System

Abstract

An object of a system according to an embodiment is to analyze data obtained from a wearable device and provide advice in accordance with an individual life rhythm or exercise performance.SOLUTION: A system includes a data collection unit, an analysis unit, and an advice provision unit. The data collection unit collects data from a wearable device. The analysis unit analyzes the data collected by the data collection unit. The advice providing unit provides advice on the basis of a result analyzed by the analysis unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology leaves room for improvement in terms of effectively utilizing data obtained from wearable devices to provide advice tailored to individual lifestyles and exercise performance.

[0005] The system according to the embodiment aims to analyze data obtained from a wearable device and provide advice tailored to an individual's lifestyle and exercise performance. [Means for solving the problem]

[0006] The system according to the embodiment includes a data collection unit, an analysis unit, and an advice provision unit. The data collection unit collects data from a wearable device. The analysis unit analyzes the data collected by the data collection unit. The advice provision unit provides advice based on the results of the analysis by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can analyze data obtained from a wearable device and provide advice tailored to an individual's lifestyle and exercise performance. [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 personalized service system according to an embodiment of the present invention uses a generation AI to analyze data collected from a wearable device and provide personalized advice based on the user's lifestyle and athletic performance. This allows the personalized service system to improve the efficiency of both training and health management through real-time feedback and analysis based on the user's lifestyle and athletic performance.

[0029] A personalized service system according to an embodiment includes a data collection unit, an analysis unit, and an advice provision unit. The data collection unit collects data from a wearable device. For example, the data collection unit collects data such as a user's heart rate, number of steps, calories burned, and sleep patterns. The data collection unit can also collect data transmitted in real time from the wearable device. The analysis unit analyzes the data collected by the data collection unit. For example, the analysis unit generates personalized advice based on the user's lifestyle and exercise performance using a generation AI. The analysis unit can also analyze the data transmitted in real time using the generation AI and provide immediate feedback. The advice provision unit provides advice based on the results of the analysis by the analysis unit. For example, the advice provision unit provides the user with advice generated by the generation AI. The advice provision unit can also provide the user with feedback provided in real time by the generation AI. As a result, the personalized service system according to an embodiment can optimize a user's lifestyle and exercise performance by analyzing the data collected from the wearable device and providing personalized advice.

[0030] The data collection unit collects the user's dietary content and fluid intake, and the analysis unit can provide nutritional balance advice based on the dietary content and fluid intake. For example, the data collection unit adds a function to input dietary content to the wearable device, allowing the user to record their daily meals. The generation AI analyzes this data and provides nutritional balance advice. For example, it points out vitamin and mineral deficiencies and suggests specific foods. The data collection unit also adds a function to record fluid intake to the wearable device, allowing the user to track the amount of water they drink. The generation AI analyzes this data and provides advice on appropriate fluid intake. For example, it suggests the timing of hydration after exercise. The data collection unit also integrates the dietary content and fluid intake data to build a system in which the generation AI evaluates overall nutritional balance. For example, it analyzes the balance between calorie intake and calorie expenditure and provides weight management advice. This allows for more efficient health management by providing nutritional balance advice based on the user's dietary content and fluid intake.

[0031] The data collection unit measures the user's stress level, and the analysis unit can suggest relaxation methods based on the stress level. For example, the data collection unit adds a function to measure stress levels to the wearable device and analyzes heart rate variability and electrodermal activity. The generation AI uses this data to suggest relaxation methods to reduce stress. For example, it provides guided deep breathing and meditation. The data collection unit also builds a system in which the generation AI suggests relaxation music or natural sounds suitable for the user based on the stress level data. For example, it automatically plays relaxing music when the user's stress level is high. The data collection unit also analyzes the stress level data collected by the wearable device, and the generation AI suggests relaxation exercises suitable for the user. For example, it suggests specific yoga or stretching poses. This allows for more efficient stress management by suggesting relaxation methods based on the user's stress level.

[0032] The data collection unit measures the ambient temperature, humidity, and noise level, and the analysis unit can suggest the optimal location and time for exercise based on the temperature, humidity, and noise level. For example, the data collection unit may install a temperature sensor in a wearable device to measure the ambient temperature in real time. The generation AI may then use this data to suggest the optimal location and time for exercise. For example, it may recommend exercising indoors during hot periods. The data collection unit may also add a humidity sensor to the wearable device to measure the ambient humidity. The generation AI may then analyze this data and suggest appropriate times to hydrate when humidity is high. The data collection unit may also install a sensor in the wearable device to measure noise levels, creating a system in which the generation AI recommends exercising in a quiet environment. For example, it may suggest a time or location with low noise levels. This improves exercise efficiency by suggesting the optimal location and time for exercise based on ambient environmental data.

[0033] The data collection unit works in conjunction with smart devices in the home, and the analysis unit can automatically adjust the environment based on the data collected from the smart devices. For example, the data collection unit may link data collected by a wearable device with a smart speaker, building a system in which the generation AI automatically plays music according to the user's exercise status. For example, it may play music with a good tempo while exercising. The data collection unit may also work in conjunction with smart lights, and the generation AI may automatically adjust the brightness and color of the lighting based on the data collected by the wearable device. For example, it may set warmer lighting when you want to relax. The data collection unit may also work in conjunction with a smart thermostat, building a system in which the generation AI automatically adjusts the room temperature based on the data collected by the wearable device. For example, it may set the room temperature to an appropriate level after exercise. In this way, the system works in conjunction with smart devices in the home to automatically adjust the environment, improving user comfort.

[0034] The analysis unit can learn from the user's past data, set long-term health goals, and provide step-by-step advice toward achieving those goals. For example, the analysis unit uses a generation AI to learn the user's past exercise and health data and build a system for setting long-term health goals. For example, the system sets a one-year weight loss goal and provides specific advice toward achieving that goal. The analysis unit also uses a generation AI to provide step-by-step advice based on the user's past data. For example, the system may adjust weekly exercise volume and dietary content to manage progress toward achieving the goal. The analysis unit also uses a generation AI to analyze the user's past data and provide advice to maintain motivation toward long-term health goals. For example, the system may send regular reminders and encouraging messages. This allows the system to learn from the user's past data and set long-term health goals, streamlining health management.

[0035] The analysis unit analyzes the user's social activity data and can adjust the timing of exercise and rest to match schedules with friends and family. For example, the analysis unit uses a generation AI to link with the user's calendar or schedule app to analyze the social activity data. For example, the timing of exercise is adjusted on days when the user has plans with friends. The analysis unit also builds a system in which the generation AI suggests timing for exercise and rest based on the user's social activity data. For example, the analysis unit advises the user to exercise before or after a meal with family. The analysis unit also uses a generation AI to analyze the user's social activity data and provide a personalized exercise plan that matches schedules with friends and family. For example, the analysis unit suggests times to exercise with friends. This allows the user to optimize their daily rhythm by adjusting the timing of exercise and rest based on the user's social activity data.

[0036] The analysis unit can analyze the user's exercise data and suggest virtual group training with other users who have the same goal. For example, the analysis unit builds a system in which a generation AI analyzes the user's exercise data and identifies other users who have the same goal. For example, it matches users who are aiming to lose weight or complete a marathon. The analysis unit also builds a system in which a generation AI creates a training plan based on the user's exercise data to suggest virtual group training. For example, it suggests a time to train together with users who have the same goal. The analysis unit also builds a system in which a generation AI analyzes the user's exercise data and shares the progress of virtual group training in real time. For example, it promotes competition and encouragement within the group. This improves training motivation by suggesting virtual group training based on the user's exercise data.

[0037] The analysis unit can automatically schedule online sessions with a personal trainer or nutritionist based on user data. The analysis unit, for example, builds a system in which a generation AI analyzes a user's exercise data and health data and automatically schedules online sessions with a personal trainer or nutritionist. For example, if the user's exercise performance is declining, the analysis unit suggests a session with a trainer. The analysis unit also builds a system in which the generation AI schedules online sessions with a personal trainer or nutritionist at the optimal time based on the user data. For example, the analysis unit suggests a session with a nutritionist if the user's diet needs improvement. The analysis unit also builds a system in which the generation AI analyzes the user's data and adjusts the frequency and content of online sessions with a personal trainer or nutritionist. For example, the analysis unit suggests regular sessions and changes the content according to progress. This allows online sessions to be automatically scheduled based on user data, making training and health management more efficient.

[0038] The analysis unit can analyze the user's exercise form in real time and provide specific suggestions for improvement. The analysis unit, for example, uses a generation AI to build a system that analyzes the user's exercise form in real time and provides specific suggestions for improvement. For example, it analyzes posture and foot movements while running and provides advice on how to improve. The analysis unit also uses a generation AI to evaluate the user's exercise form in real time based on data collected by a wearable device and suggest specific suggestions for improvement. For example, it analyzes form during strength training and provides guidance on correct posture. The analysis unit also builds a system that uses a generation AI to analyze the user's exercise form in real time and provides specific suggestions for improvement via audio and visual means. For example, it provides real-time feedback via a smartphone app. This improves the efficiency of exercise by providing real-time suggestions for improvement in exercise form.

[0039] The analysis unit can analyze the user's breathing patterns in real time and suggest optimal breathing techniques. The analysis unit, for example, builds a system in which a generating AI analyzes the user's breathing patterns in real time and suggests optimal breathing techniques. For example, it analyzes breathing rhythms while running and provides advice on efficient breathing techniques. The analysis unit also builds a system in which a generating AI evaluates the user's breathing patterns in real time based on data collected by a wearable device and suggests specific areas for improvement. For example, it analyzes breathing techniques during yoga or meditation and provides guidance on breathing techniques that enhance relaxation. The analysis unit also builds a system in which a generating AI analyzes the user's breathing patterns in real time and suggests specific areas for improvement using audio and visual feedback. For example, it provides real-time feedback through a smartphone app. This allows the system to analyze breathing patterns in real time and suggest optimal breathing techniques, improving exercise efficiency.

[0040] The analysis unit can analyze the user's exercise data in real time, allowing the virtual coach to provide audio advice. The analysis unit, for example, builds a system in which a generation AI analyzes the user's exercise data in real time and the virtual coach provides audio advice. For example, audio advice is provided on adjusting the pace while running. The analysis unit also builds a system in which a generation AI evaluates the user's exercise performance in real time based on data collected by a wearable device, allowing the virtual coach to provide specific audio advice. For example, audio guidance is provided on form during strength training. The analysis unit also builds a system in which a generation AI analyzes the user's exercise data in real time and the virtual coach provides audio advice on specific areas for improvement. For example, real-time feedback is provided via a smartphone app. This allows the exercise data to be analyzed in real time and the virtual coach to provide audio advice, improving the efficiency of exercise.

[0041] The analysis unit can analyze the user's data in real time and automatically adjust the tempo of the music played during exercise. For example, the analysis unit constructs a system in which a generation AI analyzes the user's exercise data in real time and automatically adjusts the tempo of the music played during exercise. For example, the tempo of the music may be changed to match the pace of running. The analysis unit also constructs a system in which a generation AI evaluates the user's exercise performance in real time based on data collected by the wearable device and automatically adjusts the tempo of the music. For example, the analysis unit plays music to match the rhythm of strength training. The analysis unit also constructs a system in which a generation AI analyzes the user's exercise data in real time and automatically adjusts the tempo of the music. For example, the analysis unit provides real-time feedback through a smartphone app. This allows the system to analyze data in real time and automatically adjust the tempo of the music played during exercise, improving exercise efficiency.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] The data collection unit collects environmental sounds around the user, and the analysis unit can provide advice to improve concentration based on the environmental sounds. For example, the data collection unit installs a microphone in the wearable device to measure ambient noise levels. The generation AI analyzes this data and suggests quiet places to improve concentration. The data collection unit also analyzes the type of environmental sound, and the generation AI builds a system that suggests music or white noise suitable for the user. For example, if the noise of a cafe improves concentration, that sound is played. The data collection unit also analyzes fluctuations in environmental sounds in real time, and the generation AI provides advice to the user to maintain appropriate concentration. For example, if the noise level increases, the system suggests using noise-canceling headphones. This improves work efficiency by providing advice to improve concentration based on the environmental sounds around the user.

[0044] The data collection unit monitors the user's sleep environment, and the analysis unit can provide advice to improve optimal sleep quality based on the sleep environment. For example, the data collection unit could equip a wearable device with temperature and humidity sensors to collect bedroom environmental data. The generation AI would analyze this data and suggest optimal room temperature and humidity. The data collection unit could also use a light sensor to measure the brightness of the bedroom, and the generation AI could then build a system that suggests appropriate lighting settings. For example, it could suggest using warm-colored lighting before bedtime. The data collection unit could also monitor noise levels, and the generation AI could then provide advice to create a quieter sleep environment. For example, it could suggest using earplugs or playing white noise. This could improve health management efficiency by providing advice to improve optimal sleep quality based on the user's sleep environment.

[0045] The data collection unit monitors the user's posture during exercise, and the analysis unit can provide advice to improve exercise efficiency based on the posture data. For example, the data collection unit could equip a wearable device with an acceleration sensor and gyro sensor to collect posture data while the user is exercising. The generation AI would analyze this data and provide advice on maintaining correct posture. The data collection unit could also analyze posture data in real time, creating a system in which the generation AI suggests an exercise form suitable for the user. For example, the data collection unit could analyze posture while running and suggest an efficient running style. The data collection unit could also use the posture data during exercise to suggest exercises to improve the user's posture. For example, the generation AI could suggest stretches to straighten the back. This maximizes the effectiveness of training by providing advice to improve exercise efficiency based on the user's posture data during exercise.

[0046] The data collection unit monitors the user's heart rate during exercise, and the analysis unit can provide advice for adjusting exercise intensity based on the heart rate data. For example, the data collection unit installs a heart rate sensor in a wearable device and collects heart rate data during exercise. The generation AI analyzes this data and suggests appropriate exercise intensity. The data collection unit also analyzes the heart rate data in real time, and the generation AI builds a system that adjusts exercise intensity to suit the user. For example, if the heart rate is too high, the generation AI suggests lowering the exercise intensity. The data collection unit also allows the generation AI to adjust the user's exercise plan based on the heart rate data. For example, it changes the type and duration of exercise depending on heart rate fluctuations. This allows the effectiveness of training to be maximized by providing advice for adjusting exercise intensity based on the user's heart rate data during exercise.

[0047] The data collection unit monitors the user's energy consumption during exercise, and the analysis unit can suggest meal timing and content based on the energy consumption data. For example, the data collection unit could install an energy consumption sensor on a wearable device and collect energy consumption data during exercise. The generation AI would analyze this data and suggest appropriate meal timing. The data collection unit could also analyze the energy consumption data in real time, building a system in which the generation AI suggests meal content suitable for the user. For example, it could suggest meals to replenish energy after exercise. The data collection unit could also allow the generation AI to adjust the user's meal plan based on the energy consumption data. For example, it could change the calorie and nutritional balance of meals according to fluctuations in energy consumption. This could improve the efficiency of health management by suggesting meal timing and content based on the user's energy consumption data during exercise.

[0048] The data collection unit monitors the user's muscle activity during exercise, and the analysis unit can provide advice to improve exercise efficiency based on the muscle activity data. For example, the data collection unit installs an electromyographic sensor in a wearable device to collect muscle activity data during exercise. The generation AI analyzes this data and suggests appropriate exercise form. The data collection unit also analyzes the muscle activity data in real time, and the generation AI builds a system that adjusts exercise intensity to suit the user. For example, if muscle activity is excessive, the generation AI suggests lowering the exercise intensity. The data collection unit also adjusts the user's exercise plan based on the muscle activity data. For example, it changes the type and duration of exercise depending on fluctuations in muscle activity. This maximizes the effectiveness of training by providing advice to improve exercise efficiency based on the user's muscle activity data during exercise.

[0049] The processing flow of the first embodiment will be briefly explained below.

[0050] Step 1: The data collection unit collects data from the wearable device. For example, the data collection unit collects data such as the user's heart rate, number of steps, calories burned, and sleep patterns. The data collection unit can also collect data transmitted in real time from the wearable device. Step 2: The analysis unit analyzes the data collected by the data collection unit. For example, the analysis unit uses the generation AI to generate personalized advice based on the user's lifestyle and exercise performance. The analysis unit can also use the generation AI to analyze data transmitted in real time and provide immediate feedback. Step 3: The advice providing unit provides advice based on the results of the analysis by the analysis unit. For example, the advice providing unit provides the user with advice generated by the generation AI. The advice providing unit can also provide the user with feedback provided by the generation AI in real time.

[0051] (Example 2) A personalized service system according to an embodiment of the present invention uses a generation AI to analyze data collected from a wearable device and provide personalized advice based on the user's lifestyle and athletic performance. This allows the personalized service system to improve the efficiency of both training and health management through real-time feedback and analysis based on the user's lifestyle and athletic performance.

[0052] A personalized service system according to an embodiment includes a data collection unit, an analysis unit, and an advice provision unit. The data collection unit collects data from a wearable device. For example, the data collection unit collects data such as a user's heart rate, number of steps, calories burned, and sleep patterns. The data collection unit can also collect data transmitted in real time from the wearable device. The analysis unit analyzes the data collected by the data collection unit. For example, the analysis unit generates personalized advice based on the user's lifestyle and exercise performance using a generation AI. The analysis unit can also analyze the data transmitted in real time using the generation AI and provide immediate feedback. The advice provision unit provides advice based on the results of the analysis by the analysis unit. For example, the advice provision unit provides the user with advice generated by the generation AI. The advice provision unit can also provide the user with feedback provided in real time by the generation AI. As a result, the personalized service system according to an embodiment can optimize a user's lifestyle and exercise performance by analyzing the data collected from the wearable device and providing personalized advice.

[0053] The data collection unit collects the user's dietary content and fluid intake, and the analysis unit can provide nutritional balance advice based on the dietary content and fluid intake. For example, the data collection unit adds a function to input dietary content to the wearable device, allowing the user to record their daily meals. The generation AI analyzes this data and provides nutritional balance advice. For example, it points out vitamin and mineral deficiencies and suggests specific foods. The data collection unit also adds a function to record fluid intake to the wearable device, allowing the user to track the amount of water they drink. The generation AI analyzes this data and provides advice on appropriate fluid intake. For example, it suggests the timing of hydration after exercise. The data collection unit also integrates the dietary content and fluid intake data to build a system in which the generation AI evaluates overall nutritional balance. For example, it analyzes the balance between calorie intake and calorie expenditure and provides weight management advice. This allows for more efficient health management by providing nutritional balance advice based on the user's dietary content and fluid intake.

[0054] The data collection unit measures the user's stress level, and the analysis unit can suggest relaxation methods based on the stress level. For example, the data collection unit adds a function to measure stress levels to the wearable device and analyzes heart rate variability and electrodermal activity. The generation AI uses this data to suggest relaxation methods to reduce stress. For example, it provides guided deep breathing and meditation. The data collection unit also builds a system in which the generation AI suggests relaxation music or natural sounds suitable for the user based on the stress level data. For example, it automatically plays relaxing music when the user's stress level is high. The data collection unit also analyzes the stress level data collected by the wearable device, and the generation AI suggests relaxation exercises suitable for the user. For example, it suggests specific yoga or stretching poses. This allows for more efficient stress management by suggesting relaxation methods based on the user's stress level.

[0055] The data collection unit analyzes the user's emotional state, and the analysis unit can provide exercise and rest advice based on the emotional state. For example, the data collection unit adds an emotion estimation function to a wearable device and analyzes the user's facial expressions and vocal tone. The generation AI uses this data to provide exercise and rest advice based on the user's emotional state. For example, it suggests relaxing exercise when stress is high. The data collection unit also uses the emotion estimation function to analyze the user's emotional state in real time, and the generation AI provides exercise and rest advice appropriate for each moment. For example, it suggests light jogging when feeling depressed. The data collection unit also uses the emotion estimation data to allow the generation AI to create a personalized exercise plan based on the user's emotional state. For example, it adjusts the intensity and type of exercise according to emotional fluctuations. This allows for more efficient emotional management by providing exercise and rest advice based on the user's emotional state.

[0056] The data collection unit measures the ambient temperature, humidity, and noise level, and the analysis unit can suggest the optimal location and time for exercise based on the temperature, humidity, and noise level. For example, the data collection unit may install a temperature sensor in a wearable device to measure the ambient temperature in real time. The generation AI may then use this data to suggest the optimal location and time for exercise. For example, it may recommend exercising indoors during hot periods. The data collection unit may also add a humidity sensor to the wearable device to measure the ambient humidity. The generation AI may then analyze this data and suggest appropriate times to hydrate when humidity is high. The data collection unit may also install a sensor in the wearable device to measure noise levels, creating a system in which the generation AI recommends exercising in a quiet environment. For example, it may suggest a time or location with low noise levels. This improves exercise efficiency by suggesting the optimal location and time for exercise based on ambient environmental data.

[0057] The data collection unit works in conjunction with smart devices in the home, and the analysis unit can automatically adjust the environment based on the data collected from the smart devices. For example, the data collection unit may link data collected by a wearable device with a smart speaker, building a system in which the generation AI automatically plays music according to the user's exercise status. For example, it may play music with a good tempo while exercising. The data collection unit may also work in conjunction with smart lights, and the generation AI may automatically adjust the brightness and color of the lighting based on the data collected by the wearable device. For example, it may set warmer lighting when you want to relax. The data collection unit may also work in conjunction with a smart thermostat, building a system in which the generation AI automatically adjusts the room temperature based on the data collected by the wearable device. For example, it may set the room temperature to an appropriate level after exercise. In this way, the system works in conjunction with smart devices in the home to automatically adjust the environment, improving user comfort.

[0058] The data collection unit analyzes the user's emotional state, and the analysis unit can automatically play music or podcasts based on the emotional state. For example, the data collection unit adds an emotion estimation function to a wearable device and analyzes the user's emotional state. The generation AI automatically plays music according to the emotion based on this data. For example, relaxing music is played when stress is high. The data collection unit also uses the emotion estimation function to build a system that automatically plays podcasts according to the user's emotional state. For example, a motivational podcast is played when the user is feeling depressed. The data collection unit also uses the emotion estimation data to allow the generation AI to create and automatically play a playlist according to the user's emotional state. For example, the playlist contents are adjusted according to emotional fluctuations. This allows for more efficient emotion management by automatically playing music or podcasts based on the user's emotional state.

[0059] The analysis unit can learn from the user's past data, set long-term health goals, and provide step-by-step advice toward achieving those goals. For example, the analysis unit uses a generation AI to learn the user's past exercise and health data and build a system for setting long-term health goals. For example, the system sets a one-year weight loss goal and provides specific advice toward achieving that goal. The analysis unit also uses a generation AI to provide step-by-step advice based on the user's past data. For example, the system may adjust weekly exercise volume and dietary content to manage progress toward achieving the goal. The analysis unit also uses a generation AI to analyze the user's past data and provide advice to maintain motivation toward long-term health goals. For example, the system may send regular reminders and encouraging messages. This allows the system to learn from the user's past data and set long-term health goals, streamlining health management.

[0060] The analysis unit analyzes the user's social activity data and can adjust the timing of exercise and rest to match schedules with friends and family. For example, the analysis unit uses a generation AI to link with the user's calendar or schedule app to analyze the social activity data. For example, the timing of exercise is adjusted on days when the user has plans with friends. The analysis unit also builds a system in which the generation AI suggests timing for exercise and rest based on the user's social activity data. For example, the analysis unit advises the user to exercise before or after a meal with family. The analysis unit also uses a generation AI to analyze the user's social activity data and provide a personalized exercise plan that matches schedules with friends and family. For example, the analysis unit suggests times to exercise with friends. This allows the user to optimize their daily rhythm by adjusting the timing of exercise and rest based on the user's social activity data.

[0061] The analysis unit can provide motivational messages and encouraging words based on the user's emotional state. The analysis unit, for example, uses an emotion estimation function to analyze the user's emotional state, and builds a system in which the generation AI provides motivational messages. For example, it sends encouraging words before exercise. The analysis unit also allows the generation AI to provide personalized encouraging messages based on the user's emotional state. For example, it sends a positive message when the user is feeling down. The analysis unit also allows the generation AI to provide advice to maintain the user's motivation based on the emotion estimation data. For example, it sends a message that makes the user feel a sense of accomplishment after exercise. In this way, motivation can be maintained by providing motivational messages and encouraging words based on the user's emotional state.

[0062] The analysis unit can analyze the user's exercise data and suggest virtual group training with other users who have the same goal. For example, the analysis unit builds a system in which a generation AI analyzes the user's exercise data and identifies other users who have the same goal. For example, it matches users who are aiming to lose weight or complete a marathon. The analysis unit also builds a system in which a generation AI creates a training plan based on the user's exercise data to suggest virtual group training. For example, it suggests a time to train together with users who have the same goal. The analysis unit also builds a system in which a generation AI analyzes the user's exercise data and shares the progress of virtual group training in real time. For example, it promotes competition and encouragement within the group. This improves training motivation by suggesting virtual group training based on the user's exercise data.

[0063] The analysis unit can automatically schedule online sessions with a personal trainer or nutritionist based on user data. The analysis unit, for example, builds a system in which a generation AI analyzes a user's exercise data and health data and automatically schedules online sessions with a personal trainer or nutritionist. For example, if the user's exercise performance is declining, the analysis unit suggests a session with a trainer. The analysis unit also builds a system in which the generation AI schedules online sessions with a personal trainer or nutritionist at the optimal time based on the user data. For example, the analysis unit suggests a session with a nutritionist if the user's diet needs improvement. The analysis unit also builds a system in which the generation AI analyzes the user's data and adjusts the frequency and content of online sessions with a personal trainer or nutritionist. For example, the analysis unit suggests regular sessions and changes the content according to progress. This allows online sessions to be automatically scheduled based on user data, making training and health management more efficient.

[0064] The analysis unit can introduce a reward system according to the user's emotional state and award points and badges according to the level of exercise and health management achievement. The analysis unit, for example, uses an emotion estimation function to analyze the user's emotional state, and the generation AI builds a reward system in which points and badges are awarded according to the level of exercise and health management achievement. For example, bonus points are awarded if the user has strong positive emotions after exercising. The analysis unit also allows the generation AI to adjust the reward system based on the user's emotional state. For example, if motivation is low, a special badge is awarded to encourage the user. The analysis unit also allows the generation AI to provide a reward system according to the user's emotional state based on the emotion estimation data. For example, the content and frequency of rewards are adjusted according to emotional fluctuations. This makes it easier to maintain motivation by introducing a reward system according to the user's emotional state.

[0065] The analysis unit can analyze the user's exercise form in real time and provide specific suggestions for improvement. The analysis unit, for example, uses a generation AI to build a system that analyzes the user's exercise form in real time and provides specific suggestions for improvement. For example, it analyzes posture and foot movements while running and provides advice on how to improve. The analysis unit also uses a generation AI to evaluate the user's exercise form in real time based on data collected by a wearable device and suggest specific suggestions for improvement. For example, it analyzes form during strength training and provides guidance on correct posture. The analysis unit also builds a system that uses a generation AI to analyze the user's exercise form in real time and provides specific suggestions for improvement via audio and visual means. For example, it provides real-time feedback via a smartphone app. This improves the efficiency of exercise by providing real-time suggestions for improvement in exercise form.

[0066] The analysis unit can analyze the user's breathing patterns in real time and suggest optimal breathing techniques. The analysis unit, for example, builds a system in which a generating AI analyzes the user's breathing patterns in real time and suggests optimal breathing techniques. For example, it analyzes breathing rhythms while running and provides advice on efficient breathing techniques. The analysis unit also builds a system in which a generating AI evaluates the user's breathing patterns in real time based on data collected by a wearable device and suggests specific areas for improvement. For example, it analyzes breathing techniques during yoga or meditation and provides guidance on breathing techniques that enhance relaxation. The analysis unit also builds a system in which a generating AI analyzes the user's breathing patterns in real time and suggests specific areas for improvement using audio and visual feedback. For example, it provides real-time feedback through a smartphone app. This allows the system to analyze breathing patterns in real time and suggest optimal breathing techniques, improving exercise efficiency.

[0067] The analysis unit can provide real-time encouragement and relaxation advice based on the user's emotional state. For example, the analysis unit uses an emotion estimation function to analyze the user's emotional state in real time, and build a system in which the generation AI provides encouragement and relaxation advice. For example, positive messages are sent while exercising. The analysis unit also allows the generation AI to provide personalized encouraging messages in real time based on the user's emotional state. For example, a positive message is sent when the user is feeling depressed. The analysis unit also allows the generation AI to provide relaxation advice based on the emotion estimation data according to the user's emotional state. For example, deep breathing and meditation guidance is provided when stress is high. This allows for more efficient emotional management by providing real-time encouragement and relaxation advice based on the user's emotional state.

[0068] The analysis unit can analyze the user's exercise data in real time, allowing the virtual coach to provide audio advice. The analysis unit, for example, builds a system in which a generation AI analyzes the user's exercise data in real time and the virtual coach provides audio advice. For example, audio advice is provided on adjusting the pace while running. The analysis unit also builds a system in which a generation AI evaluates the user's exercise performance in real time based on data collected by a wearable device, allowing the virtual coach to provide specific audio advice. For example, audio guidance is provided on form during strength training. The analysis unit also builds a system in which a generation AI analyzes the user's exercise data in real time and the virtual coach provides audio advice on specific areas for improvement. For example, real-time feedback is provided via a smartphone app. This allows the exercise data to be analyzed in real time and the virtual coach to provide audio advice, improving the efficiency of exercise.

[0069] The analysis unit can analyze the user's data in real time and automatically adjust the tempo of the music played during exercise. For example, the analysis unit constructs a system in which a generation AI analyzes the user's exercise data in real time and automatically adjusts the tempo of the music played during exercise. For example, the tempo of the music may be changed to match the pace of running. The analysis unit also constructs a system in which a generation AI evaluates the user's exercise performance in real time based on data collected by the wearable device and automatically adjusts the tempo of the music. For example, the analysis unit plays music to match the rhythm of strength training. The analysis unit also constructs a system in which a generation AI analyzes the user's exercise data in real time and automatically adjusts the tempo of the music. For example, the analysis unit provides real-time feedback through a smartphone app. This allows the system to analyze data in real time and automatically adjust the tempo of the music played during exercise, improving exercise efficiency.

[0070] The analysis unit can propose a post-exercise recovery plan in real time based on the user's emotional state. The analysis unit, for example, uses an emotion estimation function to analyze the user's emotional state in real time, building a system in which the generation AI proposes a post-exercise recovery plan. For example, specific stretching and massage methods are suggested. The analysis unit also allows the generation AI to provide a personalized recovery plan in real time based on the user's emotional state. For example, when feeling depressed, the generation AI suggests a relaxation recovery method. The analysis unit also allows the generation AI to provide a recovery plan based on the emotion estimation data according to the user's emotional state. For example, when stress is high, the generation AI suggests relaxation exercises. In this way, by proposing a post-exercise recovery plan in real time based on the user's emotional state, the efficiency of recovery is improved.

[0071] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0072] The data collection unit collects environmental sounds around the user, and the analysis unit can provide advice to improve concentration based on the environmental sounds. For example, the data collection unit installs a microphone in the wearable device to measure ambient noise levels. The generation AI analyzes this data and suggests quiet places to improve concentration. The data collection unit also analyzes the type of environmental sound, and the generation AI builds a system that suggests music or white noise suitable for the user. For example, if the noise of a cafe improves concentration, that sound is played. The data collection unit also analyzes fluctuations in environmental sounds in real time, and the generation AI provides advice to the user to maintain appropriate concentration. For example, if the noise level increases, the system suggests using noise-canceling headphones. This improves work efficiency by providing advice to improve concentration based on the environmental sounds around the user.

[0073] The data collection unit monitors the user's sleep environment, and the analysis unit can provide advice to improve optimal sleep quality based on the sleep environment. For example, the data collection unit could equip a wearable device with temperature and humidity sensors to collect bedroom environmental data. The generation AI would analyze this data and suggest optimal room temperature and humidity. The data collection unit could also use a light sensor to measure the brightness of the bedroom, and the generation AI could then build a system that suggests appropriate lighting settings. For example, it could suggest using warm-colored lighting before bedtime. The data collection unit could also monitor noise levels, and the generation AI could then provide advice to create a quieter sleep environment. For example, it could suggest using earplugs or playing white noise. This could improve health management efficiency by providing advice to improve optimal sleep quality based on the user's sleep environment.

[0074] The data collection unit monitors the user's posture during exercise, and the analysis unit can provide advice to improve exercise efficiency based on the posture data. For example, the data collection unit could equip a wearable device with an acceleration sensor and gyro sensor to collect posture data while the user is exercising. The generation AI would analyze this data and provide advice on maintaining correct posture. The data collection unit could also analyze posture data in real time, creating a system in which the generation AI suggests an exercise form suitable for the user. For example, the data collection unit could analyze posture while running and suggest an efficient running style. The data collection unit could also use the posture data during exercise to suggest exercises to improve the user's posture. For example, the generation AI could suggest stretches to straighten the back. This maximizes the effectiveness of training by providing advice to improve exercise efficiency based on the user's posture data during exercise.

[0075] The data collection unit analyzes the user's emotional state, and the analysis unit can suggest meals based on the emotional state. For example, the data collection unit adds an emotion estimation function to a wearable device and analyzes the user's emotional state. The generation AI uses this data to suggest meals that correspond to the emotion. For example, when stress is high, it suggests foods that have a relaxing effect. The data collection unit also uses the emotion estimation function to build a system that creates a meal plan based on the user's emotional state. For example, when feeling depressed, it suggests foods that will replenish energy. The data collection unit also uses the emotion estimation data to allow the generation AI to suggest nutritionally balanced meals that correspond to the user's emotional state. For example, it adjusts the intake of vitamins and minerals according to emotional fluctuations. This allows for more efficient health management by suggesting meals based on the user's emotional state.

[0076] The data collection unit monitors the user's heart rate during exercise, and the analysis unit can provide advice for adjusting exercise intensity based on the heart rate data. For example, the data collection unit installs a heart rate sensor in a wearable device and collects heart rate data during exercise. The generation AI analyzes this data and suggests appropriate exercise intensity. The data collection unit also analyzes the heart rate data in real time, and the generation AI builds a system that adjusts exercise intensity to suit the user. For example, if the heart rate is too high, the generation AI suggests lowering the exercise intensity. The data collection unit also allows the generation AI to adjust the user's exercise plan based on the heart rate data. For example, it changes the type and duration of exercise depending on heart rate fluctuations. This allows the effectiveness of training to be maximized by providing advice for adjusting exercise intensity based on the user's heart rate data during exercise.

[0077] The data collection unit analyzes the user's emotional state, and the analysis unit can suggest relaxation activities based on the emotional state. For example, the data collection unit adds an emotion estimation function to a wearable device and analyzes the user's emotional state. The generation AI uses this data to suggest relaxation activities according to the emotion. For example, it might suggest yoga or meditation when stress is high. The data collection unit also uses the emotion estimation function to build a system that creates a relaxation plan according to the user's emotional state. For example, it might suggest a nature walk when feeling depressed. The data collection unit also uses the emotion estimation data to allow the generation AI to suggest relaxation exercises according to the user's emotional state. For example, it might suggest deep breathing or stretching depending on emotional fluctuations. This allows for more efficient emotional management by suggesting relaxation activities based on the user's emotional state.

[0078] The data collection unit monitors the user's energy consumption during exercise, and the analysis unit can suggest meal timing and content based on the energy consumption data. For example, the data collection unit could install an energy consumption sensor on a wearable device and collect energy consumption data during exercise. The generation AI would analyze this data and suggest appropriate meal timing. The data collection unit could also analyze the energy consumption data in real time, building a system in which the generation AI suggests meal content suitable for the user. For example, it could suggest meals to replenish energy after exercise. The data collection unit could also allow the generation AI to adjust the user's meal plan based on the energy consumption data. For example, it could change the calorie and nutritional balance of meals according to fluctuations in energy consumption. This could improve the efficiency of health management by suggesting meal timing and content based on the user's energy consumption data during exercise.

[0079] The data collection unit analyzes the user's emotional state, and the analysis unit can suggest activities to increase exercise motivation based on the emotional state. For example, the data collection unit adds an emotion estimation function to a wearable device and analyzes the user's emotional state. The generation AI uses this data to suggest activities to increase motivation based on the emotion. For example, when the user is feeling depressed, it suggests exercising together with a friend. The data collection unit also uses the emotion estimation function to build a system that creates a motivation plan based on the user's emotional state. For example, it suggests relaxation exercises when the user is feeling high. The data collection unit also uses the emotion estimation data to allow the generation AI to suggest activities to increase motivation based on the user's emotional state. For example, it suggests music or podcasts depending on emotional fluctuations. This maximizes the effectiveness of training by suggesting activities to increase exercise motivation based on the user's emotional state.

[0080] The data collection unit monitors the user's muscle activity during exercise, and the analysis unit can provide advice to improve exercise efficiency based on the muscle activity data. For example, the data collection unit installs an electromyographic sensor in a wearable device to collect muscle activity data during exercise. The generation AI analyzes this data and suggests appropriate exercise form. The data collection unit also analyzes the muscle activity data in real time, and the generation AI builds a system that adjusts exercise intensity to suit the user. For example, if muscle activity is excessive, the generation AI suggests lowering the exercise intensity. The data collection unit also adjusts the user's exercise plan based on the muscle activity data. For example, it changes the type and duration of exercise depending on fluctuations in muscle activity. This maximizes the effectiveness of training by providing advice to improve exercise efficiency based on the user's muscle activity data during exercise.

[0081] The data collection unit analyzes the user's emotional state, and the analysis unit can suggest a post-exercise recovery plan based on the emotional state. For example, the data collection unit adds an emotion estimation function to a wearable device and analyzes the user's emotional state. The generation AI uses this data to suggest a recovery plan that matches the emotion. For example, it suggests relaxation exercises when stress is high. The data collection unit also uses the emotion estimation function to build a system that creates a recovery plan based on the user's emotional state. For example, it suggests a relaxation recovery method when feeling depressed. The data collection unit also uses the emotion estimation data to allow the generation AI to provide a recovery plan that matches the user's emotional state. For example, it suggests stretching or massage depending on emotional fluctuations. In this way, by suggesting a post-exercise recovery plan based on the user's emotional state, the efficiency of recovery is improved.

[0082] The processing flow of the second embodiment will be briefly explained below.

[0083] Step 1: The data collection unit collects data from the wearable device. For example, the data collection unit collects data such as the user's heart rate, number of steps, calories burned, and sleep patterns. The data collection unit can also collect data transmitted in real time from the wearable device. Step 2: The analysis unit analyzes the data collected by the data collection unit. For example, the analysis unit uses the generation AI to generate personalized advice based on the user's lifestyle and exercise performance. The analysis unit can also use the generation AI to analyze data transmitted in real time and provide immediate feedback. Step 3: The advice providing unit provides advice based on the results of the analysis by the analysis unit. For example, the advice providing unit provides the user with advice generated by the generation AI. The advice providing unit can also provide the user with feedback provided by the generation AI in real time.

[0084] 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.

[0085] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0086] 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.

[0087] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0088] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0089] 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.

[0090] 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.

[0091] 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.

[0092] 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).

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0101] 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.

[0102] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0103] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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).

[0108] 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.

[0109] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0110] 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.

[0111] 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.

[0112] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0113] 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.

[0114] 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.

[0115] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0116] 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.

[0117] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0118] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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).

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 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 processing similar to that of the specific processing unit 290 using these models.

[0129] 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.

[0130] 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.

[0131] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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).

[0137] 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.

[0138] 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."

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0151] 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 data collection unit that collects data from the wearable device; an analysis unit that analyzes the data collected by the data collection unit; an advice providing unit that provides advice based on the results of the analysis by the analysis unit. A system characterized by:

2. The data collection unit Collecting the user's dietary and water intake information; The analysis unit Providing nutritional advice based on the diet and fluid intake 2. The system of claim 1.

3. The data collection unit Measure the user's stress level, The analysis unit Suggest relaxation methods based on the stress level 2. The system of claim 1.

4. The data collection unit Analyze the user's emotional state, The analysis unit Providing exercise and rest advice based on the emotional state 2. The system of claim 1.

5. The data collection unit Measures ambient temperature, humidity, and noise levels, The analysis unit Suggests optimal exercise locations and times based on temperature, humidity, and noise levels 2. The system of claim 1.

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