System

Abstract

The system according to the embodiment aims to provide a health care plan customized based on individual health data.SOLUTION: A system according to an embodiment includes a health data input unit, a data analysis unit, an exercise plan generation unit, a nutrition guideline providing unit, and a stress management advice providing unit. The health data input unit inputs health data. The data analysis unit analyzes the health data input by the health data input unit. The motion plan generation unit generates a motion plan based on the result analyzed by the data analysis unit. The nutrition guideline providing unit provides a nutrition guideline based on the result analyzed by the data analyzing unit. The stress management advice providing unit provides stress management advice based on the result analyzed by the data 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 have not adequately provided customized health management plans based on individual health data, and there is room for improvement.

[0005] The system according to the embodiment aims to provide a customized health management plan based on individual health data. [Means for solving the problem]

[0006] The system according to the embodiment includes a health data input unit, a data analysis unit, an exercise plan generation unit, a nutritional guideline provision unit, and a stress management advice provision unit. The health data input unit inputs health data. The data analysis unit analyzes the health data input by the health data input unit. The exercise plan generation unit generates an exercise plan based on the results of the analysis by the data analysis unit. The nutritional guideline provision unit provides nutritional guidelines based on the results of the analysis by the data analysis unit. The stress management advice provision unit provides stress management advice based on the results of the analysis by the data analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can provide a customized health management plan based on individual health data. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The HealthNavigator AI system according to an embodiment of the present invention is a system that analyzes an individual's health data and lifestyle and provides a customized health management plan, thereby supporting individual health and aiming for long-term wellness.

[0029] The Health Navigator AI system according to the embodiment includes a health data input unit, a data analysis unit, an exercise plan generation unit, a nutritional guideline provision unit, and a stress management advice provision unit. The health data input unit allows a user to input health data such as daily activity, diet, and sleep patterns. For example, data such as the number of steps, calories burned, dietary content, and sleep time can be input. The data analysis unit allows a generation AI to analyze the health data input by the health data input unit. For example, the generation AI identifies cases where the user is not active enough or has an unbalanced diet. The generation AI analyzes the data using a text generation AI (e.g., LLM) or a multimodal generation AI. The exercise plan generation unit generates an exercise plan based on the results of the analysis by the data analysis unit. For example, the exercise plan suggests exercises such as walking, jogging, and strength training depending on the user's activity level and physical strength. The nutritional guideline provision unit provides nutritional guidelines based on the results of the analysis by the data analysis unit. For example, the generation AI suggests necessary nutrients and their intake amounts based on the user's dietary content. The stress management advice providing unit uses the generation AI to provide stress management advice based on the results of the analysis by the data analysis unit. For example, it suggests relaxation techniques, meditation, and appropriate resting methods. This allows the HealthNavigator AI system according to the embodiment to provide a comprehensive health management plan for each user. For example, when a user inputs daily activity data, the generation AI analyzes the data and provides optimal exercise plans and nutritional guidelines. Furthermore, receiving stress management advice allows users to maintain a balance between their mind and body.

[0030] The health data input unit provides real-time feedback to the data entered by the user, improving the accuracy of the data. For example, when a user enters their dietary information, the generation AI evaluates the nutrient balance in real time and suggests necessary corrections. For example, if the entered meal is high in calories, it suggests low-calorie alternative foods. This improves the accuracy of the data entered by the user.

[0031] The health data input unit can automatically collect the user's health data in cooperation with a wearable device or a smart home device. The health data input unit automatically collects data such as the user's heart rate, number of steps, and calories burned using, for example, a wearable device, and transmits the data to the platform. For example, the health data input unit collects data using a smart watch. The health data input unit also automatically collects the user's health data using a smart home device. For example, the health data input unit collects weight data using a smart scale. This allows the user's health data to be collected automatically.

[0032] The health data input unit uses voice recognition technology to input health data, thereby reducing the burden on the user. For example, the health data input unit allows the user to input meal details by voice, which is then converted into text data using voice recognition technology. For example, if the user says, "I had toast and coffee for breakfast," this is automatically registered in the database. This allows health data to be input using voice recognition technology, thereby reducing the burden on the user.

[0033] The health data input unit can provide a customized input interface for different age groups and health conditions. For example, the health data input unit provides an input interface for children and promotes data input using visually easy-to-understand icons and animations. For example, when selecting meal contents, illustrations of ingredients are displayed. This makes it possible to provide a customized input interface for different age groups and health conditions.

[0034] The data analysis unit can compare the user's past data to identify long-term health trends. For example, the data analysis unit analyzes the user's exercise data from the past year to identify increases, decreases, and patterns in the amount of exercise. For example, it analyzes seasonal changes in the amount of exercise to understand long-term trends. This allows the user to compare the data with the user's past data to identify long-term health trends.

[0035] The data analysis unit can compare the user's health data with the data of other users and provide a benchmark. For example, the data analysis unit compares the user's health data with the exercise data of other users of the same age and evaluates whether the user's exercise volume is average. For example, the data analysis unit compares the user's step count with the average step count for users of the same age. This allows the user's health data to be compared with the data of other users and provide a benchmark.

[0036] The data analysis unit can provide the results of the health data analysis in a visual format that is easy for the user to understand. For example, the data analysis unit visually displays the user's exercise data in graphs or charts, allowing the user to understand changes in exercise volume at a glance. For example, the amount of exercise per week is displayed as a bar graph. This allows the results of the health data analysis to be provided in a visual format that is easy for the user to understand.

[0037] The data analysis unit can perform an integrated analysis of different health indicators and perform a comprehensive health assessment. The data analysis unit can perform an integrated analysis of data such as the user's heart rate, blood pressure, and body temperature, and perform a comprehensive health assessment. For example, the heart rate and blood pressure data can be combined to evaluate the cardiovascular health condition. This allows for an integrated analysis of different health indicators and a comprehensive health assessment.

[0038] The exercise plan generation unit can analyze the user's exercise history and provide an exercise plan that takes into account past experiences of success and failure. The exercise plan generation unit, for example, analyzes the user's past exercise history and identifies successful and unsuccessful exercise plans. For example, it proposes a new exercise plan based on a successful exercise plan in the past. This makes it possible to analyze the user's exercise history and provide an exercise plan that takes into account past experiences of success and failure.

[0039] The exercise plan generation unit can collect feedback on the user's exercise plan in real time and dynamically adjust the plan. For example, the exercise plan generation unit allows the user to input feedback after exercising and adjusts the exercise plan based on that feedback. For example, if the exercise is too hard, the unit suggests lowering the intensity. This allows feedback on the user's exercise plan to be collected in real time and dynamically adjust the plan.

[0040] The exercise plan generation unit can provide a variety of exercise plans that combine different exercise types. The exercise plan generation unit provides an exercise plan that combines different exercise types, such as yoga, Pilates, and dance, depending on the user's preferences and physical strength. For example, different exercises are suggested for each week. This makes it possible to provide a variety of exercise plans that combine different exercise types.

[0041] The exercise plan generation unit can share the user's exercise plan with other users and provide community-based support. For example, the exercise plan generation unit provides a platform where users can share their exercise plans with other users and promote community-based support. For example, users can share their exercise plans and encourage each other. This allows the user's exercise plan to be shared with other users and provide community-based support.

[0042] The nutritional guideline providing unit can analyze the user's dietary history and provide nutritional guidelines based on past eating patterns. The nutritional guideline providing unit, for example, analyzes the user's past eating history and provides a nutritionally balanced meal plan. For example, it suggests meals that supplement nutrients that were previously deficient. This makes it possible to analyze the user's dietary history and provide nutritional guidelines based on past eating patterns.

[0043] The nutritional guideline providing unit can collect feedback on the user's nutritional guidelines and dynamically adjust the guidelines. For example, the nutritional guideline providing unit allows the user to input feedback on the meal plan and adjusts the nutritional guidelines based on the feedback. For example, if the meal plan is not satisfactory, the unit suggests changes. This allows the user's feedback on the nutritional guidelines to be collected and the guidelines to be dynamically adjusted.

[0044] The nutritional guideline providing unit can provide customized nutritional guidelines that correspond to different food cultures and eating habits. The nutritional guideline providing unit provides, for example, customized nutritional guidelines that correspond to the user's food culture and eating habits. For example, it proposes meal plans that correspond to food cultures such as Japanese food and Western food. This makes it possible to provide customized nutritional guidelines that correspond to different food cultures and eating habits.

[0045] The nutritional guideline providing unit can share the user's nutritional guidelines with other users and provide community-based support. For example, the nutritional guideline providing unit can provide a platform where users can share their nutritional guidelines with other users and promote community-based support. For example, users can share meal plans and encourage each other. This allows the user's nutritional guidelines to be shared with other users and provide community-based support.

[0046] The stress management advice providing unit can analyze the user's stress history and provide advice based on past stress management methods. For example, the stress management advice providing unit analyzes the user's past stress history and identifies stress management methods that have been effective. For example, if a relaxation method has been effective in the past, it will be suggested again. This makes it possible to analyze the user's stress history and provide advice based on past stress management methods.

[0047] The stress management advice providing unit can collect feedback on the user's stress level and dynamically adjust advice. For example, the stress management advice providing unit allows the user to input feedback on their stress level and adjusts advice based on that feedback. For example, if stress is high, it may suggest relaxation techniques. This allows feedback on the user's stress level to be collected and advice to be dynamically adjusted.

[0048] The stress management advice providing unit can provide a variety of advice that combines different stress management methods. For example, the stress management advice providing unit provides advice that combines different stress management methods, such as relaxation techniques, meditation, and exercise, depending on the user's preferences and stress level. For example, a different method may be suggested each week. This makes it possible to provide a variety of advice that combines different stress management methods.

[0049] The stress management advice providing unit can share the user's stress management advice with other users and provide community-based support. For example, the stress management advice providing unit provides a platform where users can share their stress management advice with other users and promote community-based support. For example, users can share advice and encourage each other. This allows the user's stress management advice to be shared with other users and provide community-based support.

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

[0051] The HealthNavigator AI system can further include a preventive medical advice provider based on the user's health data. The preventive medical advice provider, for example, analyzes the user's health data and identifies potential future health risks. For example, it predicts future risks of high blood pressure or diabetes based on blood pressure and blood sugar data and suggests preventive measures. This allows the user to address health risks early. The preventive medical advice provider can also emphasize the importance of regular health checks and encourage the user to undergo health checkups at appropriate times. Furthermore, the preventive medical advice provider can provide specific advice for maintaining health tailored to the user's lifestyle. For example, it can support the user's health over the long term by suggesting a review of exercise habits or improvements to diet.

[0052] The HealthNavigator AI system can further include a sleep improvement advice provider based on the user's health data. The sleep improvement advice provider, for example, analyzes the user's sleep patterns and provides specific advice for improving sleep quality. For example, it may suggest ways to relax before bed or creating an appropriate sleeping environment. The sleep improvement advice provider can also suggest adjusting the user's sleep schedule to suit their lifestyle. For example, it may suggest optimal bedtimes and wake-up times based on work and home schedules. Furthermore, the sleep improvement advice provider can detect signs of sleep disorders early based on the user's sleep data and take appropriate measures. This allows the user to ensure high-quality sleep and improve their daily performance.

[0053] The HealthNavigator AI system can further include a personalized fitness program provider based on the user's health data. The personalized fitness program provider, for example, analyzes the user's physical strength and exercise history to provide a fitness program tailored to individual needs. For example, it may suggest exercises for strength training, aerobic exercise, and flexibility improvement. The personalized fitness program provider can also provide a fitness plan tailored to the user's goals. For example, it may suggest a training plan tailored to goals such as weight loss, muscle building, and endurance improvement. Furthermore, the personalized fitness program provider can dynamically adjust the program based on the user's feedback to support effective training. This allows the user to effectively achieve their fitness goals and maintain their health.

[0054] The HealthNavigator AI system can further include a rehabilitation support provider based on the user's health data. The rehabilitation support provider, for example, analyzes the user's injury and illness history and provides an appropriate rehabilitation plan. For example, it may suggest exercises to improve joint range of motion or training to restore muscle strength. The rehabilitation support provider can also monitor the user's rehabilitation progress and adjust the plan as needed. For example, if rehabilitation is not showing any effect, it may suggest an alternative method. Furthermore, the rehabilitation support provider can provide support to maintain the user's motivation for rehabilitation based on user feedback. This allows the user to progress with rehabilitation effectively and recover quickly.

[0055] The HealthNavigator AI system can further include a personalized supplement suggestion unit based on the user's health data. The personalized supplement suggestion unit, for example, analyzes the user's nutritional data and suggests supplements tailored to individual needs. For example, it may suggest supplements to make up for vitamin and mineral deficiencies. The personalized supplement suggestion unit can also provide a supplement plan tailored to the user's lifestyle and health goals. For example, it may suggest proteins and amino acids to support post-exercise recovery. Furthermore, the personalized supplement suggestion unit can dynamically adjust the supplement plan based on user feedback to provide effective support. This allows the user to effectively supplement their nutrition and maintain their health.

[0056] The HealthNavigator AI system may further include a personalized health report provider based on the user's health data. The personalized health report provider, for example, comprehensively analyzes the user's health data and provides a report tailored to the user's individual health condition. For example, it may provide detailed analysis results for each item, such as exercise, nutrition, sleep, and stress. The personalized health report provider may also include specific advice in the report based on the user's health goals. For example, it may report progress toward goals such as weight loss, muscle building, and stress management. Furthermore, the personalized health report provider may dynamically adjust the content of the report based on user feedback to provide more effective support. This allows the user to understand their own health condition and take appropriate measures.

[0057] The processing flow of the first embodiment will be briefly explained below.

[0058] Step 1: The health data input unit allows the user to input health data such as daily activity, diet, sleep patterns, etc. For example, data such as the number of steps taken, calories burned, dietary content, and sleep time can be input. Step 2: In the data analysis unit, the generation AI analyzes the health data entered by the health data input unit. For example, the generation AI identifies cases where the user is not sufficiently active or has an unbalanced diet. The generation AI analyzes the data using text generation AI (e.g., LLM) and multimodal generation AI. Step 3: The exercise plan generation unit generates an exercise plan using the AI ​​generated based on the results of the analysis by the data analysis unit. For example, it suggests exercises such as walking, jogging, and strength training based on the user's activity level and physical strength. Step 4: The nutritional guideline provider uses the generated AI to provide nutritional guidelines based on the results of the data analysis. For example, it suggests the necessary nutrients and intake amounts based on the user's diet. Step 5: The stress management advice provider uses the generated AI to provide stress management advice based on the results of the analysis by the data analysis unit, such as suggesting relaxation techniques, meditation, and appropriate resting methods.

[0059] (Example 2) The HealthNavigator AI system according to an embodiment of the present invention is a system that analyzes an individual's health data and lifestyle and provides a customized health management plan, thereby supporting individual health and aiming for long-term wellness.

[0060] The Health Navigator AI system according to the embodiment includes a health data input unit, a data analysis unit, an exercise plan generation unit, a nutritional guideline provision unit, and a stress management advice provision unit. The health data input unit allows a user to input health data such as daily activity, diet, and sleep patterns. For example, data such as the number of steps, calories burned, dietary content, and sleep time can be input. The data analysis unit allows a generation AI to analyze the health data input by the health data input unit. For example, the generation AI identifies cases where the user is not active enough or has an unbalanced diet. The generation AI analyzes the data using a text generation AI (e.g., LLM) or a multimodal generation AI. The exercise plan generation unit generates an exercise plan based on the results of the analysis by the data analysis unit. For example, the exercise plan suggests exercises such as walking, jogging, and strength training depending on the user's activity level and physical strength. The nutritional guideline provision unit provides nutritional guidelines based on the results of the analysis by the data analysis unit. For example, the generation AI suggests necessary nutrients and their intake amounts based on the user's dietary content. The stress management advice providing unit uses the generation AI to provide stress management advice based on the results of the analysis by the data analysis unit. For example, it suggests relaxation techniques, meditation, and appropriate resting methods. This allows the HealthNavigator AI system according to the embodiment to provide a comprehensive health management plan for each user. For example, when a user inputs daily activity data, the generation AI analyzes the data and provides optimal exercise plans and nutritional guidelines. Furthermore, receiving stress management advice allows users to maintain a balance between their mind and body.

[0061] The health data input unit provides real-time feedback to the data entered by the user, improving the accuracy of the data. For example, when a user enters their dietary information, the generation AI evaluates the nutrient balance in real time and suggests necessary corrections. For example, if the entered meal is high in calories, it suggests low-calorie alternative foods. This improves the accuracy of the data entered by the user.

[0062] The health data input unit can automatically collect the user's health data in cooperation with a wearable device or a smart home device. The health data input unit automatically collects data such as the user's heart rate, number of steps, and calories burned using, for example, a wearable device, and transmits the data to the platform. For example, the health data input unit collects data using a smart watch. The health data input unit also automatically collects the user's health data using a smart home device. For example, the health data input unit collects weight data using a smart scale. This allows the user's health data to be collected automatically.

[0063] The health data input unit can use an emotion estimation function to analyze the user's emotion when entering data and provide an interface for eliciting positive emotions. For example, the health data input unit analyzes facial expressions and voice when the user enters data and estimates emotions in real time. For example, it can analyze the user's emotion using a camera or microphone and display a positive message if it detects a negative emotion. This makes it possible to analyze the user's emotion when entering data and elicit positive emotions.

[0064] The health data input unit uses voice recognition technology to input health data, thereby reducing the burden on the user. For example, the health data input unit allows the user to input meal details by voice, which is then converted into text data using voice recognition technology. For example, if the user says, "I had toast and coffee for breakfast," this is automatically registered in the database. This allows health data to be input using voice recognition technology, thereby reducing the burden on the user.

[0065] The health data input unit can provide a customized input interface for different age groups and health conditions. For example, the health data input unit provides an input interface for children and promotes data input using visually easy-to-understand icons and animations. For example, when selecting meal contents, illustrations of ingredients are displayed. This makes it possible to provide a customized input interface for different age groups and health conditions.

[0066] The health data input unit can use the emotion estimation function to analyze the emotional background of data entered by a user and improve the reliability of the data. For example, when a user enters meal details, the health data input unit uses the emotion estimation function to analyze the emotion at that time and evaluate the reliability of the data taking the emotional background into consideration. For example, meal data entered when stress is high can be identified. This allows the emotional background of data entered by a user to be analyzed and the reliability of the data to be improved.

[0067] The data analysis unit can compare the user's past data to identify long-term health trends. For example, the data analysis unit analyzes the user's exercise data from the past year to identify increases, decreases, and patterns in the amount of exercise. For example, it analyzes seasonal changes in the amount of exercise to understand long-term trends. This allows the user to compare the data with the user's past data to identify long-term health trends.

[0068] The data analysis unit can compare the user's health data with the data of other users and provide a benchmark. For example, the data analysis unit compares the user's health data with the exercise data of other users of the same age and evaluates whether the user's exercise volume is average. For example, the data analysis unit compares the user's step count with the average step count for users of the same age. This allows the user's health data to be compared with the data of other users and provide a benchmark.

[0069] The data analysis unit uses the emotion estimation function to analyze health data taking into account the user's emotional state, allowing for more personalized advice. For example, the data analysis unit analyzes the user's exercise data in combination with the emotion estimation function to provide advice that takes into account emotions regarding exercise. For example, if the user finds exercise enjoyable, the data analysis unit suggests increasing the amount of exercise. This allows for analysis of health data taking into account the user's emotional state, allowing for more personalized advice to be provided.

[0070] The data analysis unit can provide the results of the health data analysis in a visual format that is easy for the user to understand. For example, the data analysis unit visually displays the user's exercise data in graphs or charts, allowing the user to understand changes in exercise volume at a glance. For example, the amount of exercise per week is displayed as a bar graph. This allows the results of the health data analysis to be provided in a visual format that is easy for the user to understand.

[0071] The data analysis unit can perform an integrated analysis of different health indicators and perform a comprehensive health assessment. The data analysis unit can perform an integrated analysis of data such as the user's heart rate, blood pressure, and body temperature, and perform a comprehensive health assessment. For example, the heart rate and blood pressure data can be combined to evaluate the cardiovascular health condition. This allows for an integrated analysis of different health indicators and a comprehensive health assessment.

[0072] The data analysis unit uses the emotion estimation function to provide analysis results of health data based on the user's emotional state, allowing for emotional support. For example, the data analysis unit analyzes the user's exercise data in combination with the emotion estimation function and provides analysis results that take into account emotions regarding exercise. For example, if the user finds exercise enjoyable, the data analysis unit suggests increasing the amount of exercise. This allows for analysis results of health data based on the user's emotional state, allowing for emotional support.

[0073] The exercise plan generation unit can analyze the user's exercise history and provide an exercise plan that takes into account past experiences of success and failure. The exercise plan generation unit, for example, analyzes the user's past exercise history and identifies successful and unsuccessful exercise plans. For example, it proposes a new exercise plan based on a successful exercise plan in the past. This makes it possible to analyze the user's exercise history and provide an exercise plan that takes into account past experiences of success and failure.

[0074] The exercise plan generation unit can collect feedback on the user's exercise plan in real time and dynamically adjust the plan. For example, the exercise plan generation unit allows the user to input feedback after exercising and adjusts the exercise plan based on that feedback. For example, if the exercise is too hard, the unit suggests lowering the intensity. This allows feedback on the user's exercise plan to be collected in real time and dynamically adjust the plan.

[0075] The exercise plan generation unit can provide a variety of exercise plans that combine different exercise types. The exercise plan generation unit provides an exercise plan that combines different exercise types, such as yoga, Pilates, and dance, depending on the user's preferences and physical strength. For example, different exercises are suggested for each week. This makes it possible to provide a variety of exercise plans that combine different exercise types.

[0076] The exercise plan generation unit can share the user's exercise plan with other users and provide community-based support. For example, the exercise plan generation unit provides a platform where users can share their exercise plans with other users and promote community-based support. For example, users can share their exercise plans and encourage each other. This allows the user's exercise plan to be shared with other users and provide community-based support.

[0077] The exercise plan generation unit can use the emotion estimation function to monitor the user's emotional state during exercise and provide an exercise plan to elicit positive emotions. The exercise plan generation unit, for example, monitors the user's emotional state during exercise in real time and provides an exercise plan to elicit positive emotions. For example, it suggests exercises that are enjoyable to exercise. This makes it possible to monitor the user's emotional state during exercise and provide an exercise plan to elicit positive emotions.

[0078] The nutritional guideline providing unit can analyze the user's dietary history and provide nutritional guidelines based on past eating patterns. The nutritional guideline providing unit, for example, analyzes the user's past eating history and provides a nutritionally balanced meal plan. For example, it suggests meals that supplement nutrients that were previously deficient. This makes it possible to analyze the user's dietary history and provide nutritional guidelines based on past eating patterns.

[0079] The nutritional guideline providing unit can collect feedback on the user's nutritional guidelines and dynamically adjust the guidelines. For example, the nutritional guideline providing unit allows the user to input feedback on the meal plan and adjusts the nutritional guidelines based on the feedback. For example, if the meal plan is not satisfactory, the unit suggests changes. This allows the user's feedback on the nutritional guidelines to be collected and the guidelines to be dynamically adjusted.

[0080] The nutritional guideline providing unit can use the emotion estimation function to analyze the user's emotions regarding meals and provide nutritional guidelines to increase meal satisfaction. For example, the nutritional guideline providing unit can analyze the user's emotions during meals in real time and provide nutritional guidelines that elicit positive emotions. For example, it can suggest meals that are enjoyable to eat. This makes it possible to analyze the user's emotions regarding meals and provide nutritional guidelines to increase meal satisfaction.

[0081] The nutritional guideline providing unit can provide customized nutritional guidelines that correspond to different food cultures and eating habits. The nutritional guideline providing unit provides, for example, customized nutritional guidelines that correspond to the user's food culture and eating habits. For example, it proposes meal plans that correspond to food cultures such as Japanese food and Western food. This makes it possible to provide customized nutritional guidelines that correspond to different food cultures and eating habits.

[0082] The nutritional guideline providing unit can share the user's nutritional guidelines with other users and provide community-based support. For example, the nutritional guideline providing unit can provide a platform where users can share their nutritional guidelines with other users and promote community-based support. For example, users can share meal plans and encourage each other. This allows the user's nutritional guidelines to be shared with other users and provide community-based support.

[0083] The nutritional guideline providing unit can use the emotion estimation function to monitor the user's emotional state while eating and provide nutritional guidelines for eliciting positive emotions. The nutritional guideline providing unit can, for example, monitor the user's emotional state while eating in real time and provide nutritional guidelines for eliciting positive emotions. For example, it can suggest meals that are enjoyable while eating. This makes it possible to monitor the user's emotional state while eating and provide nutritional guidelines for eliciting positive emotions.

[0084] The stress management advice providing unit can analyze the user's stress history and provide advice based on past stress management methods. For example, the stress management advice providing unit analyzes the user's past stress history and identifies stress management methods that have been effective. For example, if a relaxation method has been effective in the past, it will be suggested again. This makes it possible to analyze the user's stress history and provide advice based on past stress management methods.

[0085] The stress management advice providing unit can collect feedback on the user's stress level and dynamically adjust advice. For example, the stress management advice providing unit allows the user to input feedback on their stress level and adjusts advice based on that feedback. For example, if stress is high, it may suggest relaxation techniques. This allows feedback on the user's stress level to be collected and advice to be dynamically adjusted.

[0086] The stress management advice providing unit can use the emotion estimation function to analyze the user's emotions regarding stress and provide advice to provide emotional support. The stress management advice providing unit, for example, analyzes the user's emotions regarding stress in real time and provides advice to provide emotional support. For example, it suggests ways to relax when stress is high. This makes it possible to analyze the user's emotions regarding stress and provide advice to provide emotional support.

[0087] The stress management advice providing unit can provide a variety of advice that combines different stress management methods. For example, the stress management advice providing unit provides advice that combines different stress management methods, such as relaxation techniques, meditation, and exercise, depending on the user's preferences and stress level. For example, a different method may be suggested each week. This makes it possible to provide a variety of advice that combines different stress management methods.

[0088] The stress management advice providing unit can share the user's stress management advice with other users and provide community-based support. For example, the stress management advice providing unit provides a platform where users can share their stress management advice with other users and promote community-based support. For example, users can share advice and encourage each other. This allows the user's stress management advice to be shared with other users and provide community-based support.

[0089] The stress management advice providing unit can use the emotion estimation function to monitor the user's emotional state during stress and provide stress management advice to elicit positive emotions. The stress management advice providing unit, for example, monitors the user's emotional state during stress in real time and provides stress management advice to elicit positive emotions. For example, it suggests ways to relax during stress. This makes it possible to monitor the user's emotional state during stress and provide stress management advice to elicit positive emotions.

[0090] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0091] The HealthNavigator AI system can further include a preventive medical advice provider based on the user's health data. The preventive medical advice provider, for example, analyzes the user's health data and identifies potential future health risks. For example, it predicts future risks of high blood pressure or diabetes based on blood pressure and blood sugar data and suggests preventive measures. This allows the user to address health risks early. The preventive medical advice provider can also emphasize the importance of regular health checks and encourage the user to undergo health checkups at appropriate times. Furthermore, the preventive medical advice provider can provide specific advice for maintaining health tailored to the user's lifestyle. For example, it can support the user's health over the long term by suggesting a review of exercise habits or improvements to diet.

[0092] The HealthNavigator AI system can further include a sleep improvement advice provider based on the user's health data. The sleep improvement advice provider, for example, analyzes the user's sleep patterns and provides specific advice for improving sleep quality. For example, it may suggest ways to relax before bed or creating an appropriate sleeping environment. The sleep improvement advice provider can also suggest adjusting the user's sleep schedule to suit their lifestyle. For example, it may suggest optimal bedtimes and wake-up times based on work and home schedules. Furthermore, the sleep improvement advice provider can detect signs of sleep disorders early based on the user's sleep data and take appropriate measures. This allows the user to ensure high-quality sleep and improve their daily performance.

[0093] The HealthNavigator AI system can also include a mental health support provider that uses the user's health data. The mental health support provider can, for example, analyze the user's stress level and emotional state and provide specific advice for maintaining and improving their mental health. For example, it can suggest relaxation and meditation techniques to relieve stress. The mental health support provider can also provide a mental health care plan tailored to the user's lifestyle. For example, it can suggest time management and communication methods to reduce stress at work and at home. Furthermore, the mental health support provider can monitor the user's emotional state in real time and encourage them to seek professional support as needed. This allows the user to maintain their mental health and maintain a balance between mind and body.

[0094] The HealthNavigator AI system can further include a personalized fitness program provider based on the user's health data. The personalized fitness program provider, for example, analyzes the user's physical strength and exercise history to provide a fitness program tailored to individual needs. For example, it may suggest exercises for strength training, aerobic exercise, and flexibility improvement. The personalized fitness program provider can also provide a fitness plan tailored to the user's goals. For example, it may suggest a training plan tailored to goals such as weight loss, muscle building, and endurance improvement. Furthermore, the personalized fitness program provider can dynamically adjust the program based on the user's feedback to support effective training. This allows the user to effectively achieve their fitness goals and maintain their health.

[0095] The HealthNavigator AI system can further include an emotional state-based meal suggestion unit that uses the user's health data. The emotional state-based meal suggestion unit, for example, analyzes the user's emotional state and suggests meals that match their mood at that time. For example, when stress levels are high, it suggests recipes using ingredients that have a relaxing effect. The emotional state-based meal suggestion unit can also provide advice to increase meal satisfaction by taking the user's emotional state into consideration. For example, it can suggest ingredients and cooking methods that elicit positive emotions. Furthermore, the emotional state-based meal suggestion unit can monitor the user's emotional state in real time and adjust the meal plan as needed. This allows users to enjoy meals that suit their emotional state and maintain their health.

[0096] The HealthNavigator AI system can further include a rehabilitation support provider based on the user's health data. The rehabilitation support provider, for example, analyzes the user's injury and illness history and provides an appropriate rehabilitation plan. For example, it may suggest exercises to improve joint range of motion or training to restore muscle strength. The rehabilitation support provider can also monitor the user's rehabilitation progress and adjust the plan as needed. For example, if rehabilitation is not showing any effect, it may suggest an alternative method. Furthermore, the rehabilitation support provider can provide support to maintain the user's motivation for rehabilitation based on user feedback. This allows the user to progress with rehabilitation effectively and recover quickly.

[0097] The HealthNavigator AI system can further include an emotional state-based exercise suggestion unit that uses the user's health data. The emotional state-based exercise suggestion unit, for example, analyzes the user's emotional state and suggests exercises that match their mood at the time. For example, when stress levels are high, it might suggest yoga or meditation, which have a relaxing effect. The emotional state-based exercise suggestion unit can also provide advice to increase exercise satisfaction, taking the user's emotional state into consideration. For example, it might suggest exercises or workouts that elicit positive emotions. Furthermore, the emotional state-based exercise suggestion unit can monitor the user's emotional state in real time and adjust the exercise plan as needed. This allows users to enjoy exercise that suits their emotional state and maintain their health.

[0098] The HealthNavigator AI system can further include a personalized supplement suggestion unit based on the user's health data. The personalized supplement suggestion unit, for example, analyzes the user's nutritional data and suggests supplements tailored to individual needs. For example, it may suggest supplements to make up for vitamin and mineral deficiencies. The personalized supplement suggestion unit can also provide a supplement plan tailored to the user's lifestyle and health goals. For example, it may suggest proteins and amino acids to support post-exercise recovery. Furthermore, the personalized supplement suggestion unit can dynamically adjust the supplement plan based on user feedback to provide effective support. This allows the user to effectively supplement their nutrition and maintain their health.

[0099] The HealthNavigator AI system can further include an emotional state-based stress management suggestion unit based on the user's health data. The emotional state-based stress management suggestion unit, for example, analyzes the user's emotional state and suggests stress management methods tailored to their mood at the time. For example, it might suggest relaxation techniques or meditation when stress levels are high. The emotional state-based stress management suggestion unit can also provide advice to enhance satisfaction with stress management, taking the user's emotional state into account. For example, it might suggest a stress management method that elicits positive emotions. Furthermore, the emotional state-based stress management suggestion unit can monitor the user's emotional state in real time and adjust the stress management plan as needed. This allows the user to manage stress according to their emotional state and maintain a balance between mind and body.

[0100] The HealthNavigator AI system may further include a personalized health report provider based on the user's health data. The personalized health report provider, for example, comprehensively analyzes the user's health data and provides a report tailored to the user's individual health condition. For example, it may provide detailed analysis results for each item, such as exercise, nutrition, sleep, and stress. The personalized health report provider may also include specific advice in the report based on the user's health goals. For example, it may report progress toward goals such as weight loss, muscle building, and stress management. Furthermore, the personalized health report provider may dynamically adjust the content of the report based on user feedback to provide more effective support. This allows the user to understand their own health condition and take appropriate measures.

[0101] The processing flow of the second embodiment will be briefly explained below.

[0102] Step 1: The health data input unit allows the user to input health data such as daily activity, diet, sleep patterns, etc. For example, data such as the number of steps taken, calories burned, dietary content, and sleep time can be input. Step 2: In the data analysis unit, the generation AI analyzes the health data entered by the health data input unit. For example, the generation AI identifies cases where the user is not sufficiently active or has an unbalanced diet. The generation AI analyzes the data using text generation AI (e.g., LLM) and multimodal generation AI. Step 3: The exercise plan generation unit generates an exercise plan using the AI ​​generated based on the results of the analysis by the data analysis unit. For example, it suggests exercises such as walking, jogging, and strength training based on the user's activity level and physical strength. Step 4: The nutritional guideline provider uses the generated AI to provide nutritional guidelines based on the results of the data analysis. For example, it suggests the necessary nutrients and intake amounts based on the user's diet. Step 5: The stress management advice provider uses the generated AI to provide stress management advice based on the results of the analysis by the data analysis unit, such as suggesting relaxation techniques, meditation, and appropriate resting methods.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0107] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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).

[0112] 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.

[0113] 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.

[0114] 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.

[0115] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0116] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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).

[0127] 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.

[0128] 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.

[0129] 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.

[0130] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0131] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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).

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0147] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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).

[0156] 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.

[0157] 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."

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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, in order to avoid confusion and to 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.

[0169] 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]

[0170] 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 health data input unit for inputting health data; a data analysis unit that analyzes the health data input by the health data input unit; and an exercise plan generation unit that generates an exercise plan based on the results of the analysis by the data analysis unit; a nutrition guideline providing unit that provides nutrition guidelines based on the results of the analysis by the data analysis unit; a stress management advice providing unit that provides stress management advice based on the results of the analysis by the data analysis unit. A system characterized by:

2. The health data input unit includes: Automatically collect the user's health data in cooperation with wearable devices and smart home devices 2. The system of claim 1.

3. The data analysis unit Compare your data with your past data to identify long-term health trends 2. The system of claim 1.

4. The exercise plan generation unit Analyze the user's exercise history and provide the exercise plan taking into account past successes and failures 2. The system of claim 1.

5. The nutritional guideline providing unit Analyzing the user's dietary history and providing said nutritional guidelines based on past eating patterns 2. The system of claim 1.

6. The stress management advice providing unit Analyzes the user's stress history and provides advice based on past stress management methods 2. The system of claim 1.

7. The health data input unit includes: Analyzes the user's emotions when inputting and provides an interface to elicit positive emotions.

2. The system of claim 1.

8. The data analysis unit Analyzing said health data while taking into account the user's emotional state to provide more personalized advice 2. The system of claim 1.

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