system

Abstract

The system according to this embodiment aims to provide customized advice and support based on the user's health goals and lifestyle. [Solution] The system according to this embodiment comprises an advice provision unit, a data analysis unit, an information provision unit, a recording unit, a music recommendation unit, and an encouragement provision unit. The advice provision unit provides customized advice based on the user's health goals and lifestyle. The data analysis unit analyzes physiological data and food photos from a wearable device. The information provision unit provides the information analyzed by the data analysis unit. The recording unit records the history of meals, exercise records, and weight changes. The music recommendation unit recommends music suitable for fitness. The encouragement provision unit provides encouragement.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, the method including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, the individual customization and continuous support of user health management have not been fully carried out, and there is room for improvement.

[0005] The system according to the embodiment aims to provide customized advice and support based on the user's health goals and lifestyle.

Means for Solving the Problems

[0006] The system according to this embodiment comprises an advice provision unit, a data analysis unit, an information provision unit, a recording unit, a music recommendation unit, and an encouragement provision unit. The advice provision unit provides customized advice based on the user's health goals and lifestyle. The data analysis unit analyzes physiological data and food photos from a wearable device. The information provision unit provides the information analyzed by the data analysis unit. The recording unit records meal history, exercise records, and weight changes. The music recommendation unit recommends music suitable for fitness. The encouragement provision unit provides encouragement. [Effects of the Invention]

[0007] The system according to this embodiment can provide customized advice and support based on the user's health goals and lifestyle. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9]This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the 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, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

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

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

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

[0028] (Example of form 1) The health management system according to an embodiment of the present invention is a system that utilizes generative AI to provide customized advice and action plans based on the user's health goals and lifestyle. The health management system receives information about the user's health goals and lifestyle through a communication application. The generative AI analyzes physiological data from a wearable device and food photos taken and uploaded by the user with a camera. This analysis provides detailed nutrient and calorie information, enabling personalized health management. Furthermore, the camera function allows users to easily and attractively record their meal history, exercise records, and weight changes. This makes daily health management enjoyable for the user. The system also integrates with music streaming services to recommend music suitable for fitness, boosting motivation during exercise. If the user stops tracking their weight or exercise, or if their motivation declines, the generative AI provides a function to offer encouragement by combining images of their efforts taken with the camera and music they were listening to on the music streaming service. This allows users to receive consistent health management and efficient support, as well as enjoyably record the process as memories and receive encouragement to maintain motivation. This allows the health management system to provide customized advice and action plans based on the user's health goals and lifestyle, enabling personalized health management.

[0029] The health management system according to this embodiment comprises an advice provision unit, a data analysis unit, an information provision unit, a recording unit, a music recommendation unit, and a cheering unit. The advice provision unit provides customized advice based on the user's health goals and lifestyle. The advice provision unit generates specific advice according to the user's health goals, for example, using a generative AI. The advice provision unit can also provide advice on diet and exercise based on the user's lifestyle, for example. The advice provision unit can also propose an action plan according to the user's health goals, for example. The data analysis unit analyzes physiological data and food photos from a wearable device. The data analysis unit analyzes physiological data using a generative AI and evaluates the user's health status, for example. The data analysis unit can also analyze food photos and provide nutrient and calorie information, for example. The data analysis unit can also analyze the user's exercise data and evaluate the effects of exercise, for example. The information provision unit provides the information analyzed by the data analysis unit. The information provision unit provides the analysis results to the user in an easy-to-understand manner, for example, using a generative AI. The information provision unit can also generate and provide a health report to the user, for example. The information provision unit can, for example, provide specific advice based on the analysis results. The recording unit records meal history, exercise records, and weight changes. The recording unit can, for example, automatically record meal history using generative AI. The recording unit can, for example, automatically record exercise. The recording unit can, for example, record and graph weight changes. The music recommendation unit recommends music suitable for fitness. The music recommendation unit can, for example, use generative AI to recommend music that matches the user's preferences. The music recommendation unit can, for example, recommend music that matches the type of exercise. The music recommendation unit can, for example, recommend music that boosts the user's motivation. The encouragement provision unit sends encouragement when the user stops managing their weight or recording exercise, or when their motivation declines. The encouragement provision unit can, for example, use generative AI to generate encouragement that matches the user's situation. The encouragement provision unit can, for example, send encouragement by combining images of the user's efforts with music.The encouragement-providing unit can, for example, provide encouragement to help users regain their motivation. This allows the health management system according to the embodiment to provide customized advice and action plans based on the user's health goals and lifestyle, thereby enabling individualized health management.

[0030] The advice service provides customized advice based on the user's health goals and lifestyle. Specifically, it uses a generative AI to generate specific advice tailored to the user's health goals. The generative AI analyzes the user's entered health goals and daily activity data to provide advice that meets individual needs. For example, if a user wants to lose weight, the generative AI considers the user's current weight, diet, exercise frequency, etc., and proposes a calorie intake target and a recommended exercise plan. It can also provide diet and exercise advice based on the user's lifestyle. For example, it can suggest effective short-duration exercises for busy business people, and provide home-based exercises and healthy recipes for housewives who spend a lot of time at home. Furthermore, it can propose action plans tailored to the user's health goals. For example, it can create weekly exercise schedules and meal plans, showing the specific steps the user needs to achieve their goals. In this way, the advice service can provide specific and actionable advice to help users achieve their health goals and support their individual health management.

[0031] The data analysis department analyzes physiological data and food photos from wearable devices. Specifically, it uses generative AI to analyze physiological data and evaluate the user's health status. For example, it analyzes data such as heart rate, blood pressure, and sleep patterns to monitor the user's health status in real time. Based on this data, the generative AI can detect abnormalities and warn of health risks early. It can also analyze food photos to provide nutritional and calorie information. When a user takes a photo of their meal, the generative AI uses image recognition technology to identify the ingredients and calculate the nutrients and calories of each ingredient. This allows users to understand their meals in detail and strive for a balanced diet. Furthermore, it can analyze the user's exercise data to evaluate the effectiveness of their exercise. For example, it analyzes data such as steps taken, calories burned, and exercise time to evaluate the user's exercise performance. This allows the data analysis department to comprehensively evaluate the user's health status and provide appropriate feedback.

[0032] The Information Provision Department provides information analyzed by the Data Analysis Department. Specifically, it uses generation AI to present analysis results to users in an easy-to-understand manner. For example, it can generate and provide health reports to users. These health reports contain detailed information about the user's health status and progress, and are displayed visually using graphs and charts. It can also provide specific advice based on the analysis results. For example, it can provide specific advice that users can implement in their daily lives, such as suggestions for improving their diet or recommending exercise. Furthermore, the Information Provision Department can send reminders and notifications tailored to the user's health status. For example, it can send reminders for regular health checks or notifications informing users of exercise times, supporting them in continuing to manage their health. In this way, the Information Provision Department can provide users with information that allows them to accurately understand their own health status and take appropriate action.

[0033] The recording unit records meal history, exercise records, and weight changes. Specifically, it uses generative AI to automatically record meal history. When a user takes a photo of a meal, the generative AI uses image recognition technology to identify the ingredients and automatically records the contents of the meal. It can also automatically record exercise. Based on exercise data acquired from the wearable device, it records the user's exercise history in detail. Furthermore, it can record and graph weight changes. When a user enters their weight, the generative AI uses that data to graph the weight changes, displaying them in an easy-to-understand visual format. This allows the user to grasp changes in their health status at a glance. The recording unit centrally manages this data, making it easy for users to refer to past data. For example, users can search for meal history and exercise history for a specific period and review their past health status. In this way, the recording unit can support users in recording and continuously managing their health status in detail.

[0034] The music recommendation department recommends music suitable for fitness. Specifically, it uses generative AI to recommend music tailored to the user's preferences. The generative AI analyzes the user's musical tastes and past playback history to identify the music the user will like. For example, if a user prefers energetic music, the generative AI will recommend upbeat and rhythmic music. It can also recommend music according to the type of exercise. For example, it will suggest fast-paced music for running and relaxing music for yoga. Furthermore, it can recommend music to boost the user's motivation. For example, if a user's motivation drops during exercise, the generative AI will play energetic music along with an encouraging message. In this way, the music recommendation department can improve the user's fitness experience and provide support to maximize the effectiveness of their exercise.

[0035] The encouragement service sends messages of support to users when they stop tracking their weight or exercise, or when their motivation declines. Specifically, it uses a generative AI to generate messages tailored to the user's situation. The generative AI analyzes the user's past data and current situation, and sends encouraging messages at the appropriate time. For example, if a user skips exercise for several days, the generative AI will send a message such as, "Keep going! Even a little exercise can lead to great results." It can also combine images of the user's efforts with music to send messages of support. For example, it can send photos of past achievements along with motivational music. Furthermore, it can provide messages of support to help users regain their motivation. For example, if a user fails to achieve a goal, the generative AI will send a message such as, "Let's try harder next time! You can do it!" In this way, the encouragement service can support users in continuing their health management and provide encouragement to maintain their motivation.

[0036] The advice-providing unit can analyze the user's past health data and select the most appropriate advice. For example, the advice-providing unit can analyze the user's past eating history and propose a nutritionally balanced meal plan. For example, the advice-providing unit can also provide an effective training plan based on the user's exercise history. For example, the advice-providing unit can analyze the user's weight changes and propose appropriate weight management methods. This allows the system to provide optimal advice based on the user's past data. Some or all of the above-described processes in the advice-providing unit may be performed using, for example, a generative AI, or without a generative AI. For example, the advice-providing unit can input the user's past health data into a generative AI and have the generative AI select the most appropriate advice.

[0037] The advice-providing unit can filter the advice provided based on the user's current lifestyle and health status. For example, if the user is busy, the advice-providing unit can provide health advice that can be implemented in a short amount of time. For example, if the user is feeling unwell, the advice-providing unit can also provide health advice that is manageable. For example, if the user is traveling, the advice-providing unit can provide health advice that can be implemented at the travel destination. This allows the system to provide appropriate advice tailored to the user's current situation. Some or all of the above-described processes in the advice-providing unit may be performed using, for example, a generative AI, or without a generative AI. For example, the advice-providing unit can input data on the user's current lifestyle and health status into a generative AI and have the generative AI perform the filtering.

[0038] The advice-providing unit can prioritize providing highly relevant advice by considering the user's geographical location information when providing advice. For example, if the user is in a cold region, the advice-providing unit can provide advice on cold weather countermeasures. For example, if the user is in a high-altitude area, the advice-providing unit can also provide advice on health management at high altitudes. For example, if the user is in an urban area, the advice-providing unit can also provide health advice suitable for urban living. This allows the system to provide appropriate advice based on the user's geographical location information. Some or all of the above processing in the advice-providing unit may be performed using, for example, a generative AI, or without a generative AI. For example, the advice-providing unit can input the user's geographical location information into a generative AI and have the generative AI select highly relevant advice.

[0039] The advice-providing unit can analyze the user's social media activity and provide relevant advice when providing advice. For example, if the user posts about health on social media, the advice-providing unit can provide advice based on that content. For example, if the user posts about feeling stressed on social media, the advice-providing unit can also provide advice on stress reduction. For example, if the user posts about exercise on social media, the advice-providing unit can also provide advice on exercise. This allows the system to provide appropriate advice based on the user's social media activity. Some or all of the above processing in the advice-providing unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the advice-providing unit can input data on the user's social media activity into a generative AI and have the generative AI generate relevant advice.

[0040] The data analysis unit can optimize its analysis algorithms by referring to the user's past health data during data analysis. For example, the data analysis unit can optimize its nutritional balance analysis algorithm based on the user's past dietary data. For example, the data analysis unit can also optimize its exercise effect analysis algorithm based on the user's exercise history. For example, the data analysis unit can optimize its weight management analysis algorithm based on the user's weight changes. This enables optimal data analysis based on the user's past data. Some or all of the above-described processes in the data analysis unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the data analysis unit can input the user's past health data into a generative AI and have the generative AI perform the optimization of the analysis algorithm.

[0041] The data analysis unit can improve the accuracy of its analysis based on the user's current lifestyle and health status. For example, if the user is busy, the data analysis unit can perform analysis to provide health advice that can be implemented in a short amount of time. For example, if the user is feeling unwell, the data analysis unit can perform analysis to provide health advice that can be implemented within reasonable limits. For example, if the user is traveling, the data analysis unit can perform analysis to provide health advice that can be implemented at the travel destination. This allows for appropriate data analysis tailored to the user's current situation. Some or all of the above-described processes in the data analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data analysis unit can input data on the user's current lifestyle and health status into a generative AI and have the generative AI perform the task of improving the accuracy of the analysis.

[0042] The data analysis unit can perform data analysis while taking into account the user's geographical location information. For example, if the user is in a cold region, the data analysis unit can analyze data related to cold weather countermeasures. For example, if the user is in a high-altitude area, the data analysis unit can also analyze data related to health management at high altitudes. For example, if the user is in an urban area, the data analysis unit can also analyze health data suitable for urban living. This enables appropriate data analysis based on the user's geographical location information. Some or all of the above-described processes in the data analysis unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the data analysis unit can input the user's geographical location information into a generative AI and have the generative AI perform the analysis.

[0043] The data analysis unit can analyze users' social media activity and analyze relevant data during data analysis. For example, if a user posts about health on social media, the data analysis unit can analyze data based on the content of those posts. For example, if a user posts about feeling stressed on social media, the data analysis unit can also analyze data related to stress reduction. For example, if a user posts about exercise on social media, the data analysis unit can also analyze data related to exercise. This enables appropriate data analysis based on users' social media activity. Some or all of the above processing in the data analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data analysis unit can input data on users' social media activity into a generative AI and have the generative AI perform the analysis of the relevant data.

[0044] The information provision unit can select the most suitable information by referring to the user's past health data when providing information. For example, the information provision unit can provide nutritionally balanced meal information based on the user's past dietary data. For example, the information provision unit can also provide effective training information based on the user's exercise history. For example, the information provision unit can provide appropriate weight management information based on the user's weight changes. This allows the system to provide optimal information based on the user's past data. Some or all of the above processing in the information provision unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the information provision unit can input the user's past health data into a generative AI and have the generative AI select the most suitable information.

[0045] The information provision unit can adjust the level of detail of the information provided based on the user's current living situation and health condition. For example, if the user is busy, the information provision unit can provide health information that can be acted upon in a short time. For example, if the user is feeling unwell, the information provision unit can also provide health information that can be acted upon at the user's destination. This allows the information provision unit to provide appropriate information according to the user's current situation. Some or all of the above processing in the information provision unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the information provision unit can input data on the user's current living situation and health condition into a generating AI and have the generating AI perform the adjustment of the level of detail of the information.

[0046] The information provision unit can prioritize providing highly relevant information by considering the user's geographical location when providing information. For example, if the user is in a cold region, the information provision unit can provide information on cold weather countermeasures. For example, if the user is in a high-altitude area, the information provision unit can also provide information on health management at high altitudes. For example, if the user is in an urban area, the information provision unit can also provide health information suitable for urban living. This allows for the provision of appropriate information based on the user's geographical location. Some or all of the above processing in the information provision unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the information provision unit can input the user's geographical location information into a generative AI and have the generative AI select highly relevant information.

[0047] The information provision unit can analyze a user's social media activity and provide relevant information when providing information. For example, if a user posts about health on social media, the information provision unit can provide information based on that content. For example, if a user posts about feeling stressed on social media, the information provision unit can also provide information on stress reduction. For example, if a user posts about exercise on social media, the information provision unit can also provide information on exercise. This allows the information provision unit to provide appropriate information based on the user's social media activity. Some or all of the above processing in the information provision unit may be performed using, for example, a generative AI, or without a generative AI. For example, the information provision unit can input data on the user's social media activity into a generative AI and have the generative AI generate relevant information.

[0048] The recording unit can select the optimal recording method by referring to the user's past health data during recording. For example, the recording unit can provide a nutritionally balanced meal recording method based on the user's past meal data. For example, the recording unit can also provide an effective exercise recording method based on the user's exercise history. For example, the recording unit can provide an appropriate weight management recording method based on the user's weight changes. This allows the recording unit to provide the optimal recording method based on the user's past data. Some or all of the above processing in the recording unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the recording unit can input the user's past health data into a generative AI and have the generative AI select the optimal recording method.

[0049] The recording unit can adjust the level of detail in the recording based on the user's current lifestyle and health condition. For example, if the user is busy, the recording unit can provide a recording method that can be completed in a short time. For example, if the user is feeling unwell, the recording unit can also provide a recording method that is manageable. For example, if the user is traveling, the recording unit can also provide a recording method that can be completed at the travel destination. This allows the recording unit to provide an appropriate recording method according to the user's current situation. Some or all of the above processing in the recording unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the recording unit can input data on the user's current lifestyle and health condition into the generating AI and have the generating AI perform the adjustment of the level of detail in the recording.

[0050] The recording unit can prioritize recordings that are highly relevant, taking into account the user's geographical location information. For example, if the user is in a cold region, the recording unit can provide records related to cold weather countermeasures. If the user is in a high-altitude region, the recording unit can also provide records related to health management at high altitudes. If the user is in an urban area, the recording unit can also provide health records suitable for urban life. This enables the recording of appropriate records based on the user's geographical location information. Some or all of the above processing in the recording unit may be performed using, for example, a generative AI, or without a generative AI. For example, the recording unit can input the user's geographical location information into a generative AI and have the generative AI select highly relevant records.

[0051] The recording unit can analyze the user's social media activity and make relevant records at the time of recording. For example, if the user posts about health on social media, the recording unit can provide records based on that content. For example, if the user posts about feeling stressed on social media, the recording unit can also provide records related to stress reduction. For example, if the user posts about exercise on social media, the recording unit can also provide records related to exercise. This allows for the creation of appropriate records based on the user's social media activity. Some or all of the above processing in the recording unit may be performed using, for example, a generative AI, or without a generative AI. For example, the recording unit can input data on the user's social media activity into a generative AI and have the generative AI generate relevant records.

[0052] The music recommendation unit can select the most suitable music by referring to the user's past music history when making recommendations. For example, the music recommendation unit can recommend music the user likes based on their past music history. For example, the music recommendation unit can also recommend music the user listened to while exercising based on their past music history. For example, the music recommendation unit can analyze the user's past music history and recommend the most relaxing music. This allows the system to recommend the most suitable music based on the user's past music history. Some or all of the above processing in the music recommendation unit may be performed using, for example, a generative AI, or without a generative AI. For example, the music recommendation unit can input the user's past music history into a generative AI and have the generative AI select the most suitable music.

[0053] The music recommendation unit can adjust the level of detail in the music recommendations based on the user's current lifestyle and health condition. For example, if the user is busy, the music recommendation unit can recommend music that allows for quick relaxation. If the user is feeling unwell, the music recommendation unit can also recommend music that allows for relaxation within a reasonable range. If the user is traveling, the music recommendation unit can also recommend music that allows for relaxation at the travel destination. This allows for the recommendation of appropriate music according to the user's current situation. Some or all of the above processing in the music recommendation unit may be performed using, for example, a generative AI, or without a generative AI. For example, the music recommendation unit can input data on the user's current lifestyle and health condition into a generative AI and have the generative AI perform the adjustment of the level of detail in the music.

[0054] The music recommendation unit can prioritize recommending music that is highly relevant to the user, taking into account the user's geographical location. For example, if the user is in a cold region, the music recommendation unit can recommend music that will make them feel warm. For example, if the user is in a high-altitude region, the music recommendation unit can also recommend music that will help them relax at high altitudes. For example, if the user is in an urban area, the music recommendation unit can also recommend music that is suitable for urban life. This allows for the recommendation of appropriate music based on the user's geographical location. Some or all of the above processing in the music recommendation unit may be performed using, for example, a generative AI, or without a generative AI. For example, the music recommendation unit can input the user's geographical location information into a generative AI and have the generative AI select highly relevant music.

[0055] The music recommendation unit can analyze a user's social media activity and recommend relevant music when making music recommendations. For example, if a user posts about music on social media, the music recommendation unit can recommend music based on that content. For example, if a user posts about feeling stressed on social media, the music recommendation unit can also recommend stress-relieving music. For example, if a user posts about exercise on social media, the music recommendation unit can also recommend exercise-related music. This allows for the recommendation of appropriate music based on the user's social media activity. Some or all of the above processing in the music recommendation unit may be performed using, for example, a generative AI, or without a generative AI. For example, the music recommendation unit can input data on the user's social media activity into a generative AI and have the generative AI generate relevant music.

[0056] The cheering unit can select the most suitable cheering message by referring to the user's past health data when providing the message. For example, the cheering unit can provide a message about nutritionally balanced meals based on the user's past eating data. For example, the cheering unit can also provide a message about effective training based on the user's exercise history. For example, the cheering unit can provide a message about appropriate weight management based on the user's weight changes. This allows the unit to provide the most suitable cheering message based on the user's past data. Some or all of the above processing in the cheering unit may be performed using, for example, a generation AI, or without a generation AI. For example, the cheering unit can input the user's past health data into a generation AI and have the generation AI select the most suitable cheering message.

[0057] The support delivery unit can adjust the level of detail of the support provided based on the user's current living situation and health condition. For example, if the user is busy, the support delivery unit can provide support that can be done in a short amount of time. For example, if the user is feeling unwell, the support delivery unit can provide support that is within a reasonable range. For example, if the user is traveling, the support delivery unit can provide support that can be done at the travel destination. This allows the support delivery unit to provide appropriate support according to the user's current situation. Some or all of the above processing in the support delivery unit may be performed using, for example, a generation AI, or without a generation AI. For example, the support delivery unit can input data on the user's current living situation and health condition into a generation AI and have the generation AI adjust the level of detail of the support.

[0058] The cheering service provider can prioritize providing highly relevant cheers by considering the user's geographical location information when providing cheers. For example, if the user is in a cold region, the cheering service provider can provide cheers that will make them feel warm. For example, if the user is in a high-altitude region, the cheering service provider can also provide cheers that will help them relax at high altitudes. For example, if the user is in an urban area, the cheering service provider can also provide cheers that are suitable for urban life. This allows the service provider to provide appropriate cheers based on the user's geographical location information. Some or all of the above processing in the cheering service provider may be performed using, for example, a generative AI, or without a generative AI. For example, the cheering service provider can input the user's geographical location information into a generative AI and have the generative AI select highly relevant cheers.

[0059] The encouragement provider can analyze a user's social media activity and provide relevant encouragement when providing encouragement. For example, if a user posts about health on social media, the encouragement provider can provide encouragement based on that content. For example, if a user posts about feeling stressed on social media, the encouragement provider can also provide stress-reducing encouragement. For example, if a user posts about exercise on social media, the encouragement provider can also provide exercise-related encouragement. This allows for the provision of appropriate encouragement based on the user's social media activity. Some or all of the above processing in the encouragement provider may be performed using, for example, a generative AI, or without a generative AI. For example, the encouragement provider can input data on the user's social media activity into a generative AI and have the generative AI generate relevant encouragement.

[0060] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0061] The health management system can also include a sleep analysis unit that acquires and analyzes the user's sleep data. For example, the sleep analysis unit can analyze the user's sleep patterns and suggest optimal sleep duration and quality. It can also provide advice on improving sleep quality based on the user's sleep data. Furthermore, it can integrate the user's sleep data with other health data to provide comprehensive health management. This allows users to receive specific advice on how to achieve better sleep, contributing to an overall improvement in their health.

[0062] The health management system may also include a diet evaluation unit that analyzes the user's dietary data and evaluates the quality of their meals. For example, the diet evaluation unit can evaluate nutritional balance based on the user's dietary data. It can also evaluate calorie intake based on the user's dietary data. Furthermore, it can provide advice on improving the quality of the user's diet based on their dietary data. This allows users to objectively evaluate the quality of their own diet and receive specific advice for improvement.

[0063] The health management system may also include an exercise evaluation unit that analyzes the user's exercise data and evaluates the effectiveness of the exercise. The exercise evaluation unit, for example, evaluates the effectiveness of the exercise based on the user's exercise data. The exercise evaluation unit can also, for example, provide advice to improve the quality of the exercise based on the user's exercise data. Furthermore, the exercise evaluation unit can integrate the user's exercise data with other health data to provide comprehensive health management. This allows the user to objectively evaluate the effectiveness of their exercise and receive specific advice for improvement.

[0064] The health management system may also include a regional health information section that takes into account the user's geographical location and provides region-specific health information. For example, if the user is in a cold region, the regional health information section may provide health information related to cold weather countermeasures. If the user is in a high-altitude area, the regional health information section may also provide information related to health management at high altitudes. If the user is in an urban area, the regional health information section may also provide health information suitable for urban life. This allows for the provision of appropriate health information based on the user's geographical location and addresses region-specific health risks.

[0065] The health management system may also include a social media analysis unit that analyzes users' social media activity and provides relevant health information. For example, if a user posts health-related content on social media, the social media analysis unit can provide health information based on that content. It can also provide health information to reduce stress if a user posts about feeling stressed on social media. Furthermore, if a user posts about exercise on social media, the social media analysis unit can provide health information related to exercise. This allows for the provision of appropriate health information based on users' social media activity, thereby supporting users' health management.

[0066] The health management system may also include a goal achievement evaluation unit that refers to the user's past health data to assess the degree to which health goals have been achieved. For example, the goal achievement evaluation unit can evaluate the degree to which nutritional balance has been achieved based on the user's past dietary data. For example, the goal achievement evaluation unit can also evaluate the degree to which exercise goals have been achieved based on the user's exercise history. For example, the goal achievement evaluation unit can also evaluate the degree to which weight management goals have been achieved based on the user's weight changes. This allows users to objectively evaluate their progress toward their health goals and receive specific advice on how to move forward to the next step.

[0067] The following briefly describes the processing flow for example form 1.

[0068] Step 1: The advice provision unit provides customized advice based on the user's health goals and lifestyle. For example, it can use generative AI to generate specific advice tailored to the user's health goals and provide advice on diet and exercise. It can also propose action plans tailored to the user's health goals. Step 2: The data analysis unit analyzes physiological data and food photos from the wearable device. For example, it can use a generative AI to analyze physiological data and evaluate the user's health status. It can also analyze food photos to provide nutrient and calorie information, and analyze the user's exercise data to evaluate the effectiveness of exercise. Step 3: The Information Provision Department provides the information analyzed by the Data Analysis Department. For example, it can use a generation AI to provide users with easy-to-understand analysis results and generate and provide health reports to users. It can also provide specific advice based on the analysis results. Step 4: The recording unit records meal history, exercise records, and weight changes. For example, it can use generation AI to automatically record meal history and exercise records. It can also record and graph weight changes. Step 5: The music recommendation section recommends music suitable for fitness. For example, it can use generative AI to recommend music tailored to the user's preferences, or recommend music based on the type of exercise. It can also recommend music that will boost the user's motivation. Step 6: The encouragement provider sends encouragement when the user stops tracking their weight or exercise, or when their motivation declines. For example, it can use a generation AI to create encouragement tailored to the user's situation, combining it with images of the user's efforts and music. It can also provide encouragement to help the user regain their motivation.

[0069] (Example of form 2) The health management system according to an embodiment of the present invention is a system that utilizes generative AI to provide customized advice and action plans based on the user's health goals and lifestyle. The health management system receives information about the user's health goals and lifestyle through a communication application. The generative AI analyzes physiological data from a wearable device and food photos taken and uploaded by the user with a camera. This analysis provides detailed nutrient and calorie information, enabling personalized health management. Furthermore, the camera function allows users to easily and attractively record their meal history, exercise records, and weight changes. This makes daily health management enjoyable for the user. The system also integrates with music streaming services to recommend music suitable for fitness, boosting motivation during exercise. If the user stops tracking their weight or exercise, or if their motivation declines, the generative AI provides a function to offer encouragement by combining images of their efforts taken with the camera and music they were listening to on the music streaming service. This allows users to receive consistent health management and efficient support, as well as enjoyably record the process as memories and receive encouragement to maintain motivation. This allows the health management system to provide customized advice and action plans based on the user's health goals and lifestyle, enabling personalized health management.

[0070] The health management system according to this embodiment comprises an advice provision unit, a data analysis unit, an information provision unit, a recording unit, a music recommendation unit, and a cheering unit. The advice provision unit provides customized advice based on the user's health goals and lifestyle. The advice provision unit generates specific advice according to the user's health goals, for example, using a generative AI. The advice provision unit can also provide advice on diet and exercise based on the user's lifestyle, for example. The advice provision unit can also propose an action plan according to the user's health goals, for example. The data analysis unit analyzes physiological data and food photos from a wearable device. The data analysis unit analyzes physiological data using a generative AI and evaluates the user's health status, for example. The data analysis unit can also analyze food photos and provide nutrient and calorie information, for example. The data analysis unit can also analyze the user's exercise data and evaluate the effects of exercise, for example. The information provision unit provides the information analyzed by the data analysis unit. The information provision unit provides the analysis results to the user in an easy-to-understand manner, for example, using a generative AI. The information provision unit can also generate and provide a health report to the user, for example. The information provision unit can, for example, provide specific advice based on the analysis results. The recording unit records meal history, exercise records, and weight changes. The recording unit can, for example, automatically record meal history using generative AI. The recording unit can, for example, automatically record exercise. The recording unit can, for example, record and graph weight changes. The music recommendation unit recommends music suitable for fitness. The music recommendation unit can, for example, use generative AI to recommend music that matches the user's preferences. The music recommendation unit can, for example, recommend music that matches the type of exercise. The music recommendation unit can, for example, recommend music that boosts the user's motivation. The encouragement provision unit sends encouragement when the user stops managing their weight or recording exercise, or when their motivation declines. The encouragement provision unit can, for example, use generative AI to generate encouragement that matches the user's situation. The encouragement provision unit can, for example, send encouragement by combining images of the user's efforts with music.The encouragement-providing unit can, for example, provide encouragement to help users regain their motivation. This allows the health management system according to the embodiment to provide customized advice and action plans based on the user's health goals and lifestyle, thereby enabling individualized health management.

[0071] The advice service provides customized advice based on the user's health goals and lifestyle. Specifically, it uses a generative AI to generate specific advice tailored to the user's health goals. The generative AI analyzes the user's entered health goals and daily activity data to provide advice that meets individual needs. For example, if a user wants to lose weight, the generative AI considers the user's current weight, diet, exercise frequency, etc., and proposes a calorie intake target and a recommended exercise plan. It can also provide diet and exercise advice based on the user's lifestyle. For example, it can suggest effective short-duration exercises for busy business people, and provide home-based exercises and healthy recipes for housewives who spend a lot of time at home. Furthermore, it can propose action plans tailored to the user's health goals. For example, it can create weekly exercise schedules and meal plans, showing the specific steps the user needs to achieve their goals. In this way, the advice service can provide specific and actionable advice to help users achieve their health goals and support their individual health management.

[0072] The data analysis department analyzes physiological data and food photos from wearable devices. Specifically, it uses generative AI to analyze physiological data and evaluate the user's health status. For example, it analyzes data such as heart rate, blood pressure, and sleep patterns to monitor the user's health status in real time. Based on this data, the generative AI can detect abnormalities and warn of health risks early. It can also analyze food photos to provide nutritional and calorie information. When a user takes a photo of their meal, the generative AI uses image recognition technology to identify the ingredients and calculate the nutrients and calories of each ingredient. This allows users to understand their meals in detail and strive for a balanced diet. Furthermore, it can analyze the user's exercise data to evaluate the effectiveness of their exercise. For example, it analyzes data such as steps taken, calories burned, and exercise time to evaluate the user's exercise performance. This allows the data analysis department to comprehensively evaluate the user's health status and provide appropriate feedback.

[0073] The Information Provision Department provides information analyzed by the Data Analysis Department. Specifically, it uses generation AI to present analysis results to users in an easy-to-understand manner. For example, it can generate and provide health reports to users. These health reports contain detailed information about the user's health status and progress, and are displayed visually using graphs and charts. It can also provide specific advice based on the analysis results. For example, it can provide specific advice that users can implement in their daily lives, such as suggestions for improving their diet or recommending exercise. Furthermore, the Information Provision Department can send reminders and notifications tailored to the user's health status. For example, it can send reminders for regular health checks or notifications informing users of exercise times, supporting them in continuing to manage their health. In this way, the Information Provision Department can provide users with information that allows them to accurately understand their own health status and take appropriate action.

[0074] The recording unit records meal history, exercise records, and weight changes. Specifically, it uses generative AI to automatically record meal history. When a user takes a photo of a meal, the generative AI uses image recognition technology to identify the ingredients and automatically records the contents of the meal. It can also automatically record exercise. Based on exercise data acquired from the wearable device, it records the user's exercise history in detail. Furthermore, it can record and graph weight changes. When a user enters their weight, the generative AI uses that data to graph the weight changes, displaying them in an easy-to-understand visual format. This allows the user to grasp changes in their health status at a glance. The recording unit centrally manages this data, making it easy for users to refer to past data. For example, users can search for meal history and exercise history for a specific period and review their past health status. In this way, the recording unit can support users in recording and continuously managing their health status in detail.

[0075] The music recommendation department recommends music suitable for fitness. Specifically, it uses generative AI to recommend music tailored to the user's preferences. The generative AI analyzes the user's musical tastes and past playback history to identify the music the user will like. For example, if a user prefers energetic music, the generative AI will recommend upbeat and rhythmic music. It can also recommend music according to the type of exercise. For example, it will suggest fast-paced music for running and relaxing music for yoga. Furthermore, it can recommend music to boost the user's motivation. For example, if a user's motivation drops during exercise, the generative AI will play energetic music along with an encouraging message. In this way, the music recommendation department can improve the user's fitness experience and provide support to maximize the effectiveness of their exercise.

[0076] The encouragement service sends messages of support to users when they stop tracking their weight or exercise, or when their motivation declines. Specifically, it uses a generative AI to generate messages tailored to the user's situation. The generative AI analyzes the user's past data and current situation, and sends encouraging messages at the appropriate time. For example, if a user skips exercise for several days, the generative AI will send a message such as, "Keep going! Even a little exercise can lead to great results." It can also combine images of the user's efforts with music to send messages of support. For example, it can send photos of past achievements along with motivational music. Furthermore, it can provide messages of support to help users regain their motivation. For example, if a user fails to achieve a goal, the generative AI will send a message such as, "Let's try harder next time! You can do it!" In this way, the encouragement service can support users in continuing their health management and provide encouragement to maintain their motivation.

[0077] The advice-providing unit can estimate the user's emotions and adjust the content of the advice based on the estimated emotions. For example, if the user is feeling stressed, the advice-providing unit can provide advice to help them relax. For example, if the user is feeling unmotivated, the advice-providing unit can provide advice that includes words of encouragement and success stories. For example, if the user is feeling agitated, the advice-providing unit can provide advice to help them calm down. This allows the system to provide appropriate advice tailored to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the advice-providing unit may be performed using AI or not. For example, the advice-providing unit can input user emotion data into a generative AI and have the generative AI generate emotion-based advice.

[0078] The advice-providing unit can analyze the user's past health data and select the most appropriate advice. For example, the advice-providing unit can analyze the user's past eating history and propose a nutritionally balanced meal plan. For example, the advice-providing unit can also provide an effective training plan based on the user's exercise history. For example, the advice-providing unit can analyze the user's weight changes and propose appropriate weight management methods. This allows the system to provide optimal advice based on the user's past data. Some or all of the above-described processes in the advice-providing unit may be performed using, for example, a generative AI, or without a generative AI. For example, the advice-providing unit can input the user's past health data into a generative AI and have the generative AI select the most appropriate advice.

[0079] The advice-providing unit can filter the advice provided based on the user's current lifestyle and health status. For example, if the user is busy, the advice-providing unit can provide health advice that can be implemented in a short amount of time. For example, if the user is feeling unwell, the advice-providing unit can also provide health advice that is manageable. For example, if the user is traveling, the advice-providing unit can provide health advice that can be implemented at the travel destination. This allows the system to provide appropriate advice tailored to the user's current situation. Some or all of the above-described processes in the advice-providing unit may be performed using, for example, a generative AI, or without a generative AI. For example, the advice-providing unit can input data on the user's current lifestyle and health status into a generative AI and have the generative AI perform the filtering.

[0080] The advice-providing unit can estimate the user's emotions and determine the priority of advice based on the estimated emotions. For example, if the user is feeling stressed, the advice-providing unit will prioritize stress-reducing advice. For example, if the user is unmotivated, the advice-providing unit may also prioritize motivation-enhancing advice. For example, if the user is relaxed, the advice-providing unit may also prioritize advice on long-term health goals. This allows for the provision of advice with priorities tailored to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the advice-providing unit may be performed using AI or not. For example, the advice-providing unit can input user emotion data into a generative AI and have the generative AI determine the priority of advice.

[0081] The advice-providing unit can prioritize providing highly relevant advice by considering the user's geographical location information when providing advice. For example, if the user is in a cold region, the advice-providing unit can provide advice on cold weather countermeasures. For example, if the user is in a high-altitude area, the advice-providing unit can also provide advice on health management at high altitudes. For example, if the user is in an urban area, the advice-providing unit can also provide health advice suitable for urban living. This allows the system to provide appropriate advice based on the user's geographical location information. Some or all of the above processing in the advice-providing unit may be performed using, for example, a generative AI, or without a generative AI. For example, the advice-providing unit can input the user's geographical location information into a generative AI and have the generative AI select highly relevant advice.

[0082] The advice-providing unit can analyze the user's social media activity and provide relevant advice when providing advice. For example, if the user posts about health on social media, the advice-providing unit can provide advice based on that content. For example, if the user posts about feeling stressed on social media, the advice-providing unit can also provide advice on stress reduction. For example, if the user posts about exercise on social media, the advice-providing unit can also provide advice on exercise. This allows the system to provide appropriate advice based on the user's social media activity. Some or all of the above processing in the advice-providing unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the advice-providing unit can input data on the user's social media activity into a generative AI and have the generative AI generate relevant advice.

[0083] The data analysis unit can estimate the user's emotions and adjust the data analysis method based on the estimated user emotions. For example, if the user is stressed, the data analysis unit may prioritize analyzing data related to stress reduction. For example, if the user is relaxed, the data analysis unit may also analyze data related to long-term health goals. For example, if the user is unmotivated, the data analysis unit may also analyze data related to motivation improvement. This allows for appropriate data analysis tailored to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the data analysis unit may be performed using AI or not. For example, the data analysis unit can input user emotion data into a generative AI and have the generative AI adjust the data analysis method.

[0084] The data analysis unit can optimize its analysis algorithms by referring to the user's past health data during data analysis. For example, the data analysis unit can optimize its nutritional balance analysis algorithm based on the user's past dietary data. For example, the data analysis unit can also optimize its exercise effect analysis algorithm based on the user's exercise history. For example, the data analysis unit can optimize its weight management analysis algorithm based on the user's weight changes. This enables optimal data analysis based on the user's past data. Some or all of the above-described processes in the data analysis unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the data analysis unit can input the user's past health data into a generative AI and have the generative AI perform the optimization of the analysis algorithm.

[0085] The data analysis unit can improve the accuracy of its analysis based on the user's current lifestyle and health status. For example, if the user is busy, the data analysis unit can perform analysis to provide health advice that can be implemented in a short amount of time. For example, if the user is feeling unwell, the data analysis unit can perform analysis to provide health advice that can be implemented within reasonable limits. For example, if the user is traveling, the data analysis unit can perform analysis to provide health advice that can be implemented at the travel destination. This allows for appropriate data analysis tailored to the user's current situation. Some or all of the above-described processes in the data analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data analysis unit can input data on the user's current lifestyle and health status into a generative AI and have the generative AI perform the task of improving the accuracy of the analysis.

[0086] The data analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is stressed, the data analysis unit can provide a simple and highly visible display method. For example, if the user is relaxed, the data analysis unit can also provide a display method that includes detailed information. For example, if the user is unmotivated, the data analysis unit can also provide a display method that includes words of encouragement. This allows for the provision of an appropriate display method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the data analysis unit may be performed using AI, for example, or without AI. For example, the data analysis unit can input user emotion data into a generative AI and have the generative AI adjust the display method of the analysis results.

[0087] The data analysis unit can perform data analysis while taking into account the user's geographical location information. For example, if the user is in a cold region, the data analysis unit can analyze data related to cold weather countermeasures. For example, if the user is in a high-altitude area, the data analysis unit can also analyze data related to health management at high altitudes. For example, if the user is in an urban area, the data analysis unit can also analyze health data suitable for urban living. This enables appropriate data analysis based on the user's geographical location information. Some or all of the above-described processes in the data analysis unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the data analysis unit can input the user's geographical location information into a generative AI and have the generative AI perform the analysis.

[0088] The data analysis unit can analyze users' social media activity and analyze relevant data during data analysis. For example, if a user posts about health on social media, the data analysis unit can analyze data based on the content of those posts. For example, if a user posts about feeling stressed on social media, the data analysis unit can also analyze data related to stress reduction. For example, if a user posts about exercise on social media, the data analysis unit can also analyze data related to exercise. This enables appropriate data analysis based on users' social media activity. Some or all of the above processing in the data analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data analysis unit can input data on users' social media activity into a generative AI and have the generative AI perform the analysis of the relevant data.

[0089] The information provision unit can estimate the user's emotions and adjust the method of information provision based on the estimated user emotions. For example, if the user is stressed, the information provision unit can provide a simple and highly visible method of information provision. For example, if the user is relaxed, the information provision unit can also provide a method of information provision that includes detailed information. For example, if the user is unmotivated, the information provision unit can also provide a method of information provision that includes words of encouragement. This allows for the provision of an appropriate method of information provision according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the information provision unit may be performed using AI, for example, or without AI. For example, the information provision unit can input user emotion data into the generative AI and have the generative AI adjust the method of information provision.

[0090] The information provision unit can select the most suitable information by referring to the user's past health data when providing information. For example, the information provision unit can provide nutritionally balanced meal information based on the user's past dietary data. For example, the information provision unit can also provide effective training information based on the user's exercise history. For example, the information provision unit can provide appropriate weight management information based on the user's weight changes. This allows the system to provide optimal information based on the user's past data. Some or all of the above processing in the information provision unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the information provision unit can input the user's past health data into a generative AI and have the generative AI select the most suitable information.

[0091] The information provision unit can adjust the level of detail of the information provided based on the user's current living situation and health condition. For example, if the user is busy, the information provision unit can provide health information that can be acted upon in a short time. For example, if the user is feeling unwell, the information provision unit can also provide health information that can be acted upon at the user's destination. This allows the information provision unit to provide appropriate information according to the user's current situation. Some or all of the above processing in the information provision unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the information provision unit can input data on the user's current living situation and health condition into a generating AI and have the generating AI perform the adjustment of the level of detail of the information.

[0092] The information provision unit can estimate the user's emotions and determine the priority of information provision based on the estimated emotions. For example, if the user is feeling stressed, the information provision unit can prioritize providing information to reduce stress. For example, if the user is unmotivated, the information provision unit can also prioritize providing information to improve motivation. For example, if the user is relaxed, the information provision unit can also prioritize providing information related to long-term health goals. This allows information to be provided with priorities according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the information provision unit may be performed using AI, for example, or without AI. For example, the information provision unit can input user emotion data into a generative AI and have the generative AI determine the priority of information provision.

[0093] The information provision unit can prioritize providing highly relevant information by considering the user's geographical location when providing information. For example, if the user is in a cold region, the information provision unit can provide information on cold weather countermeasures. For example, if the user is in a high-altitude area, the information provision unit can also provide information on health management at high altitudes. For example, if the user is in an urban area, the information provision unit can also provide health information suitable for urban living. This allows for the provision of appropriate information based on the user's geographical location. Some or all of the above processing in the information provision unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the information provision unit can input the user's geographical location information into a generative AI and have the generative AI select highly relevant information.

[0094] The information provision unit can analyze a user's social media activity and provide relevant information when providing information. For example, if a user posts about health on social media, the information provision unit can provide information based on that content. For example, if a user posts about feeling stressed on social media, the information provision unit can also provide information on stress reduction. For example, if a user posts about exercise on social media, the information provision unit can also provide information on exercise. This allows the information provision unit to provide appropriate information based on the user's social media activity. Some or all of the above processing in the information provision unit may be performed using, for example, a generative AI, or without a generative AI. For example, the information provision unit can input data on the user's social media activity into a generative AI and have the generative AI generate relevant information.

[0095] The recording unit can estimate the user's emotions and adjust the recording method based on the estimated emotions. For example, if the user is stressed, the recording unit can provide a simple and visually clear recording method. For example, if the user is relaxed, the recording unit can also provide a recording method that includes detailed information. For example, if the user is unmotivated, the recording unit can also provide a recording method that includes words of encouragement. This allows for the provision of an appropriate recording method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the recording unit may be performed using AI, for example, or without AI. For example, the recording unit can input user emotion data into the generative AI and have the generative AI adjust the recording method.

[0096] The recording unit can select the optimal recording method by referring to the user's past health data during recording. For example, the recording unit can provide a nutritionally balanced meal recording method based on the user's past meal data. For example, the recording unit can also provide an effective exercise recording method based on the user's exercise history. For example, the recording unit can provide an appropriate weight management recording method based on the user's weight changes. This allows the recording unit to provide the optimal recording method based on the user's past data. Some or all of the above processing in the recording unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the recording unit can input the user's past health data into a generative AI and have the generative AI select the optimal recording method.

[0097] The recording unit can adjust the level of detail in the recording based on the user's current lifestyle and health condition. For example, if the user is busy, the recording unit can provide a recording method that can be completed in a short time. For example, if the user is feeling unwell, the recording unit can also provide a recording method that is manageable. For example, if the user is traveling, the recording unit can also provide a recording method that can be completed at the travel destination. This allows the recording unit to provide an appropriate recording method according to the user's current situation. Some or all of the above processing in the recording unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the recording unit can input data on the user's current lifestyle and health condition into the generating AI and have the generating AI perform the adjustment of the level of detail in the recording.

[0098] The recording unit can estimate the user's emotions and determine the priority of recordings based on the estimated emotions. For example, if the user is feeling stressed, the recording unit will prioritize recordings related to stress reduction. For example, if the user is feeling unmotivated, the recording unit may also prioritize recordings related to motivation improvement. For example, if the user is relaxed, the recording unit may also prioritize recordings related to long-term health goals. This allows recordings to be made with priorities that match the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the recording unit may be performed using AI, for example, or not using AI. For example, the recording unit can input user emotion data into a generative AI and have the generative AI determine the priority of recordings.

[0099] The recording unit can prioritize recordings that are highly relevant, taking into account the user's geographical location information. For example, if the user is in a cold region, the recording unit can provide records related to cold weather countermeasures. If the user is in a high-altitude region, the recording unit can also provide records related to health management at high altitudes. If the user is in an urban area, the recording unit can also provide health records suitable for urban life. This enables the recording of appropriate records based on the user's geographical location information. Some or all of the above processing in the recording unit may be performed using, for example, a generative AI, or without a generative AI. For example, the recording unit can input the user's geographical location information into a generative AI and have the generative AI select highly relevant records.

[0100] The recording unit can analyze the user's social media activity and make relevant records at the time of recording. For example, if the user posts about health on social media, the recording unit can provide records based on that content. For example, if the user posts about feeling stressed on social media, the recording unit can also provide records related to stress reduction. For example, if the user posts about exercise on social media, the recording unit can also provide records related to exercise. This allows for the creation of appropriate records based on the user's social media activity. Some or all of the above processing in the recording unit may be performed using, for example, a generative AI, or without a generative AI. For example, the recording unit can input data on the user's social media activity into a generative AI and have the generative AI generate relevant records.

[0101] The music recommendation unit can estimate the user's emotions and adjust the music recommendation method based on the estimated emotions. For example, if the user is feeling stressed, the music recommendation unit can recommend relaxing music. For example, if the user is feeling unmotivated, the music recommendation unit can also recommend encouraging music. For example, if the user is relaxed, the music recommendation unit can also recommend music to maintain relaxation. This allows for the recommendation of appropriate music according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the music recommendation unit may be performed using AI, or not using AI. For example, the music recommendation unit can input user emotion data into a generative AI and have the generative AI adjust the music recommendation method.

[0102] The music recommendation unit can select the most suitable music by referring to the user's past music history when making recommendations. For example, the music recommendation unit can recommend music the user likes based on their past music history. For example, the music recommendation unit can also recommend music the user listened to while exercising based on their past music history. For example, the music recommendation unit can analyze the user's past music history and recommend the most relaxing music. This allows the system to recommend the most suitable music based on the user's past music history. Some or all of the above processing in the music recommendation unit may be performed using, for example, a generative AI, or without a generative AI. For example, the music recommendation unit can input the user's past music history into a generative AI and have the generative AI select the most suitable music.

[0103] The music recommendation unit can adjust the level of detail in the music recommendations based on the user's current lifestyle and health condition. For example, if the user is busy, the music recommendation unit can recommend music that allows for quick relaxation. If the user is feeling unwell, the music recommendation unit can also recommend music that allows for relaxation within a reasonable range. If the user is traveling, the music recommendation unit can also recommend music that allows for relaxation at the travel destination. This allows for the recommendation of appropriate music according to the user's current situation. Some or all of the above processing in the music recommendation unit may be performed using, for example, a generative AI, or without a generative AI. For example, the music recommendation unit can input data on the user's current lifestyle and health condition into a generative AI and have the generative AI perform the adjustment of the level of detail in the music.

[0104] The music recommendation unit can estimate the user's emotions and determine the priority of music recommendations based on the estimated emotions. For example, if the user is feeling stressed, the music recommendation unit will prioritize recommending stress-relieving music. For example, if the user is unmotivated, the music recommendation unit can also prioritize recommending motivation-enhancing music. For example, if the user is relaxed, the music recommendation unit can also prioritize recommending music to maintain relaxation. This allows for music recommendations to be made with priorities that match the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the music recommendation unit may be performed using AI, for example, or without AI. For example, the music recommendation section can input user emotion data into a generating AI and have the AI ​​determine the priority of music recommendations.

[0105] The music recommendation unit can prioritize recommending music that is highly relevant to the user, taking into account the user's geographical location. For example, if the user is in a cold region, the music recommendation unit can recommend music that will make them feel warm. For example, if the user is in a high-altitude region, the music recommendation unit can also recommend music that will help them relax at high altitudes. For example, if the user is in an urban area, the music recommendation unit can also recommend music that is suitable for urban life. This allows for the recommendation of appropriate music based on the user's geographical location. Some or all of the above processing in the music recommendation unit may be performed using, for example, a generative AI, or without a generative AI. For example, the music recommendation unit can input the user's geographical location information into a generative AI and have the generative AI select highly relevant music.

[0106] The music recommendation unit can analyze a user's social media activity and recommend relevant music when making music recommendations. For example, if a user posts about music on social media, the music recommendation unit can recommend music based on that content. For example, if a user posts about feeling stressed on social media, the music recommendation unit can also recommend stress-relieving music. For example, if a user posts about exercise on social media, the music recommendation unit can also recommend exercise-related music. This allows for the recommendation of appropriate music based on the user's social media activity. Some or all of the above processing in the music recommendation unit may be performed using, for example, a generative AI, or without a generative AI. For example, the music recommendation unit can input data on the user's social media activity into a generative AI and have the generative AI generate relevant music.

[0107] The encouragement provider can estimate the user's emotions and adjust the content of the encouragement based on the estimated emotions. For example, if the user is feeling stressed, the encouragement provider can provide encouragement to help them relax. For example, if the user is feeling unmotivated, the encouragement provider can also provide encouragement that includes words of encouragement and success stories. For example, if the user is feeling agitated, the encouragement provider can also provide encouragement to help them calm down. This allows for the provision of appropriate encouragement according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the encouragement provider may be performed using AI or not using AI. For example, the encouragement provider can input the user's emotion data into a generative AI and have the generative AI adjust the content of the encouragement.

[0108] The cheering unit can select the most suitable cheering message by referring to the user's past health data when providing the message. For example, the cheering unit can provide a message about nutritionally balanced meals based on the user's past eating data. For example, the cheering unit can also provide a message about effective training based on the user's exercise history. For example, the cheering unit can provide a message about appropriate weight management based on the user's weight changes. This allows the unit to provide the most suitable cheering message based on the user's past data. Some or all of the above processing in the cheering unit may be performed using, for example, a generation AI, or without a generation AI. For example, the cheering unit can input the user's past health data into a generation AI and have the generation AI select the most suitable cheering message.

[0109] The support delivery unit can adjust the level of detail of the support provided based on the user's current living situation and health condition. For example, if the user is busy, the support delivery unit can provide support that can be done in a short amount of time. For example, if the user is feeling unwell, the support delivery unit can provide support that is within a reasonable range. For example, if the user is traveling, the support delivery unit can provide support that can be done at the travel destination. This allows the support delivery unit to provide appropriate support according to the user's current situation. Some or all of the above processing in the support delivery unit may be performed using, for example, a generation AI, or without a generation AI. For example, the support delivery unit can input data on the user's current living situation and health condition into a generation AI and have the generation AI adjust the level of detail of the support.

[0110] The encouragement provider can estimate the user's emotions and determine the priority of encouragement based on the estimated emotions. For example, if the user is feeling stressed, the encouragement provider can prioritize stress-reducing encouragement. For example, if the user is unmotivated, the encouragement provider can also prioritize motivation-boosting encouragement. For example, if the user is relaxed, the encouragement provider can also prioritize encouragement to maintain relaxation. This allows encouragement to be provided with priorities according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the encouragement provider may be performed using AI or not using AI. For example, the encouragement provider can input user emotion data into the generative AI and have the generative AI determine the priority of encouragement.

[0111] The cheering service provider can prioritize providing highly relevant cheers by considering the user's geographical location information when providing cheers. For example, if the user is in a cold region, the cheering service provider can provide cheers that will make them feel warm. For example, if the user is in a high-altitude region, the cheering service provider can also provide cheers that will help them relax at high altitudes. For example, if the user is in an urban area, the cheering service provider can also provide cheers that are suitable for urban life. This allows the service provider to provide appropriate cheers based on the user's geographical location information. Some or all of the above processing in the cheering service provider may be performed using, for example, a generative AI, or without a generative AI. For example, the cheering service provider can input the user's geographical location information into a generative AI and have the generative AI select highly relevant cheers.

[0112] The encouragement provider can analyze a user's social media activity and provide relevant encouragement when providing encouragement. For example, if a user posts about health on social media, the encouragement provider can provide encouragement based on that content. For example, if a user posts about feeling stressed on social media, the encouragement provider can also provide stress-reducing encouragement. For example, if a user posts about exercise on social media, the encouragement provider can also provide exercise-related encouragement. This allows for the provision of appropriate encouragement based on the user's social media activity. Some or all of the above processing in the encouragement provider may be performed using, for example, a generative AI, or without a generative AI. For example, the encouragement provider can input data on the user's social media activity into a generative AI and have the generative AI generate relevant encouragement.

[0113] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0114] The health management system can also include a sleep analysis unit that acquires and analyzes the user's sleep data. For example, the sleep analysis unit can analyze the user's sleep patterns and suggest optimal sleep duration and quality. It can also provide advice on improving sleep quality based on the user's sleep data. Furthermore, it can integrate the user's sleep data with other health data to provide comprehensive health management. This allows users to receive specific advice on how to achieve better sleep, contributing to an overall improvement in their health.

[0115] The health management system may also include an emotional feedback unit that estimates the user's emotions and provides feedback based on those emotions. For example, if the user is feeling stressed, the emotional feedback unit may provide feedback to help them relax. For example, if the user is feeling unmotivated, the emotional feedback unit may provide feedback including words of encouragement and success stories. For example, if the user is feeling agitated, the emotional feedback unit may provide feedback to help them calm down. This allows for the provision of appropriate feedback tailored to the user's emotions and supports the user's psychological well-being.

[0116] The health management system may also include a diet evaluation unit that analyzes the user's dietary data and evaluates the quality of their meals. For example, the diet evaluation unit can evaluate nutritional balance based on the user's dietary data. It can also evaluate calorie intake based on the user's dietary data. Furthermore, it can provide advice on improving the quality of the user's diet based on their dietary data. This allows users to objectively evaluate the quality of their own diet and receive specific advice for improvement.

[0117] The health management system may also include an exercise evaluation unit that analyzes the user's exercise data and evaluates the effectiveness of the exercise. The exercise evaluation unit, for example, evaluates the effectiveness of the exercise based on the user's exercise data. The exercise evaluation unit can also, for example, provide advice to improve the quality of the exercise based on the user's exercise data. Furthermore, the exercise evaluation unit can integrate the user's exercise data with other health data to provide comprehensive health management. This allows the user to objectively evaluate the effectiveness of their exercise and receive specific advice for improvement.

[0118] The health management system may further include an emotion-motor adjustment unit that estimates the user's emotions and adjusts the exercise plan based on those emotions. For example, if the user is feeling stressed, the emotion-motor adjustment unit may provide an exercise plan to help them relax. For example, if the user is feeling unmotivated, the emotion-motor adjustment unit may provide an exercise plan to boost their motivation. For example, if the user is relaxed, the emotion-motor adjustment unit may provide an exercise plan to maintain that relaxation. This allows for the provision of an appropriate exercise plan tailored to the user's emotions, maximizing the effectiveness of the exercise.

[0119] The health management system may also include a regional health information section that takes into account the user's geographical location and provides region-specific health information. For example, if the user is in a cold region, the regional health information section may provide health information related to cold weather countermeasures. If the user is in a high-altitude area, the regional health information section may also provide information related to health management at high altitudes. If the user is in an urban area, the regional health information section may also provide health information suitable for urban life. This allows for the provision of appropriate health information based on the user's geographical location and addresses region-specific health risks.

[0120] The health management system may further include an emotional meal adjustment unit that estimates the user's emotions and adjusts the meal plan based on those emotions. For example, if the user is feeling stressed, the emotional meal adjustment unit may provide a meal plan to help them relax. For example, if the user is feeling unmotivated, the emotional meal adjustment unit may provide a meal plan to boost their motivation. For example, if the user is relaxed, the emotional meal adjustment unit may provide a meal plan to help them maintain that relaxation. This allows for the provision of appropriate meal plans tailored to the user's emotions, thereby improving the quality of their meals.

[0121] The health management system may also include a social media analysis unit that analyzes users' social media activity and provides relevant health information. For example, if a user posts health-related content on social media, the social media analysis unit can provide health information based on that content. It can also provide health information to reduce stress if a user posts about feeling stressed on social media. Furthermore, if a user posts about exercise on social media, the social media analysis unit can provide health information related to exercise. This allows for the provision of appropriate health information based on users' social media activity, thereby supporting users' health management.

[0122] The health management system may further include an emotion report adjustment unit that estimates the user's emotions and adjusts the content of the health report based on the estimated emotions. For example, if the user is feeling stressed, the emotion report adjustment unit may provide a simple and easy-to-understand health report. For example, if the user is relaxed, the emotion report adjustment unit may also provide a health report that includes detailed information. For example, if the user is unmotivated, the emotion report adjustment unit may also provide a health report that includes words of encouragement. This allows for the provision of appropriate health reports tailored to the user's emotions, thereby supporting the user's health management.

[0123] The health management system may also include a goal achievement evaluation unit that refers to the user's past health data to assess the degree to which health goals have been achieved. For example, the goal achievement evaluation unit can evaluate the degree to which nutritional balance has been achieved based on the user's past dietary data. For example, the goal achievement evaluation unit can also evaluate the degree to which exercise goals have been achieved based on the user's exercise history. For example, the goal achievement evaluation unit can also evaluate the degree to which weight management goals have been achieved based on the user's weight changes. This allows users to objectively evaluate their progress toward their health goals and receive specific advice on how to move forward to the next step.

[0124] The following briefly describes the processing flow for example form 2.

[0125] Step 1: The advice provision unit provides customized advice based on the user's health goals and lifestyle. For example, it can use generative AI to generate specific advice tailored to the user's health goals and provide advice on diet and exercise. It can also propose action plans tailored to the user's health goals. Step 2: The data analysis unit analyzes physiological data and food photos from the wearable device. For example, it can use a generative AI to analyze physiological data and evaluate the user's health status. It can also analyze food photos to provide nutrient and calorie information, and analyze the user's exercise data to evaluate the effectiveness of exercise. Step 3: The Information Provision Department provides the information analyzed by the Data Analysis Department. For example, it can use a generation AI to provide users with easy-to-understand analysis results and generate and provide health reports to users. It can also provide specific advice based on the analysis results. Step 4: The recording unit records meal history, exercise records, and weight changes. For example, it can use generation AI to automatically record meal history and exercise records. It can also record and graph weight changes. Step 5: The music recommendation section recommends music suitable for fitness. For example, it can use generative AI to recommend music tailored to the user's preferences, or recommend music based on the type of exercise. It can also recommend music that will boost the user's motivation. Step 6: The encouragement provider sends encouragement when the user stops tracking their weight or exercise, or when their motivation declines. For example, it can use a generation AI to create encouragement tailored to the user's situation, combining it with images of the user's efforts and music. It can also provide encouragement to help the user regain their motivation.

[0126] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0127] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0128] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0129] Each of the multiple elements described above, including the advice provision unit, data analysis unit, information provision unit, recording unit, music recommendation unit, and encouragement provision unit, is implemented, for example, in at least one of the smart device 14 and the data processing unit 12. For example, the advice provision unit is implemented by the control unit 46A of the smart device 14 and generates specific advice according to the user's health goals. The data analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and analyzes physiological data and food photos from the wearable device. The information provision unit is implemented, for example, by the control unit 46A of the smart device 14 and provides the analysis results to the user. The recording unit is implemented, for example, by the control unit 46A of the smart device 14 and records the history of meals and exercise. The music recommendation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and recommends music suitable for fitness. The encouragement provision unit is implemented, for example, by the control unit 46A of the smart device 14 and provides encouragement according to the user's situation. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0130] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0131] As shown in Figure 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.

[0132] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0133] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0134] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0135] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0136] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0137] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0138] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0139] Storage 32 stores the data generation model 58 and the 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 the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0140] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0141] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0142] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0143] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0144] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0145] Each of the multiple elements described above, including the advice provision unit, data analysis unit, information provision unit, recording unit, music recommendation unit, and encouragement provision unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the advice provision unit is implemented by the control unit 46A of the smart glasses 214 and generates specific advice according to the user's health goals. The data analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes physiological data and food photos from the wearable device. The information provision unit is implemented by the control unit 46A of the smart glasses 214 and provides the user with the analysis results. The recording unit is implemented by the control unit 46A of the smart glasses 214 and records the history of meals and exercise. The music recommendation unit is implemented by the specific processing unit 290 of the data processing unit 12 and recommends music suitable for fitness. The encouragement provision unit is implemented by the control unit 46A of the smart glasses 214 and provides encouragement according to the user's situation. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0146] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0147] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0148] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0149] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0150] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0151] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0152] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0153] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0154] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0155] Storage 32 stores the data generation model 58 and the 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 the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0156] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0157] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0158] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0159] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0160] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0161] Each of the multiple elements described above, including the advice provision unit, data analysis unit, information provision unit, recording unit, music recommendation unit, and encouragement provision unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the advice provision unit is implemented by the control unit 46A of the headset terminal 314 and generates specific advice according to the user's health goals. The data analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes physiological data and food photos from the wearable device. The information provision unit is implemented by the control unit 46A of the headset terminal 314 and provides the user with the analysis results. The recording unit is implemented by the control unit 46A of the headset terminal 314 and records the history of meals and exercise. The music recommendation unit is implemented by the specific processing unit 290 of the data processing unit 12 and recommends music suitable for fitness. The encouragement provision unit is implemented by the control unit 46A of the headset terminal 314 and provides encouragement according to the user's situation. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0162] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0163] As shown in Figure 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.

[0164] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0165] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0166] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0167] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0168] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0169] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0170] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0171] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0172] Storage 32 stores the data generation model 58 and the 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 the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0173] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0174] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0175] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0176] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0177] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0178] Each of the multiple elements described above, including the advice provision unit, data analysis unit, information provision unit, recording unit, music recommendation unit, and encouragement provision unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the advice provision unit is implemented by the control unit 46A of the robot 414 and generates specific advice according to the user's health goals. The data analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes physiological data and food photos from a wearable device. The information provision unit is implemented by, for example, the control unit 46A of the robot 414 and provides the user with the analysis results. The recording unit is implemented by, for example, the control unit 46A of the robot 414 and records the history of meals and exercise. The music recommendation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and recommends music suitable for fitness. The encouragement provision unit is implemented by, for example, the control unit 46A of the robot 414 and provides encouragement according to the user's situation. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0179] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0180] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0181] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0182] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0183] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0184] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0185] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0186] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0187] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0188] 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.

[0189] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0190] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0191] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0192] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0193] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0194] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0195] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0196] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0197] (Note 1) The advice department provides customized advice based on the user's health goals and lifestyle, The data analysis unit analyzes physiological data and food photos from wearable devices, An information provision unit that provides information analyzed by the aforementioned data analysis unit, It has a recording section for recording meal history, exercise records, and weight changes, The music recommendation department recommends music suitable for fitness, It includes a cheering section that sends cheers, A system characterized by the following features. (Note 2) The aforementioned advice-providing unit, It estimates the user's emotions and adjusts the content of the advice based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned advice-providing unit, Analyze the user's past health data to select the most suitable advice. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned advice-providing unit, When providing advice, filtering is performed based on the user's current living situation and health status. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned advice-providing unit, It estimates the user's emotions and prioritizes advice based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned advice-providing unit, When providing advice, we prioritize providing highly relevant advice by taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned advice-providing unit, When providing advice, we analyze the user's social media activity and provide relevant advice. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned data analysis unit, We estimate the user's emotions and adjust the data analysis method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned data analysis unit, During data analysis, the analysis algorithm is optimized by referencing the user's past health data. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned data analysis unit, During data analysis, the accuracy of the analysis is improved based on the user's current lifestyle and health status. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned data analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned data analysis unit, When analyzing data, the analysis will take into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned data analysis unit, During data analysis, we analyze users' social media activity and analyze related data. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned information provision unit, It estimates the user's emotions and adjusts the way information is delivered based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned information provision unit, When providing information, the system selects the most relevant information by referring to the user's past health data. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned information provision unit, When providing information, the level of detail is adjusted based on the user's current living situation and health status. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned information provision unit, The system estimates the user's emotions and prioritizes information provision based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned information provision unit, When providing information, we prioritize providing highly relevant information by taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned information provision unit, When providing information, we analyze the user's social media activity and provide relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned recording unit is The system estimates the user's emotions and adjusts the recording method based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned recording unit is During recording, the system selects the optimal recording method by referring to the user's past health data. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned recording unit is During recording, the level of detail in the recording is adjusted based on the user's current lifestyle and health status. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned recording unit is The system estimates the user's emotions and prioritizes recordings based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned recording unit is During recording, the system prioritizes recordings that are highly relevant, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned recording unit is During recording, the user's social media activity is analyzed and relevant records are made. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned music recommendation section is, It estimates the user's emotions and adjusts the music recommendation method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned music recommendation section is, When recommending music, the system selects the most suitable music by referring to the user's past music history. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned music recommendation section is, When recommending music, the level of detail in the recommendations is adjusted based on the user's current lifestyle and health status. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned music recommendation section is, It estimates the user's emotions and prioritizes music recommendations based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned music recommendation section is, When recommending music, the system prioritizes recommending music that is highly relevant to the user, taking into account their geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned music recommendation section is, When recommending music, the system analyzes the user's social media activity and recommends relevant music. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned ale-providing section, The system estimates the user's emotions and adjusts the content of the encouragement based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned ale-providing section, When providing health support, the system selects the most suitable support by referring to the user's past health data. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned ale-providing section, When providing encouragement, the level of detail in the encouragement is adjusted based on the user's current living situation and health condition. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned ale-providing section, It estimates the user's emotions and determines the priority of cheers based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned ale-providing section, When providing cheers, the system prioritizes providing highly relevant cheers by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 37) The aforementioned ale-providing section, When providing support, the system analyzes the user's social media activity and provides relevant support. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0198] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The advice department provides customized advice based on the user's health goals and lifestyle, The data analysis unit analyzes physiological data and food photos from wearable devices, An information provision unit that provides information analyzed by the aforementioned data analysis unit, It has a recording section for recording meal history, exercise records, and weight changes, The music recommendation department recommends music suitable for fitness, It includes a cheering section that sends cheers, A system characterized by the following features.

2. The aforementioned advice-providing unit, It estimates the user's emotions and adjusts the content of the advice based on the estimated user emotions. The system according to feature 1.

3. The aforementioned advice-providing unit, Analyze the user's past health data to select the most suitable advice. The system according to feature 1.

4. The aforementioned advice-providing unit, When providing advice, filtering is performed based on the user's current living situation and health status. The system according to feature 1.

5. The aforementioned advice-providing unit, It estimates the user's emotions and prioritizes advice based on those estimated emotions. The system according to feature 1.

6. The aforementioned advice-providing unit, When providing advice, we prioritize providing highly relevant advice by taking into account the user's geographical location. The system according to feature 1.

7. The aforementioned advice-providing unit, When providing advice, we analyze the user's social media activity and provide relevant advice. The system according to feature 1.

8. The aforementioned data analysis unit, We estimate the user's emotions and adjust the data analysis method based on the estimated user emotions. The system according to feature 1.

9. The aforementioned data analysis unit, During data analysis, the analysis algorithm is optimized by referencing the user's past health data. The system according to feature 1.

10. The aforementioned data analysis unit, During data analysis, the accuracy of the analysis is improved based on the user's current lifestyle and health status. The system according to feature 1.

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