System

Abstract

An object of a system according to an embodiment is to support early detection of an undeveloped disease and prevention of progression of the disease by utilizing a daily life habit of a user and data obtained from a wearable device.SOLUTION: A system includes a data collection part, an analysis part, a plan creation part, a monitoring part, and a feedback part. The data collection unit collects daily life habits of a user and data obtained from a wearable device. The analysis unit analyzes the data collected by the data collection unit. The plan creation unit creates an individual health improvement plan based on the data analyzed by the analysis unit. The monitoring unit continuously monitors the health data based on the plan created by the plan creation unit. The feedback unit provides feedback to the user based on the data monitored by the monitoring unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem of not being able to effectively utilize data obtained from users' daily lifestyle habits and wearable devices to detect pre-disease conditions early and prevent their progression.

[0005] The system according to the embodiment aims to support early detection of pre-disease and prevention of its progression by utilizing data obtained from a user's daily lifestyle habits and wearable devices. [Means for solving the problem]

[0006] The system according to the embodiment includes a data collection unit, an analysis unit, a plan creation unit, a monitoring unit, and a feedback unit. The data collection unit collects data on the user's daily lifestyle habits and data obtained from the wearable device. The analysis unit analyzes the data collected by the data collection unit. The plan creation unit creates an individual health improvement plan based on the data analyzed by the analysis unit. The monitoring unit continuously monitors health data based on the plan created by the plan creation unit. The feedback unit provides feedback to the user based on the data monitored by the monitoring unit. [Effects of the Invention]

[0007] The system according to the embodiment can utilize data obtained from a user's daily lifestyle and wearable devices to support early detection of pre-disease and prevention of its progression. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The pre-disease prevention system according to an embodiment of the present invention is a system that uses AI to analyze data obtained from a user's daily lifestyle and wearable devices, and creates an individual health improvement plan. This enables the pre-disease prevention system to support early detection of pre-disease and prevention of its progression.

[0029] The pre-disease prevention system according to the embodiment includes a data collection unit, an analysis unit, a plan creation unit, a monitoring unit, and a feedback unit. The data collection unit collects data on a user's daily habits and data obtained from a wearable device. For example, the data collection unit collects health-related data such as heart rate, sleep patterns, steps, and calorie consumption. The data collection unit can also be linked to an app that records the user's diet and stress level. The analysis unit analyzes the data collected by the data collection unit. For example, the analysis unit may analyze the collected data using AI to evaluate the user's health status. The analysis unit can also analyze the user's genetic information and identify genetic risk factors. The plan creation unit creates an individual health improvement plan based on the data analyzed by the analysis unit. For example, the plan creation unit may provide advice to improve sleep quality to a user who has consistently experienced sleep deprivation. The plan creation unit may also suggest specific activities tailored to the user's hobbies and lifestyle. The monitoring unit continuously monitors health data based on the plan created by the plan creation unit. For example, the monitoring unit detects abnormal heart rate and disturbed sleep patterns, etc., to detect pre-disease signs early. The monitoring unit can also monitor the user's emotional state using an emotion estimation function and evaluate the impact of emotional fluctuations on health. The feedback unit provides feedback to the user based on the data monitored by the monitoring unit. For example, the feedback unit creates a weekly health report and sends it to the user. The feedback unit also provides necessary support when the user implements a health improvement plan. This allows the pre-disease prevention system according to the embodiment to support early detection of pre-disease and prevention of its progression. For example, the user can avoid overlooking pre-disease signs, such as everyday fatigue and mild headaches, and take appropriate measures. Furthermore, by implementing an individual health improvement plan, lifestyle habits can be improved and health can be maintained.

[0030] The data collection unit can be linked to an app that records the user's diet and stress level. For example, the data collection unit uses an app to record the user's daily diet, and integrates that data with data obtained from the wearable device. For example, the data collection unit records the calories and nutrients of meals, and AI analyzes the data to evaluate the user's health condition. The data collection unit also installs an app that measures stress levels and records the user's stress state daily. For example, stress levels are evaluated using a stress checklist or heart rate variability, and the data is integrated with the data from the wearable device. The data collection unit also uses AI to analyze the diet and stress level data and comprehensively evaluate the user's health condition. For example, health risks are predicted taking into account the quality of the diet and the effects of stress. This allows for a more detailed evaluation of the user's health condition.

[0031] The analysis unit can analyze the user's genetic information and identify genetic risk factors. For example, the analysis unit collects the user's genetic information and uses AI to analyze the data to identify genetic risk factors. For example, if a specific gene mutation is present, the risk is assessed. The analysis unit also integrates the genetic information with data obtained from the wearable device, and the AI ​​assesses overall health risk. For example, it combines genetic risk and lifestyle data to identify high-risk behaviors. The analysis unit also creates an individualized health improvement plan based on the genetic information. For example, it provides preventative lifestyle advice to users with specific genetic risks. This makes it possible to identify genetic risk factors and create an individualized health improvement plan.

[0032] The data collection unit can also collect data from smart home devices and perform health assessments. For example, the data collection unit integrates weight data obtained from a smart scale with data from wearable devices, and AI performs a comprehensive health assessment. For example, it analyzes the relationship between weight fluctuations and exercise volume. The data collection unit also uses a smart mirror to record changes in the user's posture and body shape and integrates this with data from the wearable devices. For example, it evaluates the impact of poor posture on health. The data collection unit also uses AI to analyze data obtained from smart home devices and evaluate overall health risks. For example, it combines data on weight, posture, and exercise volume to create a health improvement plan. This allows data from smart home devices to be integrated for a comprehensive health assessment.

[0033] The data collection unit can develop specialized data collection methods for different age groups and genders to meet individual health needs. For example, the data collection unit develops specialized data collection methods for different age groups, and AI analyzes the data. For example, collecting bone density and muscle strength data for elderly people to assess health risks. The data collection unit can also develop specialized data collection methods for different genders, and AI analyzes the data. For example, collecting hormone balance and menstrual cycle data for women to assess health risks. The data collection unit can also create individual health improvement plans for different age groups and genders. For example, proposing exercise plans for younger people and providing nutritional management advice for elderly people. This allows the development of specialized data collection methods for different age groups and genders to meet individual health needs.

[0034] The plan creation unit can suggest specific activities that match the user's hobbies and lifestyle. For example, the plan creation unit collects data on the user's hobbies and lifestyle, and the AI ​​suggests specific activities based on that data. For example, it creates an exercise plan such as yoga or hiking. The plan creation unit also creates a health improvement plan that matches the user's lifestyle. For example, for a user who does a lot of desk work, it suggests stretches and exercises that can be done in the office. The plan creation unit also suggests activities based on the user's hobbies and lifestyle, allowing the user to improve their health while having fun. For example, for a user who loves the outdoors, it suggests a weekend hiking plan. This makes it possible to suggest specific activities that match the user's hobbies and lifestyle.

[0035] The plan creation unit can incorporate a support system that utilizes the user's social network. For example, the plan creation unit collects data on the user's social network, and the AI ​​builds a support system based on that data. For example, it suggests an exercise plan to do with family and friends. The plan creation unit also creates a health improvement plan that utilizes the social network. For example, it suggests a fitness challenge that can be participated in with friends. The plan creation unit also enables health improvement with the support of family and friends. For example, it suggests a plan for dietary management together with family. In this way, a support system that utilizes the user's social network can be incorporated.

[0036] The plan creation unit can suggest activities according to the season and weather. For example, the plan creation unit builds a system that suggests activities according to the season and weather. For example, it suggests swimming and outdoor activities in the summer, and indoor exercises in the winter. The plan creation unit also collects weather data, and the AI ​​creates a health improvement plan based on that data. For example, it suggests yoga or stretching that can be done indoors on rainy days. The plan creation unit also suggests activities according to the season and weather, allowing the user to carry out the activities without straining themselves. For example, it suggests a relaxation plan that includes hot drinks in the cold season. This makes it possible to suggest activities according to the season and weather.

[0037] The plan creation unit can evaluate the implementation status of the plan and introduce a reward system based on the degree of achievement. The plan creation unit, for example, evaluates the implementation status of a health improvement plan and introduces a reward system based on the degree of achievement. For example, points or badges are awarded to users who achieve their exercise goals. The plan creation unit also monitors the user's implementation status of the plan in real time and builds a system that provides rewards based on the degree of achievement. For example, rewards are set based on the degree of achievement each week. The plan creation unit also uses the reward system to increase the user's motivation. For example, a system is introduced that allows users to receive benefits or gifts by accumulating points. This makes it possible to evaluate the implementation status of the plan and introduce a reward system based on the degree of achievement.

[0038] The monitoring unit can provide specific behavioral instructions to the user when it detects signs of pre-illness. For example, when AI detects signs of pre-illness, the monitoring unit provides specific behavioral instructions to the user. For example, if an abnormal heart rate is detected, it recommends that the user see a doctor. In addition, the monitoring unit suggests specific exercises or dietary changes when it detects signs of pre-illness. For example, if a disturbance in sleep pattern is detected, it suggests relaxation exercises. In addition, the monitoring unit builds a system that provides users with behavioral instructions when signs of pre-illness are detected. For example, if specific symptoms appear, it notifies the user to immediately see a medical institution. In this way, it is possible to provide specific behavioral instructions when signs of pre-illness are detected.

[0039] The monitoring unit can develop an algorithm that compares the user's past health data and detects abnormalities early. For example, the monitoring unit collects the user's past health data and uses AI to develop an algorithm that detects abnormalities early based on that data. For example, it compares past heart rate data with current data to detect abnormalities. The monitoring unit also compares past health data with current data to build a system that detects signs of pre-illness early. For example, it analyzes changes in sleep patterns to detect abnormalities. The monitoring unit also develops an algorithm that detects signs of pre-illness early based on the user's past health data. For example, it compares past exercise data with current data to detect abnormalities. This makes it possible to develop an algorithm that compares past health data to detect abnormalities early.

[0040] The monitoring unit can build a system that notifies the user's family and medical professionals when it detects signs of pre-disease and encourages early intervention. The monitoring unit, for example, builds a system that notifies the user's family when it detects signs of pre-disease. For example, if an abnormal heart rate is detected, it notifies the family by email or SMS. The monitoring unit also builds a system that notifies medical professionals when it detects signs of pre-disease. For example, if a disturbance in sleep pattern is detected, it notifies a doctor. The monitoring unit also develops a system that notifies the user's family and medical professionals of signs of pre-disease and encourages early intervention. For example, if a specific symptom appears, it sends an alert to the family and medical professionals. This makes it possible to build a system that notifies the family and medical professionals when it detects signs of pre-disease and encourages early intervention.

[0041] The monitoring department can set up an online community in which users can participate to prevent the progression of ME-BYO, and can provide information sharing and support. The monitoring department can set up an online community in which users can participate to prevent the progression of ME-BYO, for example, by providing a forum for sharing health information and advice. The monitoring department can also create an environment in which users can support each other through the online community. For example, it can provide a mechanism for sharing the progress of health improvement plans and encouraging each other. The monitoring department can also utilize the online community to provide information on preventing the progression of ME-BYO. For example, it can hold webinars and Q&A sessions by experts. This makes it possible to set up an online community in which users can participate to prevent the progression of ME-BYO, and can provide information sharing and support.

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

[0043] The data collection unit can collect data on the user's living environment and evaluate health risks. For example, the data collection unit collects data on the user's living environment, such as air quality, temperature, and humidity, and AI analyzes this data to evaluate health risks. The data collection unit can also collect data on the user's work environment and evaluate its impact on stress levels and work efficiency. Furthermore, the data collection unit can collect data on the user's commuting route and means of transportation and evaluate commuting stress and health risks. This makes it possible to evaluate health risks that take into account the user's entire living environment.

[0044] The analysis unit can predict seasonal health risks based on the user's health data. For example, the analysis unit can analyze past health data to identify seasonal health risks. For example, the analysis unit can evaluate the risk of influenza in winter and the risk of heatstroke in summer. The analysis unit can also suggest preventive measures based on seasonal health risks. For example, the analysis unit can recommend taking vitamin D in winter and emphasize the importance of staying hydrated in summer. The analysis unit can also create an individual health improvement plan that takes seasonal health risks into account. This makes it possible to predict seasonal health risks and suggest appropriate preventive measures.

[0045] The monitoring unit can evaluate the effectiveness of exercise in real time based on the user's exercise data. For example, the monitoring unit collects data such as heart rate, calorie consumption, and exercise time, and AI analyzes this data to evaluate the effectiveness of the exercise. The monitoring unit can also suggest to the user whether to continue or adjust the exercise based on the effectiveness of the exercise. For example, if the exercise effect is low, it can provide advice on increasing the exercise intensity. The monitoring unit can also comprehensively evaluate the user's health condition based on the exercise data and adjust the exercise plan. This makes it possible to evaluate the effectiveness of exercise in real time and provide appropriate advice.

[0046] The data collection unit can collect data on the user's sleep environment and evaluate the quality of their sleep. For example, the data collection unit collects data such as the temperature, humidity, and noise level of the user's bedroom, and the AI ​​analyzes this data to evaluate the quality of their sleep. The data collection unit can also record the user's sleep patterns and sleep duration to identify factors that affect sleep quality. Furthermore, the data collection unit can suggest ways to improve the user's sleep environment based on the sleep environment data. This makes it possible to comprehensively evaluate the user's sleep environment and provide advice to improve sleep quality.

[0047] The monitoring unit can predict long-term health risks based on the user's health data. For example, the monitoring unit can analyze past health data to identify long-term health risks. For example, the monitoring unit can evaluate the risk of cardiovascular disease or diabetes. The monitoring unit can also suggest preventive measures based on the long-term health risks. For example, the monitoring unit can recommend regular exercise and dietary management. The monitoring unit can also create an individual health improvement plan that takes long-term health risks into consideration. This makes it possible to predict long-term health risks and suggest appropriate preventive measures.

[0048] The data collection unit can collect data on the user's hobbies and lifestyle and assess health risks. For example, the data collection unit collects data on the user's hobbies and lifestyle, and AI analyzes it to assess health risks. The data collection unit can also create a health improvement plan based on the user's hobbies and lifestyle. For example, for a user who enjoys outdoor activities, it can suggest exercising in nature. The data collection unit can also evaluate health risks according to the user's hobbies and lifestyle and suggest appropriate preventive measures. This makes it possible to assess health risks taking into account the user's hobbies and lifestyle.

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

[0050] Step 1: The data collection unit collects data from the user's daily habits and wearable devices. For example, it collects health-related data such as heart rate, sleep patterns, steps taken, and calorie consumption. It can also connect to apps that record the user's diet and stress levels. Step 2: The analysis unit analyzes the data collected by the data collection unit. For example, the analysis unit may use AI to analyze the collected data and evaluate the user's health status. The analysis unit may also analyze the user's genetic information and identify genetic risk factors. Step 3: The plan creation unit creates an individual health improvement plan based on the data analyzed by the analysis unit. For example, if a user has persistent sleep deprivation, it will provide advice on how to improve the quality of their sleep. It can also suggest specific activities that fit the user's hobbies and lifestyle. Step 4: The monitoring unit continuously monitors health data based on the plan created by the plan creation unit. For example, it detects abnormalities in heart rate and sleep patterns to detect early signs of illness. It can also use an emotion estimation function to monitor the user's emotional state and evaluate the impact of emotional fluctuations on health. Step 5: The feedback unit provides feedback to the user based on the data monitored by the monitoring unit. For example, it creates a weekly health report and sends it to the user. It also provides necessary support when the user implements the health improvement plan.

[0051] (Example 2) The pre-disease prevention system according to an embodiment of the present invention is a system that uses AI to analyze data obtained from a user's daily lifestyle and wearable devices, and creates an individual health improvement plan. This enables the pre-disease prevention system to support early detection of pre-disease and prevention of its progression.

[0052] The pre-disease prevention system according to the embodiment includes a data collection unit, an analysis unit, a plan creation unit, a monitoring unit, and a feedback unit. The data collection unit collects data on a user's daily habits and data obtained from a wearable device. For example, the data collection unit collects health-related data such as heart rate, sleep patterns, steps, and calorie consumption. The data collection unit can also be linked to an app that records the user's diet and stress level. The analysis unit analyzes the data collected by the data collection unit. For example, the analysis unit may analyze the collected data using AI to evaluate the user's health status. The analysis unit can also analyze the user's genetic information and identify genetic risk factors. The plan creation unit creates an individual health improvement plan based on the data analyzed by the analysis unit. For example, the plan creation unit may provide advice to improve sleep quality to a user who has consistently experienced sleep deprivation. The plan creation unit may also suggest specific activities tailored to the user's hobbies and lifestyle. The monitoring unit continuously monitors health data based on the plan created by the plan creation unit. For example, the monitoring unit detects abnormal heart rate and disturbed sleep patterns, etc., to detect pre-disease signs early. The monitoring unit can also monitor the user's emotional state using an emotion estimation function and evaluate the impact of emotional fluctuations on health. The feedback unit provides feedback to the user based on the data monitored by the monitoring unit. For example, the feedback unit creates a weekly health report and sends it to the user. The feedback unit also provides necessary support when the user implements a health improvement plan. This allows the pre-disease prevention system according to the embodiment to support early detection of pre-disease and prevention of its progression. For example, the user can avoid overlooking pre-disease signs, such as everyday fatigue and mild headaches, and take appropriate measures. Furthermore, by implementing an individual health improvement plan, lifestyle habits can be improved and health can be maintained.

[0053] The data collection unit can be linked to an app that records the user's diet and stress level. For example, the data collection unit uses an app to record the user's daily diet, and integrates that data with data obtained from the wearable device. For example, the data collection unit records the calories and nutrients of meals, and AI analyzes the data to evaluate the user's health condition. The data collection unit also installs an app that measures stress levels and records the user's stress state daily. For example, stress levels are evaluated using a stress checklist or heart rate variability, and the data is integrated with the data from the wearable device. The data collection unit also uses AI to analyze the diet and stress level data and comprehensively evaluate the user's health condition. For example, health risks are predicted taking into account the quality of the diet and the effects of stress. This allows for a more detailed evaluation of the user's health condition.

[0054] The analysis unit can analyze the user's genetic information and identify genetic risk factors. For example, the analysis unit collects the user's genetic information and uses AI to analyze the data to identify genetic risk factors. For example, if a specific gene mutation is present, the risk is assessed. The analysis unit also integrates the genetic information with data obtained from the wearable device, and the AI ​​assesses overall health risk. For example, it combines genetic risk and lifestyle data to identify high-risk behaviors. The analysis unit also creates an individualized health improvement plan based on the genetic information. For example, it provides preventative lifestyle advice to users with specific genetic risks. This makes it possible to identify genetic risk factors and create an individualized health improvement plan.

[0055] The analysis unit can use the emotion estimation function to analyze the user's emotional state and evaluate the impact of emotional fluctuations on health. The analysis unit, for example, uses the emotion estimation function to record the user's emotional state daily. For example, it calculates an emotion score using facial expression and voice analysis and integrates it with health data. The analysis unit also uses AI to analyze the emotional state data and evaluate the impact of emotional fluctuations on health. For example, it analyzes the impact of stress and anxiety on heart rate and sleep patterns. The analysis unit also creates an individual health improvement plan based on the emotional state data. For example, it provides relaxation methods for stress management and mental health advice. This makes it possible to evaluate the impact of emotional fluctuations on health and create an individual health improvement plan.

[0056] The data collection unit can also collect data from smart home devices and perform health assessments. For example, the data collection unit integrates weight data obtained from a smart scale with data from wearable devices, and AI performs a comprehensive health assessment. For example, it analyzes the relationship between weight fluctuations and exercise volume. The data collection unit also uses a smart mirror to record changes in the user's posture and body shape and integrates this with data from the wearable devices. For example, it evaluates the impact of poor posture on health. The data collection unit also uses AI to analyze data obtained from smart home devices and evaluate overall health risks. For example, it combines data on weight, posture, and exercise volume to create a health improvement plan. This allows data from smart home devices to be integrated for a comprehensive health assessment.

[0057] The data collection unit can develop specialized data collection methods for different age groups and genders to meet individual health needs. For example, the data collection unit develops specialized data collection methods for different age groups, and AI analyzes the data. For example, collecting bone density and muscle strength data for elderly people to assess health risks. The data collection unit can also develop specialized data collection methods for different genders, and AI analyzes the data. For example, collecting hormone balance and menstrual cycle data for women to assess health risks. The data collection unit can also create individual health improvement plans for different age groups and genders. For example, proposing exercise plans for younger people and providing nutritional management advice for elderly people. This allows the development of specialized data collection methods for different age groups and genders to meet individual health needs.

[0058] The data collection unit introduces a chatbot equipped with an emotion estimation function, which can record the stress and anxiety that a user feels on a daily basis in real time. The data collection unit, for example, uses a chatbot equipped with an emotion estimation function to record the stress and anxiety that a user feels on a daily basis in real time. For example, an emotion score is calculated through dialogue with the chatbot. The data collection unit also uses the chatbot to analyze the user's emotional state and identify the cause of stress or anxiety. For example, the data collection unit evaluates the impact of specific events or situations on emotions. The data collection unit also creates an individual stress management plan based on the emotion estimation data. For example, the plan provides relaxation techniques and mental health advice. This allows the user to record the stress and anxiety that they feel on a daily basis in real time.

[0059] The plan creation unit can suggest specific activities that match the user's hobbies and lifestyle. For example, the plan creation unit collects data on the user's hobbies and lifestyle, and the AI ​​suggests specific activities based on that data. For example, it creates an exercise plan such as yoga or hiking. The plan creation unit also creates a health improvement plan that matches the user's lifestyle. For example, for a user who does a lot of desk work, it suggests stretches and exercises that can be done in the office. The plan creation unit also suggests activities based on the user's hobbies and lifestyle, allowing the user to improve their health while having fun. For example, for a user who loves the outdoors, it suggests a weekend hiking plan. This makes it possible to suggest specific activities that match the user's hobbies and lifestyle.

[0060] The plan creation unit can incorporate a support system that utilizes the user's social network. For example, the plan creation unit collects data on the user's social network, and the AI ​​builds a support system based on that data. For example, it suggests an exercise plan to do with family and friends. The plan creation unit also creates a health improvement plan that utilizes the social network. For example, it suggests a fitness challenge that can be participated in with friends. The plan creation unit also enables health improvement with the support of family and friends. For example, it suggests a plan for dietary management together with family. In this way, a support system that utilizes the user's social network can be incorporated.

[0061] The plan creation unit can suggest activities according to the season and weather. For example, the plan creation unit builds a system that suggests activities according to the season and weather. For example, it suggests swimming and outdoor activities in the summer, and indoor exercises in the winter. The plan creation unit also collects weather data, and the AI ​​creates a health improvement plan based on that data. For example, it suggests yoga or stretching that can be done indoors on rainy days. The plan creation unit also suggests activities according to the season and weather, allowing the user to carry out the activities without straining themselves. For example, it suggests a relaxation plan that includes hot drinks in the cold season. This makes it possible to suggest activities according to the season and weather.

[0062] The plan creation unit can evaluate the implementation status of the plan and introduce a reward system based on the degree of achievement. The plan creation unit, for example, evaluates the implementation status of a health improvement plan and introduces a reward system based on the degree of achievement. For example, points or badges are awarded to users who achieve their exercise goals. The plan creation unit also monitors the user's implementation status of the plan in real time and builds a system that provides rewards based on the degree of achievement. For example, rewards are set based on the degree of achievement each week. The plan creation unit also uses the reward system to increase the user's motivation. For example, a system is introduced that allows users to receive benefits or gifts by accumulating points. This makes it possible to evaluate the implementation status of the plan and introduce a reward system based on the degree of achievement.

[0063] The plan creation unit can use the emotion estimation function to monitor the emotional reactions of the user when executing the plan and adjust the plan as needed. The plan creation unit, for example, uses the emotion estimation function to monitor the emotional reactions of the user when executing the plan. For example, if the emotion score is low, the content of the plan is adjusted. The plan creation unit also builds a system that adjusts the health improvement plan in real time based on the user's emotional reaction data. For example, if stress is high, relaxation methods are added. The plan creation unit also provides a plan based on the emotion estimation data according to the user's emotional state. For example, if positive emotions are strong, a challenging activity is suggested. This makes it possible to monitor the emotional reactions of the user when executing the plan and adjust the plan as needed.

[0064] The monitoring unit can provide specific behavioral instructions to the user when it detects signs of pre-illness. For example, when AI detects signs of pre-illness, the monitoring unit provides specific behavioral instructions to the user. For example, if an abnormal heart rate is detected, it recommends that the user see a doctor. In addition, the monitoring unit suggests specific exercises or dietary changes when it detects signs of pre-illness. For example, if a disturbance in sleep pattern is detected, it suggests relaxation exercises. In addition, the monitoring unit builds a system that provides users with behavioral instructions when signs of pre-illness are detected. For example, if specific symptoms appear, it notifies the user to immediately see a medical institution. In this way, it is possible to provide specific behavioral instructions when signs of pre-illness are detected.

[0065] The monitoring unit can develop an algorithm that compares the user's past health data and detects abnormalities early. For example, the monitoring unit collects the user's past health data and uses AI to develop an algorithm that detects abnormalities early based on that data. For example, it compares past heart rate data with current data to detect abnormalities. The monitoring unit also compares past health data with current data to build a system that detects signs of pre-illness early. For example, it analyzes changes in sleep patterns to detect abnormalities. The monitoring unit also develops an algorithm that detects signs of pre-illness early based on the user's past health data. For example, it compares past exercise data with current data to detect abnormalities. This makes it possible to develop an algorithm that compares past health data to detect abnormalities early.

[0066] The monitoring unit can use the emotion estimation function to evaluate the impact of the user's emotional state on the progression of pre-illness and provide emotion management advice. The monitoring unit, for example, uses the emotion estimation function to analyze the user's emotional state and evaluate the impact on the progression of pre-illness. For example, it evaluates that high stress levels increase the risk of pre-illness. The monitoring unit also provides emotion management advice to prevent the progression of pre-illness based on the user's emotional state. For example, it provides relaxation methods and mental health advice. The monitoring unit also evaluates the impact on the progression of pre-illness based on the emotion estimation data and creates an individual emotion management plan. For example, it suggests activities to maintain positive emotions. This makes it possible to evaluate the impact of the emotional state on the progression of pre-illness and provide emotion management advice.

[0067] The monitoring unit can build a system that notifies the user's family and medical professionals when it detects signs of pre-disease and encourages early intervention. The monitoring unit, for example, builds a system that notifies the user's family when it detects signs of pre-disease. For example, if an abnormal heart rate is detected, it notifies the family by email or SMS. The monitoring unit also builds a system that notifies medical professionals when it detects signs of pre-disease. For example, if a disturbance in sleep pattern is detected, it notifies a doctor. The monitoring unit also develops a system that notifies the user's family and medical professionals of signs of pre-disease and encourages early intervention. For example, if a specific symptom appears, it sends an alert to the family and medical professionals. This makes it possible to build a system that notifies the family and medical professionals when it detects signs of pre-disease and encourages early intervention.

[0068] The monitoring department can set up an online community in which users can participate to prevent the progression of ME-BYO, and can provide information sharing and support. The monitoring department can set up an online community in which users can participate to prevent the progression of ME-BYO, for example, by providing a forum for sharing health information and advice. The monitoring department can also create an environment in which users can support each other through the online community. For example, it can provide a mechanism for sharing the progress of health improvement plans and encouraging each other. The monitoring department can also utilize the online community to provide information on preventing the progression of ME-BYO. For example, it can hold webinars and Q&A sessions by experts. This makes it possible to set up an online community in which users can participate to prevent the progression of ME-BYO, and can provide information sharing and support.

[0069] The monitoring unit can propose measures that take into account the user's emotional state when pre-illness signs are detected using the emotion estimation function. The monitoring unit, for example, uses the emotion estimation function to analyze the user's emotional state when pre-illness signs are detected. For example, if stress is high, it suggests relaxation methods. The monitoring unit also proposes measures when pre-illness signs are detected based on the user's emotional state. For example, if the emotion score is low, it provides mental health advice. The monitoring unit also proposes individual measures when pre-illness signs are detected based on the emotion estimation data. For example, it suggests activities to maintain positive emotions. This makes it possible to propose measures that take into account the emotional state when pre-illness signs are detected.

[0070] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0071] The data collection unit can collect data on the user's living environment and evaluate health risks. For example, the data collection unit collects data on the user's living environment, such as air quality, temperature, and humidity, and AI analyzes this data to evaluate health risks. The data collection unit can also collect data on the user's work environment and evaluate its impact on stress levels and work efficiency. Furthermore, the data collection unit can collect data on the user's commuting route and means of transportation and evaluate commuting stress and health risks. This makes it possible to evaluate health risks that take into account the user's entire living environment.

[0072] The analysis unit can predict seasonal health risks based on the user's health data. For example, the analysis unit can analyze past health data to identify seasonal health risks. For example, the analysis unit can evaluate the risk of influenza in winter and the risk of heatstroke in summer. The analysis unit can also suggest preventive measures based on seasonal health risks. For example, the analysis unit can recommend taking vitamin D in winter and emphasize the importance of staying hydrated in summer. The analysis unit can also create an individual health improvement plan that takes seasonal health risks into account. This makes it possible to predict seasonal health risks and suggest appropriate preventive measures.

[0073] The plan creation unit can propose a meal plan that takes into account the user's emotional state. For example, the plan creation unit can analyze the user's emotional state using the emotion estimation function and, if the user is highly stressed, propose a meal plan that uses ingredients that have a relaxing effect. The plan creation unit can also consider nutritional balance according to the user's emotional state and create a meal plan to maintain positive emotions. Furthermore, the plan creation unit can adjust the timing and amount of meals based on the user's emotional state. This makes it possible to propose a meal plan that takes into account the user's emotional state and support health improvement.

[0074] The monitoring unit can evaluate the effectiveness of exercise in real time based on the user's exercise data. For example, the monitoring unit collects data such as heart rate, calorie consumption, and exercise time, and AI analyzes this data to evaluate the effectiveness of the exercise. The monitoring unit can also suggest to the user whether to continue or adjust the exercise based on the effectiveness of the exercise. For example, if the exercise effect is low, it can provide advice on increasing the exercise intensity. The monitoring unit can also comprehensively evaluate the user's health condition based on the exercise data and adjust the exercise plan. This makes it possible to evaluate the effectiveness of exercise in real time and provide appropriate advice.

[0075] The feedback unit can provide feedback that takes into account the user's emotional state. For example, the feedback unit can analyze the user's emotional state using an emotion estimation function and provide feedback to maintain positive emotions. The feedback unit can also send messages to improve motivation according to the user's emotional state. For example, if stress is high, the feedback unit can provide relaxation techniques or encouraging messages. The feedback unit can also adjust the user's health improvement plan based on the user's emotional state. This makes it possible to provide feedback that takes into account the user's emotional state and support health improvement.

[0076] The data collection unit can collect data on the user's sleep environment and evaluate the quality of their sleep. For example, the data collection unit collects data such as the temperature, humidity, and noise level of the user's bedroom, and the AI ​​analyzes this data to evaluate the quality of their sleep. The data collection unit can also record the user's sleep patterns and sleep duration to identify factors that affect sleep quality. Furthermore, the data collection unit can suggest ways to improve the user's sleep environment based on the sleep environment data. This makes it possible to comprehensively evaluate the user's sleep environment and provide advice to improve sleep quality.

[0077] The plan creation unit can propose an exercise plan that takes into account the user's emotional state. For example, the plan creation unit can analyze the user's emotional state using the emotion estimation function and propose exercises that have a relaxation effect if the user is highly stressed. The plan creation unit can also adjust the intensity and type of exercise according to the user's emotional state to create an exercise plan to maintain positive emotions. Furthermore, the plan creation unit can also adjust the timing and frequency of exercise based on the user's emotional state. This makes it possible to propose an exercise plan that takes into account the user's emotional state and support health improvement.

[0078] The monitoring unit can predict long-term health risks based on the user's health data. For example, the monitoring unit can analyze past health data to identify long-term health risks. For example, the monitoring unit can evaluate the risk of cardiovascular disease or diabetes. The monitoring unit can also suggest preventive measures based on the long-term health risks. For example, the monitoring unit can recommend regular exercise and dietary management. The monitoring unit can also create an individual health improvement plan that takes long-term health risks into consideration. This makes it possible to predict long-term health risks and suggest appropriate preventive measures.

[0079] The feedback unit can provide a health report that takes into account the user's emotional state. For example, the feedback unit can analyze the user's emotional state using an emotion estimation function and evaluate the impact of emotional fluctuations on health. The feedback unit can also create a health report based on the emotional state and provide it to the user. For example, if stress is high, the feedback unit can provide a report that includes advice on stress management. The feedback unit can also adjust the user's health improvement plan based on the emotional state. This makes it possible to provide a health report that takes into account the user's emotional state and support health improvement.

[0080] The data collection unit can collect data on the user's hobbies and lifestyle and assess health risks. For example, the data collection unit collects data on the user's hobbies and lifestyle, and AI analyzes it to assess health risks. The data collection unit can also create a health improvement plan based on the user's hobbies and lifestyle. For example, for a user who enjoys outdoor activities, it can suggest exercising in nature. The data collection unit can also evaluate health risks according to the user's hobbies and lifestyle and suggest appropriate preventive measures. This makes it possible to assess health risks taking into account the user's hobbies and lifestyle.

[0081] The processing flow of the second embodiment will be briefly explained below.

[0082] Step 1: The data collection unit collects data from the user's daily habits and wearable devices. For example, it collects health-related data such as heart rate, sleep patterns, steps taken, and calorie consumption. It can also connect to apps that record the user's diet and stress levels. Step 2: The analysis unit analyzes the data collected by the data collection unit. For example, the analysis unit may use AI to analyze the collected data and evaluate the user's health status. The analysis unit may also analyze the user's genetic information and identify genetic risk factors. Step 3: The plan creation unit creates an individual health improvement plan based on the data analyzed by the analysis unit. For example, if a user has persistent sleep deprivation, it will provide advice on how to improve the quality of their sleep. It can also suggest specific activities that fit the user's hobbies and lifestyle. Step 4: The monitoring unit continuously monitors health data based on the plan created by the plan creation unit. For example, it detects abnormalities in heart rate and sleep patterns to detect early signs of illness. It can also use an emotion estimation function to monitor the user's emotional state and evaluate the impact of emotional fluctuations on health. Step 5: The feedback unit provides feedback to the user based on the data monitored by the monitoring unit. For example, it creates a weekly health report and sends it to the user. It also provides necessary support when the user implements the health improvement plan.

[0083] 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.

[0084] 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.

[0085] 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.

[0086] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0087] 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.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] 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).

[0092] 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.

[0093] 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.

[0094] 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.

[0095] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0096] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0097] 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.

[0098] 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.

[0099] 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 AI 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.

[0100] 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.

[0101] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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).

[0107] 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.

[0108] 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.

[0109] 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.

[0110] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0111] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0112] 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.

[0113] 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.

[0114] 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 AI 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.

[0115] 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.

[0116] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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).

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0127] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0128] 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.

[0129] 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.

[0130] 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 AI 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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).

[0136] 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.

[0137] 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."

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0149] 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]

[0150] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a data collection unit that collects data obtained from the user's daily living habits and wearable devices; an analysis unit that analyzes the data collected by the data collection unit; a plan creation unit that creates an individual health improvement plan based on the data analyzed by the analysis unit; a monitoring unit that continuously monitors health data based on the plan created by the plan creation unit; a feedback unit that provides feedback to a user based on the data monitored by the monitoring unit. A system characterized by:

2. The data collection unit It works with an app that records the user's diet and stress levels.

2. The system of claim 1.

3. The analysis unit Analyzing the user's genetic information to identify genetic risk factors 2. The system of claim 1.

4. The analysis unit Analyzing the user's emotional state and assessing the impact of emotional fluctuations on health.

2. The system of claim 1.

5. The data collection unit It also collects data from smart home devices to provide health assessments.

2. The system of claim 1.

6. The data collection unit Develop specialized data collection methods for different age groups and genders to address their individual health needs 2. The system of claim 1.

7. The data collection unit A chatbot equipped with an emotion estimation function will be introduced to record the stress and anxiety that the user experiences on a daily basis in real time.

2. The system of claim 1.

8. The plan creation unit Suggest specific activities that match the user's hobbies and lifestyle 2. The system of claim 1.

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