the user's emotions.

[0120] The collection unit can prioritize collecting highly relevant data by taking into account the geographical location information of the user. For example, when the user is at the gym, it prioritizes collecting muscle mass data. For example, the collection unit prioritizes collecting muscle mass data based on the location information of the gym. The collection unit can also prioritize collecting body fat percentage data when the user is at home. For example, the collection unit prioritizes collecting body fat percentage data based on the location information of the home. Furthermore, the collection unit can also prioritize collecting hydration data when the user is out. For example, the collection unit prioritizes collecting hydration data based on the location information of the user's destination. In this way, by collecting data by taking into account the geographical location information, more relevant data can be collected.

[0121] At the time of transmission, the transmitting unit can determine the order of transmission based on the importance of the data. For example, the transmitting unit transmits the body fat percentage data with the highest priority. For example, the transmitting unit transmits the body fat percentage data with the highest priority, followed by the muscle mass data. The transmitting unit can also transmit the water content data last. For example, the transmitting unit transmits the water content data last, and postpones data of lower importance. In this way, by determining the priority of transmission based on the importance of the data, important data can be transmitted preferentially.

[0122] When making a suggestion, the suggestion unit can improve the suggestion content by referring to the user's past training history. For example, the suggestion unit suggests optimal training content based on the user's past training history. For example, the suggestion unit analyzes the past training history and suggests optimal training content. The suggestion unit can also adjust the intensity of the training by referring to the user's past training history. For example, the suggestion unit adjusts the intensity of the training based on the past training history. Furthermore, the suggestion unit can analyze the user's past training history and suggest the type of training. For example, the suggestion unit suggests the optimal type of training based on the past training history. In this way, by referring to the past training history, more effective suggestions can be provided.

[0123] During monitoring, the monitoring unit can filter the monitoring data based on the user's lifestyle habits and activity level. For example, the monitoring unit prioritizes filtering of data collected after the user exercises. For example, the monitoring unit prioritizes collecting data after exercise to consider the effects of exercise. The monitoring unit can also filter data collected after the user eats to consider the effects of meals. For example, the monitoring unit prioritizes collecting data after meals to analyze the effects of meals. Furthermore, the monitoring unit can filter data collected while the user is sleeping to consider the effects of rest. For example, the monitoring unit prioritizes collecting data during sleep to analyze the effects of rest. In this way, by filtering data based on the user's lifestyle habits and activity level, more relevant data can be collected.

[0124] The providing unit can change the providing method based on the user's device information when providing information. For example, if the user is using a smartphone, the providing unit can provide a providing method that matches the screen size. For example, the providing unit can provide a display method optimized for the smartphone screen size. Furthermore, if the user is using a tablet, the providing unit can also provide a providing method that is optimized for a large screen. For example, the providing unit can provide a display method optimized for the tablet screen size. Furthermore, if the user is using a smartwatch, the providing unit can also provide a providing method that is concise and highly visible. For example, the providing unit can provide a display method optimized for the smartwatch screen size. This enables more effective provision by customizing the providing method based on the device information.

[0125] The processing flow of the second embodiment will be briefly explained below.

[0126] Step 1: The collection unit collects the user's body composition information. The collection unit measures information such as body fat percentage, muscle mass, and water content in real time. The collection unit can measure body fat percentage using a wearable device, muscle mass using an electromyograph, and water content using a bioimpedance method. Step 2: The transmitter transmits the information collected by the collector to the generator AI. The transmitter transmits the information using wireless communication technologies such as Bluetooth or Wi-Fi, and transmits the collected information to the generator AI in real time. It can also transmit information in batches at regular intervals. Step 3: The analysis unit analyzes the information sent by the transmission unit. Using the generation AI, the analysis unit performs analysis based on past data and current body composition information. For example, it analyzes the current body fat percentage based on past body fat percentage data, analyzes changes in muscle mass based on muscle mass data, and analyzes changes in water content based on water content data. Step 4: The suggestion unit proposes training and meal plans based on the results of the analysis by the analysis unit. Using generative AI, the suggestion unit proposes optimal training and meal plans for the user. For example, if the body fat percentage is high, the suggestion unit proposes training focused on aerobic exercise, and if muscle mass is insufficient, the suggestion unit proposes strength training and a menu containing the necessary nutrients. Step 5: The providing unit provides the content suggested by the suggesting unit to the user. The providing unit can notify the user of the content suggested by the suggesting unit through the app, send the content suggested by email or SMS, or provide the content suggested by the suggesting unit through a web portal.

[0127] 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.

[0128] 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 the generative AI include a neural network (NN) and a neural network (NN). 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 (e.g., still image data or video data). 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 one or more data formats of voice data, text data, image data, etc. 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 may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0129] 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.

[0130] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0131] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0132] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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).

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0145] 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.

[0146] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0147] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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).

[0153] 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.

[0154] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0161] 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.

[0162] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0163] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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).

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0178] 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.

[0179] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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).

[0184] 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.

[0185] 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."

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] [Explanation of symbols]

[0199] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a collection unit that collects body composition information; A transmitting unit that transmits the information collected by the collecting unit to a generating AI; an analysis unit that analyzes the information transmitted by the transmission unit; a suggestion unit that suggests training content or meal content based on the results of the analysis by the analysis unit; a providing unit that provides the content proposed by the suggestion unit to the user. A system characterized by:

2. The collecting unit Measure body composition information in real time, including body fat percentage, muscle mass, and water content The system of claim 1 .

3. The analysis unit Analyze based on past data or current body composition information The system of claim 1 .

4. The proposal unit If your body fat percentage is above a certain level, we recommend training focused on aerobic exercise. The system of claim 1 .

5. The proposal unit If muscle mass is below a certain level, strength training is recommended. The system of claim 1 .

6. The proposal unit Suggesting menu items containing specific nutrients The system of claim 1 .

7. The providing unit Providing suggestions to users 2. The system of claim 1.

8. Monitor the progress of body shaping practitioners in real time, It also includes a monitoring unit that adjusts the proposal content based on specific conditions. The system of claim 1 .

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