System

Abstract

An object of a system according to an embodiment is to analyze health data of an individual and provide appropriate advice.SOLUTION: A system includes a data collection unit, an analysis unit, and an advice provision unit. The data collection unit collects health data of a user. The analysis unit analyzes the health data collected by the data collection unit. The advice providing unit provides advice on the basis of a result analyzed by the analysis unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies are not yet capable of effectively collecting and analyzing personal health data and providing appropriate advice, and there is room for improvement.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

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

[0028] (Example 1) The HealthBot Companion system according to an embodiment of the present invention tracks a user's health data, analyzes the data using a generation AI, and provides advice. This allows the HealthBot Companion system to grasp the user's health status in real time and provide personalized health advice.

[0029] The HealthBot Companion system according to the embodiment includes a data collection unit, an analysis unit, and an advice provision unit. The data collection unit collects health data from a user. For example, the data collection unit collects data such as weight, blood pressure, heart rate, dietary details, and exercise volume that are entered by the user on a daily basis. The data collection unit can also automatically collect data from a wearable device. For example, it can acquire heart rate and step count data from a smartwatch. The data collection unit can also collect data manually entered by the user. For example, the user enters dietary details into an app. The analysis unit analyzes the health data collected by the data collection unit. For example, the analysis unit can analyze the health data using a generation AI (e.g., a text generation AI or a multimodal generation AI). The analysis unit can also analyze the data using statistical analysis or machine learning algorithms. For example, the generation AI can analyze the user's dietary data and evaluate nutritional balance. The analysis unit can also analyze trends in the health data. For example, it can analyze the user's weight fluctuations and evaluate their long-term health condition. The advice provision unit provides advice based on the results of the analysis by the analysis unit. For example, the advice providing unit generates personalized health advice based on the analysis results of the generation AI. The advice providing unit can also provide specific advice tailored to the user's health condition. For example, if the user is not getting enough exercise, the advice providing unit can provide advice such as, "We recommend that you add 30 minutes of walking each day." Furthermore, the advice providing unit can also provide advice tailored to the user's lifestyle. For example, the advice providing unit can suggest, "To avoid skipping breakfast, prepare a simple breakfast the night before." This allows the HealthBot Companion system according to the embodiment to grasp the user's health condition in real time and provide personalized health advice. For example, by inputting health data on a daily basis and receiving advice from the generation AI, users can expect to improve their health. Furthermore, by providing health strategies tailored to busy modern people, they can comfortably maintain a healthy lifestyle.Furthermore, by spreading awareness of the importance of preventive medicine, it can contribute to the early detection and prevention of disease.

[0030] The data collection unit can incorporate the user's genetic information and track health data taking into account genetic risk factors. The data collection unit, for example, collects the user's genetic information and tracks health data taking into account genetic risk factors. For example, if there is a genetic risk of high blood pressure, tracking takes that risk into account. The data collection unit also analyzes the user's health data based on the genetic information and manages health based on genetic risk factors. For example, if there is a specific gene mutation, health advice taking into account its impact is provided. Furthermore, the data collection unit incorporates the user's genetic information and builds a health data tracking system that takes into account genetic risk factors. For example, if there is a genetic high risk of diabetes, tracking takes that risk into account. This makes it possible to track health data taking into account genetic risk factors.

[0031] The data collection unit can collect user environmental data and analyze the impact of environmental factors on health data. The data collection unit, for example, collects user environmental data (temperature, humidity, air quality, etc.) and integrates it with health data for analysis. For example, it analyzes whether heart rate increases on hot days. The data collection unit also builds a system that analyzes the impact on the user's health status based on the environmental data. For example, it investigates whether respiratory system data worsens on days with poor air quality. The data collection unit further collects the user's environmental data and performs correlation analysis with the health data. For example, it analyzes whether blood pressure increases on days with high humidity. This allows for more detailed health tracking by analyzing the impact of environmental factors on health.

[0032] The data collection unit simultaneously tracks the pet's health data and can analyze the correlation between the pet's and the owner's health status. The data collection unit, for example, collects the pet's health data (weight, activity level, dietary content, etc.) and integrates it with the owner's health data for analysis. For example, it investigates whether the pet's activity level affects the owner's exercise level. The data collection unit also builds a system that simultaneously tracks the health data of the pet and the owner and performs correlation analysis. For example, it analyzes whether the pet's health status affects the owner's stress level. Furthermore, the data collection unit collects the pet's health data and performs comparative analysis with the owner's health data. For example, it investigates whether the pet's weight gain affects the owner's eating habits. This enables comprehensive health management by analyzing the correlation between the pet's and the owner's health status.

[0033] The data collection unit can link with smart devices in the home to collect more detailed health data. For example, the data collection unit links with smart devices in the home (refrigerators, scales, smart mirrors, etc.) to build a system that automatically collects health data. For example, it analyzes meal contents based on data on ingredients in the refrigerator. The data collection unit also links with smart devices to collect detailed health data of users. For example, it automatically tracks data from a scale and analyzes weight fluctuations. Furthermore, the data collection unit will link with smart devices in the home to develop a system that integrates and analyzes health data. For example, it will analyze skin condition based on data from a smart mirror. This makes it possible to collect more detailed health data by linking with smart devices.

[0034] The advice providing unit can analyze the user's past health data and advice history to provide more personalized advice. The advice providing unit, for example, analyzes the user's past health data and advice history to build a system that provides more personalized health advice. For example, it suggests an exercise plan based on past exercise history. The advice providing unit also analyzes the past health data and advice history to provide optimal health advice to the user. For example, it suggests improving nutritional balance based on past dietary data. Furthermore, the advice providing unit analyzes the user's past health data and advice history to develop a system that provides personalized health advice. For example, it provides advice to improve sleep based on past sleep data. This makes it possible to provide more personalized advice based on past data and advice history.

[0035] The advice providing unit can provide health advice that utilizes social support by taking into account the user's social relationships. For example, the advice providing unit builds a system that provides health advice that utilizes social support by taking into account the user's social relationships (family, friends, colleagues). For example, it suggests exercising with family. The advice providing unit also provides optimal health advice to the user based on the social relationships. For example, it suggests enjoying healthy meals with friends. Furthermore, the advice providing unit develops a system that provides health advice that utilizes social support by taking into account the user's social relationships. For example, it suggests engaging in stress relief activities with colleagues. In this way, more effective health advice can be provided by utilizing social support.

[0036] The advice providing unit can provide health advice in a form that is easy to implement by associating it with the user's hobbies and interests. The advice providing unit, for example, builds a system that provides health advice associated with the user's hobbies and interests. For example, it suggests exercising while listening to music to a user who likes music. The advice providing unit also provides optimal health advice to a user based on the hobbies and interests. For example, it suggests healthy recipes to a user who likes cooking. Furthermore, the advice providing unit develops a system that provides health advice associated with the user's hobbies and interests. For example, it suggests hiking to a user who likes the outdoors. In this way, by associating health advice with hobbies and interests, it becomes easier to implement the advice.

[0037] The advice providing unit can customize health advice to suit the characteristics of the area where the user lives. For example, the advice providing unit builds a system that provides health advice tailored to the characteristics of the area where the user lives (climate, food culture, etc.). For example, it suggests hot meals to a user living in a cold region. The advice providing unit also provides optimal health advice to the user based on the characteristics of the area. For example, it suggests eating more seafood to a user living by the sea. Furthermore, the advice providing unit develops a system that provides health advice tailored to the characteristics of the area where the user lives. For example, it provides advice emphasizing hydration to a user living in a humid area. This allows for more effective health management by providing health advice tailored to the characteristics of the area.

[0038] The advice providing unit can analyze a user's occupation and daily activity patterns and make lifestyle improvement suggestions based on the results. The advice providing unit, for example, analyzes a user's occupation and daily activity patterns and builds a system that makes lifestyle improvement suggestions based on the results. For example, regular stretching is suggested for a user who does a lot of desk work. The advice providing unit also makes optimal lifestyle improvement suggestions for the user based on the occupation and activity patterns. For example, methods for improving sleep quality are suggested for a user who works many night shifts. Furthermore, the advice providing unit analyzes a user's occupation and daily activity patterns and develops a system that makes lifestyle improvement suggestions based on the results. For example, simple exercises are suggested for users who are not getting enough exercise. In this way, lifestyle improvement suggestions based on the occupation and activity patterns can be made to improve the user's quality of life.

[0039] The advice providing unit can analyze the user's health data and lifestyle data over the long term and make suggestions for sustainable lifestyle improvements. The advice providing unit, for example, builds a system that analyzes the user's health data and lifestyle data over the long term and makes suggestions for sustainable lifestyle improvements. For example, it suggests forming long-term exercise habits. The advice providing unit also makes suggestions for sustainable lifestyle improvements that are optimal for the user based on the health data and lifestyle data. For example, it suggests a plan to support long-term dietary improvements. Furthermore, the advice providing unit develops a system that analyzes the user's health data and lifestyle data over the long term and makes suggestions for sustainable lifestyle improvements. For example, it suggests a long-term approach for stress management. In this way, it is possible to improve the user's quality of life by making suggestions for sustainable lifestyle improvements based on long-term data analysis.

[0040] The advice providing unit provides lifestyle improvement suggestions in a form that can be implemented by the entire family, thereby improving the health of the family as a whole. For example, the advice providing unit builds a system that provides lifestyle improvement suggestions in a form that can be implemented by the entire family. For example, it suggests an exercise program that the entire family can participate in. The advice providing unit also makes lifestyle improvement suggestions to improve the health of the entire family as a whole. For example, it suggests that the family enjoy healthy meals together. Furthermore, the advice providing unit develops a system that provides lifestyle improvement suggestions in a form that can be implemented by the entire family. For example, it suggests stress relief activities that the entire family can participate in. In this way, by suggesting lifestyle improvements that can be implemented by the entire family, the health of the entire family can be improved.

[0041] The advice providing unit can provide suggestions for lifestyle improvements in a form that can be implemented even when the user is traveling or on a business trip. The advice providing unit, for example, builds a system that provides suggestions for lifestyle improvements that can be implemented even when the user is traveling or on a business trip. For example, it suggests simple exercises that can be done at the user's business trip destination. The advice providing unit also makes suggestions for lifestyle improvements that can be implemented even when the user is traveling or on a business trip. For example, it provides advice on how to eat a healthy diet while traveling. Furthermore, the advice providing unit develops a system that provides suggestions for lifestyle improvements that can be implemented even when the user is traveling or on a business trip. For example, it suggests methods for managing stress while on a business trip. In this way, by suggesting lifestyle improvements that can be implemented even when the user is traveling or on a business trip, it is possible to continuously support the user's health management.

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

[0043] The HealthBot Companion system can also collect and analyze the user's sleep data. For example, the data collection unit can acquire data from a smartwatch or smart bed to track the user's sleep patterns and quality. The analysis unit can analyze the collected sleep data and evaluate the user's sleep quality. For example, a short period of deep sleep can be determined to indicate poor sleep quality. Furthermore, the advice provision unit can provide the user with advice to improve their sleep quality based on the analysis results. For example, specific advice such as "avoid using your smartphone before bed" or "go to bed at a regular time" can be provided. This can be expected to improve the user's sleep quality and overall health.

[0044] The HealthBot Companion system can also incorporate the user's genetic information and track health data taking into account genetic risk factors. For example, the data collection unit can collect the user's genetic information and track health data taking into account genetic risk factors. For example, if a user has a genetic risk of high blood pressure, tracking can take that risk into account. The data collection unit can also analyze the user's health data based on the genetic information and manage health based on genetic risk factors. For example, if a user has a specific gene mutation, health advice can be provided that takes into account its impact. The data collection unit can also incorporate the user's genetic information and build a health data tracking system that takes genetic risk factors into account. For example, if a user has a genetic high risk of diabetes, tracking can take that risk into account. This makes it possible to track health data taking into account genetic risk factors.

[0045] The HealthBot Companion system can also collect user environmental data and analyze the impact of environmental factors on health data. For example, the data collection unit can collect user environmental data (temperature, humidity, air quality, etc.) and integrate it with health data for analysis. For example, it can analyze whether heart rate increases on hot days. The data collection unit can also build a system that analyzes the impact of environmental data on the user's health status. For example, it can investigate whether respiratory system data worsens on days with poor air quality. Furthermore, the data collection unit can collect user environmental data and perform correlation analysis with health data. For example, it can analyze whether blood pressure increases on days with high humidity. This allows for more detailed health tracking by analyzing the impact of environmental factors on health.

[0046] The HealthBot Companion system can also simultaneously track pet health data and analyze the correlation between pet and owner health. For example, the data collection unit can collect pet health data (weight, activity level, dietary content, etc.) and integrate it with the owner's health data for analysis. For example, it can investigate whether a pet's activity level affects the owner's exercise level. The data collection unit can also build a system that simultaneously tracks pet and owner health data and performs correlation analysis. For example, it can analyze whether a pet's health status affects the owner's stress level. Furthermore, the data collection unit can collect pet health data and compare it with the owner's health data for analysis. For example, it can investigate whether a pet's weight gain affects the owner's eating habits. This allows for comprehensive health management by analyzing the correlation between pet and owner health.

[0047] The HealthBot Companion system can also connect with smart devices in the home to collect more detailed health data. For example, the data collection unit can connect with smart devices in the home (refrigerators, scales, smart mirrors, etc.) to build a system that automatically collects health data. For example, it can analyze dietary information based on the food ingredients in the refrigerator. The data collection unit can also connect with smart devices to collect detailed health data for users. For example, it can automatically track data from a scale and analyze weight fluctuations. Furthermore, the data collection unit can connect with smart devices in the home to develop a system that integrates and analyzes health data. For example, it can analyze skin condition based on data from a smart mirror. This allows for the collection of more detailed health data by connecting with smart devices.

[0048] The HealthBot Companion system can further analyze a user's past health data and advice history to provide more personalized advice. For example, the advice providing unit can analyze a user's past health data and advice history to build a system that provides more personalized health advice. For example, it can suggest an exercise plan based on past exercise history. The advice providing unit can also analyze past health data and advice history to provide optimal health advice to the user. For example, it can suggest improvements to nutritional balance based on past dietary data. Furthermore, the advice providing unit can analyze a user's past health data and advice history to develop a system that provides personalized health advice. For example, it can provide advice to improve sleep based on past sleep data. This allows for more personalized advice to be provided based on past data and advice history.

[0049] The HealthBot Companion system can further consider the user's social relationships and provide health advice that utilizes social support. For example, the advice providing unit can build a system that considers the user's social relationships (family, friends, colleagues) and provides health advice that utilizes social support. For example, it can suggest exercising with family. The advice providing unit can also provide optimal health advice to the user based on the social relationships. For example, it can suggest enjoying healthy meals with friends. Furthermore, the advice providing unit can develop a system that considers the user's social relationships and provides health advice that utilizes social support. For example, it can suggest engaging in stress-relieving activities with colleagues. In this way, more effective health advice can be provided by utilizing social support.

[0050] The HealthBot Companion system can further analyze a user's occupation and daily activity patterns and make lifestyle improvement suggestions based on the results. For example, the advice providing unit can build a system that analyzes a user's occupation and daily activity patterns and makes lifestyle improvement suggestions based on the results. For example, it can suggest regular stretching to a user who does a lot of desk work. The advice providing unit can also make optimal lifestyle improvement suggestions to a user based on the user's occupation and activity patterns. For example, it can suggest ways to improve sleep quality to a user who often works night shifts. Furthermore, the advice providing unit can develop a system that analyzes a user's occupation and daily activity patterns and makes lifestyle improvement suggestions based on the results. For example, it can suggest simple exercises to a user who is not getting enough exercise. In this way, lifestyle improvement suggestions based on the user's occupation and activity patterns can improve the user's quality of life.

[0051] The HealthBot Companion system can further analyze a user's health and lifestyle data over the long term and make suggestions for sustainable lifestyle improvements. For example, the advice providing unit can build a system that analyzes a user's health and lifestyle data over the long term and makes suggestions for sustainable lifestyle improvements. For example, it can suggest the formation of long-term exercise habits. The advice providing unit can also make suggestions for optimal sustainable lifestyle improvements to the user based on the health and lifestyle data. For example, it can suggest a plan to support long-term dietary improvements. Furthermore, the advice providing unit can develop a system that analyzes a user's health and lifestyle data over the long term and makes suggestions for sustainable lifestyle improvements. For example, it can suggest a long-term approach for stress management. This makes it possible to improve the user's quality of life by making suggestions for sustainable lifestyle improvements based on long-term data analysis.

[0052] The HealthBot Companion system can further provide lifestyle improvement suggestions in a form that can be implemented by the entire family, thereby improving the overall health of the family. For example, the advice providing unit can build a system that provides lifestyle improvement suggestions in a form that can be implemented by the entire family. For example, it can suggest an exercise program that the entire family can participate in. The advice providing unit can also make lifestyle improvement suggestions to improve the overall health of the entire family. For example, it can suggest that the family enjoy healthy meals together. Furthermore, the advice providing unit can develop a system that provides lifestyle improvement suggestions in a form that can be implemented by the entire family. For example, it can suggest stress relief activities that the entire family can participate in. In this way, by suggesting lifestyle improvements that can be implemented by the entire family, the health of the entire family can be improved.

[0053] The HealthBot Companion system can further provide lifestyle improvement suggestions that can be implemented even when the user is traveling or on a business trip. For example, the advice providing unit can be configured to provide a system that provides lifestyle improvement suggestions that can be implemented even when the user is traveling or on a business trip. For example, it can suggest simple exercises that can be done at the user's business trip destination. The advice providing unit can also provide lifestyle improvement suggestions that can be implemented even when the user is traveling or on a business trip. For example, it can provide advice on how to eat a healthy diet while traveling. Furthermore, the advice providing unit can develop a system that provides lifestyle improvement suggestions that can be implemented even when the user is traveling or on a business trip. For example, it can suggest stress management methods while on a business trip. This allows the system to continuously support the user's health management by providing lifestyle improvement suggestions that can be implemented even when the user is traveling or on a business trip.

[0054] The processing flow of the first embodiment will be briefly explained below.

[0055] Step 1: The data collection unit collects health data of the user. For example, the data collection unit collects data such as weight, blood pressure, heart rate, dietary details, and exercise volume that are entered by the user on a daily basis. The data collection unit can also automatically collect data from wearable devices. For example, it acquires heart rate and step count data from a smartwatch. Furthermore, the data collection unit can also collect data manually entered by the user. For example, the user enters dietary details into the app. Step 2: The analysis unit analyzes the health data collected by the data collection unit. For example, the analysis unit analyzes the health data using a generation AI (e.g., a text generation AI or a multimodal generation AI). The analysis unit can also analyze the data using statistical analysis or machine learning algorithms. For example, the generation AI analyzes the user's dietary data and evaluates nutritional balance. Furthermore, the analysis unit can analyze trends in the health data. For example, it can analyze fluctuations in the user's weight and evaluate their long-term health condition. Step 3: The advice providing unit provides advice based on the results of the analysis by the analysis unit. For example, the advice providing unit generates individual health advice based on the analysis results of the generation AI. The advice providing unit can also provide specific advice according to the user's health condition. For example, if the user is not getting enough exercise, the advice providing unit may provide advice such as, "We recommend that you add 30 minutes of walking every day." Furthermore, the advice providing unit can also provide advice tailored to the user's lifestyle. For example, the advice providing unit may suggest, "To avoid skipping breakfast, prepare a simple breakfast the night before."

[0056] (Example 2) The HealthBot Companion system according to an embodiment of the present invention tracks a user's health data, analyzes the data using a generation AI, and provides advice. This allows the HealthBot Companion system to grasp the user's health status in real time and provide personalized health advice.

[0057] The HealthBot Companion system according to the embodiment includes a data collection unit, an analysis unit, and an advice provision unit. The data collection unit collects health data from a user. For example, the data collection unit collects data such as weight, blood pressure, heart rate, dietary details, and exercise volume that are entered by the user on a daily basis. The data collection unit can also automatically collect data from a wearable device. For example, it can acquire heart rate and step count data from a smartwatch. The data collection unit can also collect data manually entered by the user. For example, the user enters dietary details into an app. The analysis unit analyzes the health data collected by the data collection unit. For example, the analysis unit can analyze the health data using a generation AI (e.g., a text generation AI or a multimodal generation AI). The analysis unit can also analyze the data using statistical analysis or machine learning algorithms. For example, the generation AI can analyze the user's dietary data and evaluate nutritional balance. The analysis unit can also analyze trends in the health data. For example, it can analyze the user's weight fluctuations and evaluate their long-term health condition. The advice provision unit provides advice based on the results of the analysis by the analysis unit. For example, the advice providing unit generates personalized health advice based on the analysis results of the generation AI. The advice providing unit can also provide specific advice tailored to the user's health condition. For example, if the user is not getting enough exercise, the advice providing unit can provide advice such as, "We recommend that you add 30 minutes of walking each day." Furthermore, the advice providing unit can also provide advice tailored to the user's lifestyle. For example, the advice providing unit can suggest, "To avoid skipping breakfast, prepare a simple breakfast the night before." This allows the HealthBot Companion system according to the embodiment to grasp the user's health condition in real time and provide personalized health advice. For example, by inputting health data on a daily basis and receiving advice from the generation AI, users can expect to improve their health. Furthermore, by providing health strategies tailored to busy modern people, they can comfortably maintain a healthy lifestyle.Furthermore, by spreading awareness of the importance of preventive medicine, it can contribute to the early detection and prevention of disease.

[0058] The data collection unit can estimate the user's emotional state and analyze the correlation between emotional fluctuations and health data. For example, the data collection unit estimates the user's emotional state in real time and analyzes the correlation between emotional fluctuations and health data (e.g., heart rate and blood pressure). For example, emotion estimation technology is used to quantify the user's emotional fluctuations and integrate the data into a health tracking system. For example, health data on days with high and low emotional scores is compared and analyzed. The data collection unit also estimates the user's emotional state and analyzes the impact of emotional fluctuations on health data. For example, it analyzes whether heart rate tends to increase on days with high stress. In this way, by analyzing the correlation between emotional fluctuations and health data, health tracking that takes into account the impact of stress and emotions is possible.

[0059] The data collection unit can incorporate the user's genetic information and track health data taking into account genetic risk factors. The data collection unit, for example, collects the user's genetic information and tracks health data taking into account genetic risk factors. For example, if there is a genetic risk of high blood pressure, tracking takes that risk into account. The data collection unit also analyzes the user's health data based on the genetic information and manages health based on genetic risk factors. For example, if there is a specific gene mutation, health advice taking into account its impact is provided. Furthermore, the data collection unit incorporates the user's genetic information and builds a health data tracking system that takes into account genetic risk factors. For example, if there is a genetic high risk of diabetes, tracking takes that risk into account. This makes it possible to track health data taking into account genetic risk factors.

[0060] The data collection unit can collect user environmental data and analyze the impact of environmental factors on health data. The data collection unit, for example, collects user environmental data (temperature, humidity, air quality, etc.) and integrates it with health data for analysis. For example, it analyzes whether heart rate increases on hot days. The data collection unit also builds a system that analyzes the impact on the user's health status based on the environmental data. For example, it investigates whether respiratory system data worsens on days with poor air quality. The data collection unit further collects the user's environmental data and performs correlation analysis with the health data. For example, it analyzes whether blood pressure increases on days with high humidity. This allows for more detailed health tracking by analyzing the impact of environmental factors on health.

[0061] The data collection unit simultaneously tracks the pet's health data and can analyze the correlation between the pet's and the owner's health status. The data collection unit, for example, collects the pet's health data (weight, activity level, dietary content, etc.) and integrates it with the owner's health data for analysis. For example, it investigates whether the pet's activity level affects the owner's exercise level. The data collection unit also builds a system that simultaneously tracks the health data of the pet and the owner and performs correlation analysis. For example, it analyzes whether the pet's health status affects the owner's stress level. Furthermore, the data collection unit collects the pet's health data and performs comparative analysis with the owner's health data. For example, it investigates whether the pet's weight gain affects the owner's eating habits. This enables comprehensive health management by analyzing the correlation between the pet's and the owner's health status.

[0062] The data collection unit can link with smart devices in the home to collect more detailed health data. For example, the data collection unit links with smart devices in the home (refrigerators, scales, smart mirrors, etc.) to build a system that automatically collects health data. For example, it analyzes meal contents based on data on ingredients in the refrigerator. The data collection unit also links with smart devices to collect detailed health data of users. For example, it automatically tracks data from a scale and analyzes weight fluctuations. Furthermore, the data collection unit will link with smart devices in the home to develop a system that integrates and analyzes health data. For example, it will analyze skin condition based on data from a smart mirror. This makes it possible to collect more detailed health data by linking with smart devices.

[0063] The data collection unit uses the emotion estimation function to analyze the emotions of a user when entering health data in real time, thereby improving the accuracy of the input. For example, the data collection unit uses the emotion estimation function to build a system that analyzes the emotions of a user when entering health data in real time. For example, the reliability of the data is evaluated based on the emotional state at the time of input. The data collection unit also analyzes the user's emotional state in real time to improve the accuracy of the health data input. For example, if the user is in a negative emotional state, the system prompts the user to reconfirm the input content. Furthermore, the data collection unit uses the emotion estimation function to develop a system that analyzes the emotions of a user when entering health data and improves the accuracy of the input. For example, if the user is in a positive emotional state, the system prioritizes saving the input content. In this way, the emotion estimation function can improve the accuracy of the health data input.

[0064] The advice providing unit can estimate the user's emotional state and provide health advice according to the emotion. The advice providing unit, for example, builds a system that estimates the user's emotional state in real time and provides health advice according to the emotion. For example, if stress is high, it suggests a relaxation method. The advice providing unit also uses emotion estimation technology to analyze the user's emotional state and provides health advice based on the results. For example, it recommends exercise when the user is in a positive emotional state. Furthermore, the advice providing unit develops a system that estimates the user's emotional state and provides health advice according to the emotion. For example, it suggests a relaxation method when the user is in a negative emotional state. In this way, health advice according to the emotion can be provided, thereby more effectively supporting the user's health management.

[0065] The advice providing unit can analyze the user's past health data and advice history to provide more personalized advice. The advice providing unit, for example, analyzes the user's past health data and advice history to build a system that provides more personalized health advice. For example, it suggests an exercise plan based on past exercise history. The advice providing unit also analyzes the past health data and advice history to provide optimal health advice to the user. For example, it suggests improving nutritional balance based on past dietary data. Furthermore, the advice providing unit analyzes the user's past health data and advice history to develop a system that provides personalized health advice. For example, it provides advice to improve sleep based on past sleep data. This makes it possible to provide more personalized advice based on past data and advice history.

[0066] The advice providing unit can provide health advice that utilizes social support by taking into account the user's social relationships. For example, the advice providing unit builds a system that provides health advice that utilizes social support by taking into account the user's social relationships (family, friends, colleagues). For example, it suggests exercising with family. The advice providing unit also provides optimal health advice to the user based on the social relationships. For example, it suggests enjoying healthy meals with friends. Furthermore, the advice providing unit develops a system that provides health advice that utilizes social support by taking into account the user's social relationships. For example, it suggests engaging in stress relief activities with colleagues. In this way, more effective health advice can be provided by utilizing social support.

[0067] The advice providing unit can provide health advice in a form that is easy to implement by associating it with the user's hobbies and interests. The advice providing unit, for example, builds a system that provides health advice associated with the user's hobbies and interests. For example, it suggests exercising while listening to music to a user who likes music. The advice providing unit also provides optimal health advice to a user based on the hobbies and interests. For example, it suggests healthy recipes to a user who likes cooking. Furthermore, the advice providing unit develops a system that provides health advice associated with the user's hobbies and interests. For example, it suggests hiking to a user who likes the outdoors. In this way, by associating health advice with hobbies and interests, it becomes easier to implement the advice.

[0068] The advice providing unit can customize health advice to suit the characteristics of the area where the user lives. For example, the advice providing unit builds a system that provides health advice tailored to the characteristics of the area where the user lives (climate, food culture, etc.). For example, it suggests hot meals to a user living in a cold region. The advice providing unit also provides optimal health advice to the user based on the characteristics of the area. For example, it suggests eating more seafood to a user living by the sea. Furthermore, the advice providing unit develops a system that provides health advice tailored to the characteristics of the area where the user lives. For example, it provides advice emphasizing hydration to a user living in a humid area. This allows for more effective health management by providing health advice tailored to the characteristics of the area.

[0069] The advice providing unit uses the emotion estimation function to analyze the emotional response of the user when receiving advice and evaluate the effectiveness of the advice. The advice providing unit, for example, uses the emotion estimation function to build a system that analyzes the emotional response of the user when receiving health advice in real time. For example, it analyzes facial expressions and voice when receiving the advice. The advice providing unit also analyzes the user's emotional response and evaluates the effectiveness of the health advice. For example, it prioritizes providing advice that has a high positive emotional response. Furthermore, the advice providing unit uses the emotion estimation function to develop a system that analyzes the emotional response of the user when receiving advice and evaluates the effectiveness of the advice based on the results. For example, it improves advice that has a high negative emotional response. In this way, by analyzing the emotional response, the effectiveness of advice can be evaluated and more effective advice can be provided.

[0070] The advice providing unit can estimate the user's emotional state and make suggestions for lifestyle improvements based on the emotions. The advice providing unit, for example, builds a system that estimates the user's emotional state in real time and makes suggestions for lifestyle improvements based on the emotions. For example, if the user is feeling depressed, it suggests ways to change their mood. The advice providing unit also uses emotion estimation technology to analyze the user's emotional state and makes suggestions for lifestyle improvements based on the results. For example, if the user is in a positive emotional state, it suggests taking up a new hobby. Furthermore, the advice providing unit develops a system that estimates the user's emotional state and makes suggestions for lifestyle improvements based on the emotions. For example, if the user is in a negative emotional state, it suggests relaxation methods. In this way, by making suggestions for lifestyle improvements based on emotions, it is possible to improve the user's quality of life.

[0071] The advice providing unit can analyze a user's occupation and daily activity patterns and make lifestyle improvement suggestions based on the results. The advice providing unit, for example, analyzes a user's occupation and daily activity patterns and builds a system that makes lifestyle improvement suggestions based on the results. For example, regular stretching is suggested for a user who does a lot of desk work. The advice providing unit also makes optimal lifestyle improvement suggestions for the user based on the occupation and activity patterns. For example, methods for improving sleep quality are suggested for a user who works many night shifts. Furthermore, the advice providing unit analyzes a user's occupation and daily activity patterns and develops a system that makes lifestyle improvement suggestions based on the results. For example, simple exercises are suggested for users who are not getting enough exercise. In this way, lifestyle improvement suggestions based on the occupation and activity patterns can be made to improve the user's quality of life.

[0072] The advice providing unit can analyze the user's health data and lifestyle data over the long term and make suggestions for sustainable lifestyle improvements. The advice providing unit, for example, builds a system that analyzes the user's health data and lifestyle data over the long term and makes suggestions for sustainable lifestyle improvements. For example, it suggests forming long-term exercise habits. The advice providing unit also makes suggestions for sustainable lifestyle improvements that are optimal for the user based on the health data and lifestyle data. For example, it suggests a plan to support long-term dietary improvements. Furthermore, the advice providing unit develops a system that analyzes the user's health data and lifestyle data over the long term and makes suggestions for sustainable lifestyle improvements. For example, it suggests a long-term approach for stress management. In this way, it is possible to improve the user's quality of life by making suggestions for sustainable lifestyle improvements based on long-term data analysis.

[0073] The advice providing unit provides lifestyle improvement suggestions in a form that can be implemented by the entire family, thereby improving the health of the family as a whole. For example, the advice providing unit builds a system that provides lifestyle improvement suggestions in a form that can be implemented by the entire family. For example, it suggests an exercise program that the entire family can participate in. The advice providing unit also makes lifestyle improvement suggestions to improve the health of the entire family as a whole. For example, it suggests that the family enjoy healthy meals together. Furthermore, the advice providing unit develops a system that provides lifestyle improvement suggestions in a form that can be implemented by the entire family. For example, it suggests stress relief activities that the entire family can participate in. In this way, by suggesting lifestyle improvements that can be implemented by the entire family, the health of the entire family can be improved.

[0074] The advice providing unit can provide suggestions for lifestyle improvements in a form that can be implemented even when the user is traveling or on a business trip. The advice providing unit, for example, builds a system that provides suggestions for lifestyle improvements that can be implemented even when the user is traveling or on a business trip. For example, it suggests simple exercises that can be done at the user's business trip destination. The advice providing unit also makes suggestions for lifestyle improvements that can be implemented even when the user is traveling or on a business trip. For example, it provides advice on how to eat a healthy diet while traveling. Furthermore, the advice providing unit develops a system that provides suggestions for lifestyle improvements that can be implemented even when the user is traveling or on a business trip. For example, it suggests methods for managing stress while on a business trip. In this way, by suggesting lifestyle improvements that can be implemented even when the user is traveling or on a business trip, it is possible to continuously support the user's health management.

[0075] The advice providing unit uses the emotion estimation function to analyze the emotional response of the user when the user implements a lifestyle improvement suggestion, and can evaluate the effectiveness of the suggestion. The advice providing unit, for example, uses the emotion estimation function to build a system that analyzes the emotional response of the user when the user implements the lifestyle improvement suggestion in real time. For example, the emotional state after the suggestion is implemented is monitored. The advice providing unit also analyzes the user's emotional response and evaluates the effectiveness of the lifestyle improvement suggestion. For example, suggestions that have a high number of positive emotional responses are provided preferentially. Furthermore, the advice providing unit uses the emotion estimation function to analyze the emotional response of the user when the user implements the lifestyle improvement suggestion, and develops a system that evaluates the effectiveness of the suggestion based on the results. For example, suggestions that have a high number of negative emotional responses are improved. In this way, by analyzing the emotional response, the effectiveness of the lifestyle improvement suggestion can be evaluated and more effective suggestions can be provided.

[0076] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0077] The HealthBot Companion system can also collect and analyze the user's sleep data. For example, the data collection unit can acquire data from a smartwatch or smart bed to track the user's sleep patterns and quality. The analysis unit can analyze the collected sleep data and evaluate the user's sleep quality. For example, a short period of deep sleep can be determined to indicate poor sleep quality. Furthermore, the advice provision unit can provide the user with advice to improve their sleep quality based on the analysis results. For example, specific advice such as "avoid using your smartphone before bed" or "go to bed at a regular time" can be provided. This can be expected to improve the user's sleep quality and overall health.

[0078] The HealthBot Companion system can also estimate a user's emotional state and analyze the correlation between emotional fluctuations and health data. For example, the data collection unit can estimate a user's emotional state in real time and analyze the correlation between emotional fluctuations and health data (e.g., heart rate and blood pressure). For example, emotion estimation technology can be used to quantify a user's emotional fluctuations and integrate the data into a health tracking system. For example, health data from days with high and low emotional scores can be compared and analyzed. The data collection unit can also estimate a user's emotional state and analyze the impact of emotional fluctuations on health data. For example, it can analyze whether heart rate tends to increase on days with high stress. This allows health tracking that takes into account the effects of stress and emotions by analyzing the correlation between emotional fluctuations and health data.

[0079] The HealthBot Companion system can also incorporate the user's genetic information and track health data taking into account genetic risk factors. For example, the data collection unit can collect the user's genetic information and track health data taking into account genetic risk factors. For example, if a user has a genetic risk of high blood pressure, tracking can take that risk into account. The data collection unit can also analyze the user's health data based on the genetic information and manage health based on genetic risk factors. For example, if a user has a specific gene mutation, health advice can be provided that takes into account its impact. The data collection unit can also incorporate the user's genetic information and build a health data tracking system that takes genetic risk factors into account. For example, if a user has a genetic high risk of diabetes, tracking can take that risk into account. This makes it possible to track health data taking into account genetic risk factors.

[0080] The HealthBot Companion system can also collect user environmental data and analyze the impact of environmental factors on health data. For example, the data collection unit can collect user environmental data (temperature, humidity, air quality, etc.) and integrate it with health data for analysis. For example, it can analyze whether heart rate increases on hot days. The data collection unit can also build a system that analyzes the impact of environmental data on the user's health status. For example, it can investigate whether respiratory system data worsens on days with poor air quality. Furthermore, the data collection unit can collect user environmental data and perform correlation analysis with health data. For example, it can analyze whether blood pressure increases on days with high humidity. This allows for more detailed health tracking by analyzing the impact of environmental factors on health.

[0081] The HealthBot Companion system can also simultaneously track pet health data and analyze the correlation between pet and owner health. For example, the data collection unit can collect pet health data (weight, activity level, dietary content, etc.) and integrate it with the owner's health data for analysis. For example, it can investigate whether a pet's activity level affects the owner's exercise level. The data collection unit can also build a system that simultaneously tracks pet and owner health data and performs correlation analysis. For example, it can analyze whether a pet's health status affects the owner's stress level. Furthermore, the data collection unit can collect pet health data and compare it with the owner's health data for analysis. For example, it can investigate whether a pet's weight gain affects the owner's eating habits. This allows for comprehensive health management by analyzing the correlation between pet and owner health.

[0082] The HealthBot Companion system can also connect with smart devices in the home to collect more detailed health data. For example, the data collection unit can connect with smart devices in the home (refrigerators, scales, smart mirrors, etc.) to build a system that automatically collects health data. For example, it can analyze dietary information based on the food ingredients in the refrigerator. The data collection unit can also connect with smart devices to collect detailed health data for users. For example, it can automatically track data from a scale and analyze weight fluctuations. Furthermore, the data collection unit can connect with smart devices in the home to develop a system that integrates and analyzes health data. For example, it can analyze skin condition based on data from a smart mirror. This allows for the collection of more detailed health data by connecting with smart devices.

[0083] The HealthBot Companion system can further use an emotion estimation function to analyze a user's emotions when entering health data in real time, thereby improving the accuracy of the data entry. For example, the data collection unit can use the emotion estimation function to build a system that analyzes a user's emotions when entering health data in real time. For example, the reliability of the data can be evaluated based on the user's emotional state at the time of entry. The data collection unit can also analyze a user's emotional state in real time to improve the accuracy of the health data entry. For example, the data collection unit can prompt the user to reconfirm the content of the entry when the user is in a negative emotional state. Furthermore, the data collection unit can use the emotion estimation function to develop a system that analyzes a user's emotions when entering health data, thereby improving the accuracy of the entry. For example, the data collection unit can prioritize saving the content of the entry when the user is in a positive emotional state. In this way, the emotion estimation function can improve the accuracy of the health data entry.

[0084] The HealthBot Companion system can further estimate a user's emotional state and provide health advice tailored to the user's emotions. For example, the advice providing unit can build a system that estimates a user's emotional state in real time and provides health advice tailored to the user's emotions. For example, relaxation methods can be suggested when stress levels are high. The advice providing unit can also use emotion estimation technology to analyze a user's emotional state and provide health advice based on the results. For example, exercise can be recommended when the user is in a positive emotional state. Furthermore, the advice providing unit can develop a system that estimates a user's emotional state and provides health advice tailored to the user's emotions. For example, relaxation methods can be suggested when the user is in a negative emotional state. This allows for more effective support for the user's health management by providing health advice tailored to the user's emotions.

[0085] The HealthBot Companion system can further analyze a user's past health data and advice history to provide more personalized advice. For example, the advice providing unit can analyze a user's past health data and advice history to build a system that provides more personalized health advice. For example, it can suggest an exercise plan based on past exercise history. The advice providing unit can also analyze past health data and advice history to provide optimal health advice to the user. For example, it can suggest improvements to nutritional balance based on past dietary data. Furthermore, the advice providing unit can analyze a user's past health data and advice history to develop a system that provides personalized health advice. For example, it can provide advice to improve sleep based on past sleep data. This allows for more personalized advice to be provided based on past data and advice history.

[0086] The HealthBot Companion system can further consider the user's social relationships and provide health advice that utilizes social support. For example, the advice providing unit can build a system that considers the user's social relationships (family, friends, colleagues) and provides health advice that utilizes social support. For example, it can suggest exercising with family. The advice providing unit can also provide optimal health advice to the user based on the social relationships. For example, it can suggest enjoying healthy meals with friends. Furthermore, the advice providing unit can develop a system that considers the user's social relationships and provides health advice that utilizes social support. For example, it can suggest engaging in stress-relieving activities with colleagues. In this way, more effective health advice can be provided by utilizing social support.

[0087] The HealthBot Companion system can further estimate a user's emotional state and make suggestions for lifestyle improvements based on their emotions. For example, the advice providing unit can build a system that estimates a user's emotional state in real time and makes suggestions for lifestyle improvements based on their emotions. For example, if a user is feeling depressed, the advice providing unit can suggest ways to change their mood. The advice providing unit can also use emotion estimation technology to analyze a user's emotional state and make suggestions for lifestyle improvements based on the results. For example, if a user is in a positive emotional state, the advice providing unit can suggest taking up a new hobby. The advice providing unit can also develop a system that estimates a user's emotional state and makes suggestions for lifestyle improvements based on their emotions. For example, if a user is in a negative emotional state, the advice providing unit can suggest relaxation methods. This makes it possible to improve the user's quality of life by making suggestions for lifestyle improvements based on their emotions.

[0088] The HealthBot Companion system can further analyze a user's occupation and daily activity patterns and make lifestyle improvement suggestions based on the results. For example, the advice providing unit can build a system that analyzes a user's occupation and daily activity patterns and makes lifestyle improvement suggestions based on the results. For example, it can suggest regular stretching to a user who does a lot of desk work. The advice providing unit can also make optimal lifestyle improvement suggestions to a user based on the user's occupation and activity patterns. For example, it can suggest ways to improve sleep quality to a user who often works night shifts. Furthermore, the advice providing unit can develop a system that analyzes a user's occupation and daily activity patterns and makes lifestyle improvement suggestions based on the results. For example, it can suggest simple exercises to a user who is not getting enough exercise. In this way, lifestyle improvement suggestions based on the user's occupation and activity patterns can improve the user's quality of life.

[0089] The HealthBot Companion system can further analyze a user's health and lifestyle data over the long term and make suggestions for sustainable lifestyle improvements. For example, the advice providing unit can build a system that analyzes a user's health and lifestyle data over the long term and makes suggestions for sustainable lifestyle improvements. For example, it can suggest the formation of long-term exercise habits. The advice providing unit can also make suggestions for optimal sustainable lifestyle improvements to the user based on the health and lifestyle data. For example, it can suggest a plan to support long-term dietary improvements. Furthermore, the advice providing unit can develop a system that analyzes a user's health and lifestyle data over the long term and makes suggestions for sustainable lifestyle improvements. For example, it can suggest a long-term approach for stress management. This makes it possible to improve the user's quality of life by making suggestions for sustainable lifestyle improvements based on long-term data analysis.

[0090] The HealthBot Companion system can further provide lifestyle improvement suggestions in a form that can be implemented by the entire family, thereby improving the overall health of the family. For example, the advice providing unit can build a system that provides lifestyle improvement suggestions in a form that can be implemented by the entire family. For example, it can suggest an exercise program that the entire family can participate in. The advice providing unit can also make lifestyle improvement suggestions to improve the overall health of the entire family. For example, it can suggest that the family enjoy healthy meals together. Furthermore, the advice providing unit can develop a system that provides lifestyle improvement suggestions in a form that can be implemented by the entire family. For example, it can suggest stress relief activities that the entire family can participate in. In this way, by suggesting lifestyle improvements that can be implemented by the entire family, the health of the entire family can be improved.

[0091] The HealthBot Companion system can further provide lifestyle improvement suggestions that can be implemented even when the user is traveling or on a business trip. For example, the advice providing unit can be configured to provide a system that provides lifestyle improvement suggestions that can be implemented even when the user is traveling or on a business trip. For example, it can suggest simple exercises that can be done at the user's business trip destination. The advice providing unit can also provide lifestyle improvement suggestions that can be implemented even when the user is traveling or on a business trip. For example, it can provide advice on how to eat a healthy diet while traveling. Furthermore, the advice providing unit can develop a system that provides lifestyle improvement suggestions that can be implemented even when the user is traveling or on a business trip. For example, it can suggest stress management methods while on a business trip. This allows the system to continuously support the user's health management by providing lifestyle improvement suggestions that can be implemented even when the user is traveling or on a business trip.

[0092] The HealthBot Companion system can further use the emotion estimation function to analyze the emotional reactions of a user when implementing a lifestyle improvement suggestion and evaluate the effectiveness of the suggestion. For example, the advice providing unit can use the emotion estimation function to build a system that analyzes the emotional reactions of a user when implementing a lifestyle improvement suggestion in real time. For example, the emotional state after implementing the suggestion can be monitored. The advice providing unit can also analyze the user's emotional reactions and evaluate the effectiveness of the lifestyle improvement suggestion. For example, suggestions that have a high number of positive emotional reactions can be preferentially provided. Furthermore, the advice providing unit can use the emotion estimation function to develop a system that analyzes the emotional reactions of a user when implementing a lifestyle improvement suggestion and evaluates the effectiveness of the suggestion based on the results. For example, suggestions that have a high number of negative emotional reactions can be improved. In this way, the effectiveness of lifestyle improvement suggestions can be evaluated by analyzing the emotional reactions, and more effective suggestions can be provided.

[0093] The processing flow of the second embodiment will be briefly explained below.

[0094] Step 1: The data collection unit collects health data of the user. For example, the data collection unit collects data such as weight, blood pressure, heart rate, dietary details, and exercise volume that are entered by the user on a daily basis. The data collection unit can also automatically collect data from wearable devices. For example, it acquires heart rate and step count data from a smartwatch. Furthermore, the data collection unit can also collect data manually entered by the user. For example, the user enters dietary details into the app. Step 2: The analysis unit analyzes the health data collected by the data collection unit. For example, the analysis unit analyzes the health data using a generation AI (e.g., a text generation AI or a multimodal generation AI). The analysis unit can also analyze the data using statistical analysis or machine learning algorithms. For example, the generation AI analyzes the user's dietary data and evaluates nutritional balance. Furthermore, the analysis unit can analyze trends in the health data. For example, it can analyze fluctuations in the user's weight and evaluate their long-term health condition. Step 3: The advice providing unit provides advice based on the results of the analysis by the analysis unit. For example, the advice providing unit generates individual health advice based on the analysis results of the generation AI. The advice providing unit can also provide specific advice according to the user's health condition. For example, if the user is not getting enough exercise, the advice providing unit may provide advice such as, "We recommend that you add 30 minutes of walking every day." Furthermore, the advice providing unit can also provide advice tailored to the user's lifestyle. For example, the advice providing unit may suggest, "To avoid skipping breakfast, prepare a simple breakfast the night before."

[0095] 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.

[0096] 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.

[0097] 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.

[0098] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0099] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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).

[0104] 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.

[0105] 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.

[0106] 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.

[0107] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0108] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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).

[0119] 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.

[0120] 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.

[0121] 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.

[0122] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0123] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0129] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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).

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0139] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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).

[0148] 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.

[0149] 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."

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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]

[0162] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a data collection unit that collects health data of a user; an analysis unit that analyzes the health data collected by the data collection unit; an advice providing unit that provides advice based on the results of the analysis by the analysis unit. A system characterized by:

2. The data collection unit Estimating the emotional state of the user and analyzing the correlation between the emotional fluctuations and the health data.

2. The system of claim 1.

3. The data collection unit Incorporating genetic information about the user and tracking the health data taking into account genetic risk factors 2. The system of claim 1.

4. The data collection unit Collecting environmental data of the user and analyzing the impact of environmental factors on the health data.

2. The system of claim 1.

5. The data collection unit The health data of the pet is also tracked at the same time, and the correlation between the health status of the pet and the owner is analyzed.

2. The system of claim 1.

Images