System

Abstract

An object of a system according to an embodiment is to efficiently collect and analyze health data of an individual.SOLUTION: A system according to an embodiment includes a wearable device, a physical condition information share application, a generation AI, and a reward providing unit. The wearable device collects physical condition data of the user. The physical condition information sharing application transmits the physical condition data collected by the wearable device to the cloud. The generation AI analyzes the physical condition information transmitted by the physical condition information share application. The reward provision unit provides a reward to the user who has provided the physical condition data.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have not been able to efficiently collect and analyze personal health data, and there is room for improvement.

[0005] The system according to the embodiment aims to efficiently collect and analyze personal health data. [Means for solving the problem]

[0006] The system according to the embodiment includes a wearable device, a physical condition information sharing app, a generation AI, and a reward providing unit. The wearable device collects a user's physical condition data. The physical condition information sharing app transmits the physical condition data collected by the wearable device to the cloud. The generation AI analyzes the physical condition data transmitted by the physical condition information sharing app. The reward providing unit provides a reward to a user who provides physical condition data. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently collect and analyze personal health data. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

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

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

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

[0017] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0024] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0027] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) A health data collection system according to an embodiment of the present invention uses free or inexpensive wearable devices and a health information sharing app to collect personal health data, and a generative AI develops learning models based on that data and provides them to other companies. This health data collection system can contribute to personalized medicine, disease prevention, and optimization of health management.

[0029] A health data collection system according to an embodiment includes a wearable device, a health information sharing app, a generation AI, and a reward provider. The wearable device collects a user's health data. For example, the wearable device collects data such as the user's body temperature, humidity, sweat sodium content, drunkenness level, age, skin moisture content, and health condition. The wearable device can also monitor the user's heart rate and blood pressure. The wearable device can also record the user's exercise level and sleep patterns. The health information sharing app transmits the health data collected by the wearable device to the cloud. For example, the health information sharing app transmits data to the cloud using Bluetooth or Wi-Fi. The health information sharing app can also allow the user to set the frequency of data transmission. The health information sharing app can also display the data transmission status in real time. The generation AI analyzes the health data transmitted by the health information sharing app. For example, the generation AI can analyze the data using a machine learning model to predict health status and assess disease risk. The generation AI can also analyze correlations in the data to discover new insights for health management. The generation AI can also detect abnormal values ​​in the data and send an alert to the user. The reward providing unit provides rewards to users who provide physical condition data. For example, the reward providing unit may grant electronic money according to the quantity and quality of data provided by the user. The reward providing unit can also provide points or coupons. The reward providing unit can also notify the user of the reward provision status. As a result, the health data collection system according to the embodiment can contribute to personalized medicine, disease prevention, and optimization of health management. For example, users can understand their health condition on a daily basis and take appropriate measures. Furthermore, medical institutions and healthcare companies can develop new treatments and preventive measures based on the collected data.

[0030] A wearable device can use a material that automatically adjusts to the user's body shape or skin type. For example, a wearable device can be designed to automatically fit to the user's body shape by using a shape memory alloy in the band. For example, the shape memory alloy can deform to fit the shape of the user's wrist, providing a comfortable fit. A wearable device can also use a skin-friendly silicone material to ensure comfortable wear even for users with sensitive skin. A wearable device can also use a breathable nylon material to prevent stuffiness even when worn for long periods of time. By using a material that automatically adjusts to the user's body shape and skin type, the wearing comfort can be improved.

[0031] Wearable devices can be equipped with an energy collection function that utilizes sunlight or body heat. For example, a wearable device may be equipped with a small solar cell and use sunlight during the day to charge the battery. For example, the solar cell generates electricity from sunlight and supplies power to the battery. Wearable devices can also convert a user's body heat into electricity using thermoelectric conversion technology. For example, a body temperature sensor detects the user's body temperature, and a thermoelectric conversion element converts the heat into electricity. Furthermore, wearable devices can extend battery life by using the energy collection function. Thus, adding an energy collection function that utilizes sunlight or body heat can extend battery life.

[0032] Wearable devices have been modified for use with pets to collect health data about the pet. For example, a collar-type wearable device has been developed for use with pets to monitor the pet's body temperature and heart rate. For example, the wearable device is worn around the pet's neck, and a temperature sensor measures the pet's body temperature. The wearable device can also monitor the pet's health by measuring the pet's heart rate using a heart rate sensor. The wearable device can also record the pet's activity level and sleep patterns. For example, the wearable device can measure the pet's movement level using an acceleration sensor to collect activity data. The wearable device can also analyze the pet's sleep patterns and evaluate the quality of sleep. Thus, pet health data can be collected using wearable devices modified for pets.

[0033] Wearable devices are specialized for athletes and can collect performance data during exercise. For example, wearable devices can add functions for athletes to monitor their heart rate and oxygen saturation in real time to evaluate the effectiveness of their training. For example, a wearable device can use a heart rate sensor to measure heart rate during exercise and evaluate the intensity of the training. A wearable device can also use an oxygen sensor to measure oxygen saturation and monitor the oxygen supply status during exercise. Wearable devices can also be equipped with a GPS function to record the distance and speed traveled during exercise. For example, a wearable device can measure running distance and pace based on GPS data to evaluate training progress. Furthermore, a wearable device can use an accelerometer to analyze exercise form and movements, which can be used to improve performance. This makes it possible to collect performance data during exercise using wearable devices specialized for athletes.

[0034] When collecting data, the generation AI can simultaneously record the user's lifestyle habits and dietary details, allowing for more detailed health predictions. For example, the generation AI can add a feature that allows the user to input their daily dietary details into the app and collect dietary data. For example, the generation AI can input the types of food and calories consumed by the user and analyze the data. The generation AI can also record the user's exercise habits and sleep patterns to collect lifestyle data. For example, the generation AI can input the type and frequency of exercise and analyze the data. The generation AI can also record the user's sleep time and quality and analyze the sleep data. The generation AI can also combine and analyze lifestyle data and health data to predict health status and assess disease risk. For example, the generation AI can analyze the correlation between dietary details and health data to evaluate the impact of specific meals on health. The generation AI can also analyze the correlation between exercise habits and health data to evaluate the impact of exercise on health status. This allows for more detailed health predictions by simultaneously recording the user's lifestyle habits and dietary details.

[0035] The generation AI dynamically adjusts the data collection frequency according to the user's physical condition, efficiently collecting only the necessary data. For example, the generation AI can set the data collection frequency low when the user's physical condition is stable and increase the collection frequency when the user's physical condition fluctuates. For example, the generation AI can increase data collection to collect detailed physical condition data if the user's body temperature suddenly rises. The generation AI can also increase data collection if the user's heart rate or blood pressure shows abnormal values. The generation AI can also monitor the user's physical condition data in real time and dynamically adjust the collection frequency. For example, the generation AI can collect data once a day when the user's physical condition is stable and every hour when the user's physical condition fluctuates. The generation AI can also analyze the user's physical condition data and use an algorithm to optimize the collection frequency. This allows the data collection frequency to be dynamically adjusted according to the user's physical condition, efficiently collecting only the necessary data.

[0036] When collecting health data, the generative AI can simultaneously collect the user's voice data and estimate their health status from changes in their voice. For example, the generative AI can add a feature to the app that allows the user to record voice memos, and then analyze the tone and pitch of the voice to estimate their health status. For example, the generative AI can detect hoarseness or changes in tone in the user's voice and estimate abnormalities in their health status. The generative AI can also analyze the user's breathing sounds and monitor their respiratory status. For example, the generative AI can detect changes in the user's breathing sounds and evaluate the health status of their respiratory system. The generative AI can also combine and analyze the user's voice data and health data to predict their health status and assess their disease risk. For example, the generative AI can analyze the correlation between changes in voice tone and health data to evaluate the impact of specific voice changes on their health status. The generative AI can also estimate the user's emotional state based on the voice data and analyze the correlation between emotions and health status. This makes it possible to simultaneously collect the user's voice data and estimate their health status from changes in their voice.

[0037] Generative AI can also collect environmental data by linking data collection with smart devices in the home. For example, generative AI can link with a smart refrigerator to collect data on food consumption and storage conditions. For example, generative AI can record the temperature and humidity inside the refrigerator to monitor food storage conditions. Generative AI can also link with a smart air conditioner to collect indoor temperature and humidity data. For example, generative AI can record the air conditioner's set temperature and indoor humidity to collect environmental data. Generative AI can also link with a smart speaker to collect user voice commands. For example, generative AI can record voice commands given by the user to the smart speaker and collect user behavior data. Generative AI can also combine and analyze environmental data and health data to predict health status and assess disease risk. For example, generative AI can analyze the correlation between indoor temperature and humidity and health data to evaluate the impact of specific environmental conditions on health. Generative AI can also provide users with appropriate health management advice based on environmental data. This allows data collection to be linked to smart devices in the home, allowing environmental data to be collected at the same time.

[0038] The generation AI can customize the content of advice based on the user's past behavioral history and preferences, making it easier to implement. For example, the generation AI can analyze the user's past behavioral history and provide easy-to-implement advice. For example, the generation AI can analyze the user's exercise history and make meal suggestions based on past successful diet methods. The generation AI can also analyze the user's diet history and suggest meals that match their preferences. For example, the generation AI can suggest healthy recipes based on the user's favorite ingredients. The generation AI can also analyze the user's sleep history and provide advice to improve sleep quality. For example, the generation AI can analyze the user's sleep patterns and suggest appropriate bedtimes and wake-up times. The generation AI can also customize the content of advice based on the user's preferences. For example, the generation AI can suggest exercises and meals that the user prefers and provide easy-to-implement advice. This makes it possible to customize advice based on the user's past behavioral history and preferences, making it easier to implement.

[0039] The generation AI can monitor the effectiveness of the advice in real time and automatically update the advice content as needed. For example, the generation AI can monitor the user's physical condition data in real time after the advice is implemented and evaluate its effectiveness. For example, the generation AI can analyze changes in heart rate and body temperature after exercise to evaluate the effectiveness of the advice. The generation AI can also monitor the user's sleep data and evaluate the effectiveness of the advice. For example, the generation AI can analyze the user's sleep time and sleep quality to evaluate the effectiveness of the advice. The generation AI can also automatically update the advice content based on the effectiveness of the advice. For example, the generation AI can analyze the user's physical condition data and change the advice content if it is not effective. The generation AI can also customize the advice content based on user feedback. For example, the generation AI can analyze the feedback the user provides in response to the advice and improve the advice content. This enables more effective health management by monitoring the effectiveness of the advice in real time and automatically updating the advice content as needed.

[0040] The generation AI can use animations or infographics to make the advice easier to understand visually. For example, the generation AI can display the advice content using animations to make it easier for users to understand visually. For example, the generation AI can use animations to show exercise methods so that users can exercise with the correct form. The generation AI can also display meal suggestions using infographics to make it easier for users to understand visually. For example, the generation AI can use infographics to show the nutritional balance of meals so that users can visually understand which ingredients they should consume. The generation AI can also display health management progress using infographics so that users can visually check their progress toward their goals. For example, the generation AI can display graphs of the user's exercise volume and weight changes to visually show their progress toward their health management. The generation AI can also use animations and infographics to provide motivational messages to users. For example, the generation AI can display an animated congratulatory message when the user achieves their goal. This makes the advice easier to understand visually, making it easier for users to understand.

[0041] The generation AI can integrate advice with a voice assistant, allowing users to receive advice via voice. For example, the generation AI can integrate with a voice assistant to allow users to receive advice via voice. For example, the generation AI can provide exercise and diet advice through a smart speaker. The generation AI can also allow users to request advice via voice command. For example, when a user inputs the voice command "Tell me today's exercise advice," the smart speaker will provide the exercise advice via voice. The generation AI can also set reminders for users through a voice assistant. For example, when a user inputs the voice command "Set a reminder to exercise tomorrow morning," the smart speaker will set the reminder and notify the user via voice at the specified time. The generation AI can also provide motivational messages to users through a voice assistant. For example, when a user completes an exercise session, the generation AI can have the smart speaker provide an encouraging voice message such as "Well done!". In this way, by integrating advice with a voice assistant, users can receive advice via voice.

[0042] The reward providing unit may diversify the types of rewards and allow the user to select from them. For example, the reward providing unit may provide electronic money, points, gift cards, etc. The reward providing unit may also allow the user to select the type of reward. For example, the reward providing unit may allow the user to select the type of reward within the app and receive the desired reward. The reward providing unit may also periodically update the types of rewards and provide new rewards to the user. For example, the reward providing unit may provide different gift cards or coupons for each season to attract the user's interest. The reward providing unit may also customize the types of rewards based on the user's preferences. For example, the reward providing unit may provide gift cards of a brand that the user prefers, thereby improving user satisfaction. In this way, the types of rewards may be diversified and the user may be able to select from them, thereby improving user satisfaction.

[0043] The reward providing unit can dynamically adjust the frequency of reward provision depending on the frequency and quality of data provided by the user. The reward providing unit dynamically adjusts the frequency of reward provision depending on, for example, the frequency of data provided by the user. For example, the reward providing unit frequently provides rewards to users who provide a lot of data, and reduces the frequency of reward provision to users who provide a little data. The reward providing unit can also adjust the frequency of reward provision depending on the quality of the user's data. For example, the reward providing unit frequently provides rewards to users who provide high-quality data, and reduces the frequency of reward provision to users who provide low-quality data. The reward providing unit can also monitor the user's data provision status in real time and dynamically adjust the frequency of reward provision. For example, the reward providing unit frequently provides rewards immediately after the user starts providing data, and reduces the frequency of reward provision once the data provision becomes stable. The reward providing unit can also adjust the type of reward depending on the user's data provision status. For example, the reward providing unit provides expensive gift cards to users who provide a lot of data, and provides points to users who provide a little data. In this way, the frequency of reward provision can be dynamically adjusted depending on the frequency and quality of the user's data provision, thereby maintaining the user's motivation.

[0044] The reward providing unit can contribute to revitalizing the local economy by partnering with local shops and services to provide rewards. For example, the reward providing unit can partner with local shops and services to provide rewards so that users can use them in the local area. For example, the reward providing unit can provide coupons that can be used at local restaurants and cafes. The reward providing unit can also partner with local fitness gyms and health food stores to allow users to use health-related services. For example, the reward providing unit can provide vouchers for local fitness gyms and coupons for health foods. The reward providing unit can also contribute to revitalizing the local economy through partnerships with local shops and services. For example, the reward providing unit can cooperate with local shops and services to hold local events and campaigns. The reward providing unit can also convey the appeal of the local area to users through partnerships with local shops and services. For example, the reward providing unit can introduce local tourist spots and local specialties to encourage users to visit the local area. In this way, partnering with local shops and services to provide rewards can contribute to revitalizing the local economy.

[0045] The reward providing unit can enable the user to exchange the reward for health-related products or services. For example, the reward providing unit builds a system that enables the user to exchange the reward for health-related products or services. For example, the reward providing unit provides fitness gym vouchers or health food coupons. The reward providing unit can also enable the user to exchange the reward for health-related products or services. For example, the reward providing unit provides a function for exchanging rewards within the app, allowing the user to select the health-related products or services they desire. The reward providing unit can also periodically update the reward exchange destinations and provide the user with new health-related products or services. For example, the reward providing unit can provide different fitness gym vouchers or health food coupons for each season to attract the user's interest. The reward providing unit can also customize the reward exchange destinations based on the user's preferences. For example, the reward providing unit can provide coupons for the user's favorite fitness gym or health food store to increase user satisfaction. By enabling the user to exchange the reward for health-related products or services, the user's motivation to manage their health can be increased.

[0046] Generative AI can incorporate advanced anonymization techniques to protect user privacy when providing data. For example, when providing data, it can incorporate techniques to anonymize users' personal information. For example, it can remove personally identifiable information such as names and addresses and provide anonymized data. Generative AI can also use data masking techniques to conceal users' personal information. For example, it can replace users' personal information with random strings of characters to anonymize them. Generative AI can also protect users' personal information using pseudo-anonymization techniques. For example, it can encrypt and anonymize users' personal information. Generative AI can also incorporate access control to protect users' privacy when providing data. For example, it can allow data recipients to access only specific data and restrict access to other data. Generative AI can also incorporate a mechanism to obtain users' consent when providing data. For example, it can explain the purpose and scope of data provision to users and provide the data only after obtaining their consent. This can increase users' sense of security by incorporating advanced anonymization techniques to protect users' privacy when providing data.

[0047] By providing data to other companies, generative AI can promote joint research and development projects and create new medical technologies and services. For example, generative AI can launch joint research projects with other companies and develop new medical technologies based on collected data. For example, generative AI can jointly develop new algorithms for disease prevention. Generative AI can also promote development projects with other companies and create new medical services. For example, generative AI can work with other companies to develop new diagnostic tools for personalized medicine. Generative AI can also promote innovation in the medical field through joint research with other companies. For example, generative AI can publish research papers in collaboration with other companies and share new knowledge. Generative AI can also promote the practical application of medical technology through joint development projects with other companies. For example, generative AI can work with other companies to develop new medical devices and conduct clinical trials. In this way, by providing data to other companies, joint research and development projects can be promoted and new medical technologies and services can be created, thereby promoting innovation in the medical field.

[0048] The generating AI can link its own app with other companies' health-related services to provide users with a consistent health management experience. For example, the generating AI can link its own app with other companies' health-related services to provide users with a consistent health management experience. For example, the generating AI can link with a fitness app to share exercise data. The generating AI can also link with a diet management app to share diet data. For example, the generating AI can import diet data entered by a user into a diet management app into its own app and use it for health management. The generating AI can also link with a sleep management app to share sleep data. For example, the generating AI can import sleep data entered by a user into a sleep management app into its own app and use it for health management. The generating AI can also link with other companies' health-related services to provide users with comprehensive health management advice. For example, the generating AI can combine data from a fitness app and a diet management app to provide users with advice that takes into account the balance between exercise and diet. The generating AI can also link with other companies' health-related services to provide users with a consistent health management experience. For example, generative AI can maintain data consistency when a user uses multiple apps, providing a seamless health management experience. This allows companies to link their own apps with other companies' health-related services to provide users with a consistent health management experience.

[0049] Generative AI can promote collaboration with different industries by providing data to other companies. For example, generative AI can promote collaboration with the insurance industry by providing data to other companies. For example, generative AI can customize insurance products based on health data. Generative AI can also promote collaboration with the food industry. For example, generative AI can develop health foods based on health data. Generative AI can also create new business opportunities through collaboration with different industries. For example, generative AI can work with different industries based on health data to develop new services and products. Generative AI can also provide comprehensive health management services to users through collaboration with different industries. For example, generative AI can combine data from the insurance and food industries to suggest health management and insurance products to users. Generative AI can also provide users with a consistent health management experience through collaboration with different industries. For example, generative AI can maintain data consistency when users use services from multiple industries, providing a seamless health management experience. This can create new business opportunities by promoting collaboration with different industries through data provision.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] Wearable devices not only collect data on a user's physical condition, but also data on the user's living environment. For example, wearable devices can monitor the temperature, humidity, and air quality around the user using built-in environmental sensors. Wearable devices can also acquire the user's location information using GPS and record outdoor activity. Furthermore, wearable devices can send the user's living environment data to the cloud, where a generative AI can analyze the data to identify environmental factors that affect the user's health. This allows users to understand the impact of their living environment on their health and take appropriate measures.

[0052] Wearable devices can use materials that automatically adjust to the user's body type and skin type, as well as dynamically adjust to the user's activity level. For example, a wearable device can change the structure of its material to increase breathability when the user sweats during exercise. Furthermore, a wearable device can change to a softer material when the user is relaxed, providing a comfortable fit. Furthermore, a wearable device can automatically adjust the tightness of the band according to the user's activity level. This allows the user to use the wearable device while always maintaining a comfortable fit.

[0053] Wearable devices can not only add energy harvesting capabilities using sunlight or body heat, but also have the ability to generate energy using the user's movements. For example, a wearable device can use a built-in vibration-powered element to convert vibrations generated by the user's walking or exercise into electricity. A wearable device can also generate electricity using friction generated while the user is exercising. Furthermore, wearable devices can use the energy harvesting capability to not only extend battery life but also collect user activity data. This allows a wearable device to generate energy using the user's movements and extend battery life.

[0054] Wearable devices have been adapted for pets, and can now collect not only pet health data but also behavioral data. For example, wearable devices can use built-in acceleration sensors to record a pet's activity level and frequency of play. They can also use GPS to record a pet's route and range of activity. Furthermore, wearable devices can transmit pet behavioral data to the cloud, where AI can analyze the data to understand the pet's health condition and behavioral patterns. This allows pet owners to understand their pet's health condition and behavioral patterns and take appropriate measures.

[0055] Wearable devices are specialized for athletes and can collect not only performance data during exercise but also post-exercise recovery data. For example, wearable devices can monitor the recovery rate of heart rate and blood pressure after exercise to evaluate the effectiveness of recovery. Wearable devices can also measure muscle fatigue and determine the need for recovery. Furthermore, wearable devices can collect sleep data after exercise and evaluate the quality of recovery. This allows athletes to create appropriate recovery plans based on post-exercise recovery data, which can help improve their performance.

[0056] When collecting data, the Generator AI simultaneously records the user's lifestyle and dietary habits, enabling it to not only make more detailed health predictions but also provide appropriate health management advice based on the user's lifestyle and dietary habits. For example, the Generator AI can add a feature that allows the user to input their daily dietary habits into the app, analyze the dietary data, and suggest nutritionally balanced meals. The Generator AI can also record the user's exercise habits and sleep patterns, analyze the lifestyle data, and provide appropriate exercise and sleep advice. Furthermore, the Generator AI can combine and analyze lifestyle data and physical condition data to not only predict health status and assess disease risk, but also provide appropriate health management advice to the user. This allows users to receive appropriate health management advice based on their lifestyle and dietary habits, making health management more effective.

[0057] The generating AI dynamically adjusts the data collection frequency according to the user's physical condition, not only efficiently collecting only the necessary data but also optimizing the timing of data collection according to the user's lifestyle. For example, if the user is active during the day, the generating AI can increase the frequency of data collection and reduce the frequency of data collection at night. The generating AI can also reduce the burden on the user by adjusting the timing of data collection according to the user's lifestyle. Furthermore, the generating AI monitors the user's physical condition data in real time and dynamically adjusts the collection frequency, not only dynamically adjusting the collection frequency but also optimizing the timing of data collection according to the user's lifestyle, thereby efficiently collecting only the necessary data. This allows users to receive optimal data collection according to their lifestyle, making health management more effective.

[0058] The processing flow of the first embodiment will be briefly explained below.

[0059] Step 1: The wearable device collects data on the user's physical condition, such as body temperature, humidity, sweat sodium content, intoxication level, age, skin moisture content, heart rate, blood pressure, exercise volume, and sleep patterns. Step 2: The health information sharing app sends the health data collected by the wearable device to the cloud. For example, it can send data to the cloud using Bluetooth or Wi-Fi, and allow the user to set the frequency of data transmission. It can also display the data transmission status in real time. Step 3: The generating AI analyzes the health data sent by the health information sharing app. For example, it can analyze the data using a machine learning model to predict health status and assess disease risk. It can also analyze data correlations to discover new insights for health management. It can also detect abnormal values ​​in the data and send alerts to the user. Step 4: The reward provider provides rewards to users who provide their physical condition data. For example, the reward provider may provide electronic money, points, or coupons depending on the quantity and quality of the data provided by the user. The reward provider may also notify the user of the status of reward provision.

[0060] (Example 2) A health data collection system according to an embodiment of the present invention uses free or inexpensive wearable devices and a health information sharing app to collect personal health data, and a generative AI develops learning models based on that data and provides them to other companies. This health data collection system can contribute to personalized medicine, disease prevention, and optimization of health management.

[0061] A health data collection system according to an embodiment includes a wearable device, a health information sharing app, a generation AI, and a reward provider. The wearable device collects a user's health data. For example, the wearable device collects data such as the user's body temperature, humidity, sweat sodium content, drunkenness level, age, skin moisture content, and health condition. The wearable device can also monitor the user's heart rate and blood pressure. The wearable device can also record the user's exercise level and sleep patterns. The health information sharing app transmits the health data collected by the wearable device to the cloud. For example, the health information sharing app transmits data to the cloud using Bluetooth or Wi-Fi. The health information sharing app can also allow the user to set the frequency of data transmission. The health information sharing app can also display the data transmission status in real time. The generation AI analyzes the health data transmitted by the health information sharing app. For example, the generation AI can analyze the data using a machine learning model to predict health status and assess disease risk. The generation AI can also analyze correlations in the data to discover new insights for health management. The generation AI can also detect abnormal values ​​in the data and send an alert to the user. The reward providing unit provides rewards to users who provide physical condition data. For example, the reward providing unit may grant electronic money according to the quantity and quality of data provided by the user. The reward providing unit can also provide points or coupons. The reward providing unit can also notify the user of the reward provision status. As a result, the health data collection system according to the embodiment can contribute to personalized medicine, disease prevention, and optimization of health management. For example, users can understand their health condition on a daily basis and take appropriate measures. Furthermore, medical institutions and healthcare companies can develop new treatments and preventive measures based on the collected data.

[0062] Wearable devices are equipped with an emotion estimation function and can monitor a user's emotional state in real time and collect it along with physical condition data. Wearable devices, for example, use built-in sensors to measure a user's heart rate and electrodermal activity to estimate their emotional state. For example, wearable devices can analyze heart rate fluctuations to estimate stress and relaxation levels. Wearable devices can also estimate emotional states by analyzing changes in electrodermal activity. Wearable devices can also capture a user's facial expression with a camera and estimate their emotional state using facial recognition technology. This allows users to monitor their emotional state in real time and collect it along with physical condition data, enabling more detailed health management.

[0063] A wearable device can use a material that automatically adjusts to the user's body shape or skin type. For example, a wearable device can be designed to automatically fit to the user's body shape by using a shape memory alloy in the band. For example, the shape memory alloy can deform to fit the shape of the user's wrist, providing a comfortable fit. A wearable device can also use a skin-friendly silicone material to ensure comfortable wear even for users with sensitive skin. A wearable device can also use a breathable nylon material to prevent stuffiness even when worn for long periods of time. By using a material that automatically adjusts to the user's body shape and skin type, the wearing comfort can be improved.

[0064] Wearable devices can be equipped with an energy collection function that utilizes sunlight or body heat. For example, a wearable device may be equipped with a small solar cell and use sunlight during the day to charge the battery. For example, the solar cell generates electricity from sunlight and supplies power to the battery. Wearable devices can also convert a user's body heat into electricity using thermoelectric conversion technology. For example, a body temperature sensor detects the user's body temperature, and a thermoelectric conversion element converts the heat into electricity. Furthermore, wearable devices can extend battery life by using the energy collection function. Thus, adding an energy collection function that utilizes sunlight or body heat can extend battery life.

[0065] Wearable devices have been modified for use with pets to collect health data about the pet. For example, a collar-type wearable device has been developed for use with pets to monitor the pet's body temperature and heart rate. For example, the wearable device is worn around the pet's neck, and a temperature sensor measures the pet's body temperature. The wearable device can also monitor the pet's health by measuring the pet's heart rate using a heart rate sensor. The wearable device can also record the pet's activity level and sleep patterns. For example, the wearable device can measure the pet's movement level using an acceleration sensor to collect activity data. The wearable device can also analyze the pet's sleep patterns and evaluate the quality of sleep. Thus, pet health data can be collected using wearable devices modified for pets.

[0066] Wearable devices are specialized for athletes and can collect performance data during exercise. For example, wearable devices can add functions for athletes to monitor their heart rate and oxygen saturation in real time to evaluate the effectiveness of their training. For example, a wearable device can use a heart rate sensor to measure heart rate during exercise and evaluate the intensity of the training. A wearable device can also use an oxygen sensor to measure oxygen saturation and monitor the oxygen supply status during exercise. Wearable devices can also be equipped with a GPS function to record the distance and speed traveled during exercise. For example, a wearable device can measure running distance and pace based on GPS data to evaluate training progress. Furthermore, a wearable device can use an accelerometer to analyze exercise form and movements, which can be used to improve performance. This makes it possible to collect performance data during exercise using wearable devices specialized for athletes.

[0067] A wearable device can use its emotion estimation function to automatically provide music or aromas to help the user relax when they feel stressed. For example, the wearable device can use the emotion estimation function to automatically play relaxing music when the user feels stressed. For example, the wearable device can analyze the user's heart rate and electrodermal activity and, when it detects a state of stress, play classical music or natural sounds. The wearable device can also link with an aroma diffuser to automatically provide relaxing aromas. For example, when it detects a state of stress, the wearable device can emit lavender or peppermint aromas from the diffuser. The wearable device can also suggest relaxation methods based on the user's emotional state. For example, the wearable device can provide deep breathing or meditation guides to help the user relax. This allows the user to reduce stress by automatically providing relaxing music or aromas when they feel stressed.

[0068] The generative AI can estimate a user's emotional state from collected data and analyze the correlation between emotions and physical condition. The generative AI can estimate a user's emotional state from collected heart rate and electrodermal activity data, for example. For example, the generative AI can analyze heart rate fluctuations to estimate stress and relaxation levels. The generative AI can also analyze changes in electrodermal activity to estimate emotional state. The generative AI can also analyze a user's facial expression data to estimate emotional state. For example, the generative AI can use facial expression recognition technology to estimate emotions such as joy and sadness from a user's facial expression. The generative AI can also analyze the correlation between emotional state and physical condition data to predict health status and assess disease risk. For example, the generative AI can analyze the correlation between emotional state and physical condition data to discover a tendency for health to deteriorate under high stress. The generative AI can also provide appropriate health management advice to users based on the correlation between emotional state and physical condition data. This enables more detailed health management by estimating a user's emotional state and analyzing the correlation between emotions and physical condition.

[0069] When collecting data, the generation AI can simultaneously record the user's lifestyle habits and dietary details, allowing for more detailed health predictions. For example, the generation AI can add a feature that allows the user to input their daily dietary details into the app and collect dietary data. For example, the generation AI can input the types of food and calories consumed by the user and analyze the data. The generation AI can also record the user's exercise habits and sleep patterns to collect lifestyle data. For example, the generation AI can input the type and frequency of exercise and analyze the data. The generation AI can also record the user's sleep time and quality and analyze the sleep data. The generation AI can also combine and analyze lifestyle data and health data to predict health status and assess disease risk. For example, the generation AI can analyze the correlation between dietary details and health data to evaluate the impact of specific meals on health. The generation AI can also analyze the correlation between exercise habits and health data to evaluate the impact of exercise on health status. This allows for more detailed health predictions by simultaneously recording the user's lifestyle habits and dietary details.

[0070] The generation AI dynamically adjusts the data collection frequency according to the user's physical condition, efficiently collecting only the necessary data. For example, the generation AI can set the data collection frequency low when the user's physical condition is stable and increase the collection frequency when the user's physical condition fluctuates. For example, the generation AI can increase data collection to collect detailed physical condition data if the user's body temperature suddenly rises. The generation AI can also increase data collection if the user's heart rate or blood pressure shows abnormal values. The generation AI can also monitor the user's physical condition data in real time and dynamically adjust the collection frequency. For example, the generation AI can collect data once a day when the user's physical condition is stable and every hour when the user's physical condition fluctuates. The generation AI can also analyze the user's physical condition data and use an algorithm to optimize the collection frequency. This allows the data collection frequency to be dynamically adjusted according to the user's physical condition, efficiently collecting only the necessary data.

[0071] When collecting health data, the generative AI can simultaneously collect the user's voice data and estimate their health status from changes in their voice. For example, the generative AI can add a feature to the app that allows the user to record voice memos, and then analyze the tone and pitch of the voice to estimate their health status. For example, the generative AI can detect hoarseness or changes in tone in the user's voice and estimate abnormalities in their health status. The generative AI can also analyze the user's breathing sounds and monitor their respiratory status. For example, the generative AI can detect changes in the user's breathing sounds and evaluate the health status of their respiratory system. The generative AI can also combine and analyze the user's voice data and health data to predict their health status and assess their disease risk. For example, the generative AI can analyze the correlation between changes in voice tone and health data to evaluate the impact of specific voice changes on their health status. The generative AI can also estimate the user's emotional state based on the voice data and analyze the correlation between emotions and health status. This makes it possible to simultaneously collect the user's voice data and estimate their health status from changes in their voice.

[0072] Generative AI can also collect environmental data by linking data collection with smart devices in the home. For example, generative AI can link with a smart refrigerator to collect data on food consumption and storage conditions. For example, generative AI can record the temperature and humidity inside the refrigerator to monitor food storage conditions. Generative AI can also link with a smart air conditioner to collect indoor temperature and humidity data. For example, generative AI can record the air conditioner's set temperature and indoor humidity to collect environmental data. Generative AI can also link with a smart speaker to collect user voice commands. For example, generative AI can record voice commands given by the user to the smart speaker and collect user behavior data. Generative AI can also combine and analyze environmental data and health data to predict health status and assess disease risk. For example, generative AI can analyze the correlation between indoor temperature and humidity and health data to evaluate the impact of specific environmental conditions on health. Generative AI can also provide users with appropriate health management advice based on environmental data. This allows data collection to be linked to smart devices in the home, allowing environmental data to be collected at the same time.

[0073] The generation AI can improve data quality by using the emotion estimation function to collect data during times when the user is in a positive emotional state. For example, the generation AI can use the emotion estimation function to identify times when the user is in a positive emotional state and collect data during those times. For example, the generation AI can analyze the user's heart rate and electrodermal activity to detect a positive emotional state. The generation AI can also analyze the user's facial expression data to detect a positive emotional state. For example, the generation AI can use facial expression recognition technology to detect a user's smile and identify a positive emotional state. The generation AI can also analyze the user's voice data to detect a positive emotional state. For example, the generation AI can analyze the tone and pitch of the user's voice to identify a positive emotional state. The generation AI can also improve data quality by collecting data during times when the user is in a positive emotional state. For example, the generation AI can collect physical condition data during times when the user is relaxed to improve data accuracy. The generation AI can also predict health status and assess disease risk based on data collected during times when the user is in a positive emotional state. This allows the emotion estimation function to collect data during times when the user is in a positive emotional state and improve data quality.

[0074] Generative AI can provide health management advice that takes into account the user's emotional state. For example, generative AI can analyze the user's emotional state and suggest relaxation methods when stress levels are high. For example, generative AI can analyze the user's heart rate and electrodermal activity and, if it detects stress levels, provide deep breathing or meditation guidance. Generative AI can also analyze the user's facial expression data to detect stress levels. For example, generative AI can use facial recognition technology to identify stress levels from the user's facial expressions and suggest relaxation methods. Generative AI can also analyze the user's voice data to detect stress levels. For example, generative AI can analyze the tone and pitch of the user's voice to identify stress levels and suggest relaxation methods. Generative AI can also customize the content of health management advice according to the user's emotional state. For example, generative AI can provide exercise and dietary advice when the user is relaxed, and suggest relaxation methods when stress levels are high. This allows for more effective health management by providing health management advice that takes the user's emotional state into account.

[0075] The generation AI can customize the content of advice based on the user's past behavioral history and preferences, making it easier to implement. For example, the generation AI can analyze the user's past behavioral history and provide easy-to-implement advice. For example, the generation AI can analyze the user's exercise history and make meal suggestions based on past successful diet methods. The generation AI can also analyze the user's diet history and suggest meals that match their preferences. For example, the generation AI can suggest healthy recipes based on the user's favorite ingredients. The generation AI can also analyze the user's sleep history and provide advice to improve sleep quality. For example, the generation AI can analyze the user's sleep patterns and suggest appropriate bedtimes and wake-up times. The generation AI can also customize the content of advice based on the user's preferences. For example, the generation AI can suggest exercises and meals that the user prefers and provide easy-to-implement advice. This makes it possible to customize advice based on the user's past behavioral history and preferences, making it easier to implement.

[0076] The generation AI can monitor the effectiveness of the advice in real time and automatically update the advice content as needed. For example, the generation AI can monitor the user's physical condition data in real time after the advice is implemented and evaluate its effectiveness. For example, the generation AI can analyze changes in heart rate and body temperature after exercise to evaluate the effectiveness of the advice. The generation AI can also monitor the user's sleep data and evaluate the effectiveness of the advice. For example, the generation AI can analyze the user's sleep time and sleep quality to evaluate the effectiveness of the advice. The generation AI can also automatically update the advice content based on the effectiveness of the advice. For example, the generation AI can analyze the user's physical condition data and change the advice content if it is not effective. The generation AI can also customize the advice content based on user feedback. For example, the generation AI can analyze the feedback the user provides in response to the advice and improve the advice content. This enables more effective health management by monitoring the effectiveness of the advice in real time and automatically updating the advice content as needed.

[0077] The generation AI can use animations or infographics to make the advice easier to understand visually. For example, the generation AI can display the advice content using animations to make it easier for users to understand visually. For example, the generation AI can use animations to show exercise methods so that users can exercise with the correct form. The generation AI can also display meal suggestions using infographics to make it easier for users to understand visually. For example, the generation AI can use infographics to show the nutritional balance of meals so that users can visually understand which ingredients they should consume. The generation AI can also display health management progress using infographics so that users can visually check their progress toward their goals. For example, the generation AI can display graphs of the user's exercise volume and weight changes to visually show their progress toward their health management. The generation AI can also use animations and infographics to provide motivational messages to users. For example, the generation AI can display an animated congratulatory message when the user achieves their goal. This makes the advice easier to understand visually, making it easier for users to understand.

[0078] The generation AI can integrate advice with a voice assistant, allowing users to receive advice via voice. For example, the generation AI can integrate with a voice assistant to allow users to receive advice via voice. For example, the generation AI can provide exercise and diet advice through a smart speaker. The generation AI can also allow users to request advice via voice command. For example, when a user inputs the voice command "Tell me today's exercise advice," the smart speaker will provide the exercise advice via voice. The generation AI can also set reminders for users through a voice assistant. For example, when a user inputs the voice command "Set a reminder to exercise tomorrow morning," the smart speaker will set the reminder and notify the user via voice at the specified time. The generation AI can also provide motivational messages to users through a voice assistant. For example, when a user completes an exercise session, the generation AI can have the smart speaker provide an encouraging voice message such as "Well done!". In this way, by integrating advice with a voice assistant, users can receive advice via voice.

[0079] The generation AI can use its emotion estimation function to analyze the user's emotional state when receiving advice and provide the advice at the optimal time. For example, the generation AI can use its emotion estimation function to provide advice when the user is relaxed. For example, the generation AI can analyze the user's heart rate and electrodermal activity and, if it detects a relaxed state, suggest exercise. The generation AI can also analyze the user's facial expression data to detect a relaxed state. For example, the generation AI can use facial expression recognition technology to identify a relaxed state from the user's facial expression and provide dietary suggestions. The generation AI can also analyze the user's voice data to detect a relaxed state. For example, the generation AI can analyze the tone and pitch of the user's voice to identify a relaxed state and provide sleep advice. The generation AI can also adjust the timing of advice depending on the user's emotional state. For example, if the user is feeling stressed, the generation AI can provide advice on relaxation methods, and if they are relaxed, the generation AI can provide exercise and dietary advice. This allows the generation AI to analyze the user's emotional state when receiving advice and provide advice at the optimal time, enabling more effective health management.

[0080] The reward providing unit uses the emotion estimation function to provide a reward when the user feels positive emotions, thereby improving the reward receiving experience. The reward providing unit, for example, uses the emotion estimation function to provide a reward when the user feels positive emotions. For example, the reward providing unit analyzes the user's heart rate or electrodermal activity and grants electronic money when a positive emotional state is detected. The reward providing unit can also analyze the user's facial expression data to detect a positive emotional state. For example, the reward providing unit uses facial expression recognition technology to detect a user's smile and grant electronic money. The reward providing unit can also analyze the user's voice data to detect a positive emotional state. For example, the reward providing unit analyzes the tone and pitch of the user's voice to identify a positive emotional state and grant points. The reward providing unit can also adjust the type and timing of the reward depending on the user's emotional state. For example, the reward providing unit grants electronic money when the user is relaxed and a coupon when the user is feeling stressed. In this way, the reward providing unit can improve the reward receiving experience by providing a reward when the user feels positive emotions.

[0081] The reward providing unit may diversify the types of rewards and allow the user to select from them. For example, the reward providing unit may provide electronic money, points, gift cards, etc. The reward providing unit may also allow the user to select the type of reward. For example, the reward providing unit may allow the user to select the type of reward within the app and receive the desired reward. The reward providing unit may also periodically update the types of rewards and provide new rewards to the user. For example, the reward providing unit may provide different gift cards or coupons for each season to attract the user's interest. The reward providing unit may also customize the types of rewards based on the user's preferences. For example, the reward providing unit may provide gift cards of a brand that the user prefers, thereby improving user satisfaction. In this way, the types of rewards may be diversified and the user may be able to select from them, thereby improving user satisfaction.

[0082] The reward providing unit can dynamically adjust the frequency of reward provision depending on the frequency and quality of data provided by the user. The reward providing unit dynamically adjusts the frequency of reward provision depending on, for example, the frequency of data provided by the user. For example, the reward providing unit frequently provides rewards to users who provide a lot of data, and reduces the frequency of reward provision to users who provide a little data. The reward providing unit can also adjust the frequency of reward provision depending on the quality of the user's data. For example, the reward providing unit frequently provides rewards to users who provide high-quality data, and reduces the frequency of reward provision to users who provide low-quality data. The reward providing unit can also monitor the user's data provision status in real time and dynamically adjust the frequency of reward provision. For example, the reward providing unit frequently provides rewards immediately after the user starts providing data, and reduces the frequency of reward provision once the data provision becomes stable. The reward providing unit can also adjust the type of reward depending on the user's data provision status. For example, the reward providing unit provides expensive gift cards to users who provide a lot of data, and provides points to users who provide a little data. In this way, the frequency of reward provision can be dynamically adjusted depending on the frequency and quality of the user's data provision, thereby maintaining the user's motivation.

[0083] The reward providing unit can contribute to revitalizing the local economy by partnering with local shops and services to provide rewards. For example, the reward providing unit can partner with local shops and services to provide rewards so that users can use them in the local area. For example, the reward providing unit can provide coupons that can be used at local restaurants and cafes. The reward providing unit can also partner with local fitness gyms and health food stores to allow users to use health-related services. For example, the reward providing unit can provide vouchers for local fitness gyms and coupons for health foods. The reward providing unit can also contribute to revitalizing the local economy through partnerships with local shops and services. For example, the reward providing unit can cooperate with local shops and services to hold local events and campaigns. The reward providing unit can also convey the appeal of the local area to users through partnerships with local shops and services. For example, the reward providing unit can introduce local tourist spots and local specialties to encourage users to visit the local area. In this way, partnering with local shops and services to provide rewards can contribute to revitalizing the local economy.

[0084] The reward providing unit can enable the user to exchange the reward for health-related products or services. For example, the reward providing unit builds a system that enables the user to exchange the reward for health-related products or services. For example, the reward providing unit provides fitness gym vouchers or health food coupons. The reward providing unit can also enable the user to exchange the reward for health-related products or services. For example, the reward providing unit provides a function for exchanging rewards within the app, allowing the user to select the health-related products or services they desire. The reward providing unit can also periodically update the reward exchange destinations and provide the user with new health-related products or services. For example, the reward providing unit can provide different fitness gym vouchers or health food coupons for each season to attract the user's interest. The reward providing unit can also customize the reward exchange destinations based on the user's preferences. For example, the reward providing unit can provide coupons for the user's favorite fitness gym or health food store to increase user satisfaction. By enabling the user to exchange the reward for health-related products or services, the user's motivation to manage their health can be increased.

[0085] The reward providing unit can use the emotion estimation function to analyze the emotional state of the user when receiving a reward and optimize the type and timing of the reward. The reward providing unit can, for example, use the emotion estimation function to analyze the emotional state of the user when receiving a reward. For example, the reward providing unit can analyze the user's heart rate or electrodermal activity and provide a reward when it detects a relaxed state. The reward providing unit can also analyze the user's facial expression data to detect the relaxed state. For example, the reward providing unit can use facial expression recognition technology to identify the relaxed state from the user's facial expression and provide a reward. The reward providing unit can also analyze the user's voice data to detect the relaxed state. For example, the reward providing unit can analyze the tone and pitch of the user's voice to identify the relaxed state and provide a reward. The reward providing unit can also optimize the type and timing of the reward according to the user's emotional state. For example, the reward providing unit can grant electronic money when the user is relaxed and provide a coupon when the user is feeling stressed. In this way, the reward receiving experience can be improved by analyzing the user's emotional state when receiving a reward and optimizing the type and timing of the reward.

[0086] Generative AI can add emotional data using emotion estimation functions to data provided to other companies, enabling more detailed user analysis. Generative AI can add emotional data using emotion estimation functions to data provided to other companies, enabling more detailed analysis of a user's emotional state. For example, generative AI can analyze a user's heart rate and electrodermal activity to estimate their emotional state. Generative AI can also analyze a user's facial expression data to estimate their emotional state. For example, generative AI can use facial expression recognition technology to estimate emotions such as joy or sadness from a user's facial expression. Generative AI can also analyze a user's voice data to estimate their emotional state. For example, generative AI can analyze the tone and pitch of a user's voice to identify their emotional state. Generative AI can also combine and analyze emotional data and physical condition data to perform detailed analysis of a user's health condition and behavioral patterns. For example, generative AI can analyze the correlation between emotional state and physical condition data to evaluate the impact of specific emotions on physical condition. Generative AI can also provide users with appropriate health management advice based on the emotional data. This will enable more detailed user analysis by adding emotional data to the data provided to other companies.

[0087] Generative AI can incorporate advanced anonymization techniques to protect user privacy when providing data. For example, when providing data, it can incorporate techniques to anonymize users' personal information. For example, it can remove personally identifiable information such as names and addresses and provide anonymized data. Generative AI can also use data masking techniques to conceal users' personal information. For example, it can replace users' personal information with random strings of characters to anonymize them. Generative AI can also protect users' personal information using pseudo-anonymization techniques. For example, it can encrypt and anonymize users' personal information. Generative AI can also incorporate access control to protect users' privacy when providing data. For example, it can allow data recipients to access only specific data and restrict access to other data. Generative AI can also incorporate a mechanism to obtain users' consent when providing data. For example, it can explain the purpose and scope of data provision to users and provide the data only after obtaining their consent. This can increase users' sense of security by incorporating advanced anonymization techniques to protect users' privacy when providing data.

[0088] By providing data to other companies, generative AI can promote joint research and development projects and create new medical technologies and services. For example, generative AI can launch joint research projects with other companies and develop new medical technologies based on collected data. For example, generative AI can jointly develop new algorithms for disease prevention. Generative AI can also promote development projects with other companies and create new medical services. For example, generative AI can work with other companies to develop new diagnostic tools for personalized medicine. Generative AI can also promote innovation in the medical field through joint research with other companies. For example, generative AI can publish research papers in collaboration with other companies and share new knowledge. Generative AI can also promote the practical application of medical technology through joint development projects with other companies. For example, generative AI can work with other companies to develop new medical devices and conduct clinical trials. In this way, by providing data to other companies, joint research and development projects can be promoted and new medical technologies and services can be created, thereby promoting innovation in the medical field.

[0089] The generating AI can link its own app with other companies' health-related services to provide users with a consistent health management experience. For example, the generating AI can link its own app with other companies' health-related services to provide users with a consistent health management experience. For example, the generating AI can link with a fitness app to share exercise data. The generating AI can also link with a diet management app to share diet data. For example, the generating AI can import diet data entered by a user into a diet management app into its own app and use it for health management. The generating AI can also link with a sleep management app to share sleep data. For example, the generating AI can import sleep data entered by a user into a sleep management app into its own app and use it for health management. The generating AI can also link with other companies' health-related services to provide users with comprehensive health management advice. For example, the generating AI can combine data from a fitness app and a diet management app to provide users with advice that takes into account the balance between exercise and diet. The generating AI can also link with other companies' health-related services to provide users with a consistent health management experience. For example, generative AI can maintain data consistency when a user uses multiple apps, providing a seamless health management experience. This allows companies to link their own apps with other companies' health-related services to provide users with a consistent health management experience.

[0090] Generative AI can promote collaboration with different industries by providing data to other companies. For example, generative AI can promote collaboration with the insurance industry by providing data to other companies. For example, generative AI can customize insurance products based on health data. Generative AI can also promote collaboration with the food industry. For example, generative AI can develop health foods based on health data. Generative AI can also create new business opportunities through collaboration with different industries. For example, generative AI can work with different industries based on health data to develop new services and products. Generative AI can also provide comprehensive health management services to users through collaboration with different industries. For example, generative AI can combine data from the insurance and food industries to suggest health management and insurance products to users. Generative AI can also provide users with a consistent health management experience through collaboration with different industries. For example, generative AI can maintain data consistency when users use services from multiple industries, providing a seamless health management experience. This can create new business opportunities by promoting collaboration with different industries through data provision.

[0091] Generative AI can use its emotion estimation function to improve the quality of data provided to other companies, enabling more accurate analysis. Generative AI can improve the quality of data provided to other companies using its emotion estimation function. For example, generative AI can analyze a user's heart rate and electrodermal activity to estimate their emotional state. Generative AI can also analyze a user's facial expression data to estimate their emotional state. For example, generative AI can use facial expression recognition technology to estimate emotions such as joy or sadness from a user's facial expression. Generative AI can also analyze a user's voice data to estimate their emotional state. For example, generative AI can analyze the tone and pitch of a user's voice to identify their emotional state. Generative AI can also combine and analyze emotional data and physical condition data to perform detailed analysis of a user's health status and behavioral patterns. For example, generative AI can analyze the correlation between emotional state and physical condition data to evaluate the impact of specific emotions on physical condition. Generative AI can also provide appropriate health management advice to users based on the emotional data. By improving the quality of data using the emotion estimation function, other companies can perform more accurate analyses.

[0092] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0093] Wearable devices not only collect data on a user's physical condition, but also data on the user's living environment. For example, wearable devices can monitor the temperature, humidity, and air quality around the user using built-in environmental sensors. Wearable devices can also acquire the user's location information using GPS and record outdoor activity. Furthermore, wearable devices can send the user's living environment data to the cloud, where a generative AI can analyze the data to identify environmental factors that affect the user's health. This allows users to understand the impact of their living environment on their health and take appropriate measures.

[0094] Using emotion estimation capabilities, wearable devices can provide appropriate feedback when a user is in a specific emotional state. For example, when a user is feeling stressed, a wearable device can provide breathing exercises or meditation guides to help the user relax. Also, when a user is feeling positive, a wearable device can display encouraging messages to motivate the user. Furthermore, a wearable device can provide appropriate exercise and dietary advice based on the user's emotional state. This allows users to receive appropriate feedback based on their emotional state, making health management more effective.

[0095] Wearable devices can use materials that automatically adjust to the user's body type and skin type, as well as dynamically adjust to the user's activity level. For example, a wearable device can change the structure of its material to increase breathability when the user sweats during exercise. Furthermore, a wearable device can change to a softer material when the user is relaxed, providing a comfortable fit. Furthermore, a wearable device can automatically adjust the tightness of the band according to the user's activity level. This allows the user to use the wearable device while always maintaining a comfortable fit.

[0096] Wearable devices can not only add energy harvesting capabilities using sunlight or body heat, but also have the ability to generate energy using the user's movements. For example, a wearable device can use a built-in vibration-powered element to convert vibrations generated by the user's walking or exercise into electricity. A wearable device can also generate electricity using friction generated while the user is exercising. Furthermore, wearable devices can use the energy harvesting capability to not only extend battery life but also collect user activity data. This allows a wearable device to generate energy using the user's movements and extend battery life.

[0097] Wearable devices have been adapted for pets, and can now collect not only pet health data but also behavioral data. For example, wearable devices can use built-in acceleration sensors to record a pet's activity level and frequency of play. They can also use GPS to record a pet's route and range of activity. Furthermore, wearable devices can transmit pet behavioral data to the cloud, where AI can analyze the data to understand the pet's health condition and behavioral patterns. This allows pet owners to understand their pet's health condition and behavioral patterns and take appropriate measures.

[0098] Wearable devices are specialized for athletes and can collect not only performance data during exercise but also post-exercise recovery data. For example, wearable devices can monitor the recovery rate of heart rate and blood pressure after exercise to evaluate the effectiveness of recovery. Wearable devices can also measure muscle fatigue and determine the need for recovery. Furthermore, wearable devices can collect sleep data after exercise and evaluate the quality of recovery. This allows athletes to create appropriate recovery plans based on post-exercise recovery data, which can help improve their performance.

[0099] Using its emotion estimation function, a wearable device can not only automatically provide music or aromas to help a user relax when they feel stressed, but also suggest appropriate exercises or stretches based on the user's emotional state. For example, a wearable device can provide a guide to yoga or stretches that are effective for relaxation when the user is feeling stressed. The wearable device can also suggest light exercises or walking when the user is relaxed. Furthermore, the wearable device can support the user's health management by suggesting appropriate exercises or stretches based on the user's emotional state. This allows the user to perform appropriate exercises or stretches based on their emotional state, making health management more effective.

[0100] Generative AI can not only estimate a user's emotional state from collected data and analyze the correlation between emotions and physical condition, but also provide appropriate health management advice based on the user's emotional state. For example, when a user is feeling stressed, generative AI can provide breathing exercises or meditation guides to help them relax. Also, when a user is feeling positive, generative AI can provide exercise and diet advice to increase motivation. Furthermore, generative AI can support the user's health management by providing appropriate health management advice based on the user's emotional state. This allows users to receive appropriate health management advice based on their emotional state, making health management more effective.

[0101] When collecting data, the Generator AI simultaneously records the user's lifestyle and dietary habits, enabling it to not only make more detailed health predictions but also provide appropriate health management advice based on the user's lifestyle and dietary habits. For example, the Generator AI can add a feature that allows the user to input their daily dietary habits into the app, analyze the dietary data, and suggest nutritionally balanced meals. The Generator AI can also record the user's exercise habits and sleep patterns, analyze the lifestyle data, and provide appropriate exercise and sleep advice. Furthermore, the Generator AI can combine and analyze lifestyle data and physical condition data to not only predict health status and assess disease risk, but also provide appropriate health management advice to the user. This allows users to receive appropriate health management advice based on their lifestyle and dietary habits, making health management more effective.

[0102] The generating AI dynamically adjusts the data collection frequency according to the user's physical condition, not only efficiently collecting only the necessary data but also optimizing the timing of data collection according to the user's lifestyle. For example, if the user is active during the day, the generating AI can increase the frequency of data collection and reduce the frequency of data collection at night. The generating AI can also reduce the burden on the user by adjusting the timing of data collection according to the user's lifestyle. Furthermore, the generating AI monitors the user's physical condition data in real time and dynamically adjusts the collection frequency, not only dynamically adjusting the collection frequency but also optimizing the timing of data collection according to the user's lifestyle, thereby efficiently collecting only the necessary data. This allows users to receive optimal data collection according to their lifestyle, making health management more effective.

[0103] The processing flow of the second embodiment will be briefly explained below.

[0104] Step 1: The wearable device collects data on the user's physical condition, such as body temperature, humidity, sweat sodium content, intoxication level, age, skin moisture content, heart rate, blood pressure, exercise volume, and sleep patterns. Step 2: The health information sharing app sends the health data collected by the wearable device to the cloud. For example, it can send data to the cloud using Bluetooth or Wi-Fi, and allow the user to set the frequency of data transmission. It can also display the data transmission status in real time. Step 3: The generating AI analyzes the health data sent by the health information sharing app. For example, it can analyze the data using a machine learning model to predict health status and assess disease risk. It can also analyze data correlations to discover new insights for health management. It can also detect abnormal values ​​in the data and send alerts to the user. Step 4: The reward provider provides rewards to users who provide their physical condition data. For example, the reward provider may provide electronic money, points, or coupons depending on the quantity and quality of the data provided by the user. The reward provider may also notify the user of the status of reward provision.

[0105] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0106] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0107] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0108] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0109] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0110] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0111] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0112] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0113] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0114] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0115] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0116] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0117] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0118] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0119] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0120] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0121] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0122] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0123] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0124] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0125] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0126] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0127] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0128] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0129] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0130] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0131] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0132] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0133] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0134] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0135] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0136] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0137] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0138] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0139] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0140] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0141] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0142] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0143] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0144] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0145] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0146] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0147] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0148] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0149] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0150] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0151] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0152] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0153] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0154] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0155] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0156] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0157] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0158] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0159] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0160] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0161] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0162] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0163] 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.

[0164] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0165] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0166] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0167] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0168] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0169] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0170] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0171] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0172] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. Wearable devices and A health information sharing app, Generative AI and a reward providing unit, The wearable device is Collecting user's physical condition data, The health information sharing app is Transmitting the physical condition data collected by the wearable device to a cloud; The generated AI is Analyzing the physical condition data transmitted by the physical condition information sharing app; The reward providing unit: Reward the user who provided the physical condition data A system characterized by:

2. The wearable device is Equipped with an emotion estimation function, the user's emotional state is monitored in real time and collected together with the physical condition data.

2. The system of claim 1.

3. The wearable device is Enhanced for pets, collecting pet health data 2. The system of claim 1.

4. The generated AI is The emotional state of the user is estimated from the collected data, and the correlation between the emotion and the physical condition is analyzed.

2. The system of claim 1.

5. The generated AI is Providing health care advice that takes into account the emotional state of the user 2. The system of claim 1.

6. The reward providing unit: The reward is provided at a timing when the user feels positive emotions, thereby improving the reward receiving experience.

2. The system of claim 1.

7. The generated AI is Adding emotion data using emotion estimation functionality to data provided to other companies enables more detailed user analysis 2. The system of claim 1.

8. The generated AI is Improve the quality of data provided to other companies, enabling more accurate analysis 2. The system of claim 1.

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