system

Abstract

The system according to the embodiment aims to provide individualized dietary and lifestyle support based on the health condition of a subject. [Solution] A system according to an embodiment includes a collection unit, an analysis unit, a meal provision unit, and a life support unit. The collection unit collects health data of a subject. The analysis unit analyzes the data collected by the collection unit and evaluates the health condition. The meal provision unit provides meals suited to the subject based on the health condition evaluated by the analysis unit. The life support unit supports the subject's daily life based on the health condition evaluated by the analysis unit.

Description

[Technical Field]

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

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

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

[0004] Conventional technologies do not adequately provide individualized dietary and lifestyle support based on the subject's health condition, and there is room for improvement.

[0005] The system according to the embodiment aims to provide individualized dietary and lifestyle support based on the health condition of a subject. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a meal provision unit, and a life support unit. The collection unit collects health data of the subject. The analysis unit analyzes the data collected by the collection unit and evaluates the health condition. The meal provision unit provides meals suited to the subject based on the health condition evaluated by the analysis unit. The life support unit supports the subject's daily life based on the health condition evaluated by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can provide individualized dietary and lifestyle support based on the health status of the subject. [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) A health management system according to an embodiment of the present invention monitors a subject's health status in real time and provides necessary medical support. This health management system works with hospitals and other institutions to collect real-time data on the subject's health status and understands the subject's health status based on their movements. Next, it delivers meals with calorie and ingredient content calculated to suit the subject's health status. Furthermore, to support the subject's daily life, it creates and distributes images and audio that interest the subject, interactively detects their needs, and automatically connects with necessary support facilities for daily walks and other services. This system enables the subject's health to be constantly maintained at an optimal level and supports daily life. For example, it works with hospitals and other institutions to collect real-time data on the subject's health status. Detailed data, such as the subject's movements and vital signs, is collected. For example, data such as the subject's heart rate, number of steps, and body temperature is collected to understand the subject's health status. Next, it analyzes the collected data and evaluates the subject's health status. For example, it analyzes fluctuations in heart rate and body temperature, and if any abnormalities are detected, it contacts medical support facilities. Furthermore, it delivers meals with calorie and ingredient content calculated to suit the subject's health status. For example, it can provide low-calorie meals or meals containing specific nutrients depending on the subject's health condition. Finally, to support the subject's daily life, it can create and distribute images and audio that interest the subject, and provide services such as daily walks and automatically linking with necessary support facilities by detecting needs through a conversational approach. For example, it can enrich the subject's daily life by providing information on hobbies and activities that interest the subject. In this way, the health management system can always maintain the subject's optimal health and support their daily life.

[0029] A health management system according to an embodiment includes a collection unit, an analysis unit, a meal provision unit, and a life support unit. The collection unit collects health data of a subject. The health data of the subject includes, but is not limited to, vital signs, blood test results, and exercise data. The collection unit collects data such as the subject's movements and vital signs. For example, the collection unit can collect data such as the subject's heart rate, blood pressure, and body temperature. The collection unit can also collect data such as the subject's step count and exercise amount. The collection unit can also collect data such as the subject's sleep pattern and stress level. The analysis unit analyzes the data collected by the collection unit and evaluates the subject's health condition. The analysis is performed using, for example, statistical analysis or a machine learning algorithm, but is not limited to, examples. For example, the analysis unit evaluates the subject's health condition based on the collected data. For example, the analysis unit can analyze fluctuations in heart rate and body temperature and, if abnormalities are detected, can connect to a medical support facility. The analysis unit can also evaluate the subject's health risk based on the collected data. For example, the analysis unit analyzes blood pressure and blood glucose level data to evaluate health risks. The meal provision unit provides meals tailored to the subject based on the health condition assessed by the analysis unit. Meals may be, for example, low-calorie meals or meals containing specific nutrients, but are not limited to these examples. For example, the meal provision unit may provide low-calorie meals depending on the subject's health condition. The meal provision unit may also provide meals fortified with specific nutrients. Furthermore, the meal provision unit may provide different meal menus based on the subject's dietary history. The life support unit supports the subject's daily life based on the health condition assessed by the analysis unit. For example, life support may create and distribute images and audio that interest the subject, and provide services by automatically linking with daily walks and necessary support facilities through a conversational system to detect needs, but is not limited to these examples. For example, the life support unit may provide information on hobbies and activities that the subject is interested in. The life support unit may also provide relaxing music and videos to enrich the subject's daily life.Furthermore, the life support unit can estimate the subject's emotions and adjust the support content based on the estimated emotions. This allows the health management system according to the embodiment to grasp the subject's health condition in real time and provide appropriate medical support and life support.

[0030] The collection unit can collect data on the subject's movements or vital signs. For example, the collection unit detects the subject's movements using a sensor and collects the data. For example, the collection unit can measure the subject's steps and exercise volume. The collection unit can also measure the subject's vital signs using a sensor and collect the data. For example, the collection unit can measure the subject's heart rate and blood pressure. The collection unit can also measure the subject's body temperature and respiratory rate. Furthermore, the collection unit can measure the subject's sleep patterns and stress level. This allows for a more detailed understanding of the subject's health condition by collecting the subject's movements and vital signs. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data acquired by a sensor into a generation AI and have the generation AI analyze the data.

[0031] The analysis unit can analyze the collected data and evaluate the health condition. The analysis unit analyzes the collected data using, for example, statistical analysis or machine learning algorithms. For example, the analysis unit can analyze collected heart rate and body temperature data and evaluate the health condition. The analysis unit can also analyze collected blood pressure and blood glucose level data and evaluate health risks. Furthermore, the analysis unit can analyze collected exercise data and sleep data and comprehensively evaluate the health condition. In this way, the health condition of the subject can be accurately evaluated by analyzing the collected data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the collected data to a generation AI and have the generation AI perform a health condition evaluation.

[0032] The meal provision unit can provide low-calorie meals or meals containing specific nutrients depending on the subject's health condition. The meal provision unit can provide low-calorie meals depending on the subject's health condition, for example. For example, the meal provision unit can calculate the calorie amount per meal based on the subject's health condition and provide low-calorie meals. The meal provision unit can also provide meals fortified with specific nutrients. For example, the meal provision unit can provide meals fortified with nutrients such as vitamins, minerals, and proteins based on the subject's health condition. Furthermore, the meal provision unit can provide different meal menus based on the subject's meal history. For example, the meal provision unit can provide a variety of meals by referring to meal menus provided in the past. This can support the subject's health maintenance by providing meals according to the subject's health condition. Some or all of the above-described processing by the meal provision unit can be performed using, for example, AI, or without AI. For example, the meal provision unit can input the subject's health condition data into a generation AI and have the generation AI select an appropriate meal menu.

[0033] The life support unit can create and distribute images and audio of the subject's interest, detect needs through a conversational approach, and provide services for daily walks or by automatically linking with necessary support facilities. The life support unit, for example, creates and distributes images and audio of the subject's interest. For example, the life support unit can create and distribute images and audio related to the subject's hobbies and activities. The life support unit can also detect needs through a conversational approach and provide services for daily walks or by automatically linking with necessary support facilities. For example, the life support unit can detect the subject's needs through conversation with the subject and provide appropriate services. This can enrich the subject's daily life and provide appropriate support. Some or all of the above-described processing in the life support unit may be performed using, for example, AI, or may be performed without AI. For example, the life support unit can input the subject's conversational data into a generation AI and have the generation AI detect needs.

[0034] The collection unit can analyze the subject's past health data and select an appropriate data collection method. The collection unit, for example, analyzes the subject's past health data using statistical analysis or machine learning algorithms. For example, the collection unit can concentrate data collection during a specific time period based on the past health data. The collection unit can also prioritize collection of specific vital signs based on the past health data. Furthermore, the collection unit can analyze the past health data and select the most effective data collection method. In this way, the optimal data collection method can be selected by analyzing the past health data. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input past health data into a generation AI and have the generation AI select the optimal data collection method.

[0035] The collection unit can perform filtering based on the subject's current activity status when collecting data. The collection unit, for example, detects the subject's current activity status using a sensor and performs filtering when collecting data. For example, when the subject is exercising, the collection unit can prioritize collecting data related to exercise. Furthermore, when the subject is resting, the collection unit can collect data related to a relaxed state. Furthermore, when the subject is working, the collection unit can collect data related to stress levels. In this way, by filtering data based on the current activity status, highly relevant data can be collected. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input current activity status data to a generation AI and have the generation AI perform data filtering.

[0036] When collecting data, the collection unit can prioritize collecting highly relevant data by taking into account the subject's geographical location information. The collection unit, for example, obtains the subject's geographical location information using GPS data or address information and considers it when collecting data. For example, when the subject is in a hospital, the collection unit can prioritize collecting medical-related data. Furthermore, when the subject is at home, the collection unit can prioritize collecting data related to daily life. Furthermore, when the subject is out, the collection unit can prioritize collecting data related to movement. In this way, by taking the geographical location information into consideration, highly relevant data can be collected preferentially. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input geographical location information data to the generation AI and cause the generation AI to prioritize the collection of highly relevant data.

[0037] The collection unit can analyze the subject's social media activity and collect related data when collecting data. For example, the collection unit analyzes the subject's social media activity and takes it into consideration when collecting data. For example, if the subject posts about health on social media, the collection unit can collect data related to the content. Furthermore, if the subject posts about stress on social media, the collection unit can collect data related to stress levels. Furthermore, if the subject posts about exercise on social media, the collection unit can collect data related to exercise. This allows for efficient collection of related data by analyzing social media activity. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input social media activity data into a generation AI and cause the generation AI to collect related data.

[0038] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the collected data. The analysis unit, for example, evaluates the importance of the collected data and adjusts the level of detail of the analysis. For example, the analysis unit can perform a detailed analysis on data of high importance. The analysis unit can also perform a simplified analysis on data of low importance. Furthermore, the analysis unit can also perform an analysis at an appropriate level of detail on data of medium importance. This enables efficient analysis by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the collected data to a generation AI and have the generation AI adjust the level of detail of the analysis.

[0039] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit classifies the collected data into categories and applies different analysis algorithms during analysis. For example, the analysis unit can apply a medical analysis algorithm to vital sign data. The analysis unit can also apply a fitness analysis algorithm to exercise data. Furthermore, the analysis unit can apply a nutritional management analysis algorithm to dietary data. This enables more accurate analysis by applying an analysis algorithm depending on the data category. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the collected data into a generation AI and cause the generation AI to apply an analysis algorithm depending on the category.

[0040] During analysis, the analysis unit can determine the analysis priority based on the time when the data was collected. The analysis unit, for example, evaluates the time when the collected data was collected and determines the analysis priority. For example, the analysis unit can prioritize analyzing the most recent data. The analysis unit can also refer to past data and emphasize the most recent data. Furthermore, the analysis unit can prioritize analyzing data collected during a specific period. In this way, by determining the analysis priority based on the time when the data was collected, the most recent data can be analyzed preferentially. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the collected data to a generation AI and have the generation AI determine the analysis priority.

[0041] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the data. The analysis unit, for example, evaluates the relevance of the collected data and adjusts the order of analysis. For example, the analysis unit can prioritize analysis of data with high relevance. The analysis unit can also analyze data with medium relevance next. Furthermore, the analysis unit can analyze data with low relevance last. This enables efficient analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the collected data to a generation AI and have the generation AI adjust the order of analysis.

[0042] The meal provision unit can adjust the level of detail of the meal based on the subject's health condition when providing the meal. The meal provision unit, for example, evaluates the subject's health condition and adjusts the level of detail of the meal when providing the meal. For example, the meal provision unit can provide a balanced meal if the subject's health condition is good. Furthermore, the meal provision unit can provide a meal fortified with specific nutrients if the subject's health condition is deteriorating. Furthermore, the meal provision unit can provide a regular meal if the subject's health condition is stable. In this way, by adjusting the level of detail of the meal based on the health condition, a more appropriate meal can be provided. Some or all of the above-mentioned processing in the meal provision unit may be performed, for example, using AI or without AI. For example, the meal provision unit can input the subject's health condition data into the generation AI and cause the generation AI to adjust the level of detail of the meal.

[0043] The meal provision unit can apply different meal menus depending on the subject's meal history when providing a meal. The meal provision unit, for example, evaluates the subject's meal history and applies different meal menus when providing a meal. For example, the meal provision unit can provide a variety of meals by referring to meal menus provided in the past. The meal provision unit can also provide a menu using ingredients that the subject prefers to eat. Furthermore, the meal provision unit can also provide a menu that excludes ingredients that the subject avoids. In this way, by providing a menu according to the meal history, a meal that suits the subject's preferences can be provided. Some or all of the above-mentioned processing in the meal provision unit may be performed, for example, using AI, or may be performed without using AI. For example, the meal provision unit can input the subject's meal history data into a generation AI and have the generation AI apply different meal menus.

[0044] The meal provision unit can provide an appropriate meal by taking into account the geographical location information of the subject when providing the meal. The meal provision unit, for example, acquires the geographical location information of the subject using GPS data or address information and takes the information into consideration when providing the meal. For example, if the subject is at home, the meal provision unit can provide a meal that can be cooked at home. Also, if the subject is out, the meal provision unit can provide a meal that is easy to carry. Furthermore, if the subject is at a specific facility, the meal provision unit can provide a meal that can be provided at that facility. In this way, the optimal meal can be provided by taking into account the geographical location information. Some or all of the above-described processing in the meal provision unit may be performed, for example, using AI, or may be performed without using AI. For example, the meal provision unit can input geographical location information data into a generation AI and have the generation AI provide an appropriate meal.

[0045] The meal provision unit can analyze the social media activity of the subject and provide a related meal when providing the meal. For example, the meal provision unit analyzes the social media activity of the subject and takes it into consideration when providing the meal. For example, if the subject posts about health on social media, the meal provision unit can provide a meal related to that content. Furthermore, if the subject posts about a specific ingredient on social media, the meal provision unit can provide a meal using that ingredient. Furthermore, if the subject posts about dieting on social media, the meal provision unit can provide a low-calorie meal. In this way, related meals can be provided by analyzing social media activity. Some or all of the above-mentioned processing in the meal provision unit may be performed, for example, using AI, or may be performed without using AI. For example, the meal provision unit can input social media activity data into a generation AI and have the generation AI provide related meals.

[0046] The life support unit can provide optimal support by referring to the subject's past life history during life support. The life support unit, for example, evaluates the subject's past life history and provides optimal support during life support. For example, the life support unit can provide optimal support based on the support content used by the subject in the past. The life support unit can also provide support needed at a specific time period based on the subject's past life history. Furthermore, the life support unit can analyze the subject's past life history and provide the most effective support. In this way, optimal support can be provided by referring to the past life history. Some or all of the above-mentioned processing in the life support unit may be performed using, for example, AI, or may be performed without using AI. For example, the life support unit can input the subject's past life history data into a generation AI and cause the generation AI to provide optimal support.

[0047] The life support unit can customize the means of support based on the subject's current living situation during life support. The life support unit, for example, evaluates the subject's current living situation and customizes the means of support during life support. For example, if the subject is at home, the life support unit can provide support that can be used at home. Also, if the subject is out, the life support unit can provide support that can be used while away from home. Furthermore, if the subject is in a specific facility, the life support unit can provide support that can be used at that facility. In this way, by customizing the means of support based on the current living situation, more appropriate support can be provided. Some or all of the above-mentioned processing in the life support unit may be performed, for example, using AI, or may be performed without using AI. For example, the life support unit can input the subject's current living situation data into a generation AI and cause the generation AI to customize the means of support.

[0048] The life support unit can provide appropriate support by taking into account the geographical location information of the subject when providing life support. The life support unit, for example, acquires the geographical location information of the subject using GPS data or address information and takes it into account when providing life support. For example, if the subject is at home, the life support unit can provide support that can be used at home. Also, if the subject is out, the life support unit can provide support that can be used while away from home. Furthermore, if the subject is in a specific facility, the life support unit can provide support that can be used at that facility. In this way, optimal support can be provided by taking into account the geographical location information. Some or all of the above-described processing in the life support unit may be performed, for example, using AI, or may be performed without using AI. For example, the life support unit can input geographical location information data to a generation AI and cause the generation AI to provide appropriate support.

[0049] The life support unit can analyze the social media activity of the subject and provide relevant support during life support. The life support unit, for example, analyzes the social media activity of the subject and takes it into consideration during life support. For example, if the subject posts about health on social media, the life support unit can provide support related to that content. Furthermore, if the subject posts about a particular hobby on social media, the life support unit can provide support related to that hobby. Furthermore, if the subject posts about stress on social media, the life support unit can provide support related to stress reduction. In this way, relevant support can be provided by analyzing social media activity. Some or all of the above-mentioned processing in the life support unit may be performed, for example, using AI, or may be performed without using AI. For example, the life support unit can input social media activity data into a generation AI and cause the generation AI to provide relevant support.

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

[0051] The health management system may further include an exercise provider that provides an exercise program based on the subject's health condition. The exercise provider provides an exercise program appropriate for the subject based on the health condition evaluated by the analysis unit. For example, the exercise provider may provide a program of light stretching or walking based on the subject's heart rate and physical fitness level. The exercise provider may also provide the subject with a program of strength training or aerobic exercise. Furthermore, the exercise provider may track the subject's progress and adjust the exercise program based on the subject's exercise history. This allows the subject to maintain their health by providing an exercise program appropriate to their health condition.

[0052] When collecting the subject's health data, the collection unit can also collect data on the subject's living environment. For example, the collection unit can collect data on the temperature, humidity, air quality, and other aspects of the subject's living environment. The collection unit can also collect data on the subject's lifestyle habits and eating patterns. Furthermore, the collection unit can collect data on the subject's stress level and sleep quality. Thus, by collecting the subject's living environment data, it is possible to understand the subject's health condition in more detail.

[0053] The analysis unit can take into account the subject's genetic information when analyzing the collected data. For example, the analysis unit can evaluate the subject's risk of a particular disease based on the subject's genetic information. The analysis unit can also suggest a diet and exercise program appropriate for the subject based on the genetic information. Furthermore, the analysis unit can evaluate the subject's drug responsiveness based on the genetic information and provide appropriate medical support. This makes it possible to provide more personalized health management by taking genetic information into account.

[0054] The meal provision unit can customize the method of providing meals according to the subject's health condition. For example, if the subject cooks at home, the meal provision unit can provide easy-to-cook recipes. In addition, if the subject is out and about, the meal provision unit can also provide meals that are easy to carry. Furthermore, if the subject is in a specific facility, the meal provision unit can also provide meals that can be provided at that facility. In this way, by customizing the method of providing meals according to the subject's health condition, more appropriate meals can be provided.

[0055] The collection unit can analyze the subject's past health data and select the appropriate timing for data collection. For example, the collection unit can concentrate data collection during a specific time period based on the past health data. The collection unit can also prioritize the collection of specific vital signs based on the past health data. Furthermore, the collection unit can analyze the past health data and select the most effective timing for data collection. In this way, the optimal timing for data collection can be selected by analyzing the past health data.

[0056] When collecting data, the collection unit can prioritize data based on the subject's current activity status. For example, when the subject is exercising, the collection unit can prioritize collecting data related to exercise. When the subject is resting, the collection unit can also collect data related to a relaxed state. Furthermore, when the subject is working, the collection unit can prioritize collecting data related to stress levels. Thus, by prioritizing data based on the subject's current activity status, highly relevant data can be collected.

[0057] When collecting data, the collection unit can adjust the data collection method by taking into account the geographical location information of the subject. For example, if the subject is in a hospital, the collection unit can prioritize collecting medical-related data. Also, if the subject is at home, the collection unit can prioritize collecting data related to daily life. Furthermore, if the subject is out, the collection unit can prioritize collecting data related to movement. In this way, by taking into account the geographical location information, highly relevant data can be collected preferentially.

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

[0059] Step 1: The collection unit collects the subject's health data, including vital signs, blood test results, exercise data, heart rate, blood pressure, body temperature, number of steps, amount of exercise, sleep patterns, stress levels, etc. Step 2: The analysis unit analyzes the data collected by the collection unit and evaluates the health status. The analysis is performed using statistical analysis and machine learning algorithms, and health risks are assessed based on data on heart rate, body temperature fluctuations, blood pressure, and blood sugar levels. Step 3: The meal provision unit provides a meal tailored to the subject based on the health status assessed by the analysis unit. The meal may be a low-calorie meal, a meal containing specific nutrients, or a menu that differs based on the subject's dietary history. Step 4: The life support unit supports the subject's daily life based on the health status assessed by the analysis unit. It creates and distributes images and audio that interest the subject, and provides services by automatically linking with necessary support facilities and providing daily walks.

[0060] (Example 2) A health management system according to an embodiment of the present invention monitors a subject's health status in real time and provides necessary medical support. This health management system works with hospitals and other institutions to collect real-time data on the subject's health status and understands the subject's health status based on their movements. Next, it delivers meals with calorie and ingredient content calculated to suit the subject's health status. Furthermore, to support the subject's daily life, it creates and distributes images and audio that interest the subject, interactively detects their needs, and automatically connects with necessary support facilities for daily walks and other services. This system enables the subject's health to be constantly maintained at an optimal level and supports daily life. For example, it works with hospitals and other institutions to collect real-time data on the subject's health status. Detailed data, such as the subject's movements and vital signs, is collected. For example, data such as the subject's heart rate, number of steps, and body temperature is collected to understand the subject's health status. Next, it analyzes the collected data and evaluates the subject's health status. For example, it analyzes fluctuations in heart rate and body temperature, and if any abnormalities are detected, it contacts medical support facilities. Furthermore, it delivers meals with calorie and ingredient content calculated to suit the subject's health status. For example, it can provide low-calorie meals or meals containing specific nutrients depending on the subject's health condition. Finally, to support the subject's daily life, it can create and distribute images and audio that interest the subject, and provide services such as daily walks and automatically linking with necessary support facilities by detecting needs through a conversational approach. For example, it can enrich the subject's daily life by providing information on hobbies and activities that interest the subject. In this way, the health management system can always maintain the subject's optimal health and support their daily life.

[0061] A health management system according to an embodiment includes a collection unit, an analysis unit, a meal provision unit, and a life support unit. The collection unit collects health data of a subject. The health data of the subject includes, but is not limited to, vital signs, blood test results, and exercise data. The collection unit collects data such as the subject's movements and vital signs. For example, the collection unit can collect data such as the subject's heart rate, blood pressure, and body temperature. The collection unit can also collect data such as the subject's step count and exercise amount. The collection unit can also collect data such as the subject's sleep pattern and stress level. The analysis unit analyzes the data collected by the collection unit and evaluates the subject's health condition. The analysis is performed using, for example, statistical analysis or a machine learning algorithm, but is not limited to, examples. For example, the analysis unit evaluates the subject's health condition based on the collected data. For example, the analysis unit can analyze fluctuations in heart rate and body temperature and, if abnormalities are detected, can connect to a medical support facility. The analysis unit can also evaluate the subject's health risk based on the collected data. For example, the analysis unit analyzes blood pressure and blood glucose level data to evaluate health risks. The meal provision unit provides meals tailored to the subject based on the health condition assessed by the analysis unit. Meals may be, for example, low-calorie meals or meals containing specific nutrients, but are not limited to these examples. For example, the meal provision unit may provide low-calorie meals depending on the subject's health condition. The meal provision unit may also provide meals fortified with specific nutrients. Furthermore, the meal provision unit may provide different meal menus based on the subject's dietary history. The life support unit supports the subject's daily life based on the health condition assessed by the analysis unit. For example, life support may create and distribute images and audio that interest the subject, and provide services by automatically linking with daily walks and necessary support facilities through a conversational system to detect needs, but is not limited to these examples. For example, the life support unit may provide information on hobbies and activities that the subject is interested in. The life support unit may also provide relaxing music and videos to enrich the subject's daily life.Furthermore, the life support unit can estimate the subject's emotions and adjust the support content based on the estimated emotions. This allows the health management system according to the embodiment to grasp the subject's health condition in real time and provide appropriate medical support and life support.

[0062] The collection unit can collect data on the subject's movements or vital signs. For example, the collection unit detects the subject's movements using a sensor and collects the data. For example, the collection unit can measure the subject's steps and exercise volume. The collection unit can also measure the subject's vital signs using a sensor and collect the data. For example, the collection unit can measure the subject's heart rate and blood pressure. The collection unit can also measure the subject's body temperature and respiratory rate. Furthermore, the collection unit can measure the subject's sleep patterns and stress level. This allows for a more detailed understanding of the subject's health condition by collecting the subject's movements and vital signs. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data acquired by a sensor into a generation AI and have the generation AI analyze the data.

[0063] The analysis unit can analyze the collected data and evaluate the health condition. The analysis unit analyzes the collected data using, for example, statistical analysis or machine learning algorithms. For example, the analysis unit can analyze collected heart rate and body temperature data and evaluate the health condition. The analysis unit can also analyze collected blood pressure and blood glucose level data and evaluate health risks. Furthermore, the analysis unit can analyze collected exercise data and sleep data and comprehensively evaluate the health condition. In this way, the health condition of the subject can be accurately evaluated by analyzing the collected data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the collected data to a generation AI and have the generation AI perform a health condition evaluation.

[0064] The meal provision unit can provide low-calorie meals or meals containing specific nutrients depending on the subject's health condition. The meal provision unit can provide low-calorie meals depending on the subject's health condition, for example. For example, the meal provision unit can calculate the calorie amount per meal based on the subject's health condition and provide low-calorie meals. The meal provision unit can also provide meals fortified with specific nutrients. For example, the meal provision unit can provide meals fortified with nutrients such as vitamins, minerals, and proteins based on the subject's health condition. Furthermore, the meal provision unit can provide different meal menus based on the subject's meal history. For example, the meal provision unit can provide a variety of meals by referring to meal menus provided in the past. This can support the subject's health maintenance by providing meals according to the subject's health condition. Some or all of the above-described processing by the meal provision unit can be performed using, for example, AI, or without AI. For example, the meal provision unit can input the subject's health condition data into a generation AI and have the generation AI select an appropriate meal menu.

[0065] The life support unit can create and distribute images and audio of the subject's interest, detect needs through a conversational approach, and provide services for daily walks or by automatically linking with necessary support facilities. The life support unit, for example, creates and distributes images and audio of the subject's interest. For example, the life support unit can create and distribute images and audio related to the subject's hobbies and activities. The life support unit can also detect needs through a conversational approach and provide services for daily walks or by automatically linking with necessary support facilities. For example, the life support unit can detect the subject's needs through conversation with the subject and provide appropriate services. This can enrich the subject's daily life and provide appropriate support. Some or all of the above-described processing in the life support unit may be performed using, for example, AI, or may be performed without AI. For example, the life support unit can input the subject's conversational data into a generation AI and have the generation AI detect needs.

[0066] The collection unit can estimate the subject's emotions and adjust the frequency of data collection based on the estimated emotions. The collection unit estimates the subject's emotions using, for example, facial expression recognition or voice analysis. For example, the collection unit can analyze the subject's facial expression data to estimate the emotions. The collection unit can also analyze the subject's voice data to estimate the emotions. Furthermore, the collection unit can adjust the frequency of data collection based on the estimated emotions. For example, if the subject is feeling stressed, the collection unit can reduce the frequency of data collection to reduce the burden on the subject. Furthermore, if the subject is relaxed, the collection unit can increase the frequency of data collection to collect detailed data. In this way, by adjusting the frequency of data collection according to the subject's emotions, detailed data can be collected while reducing the burden. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the subject's emotion data to the generation AI and cause the generation AI to adjust the frequency of data collection.

[0067] The collection unit can analyze the subject's past health data and select an appropriate data collection method. The collection unit, for example, analyzes the subject's past health data using statistical analysis or machine learning algorithms. For example, the collection unit can concentrate data collection during a specific time period based on the past health data. The collection unit can also prioritize collection of specific vital signs based on the past health data. Furthermore, the collection unit can analyze the past health data and select the most effective data collection method. In this way, the optimal data collection method can be selected by analyzing the past health data. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input past health data into a generation AI and have the generation AI select the optimal data collection method.

[0068] The collection unit can perform filtering based on the subject's current activity status when collecting data. The collection unit, for example, detects the subject's current activity status using a sensor and performs filtering when collecting data. For example, when the subject is exercising, the collection unit can prioritize collecting data related to exercise. Furthermore, when the subject is resting, the collection unit can collect data related to a relaxed state. Furthermore, when the subject is working, the collection unit can collect data related to stress levels. In this way, by filtering data based on the current activity status, highly relevant data can be collected. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input current activity status data to a generation AI and have the generation AI perform data filtering.

[0069] The collection unit can estimate the subject's emotions and determine the priority of data to be collected based on the estimated emotions. The collection unit estimates the subject's emotions using, for example, facial expression recognition or voice analysis. For example, the collection unit can analyze the subject's facial expression data to estimate the emotions. The collection unit can also analyze the subject's voice data to estimate the emotions. Furthermore, the collection unit can determine the priority of data to be collected based on the estimated emotions. For example, if the subject is feeling stressed, the collection unit can prioritize collecting data related to the stress level. Also, if the subject is relaxed, the collection unit can prioritize collecting data related to the relaxed state. In this way, by prioritizing data based on emotions, important data can be collected preferentially. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the subject's emotion data to a generation AI and have the generation AI determine the priority of the data.

[0070] When collecting data, the collection unit can prioritize collecting highly relevant data by taking into account the subject's geographical location information. The collection unit, for example, obtains the subject's geographical location information using GPS data or address information and considers it when collecting data. For example, when the subject is in a hospital, the collection unit can prioritize collecting medical-related data. Furthermore, when the subject is at home, the collection unit can prioritize collecting data related to daily life. Furthermore, when the subject is out, the collection unit can prioritize collecting data related to movement. In this way, by taking the geographical location information into consideration, highly relevant data can be collected preferentially. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input geographical location information data to the generation AI and cause the generation AI to prioritize the collection of highly relevant data.

[0071] The collection unit can analyze the subject's social media activity and collect related data when collecting data. For example, the collection unit analyzes the subject's social media activity and takes it into consideration when collecting data. For example, if the subject posts about health on social media, the collection unit can collect data related to the content. Furthermore, if the subject posts about stress on social media, the collection unit can collect data related to stress levels. Furthermore, if the subject posts about exercise on social media, the collection unit can collect data related to exercise. This allows for efficient collection of related data by analyzing social media activity. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input social media activity data into a generation AI and cause the generation AI to collect related data.

[0072] The analysis unit can estimate the subject's emotions and adjust the analysis algorithm based on the estimated emotions. The analysis unit estimates the subject's emotions using, for example, facial expression recognition or voice analysis. For example, the analysis unit can analyze the subject's facial expression data to estimate the emotions. The analysis unit can also analyze the subject's voice data to estimate the emotions. The analysis unit can also adjust the analysis algorithm based on the estimated emotions. For example, if the subject is feeling stressed, the analysis unit can apply an analysis algorithm that emphasizes the stress level. Also, if the subject is relaxed, the analysis unit can apply an analysis algorithm that emphasizes the relaxed state. This enables more appropriate analysis by adjusting the analysis algorithm based on the emotions. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the subject's emotion data into the generation AI and have the generation AI adjust the analysis algorithm.

[0073] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the collected data. The analysis unit, for example, evaluates the importance of the collected data and adjusts the level of detail of the analysis. For example, the analysis unit can perform a detailed analysis on data of high importance. The analysis unit can also perform a simplified analysis on data of low importance. Furthermore, the analysis unit can also perform an analysis at an appropriate level of detail on data of medium importance. This enables efficient analysis by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the collected data to a generation AI and have the generation AI adjust the level of detail of the analysis.

[0074] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit classifies the collected data into categories and applies different analysis algorithms during analysis. For example, the analysis unit can apply a medical analysis algorithm to vital sign data. The analysis unit can also apply a fitness analysis algorithm to exercise data. Furthermore, the analysis unit can apply a nutritional management analysis algorithm to dietary data. This enables more accurate analysis by applying an analysis algorithm depending on the data category. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the collected data into a generation AI and cause the generation AI to apply an analysis algorithm depending on the category.

[0075] The analysis unit can estimate the subject's emotions and adjust the display method of the analysis results based on the estimated emotions. The analysis unit estimates the subject's emotions using, for example, facial expression recognition or voice analysis. For example, the analysis unit can analyze the subject's facial expression data to estimate the emotions. The analysis unit can also analyze the subject's voice data to estimate the emotions. Furthermore, the analysis unit can adjust the display method of the analysis results based on the estimated emotions. For example, if the subject is feeling stressed, the analysis unit can provide a simple, highly visible display method. If the subject is relaxed, the analysis unit can provide a display method including detailed information. This enables highly visible display by adjusting the display method based on the emotions. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the subject's emotion data into a generation AI and have the generation AI adjust the display method.

[0076] During analysis, the analysis unit can determine the analysis priority based on the time when the data was collected. The analysis unit, for example, evaluates the time when the collected data was collected and determines the analysis priority. For example, the analysis unit can prioritize analyzing the most recent data. The analysis unit can also refer to past data and emphasize the most recent data. Furthermore, the analysis unit can prioritize analyzing data collected during a specific period. In this way, by determining the analysis priority based on the time when the data was collected, the most recent data can be analyzed preferentially. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the collected data to a generation AI and have the generation AI determine the analysis priority.

[0077] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the data. The analysis unit, for example, evaluates the relevance of the collected data and adjusts the order of analysis. For example, the analysis unit can prioritize analysis of data with high relevance. The analysis unit can also analyze data with medium relevance next. Furthermore, the analysis unit can analyze data with low relevance last. This enables efficient analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the collected data to a generation AI and have the generation AI adjust the order of analysis.

[0078] The meal provision unit can estimate the subject's emotions and adjust the meal contents based on the estimated emotions. The meal provision unit estimates the subject's emotions using, for example, facial expression recognition or voice analysis. For example, the meal provision unit can analyze the subject's facial expression data to estimate the emotions. The meal provision unit can also analyze the subject's voice data to estimate the emotions. Furthermore, the meal provision unit can adjust the meal contents based on the estimated emotions. For example, if the subject is feeling stressed, the meal provision unit can provide a meal using ingredients that have a relaxing effect. Furthermore, if the subject is relaxed, the meal provision unit can provide a nutritionally balanced meal. In this way, by adjusting the meal contents based on the emotions, a more appropriate meal can be provided. Some or all of the above-described processing in the meal provision unit may be performed using, for example, AI, or may be performed without using AI. For example, the meal provision unit can input the subject's emotional data into a generation AI and have the generation AI adjust the meal contents.

[0079] The meal provision unit can adjust the level of detail of the meal based on the subject's health condition when providing the meal. The meal provision unit, for example, evaluates the subject's health condition and adjusts the level of detail of the meal when providing the meal. For example, the meal provision unit can provide a balanced meal if the subject's health condition is good. Furthermore, the meal provision unit can provide a meal fortified with specific nutrients if the subject's health condition is deteriorating. Furthermore, the meal provision unit can provide a regular meal if the subject's health condition is stable. In this way, by adjusting the level of detail of the meal based on the health condition, a more appropriate meal can be provided. Some or all of the above-mentioned processing in the meal provision unit may be performed, for example, using AI or without AI. For example, the meal provision unit can input the subject's health condition data into the generation AI and cause the generation AI to adjust the level of detail of the meal.

[0080] The meal provision unit can apply different meal menus depending on the subject's meal history when providing a meal. The meal provision unit, for example, evaluates the subject's meal history and applies different meal menus when providing a meal. For example, the meal provision unit can provide a variety of meals by referring to meal menus provided in the past. The meal provision unit can also provide a menu using ingredients that the subject prefers to eat. Furthermore, the meal provision unit can also provide a menu that excludes ingredients that the subject avoids. In this way, by providing a menu according to the meal history, a meal that suits the subject's preferences can be provided. Some or all of the above-mentioned processing in the meal provision unit may be performed, for example, using AI, or may be performed without using AI. For example, the meal provision unit can input the subject's meal history data into a generation AI and have the generation AI apply different meal menus.

[0081] The meal provision unit can estimate the subject's emotions and adjust the timing of meal provision based on the estimated emotions. The meal provision unit estimates the subject's emotions using, for example, facial expression recognition or voice analysis. For example, the meal provision unit can analyze the subject's facial expression data to estimate the emotions. The meal provision unit can also analyze the subject's voice data to estimate the emotions. Furthermore, the meal provision unit can adjust the timing of meal provision based on the estimated emotions. For example, if the subject is feeling stressed, the meal provision unit can provide the subject with a meal at a time when the subject is able to relax. Also, if the subject is relaxed, the meal provision unit can provide the subject with a meal at a normal mealtime. In this way, by adjusting the timing of meal provision based on the emotions, the meal can be provided at a more appropriate time. Some or all of the above-described processing in the meal provision unit may be performed using, for example, AI, or may be performed without using AI. For example, the meal provision unit can input the subject's emotion data into a generation AI and have the generation AI adjust the timing of meal provision.

[0082] The meal provision unit can provide an appropriate meal by taking into account the geographical location information of the subject when providing the meal. The meal provision unit, for example, acquires the geographical location information of the subject using GPS data or address information and takes the information into consideration when providing the meal. For example, if the subject is at home, the meal provision unit can provide a meal that can be cooked at home. Also, if the subject is out, the meal provision unit can provide a meal that is easy to carry. Furthermore, if the subject is at a specific facility, the meal provision unit can provide a meal that can be provided at that facility. In this way, the optimal meal can be provided by taking into account the geographical location information. Some or all of the above-described processing in the meal provision unit may be performed, for example, using AI, or may be performed without using AI. For example, the meal provision unit can input geographical location information data into a generation AI and have the generation AI provide an appropriate meal.

[0083] The meal provision unit can analyze the social media activity of the subject and provide a related meal when providing the meal. For example, the meal provision unit analyzes the social media activity of the subject and takes it into consideration when providing the meal. For example, if the subject posts about health on social media, the meal provision unit can provide a meal related to that content. Furthermore, if the subject posts about a specific ingredient on social media, the meal provision unit can provide a meal using that ingredient. Furthermore, if the subject posts about dieting on social media, the meal provision unit can provide a low-calorie meal. In this way, related meals can be provided by analyzing social media activity. Some or all of the above-mentioned processing in the meal provision unit may be performed, for example, using AI, or may be performed without using AI. For example, the meal provision unit can input social media activity data into a generation AI and have the generation AI provide related meals.

[0084] The life support unit can estimate the subject's emotions and adjust the support content based on the estimated emotions. The life support unit estimates the subject's emotions using, for example, facial expression recognition or voice analysis. For example, the life support unit can analyze the subject's facial expression data to estimate the emotions. The life support unit can also analyze the subject's voice data to estimate the emotions. Furthermore, the life support unit can adjust the support content based on the estimated emotions. For example, the life support unit can provide relaxing content when the subject is feeling stressed. Furthermore, the life support unit can provide interesting content when the subject is relaxed. In this way, by adjusting the support content based on the emotions, more appropriate support can be provided. Some or all of the above-described processing in the life support unit may be performed using, for example, AI, or may be performed without using AI. For example, the life support unit can input the subject's emotion data into a generation AI and have the generation AI adjust the support content.

[0085] The life support unit can provide optimal support by referring to the subject's past life history during life support. The life support unit, for example, evaluates the subject's past life history and provides optimal support during life support. For example, the life support unit can provide optimal support based on the support content used by the subject in the past. The life support unit can also provide support needed at a specific time period based on the subject's past life history. Furthermore, the life support unit can analyze the subject's past life history and provide the most effective support. In this way, optimal support can be provided by referring to the past life history. Some or all of the above-mentioned processing in the life support unit may be performed using, for example, AI, or may be performed without using AI. For example, the life support unit can input the subject's past life history data into a generation AI and cause the generation AI to provide optimal support.

[0086] The life support unit can customize the means of support based on the subject's current living situation during life support. The life support unit, for example, evaluates the subject's current living situation and customizes the means of support during life support. For example, if the subject is at home, the life support unit can provide support that can be used at home. Also, if the subject is out, the life support unit can provide support that can be used while away from home. Furthermore, if the subject is in a specific facility, the life support unit can provide support that can be used at that facility. In this way, by customizing the means of support based on the current living situation, more appropriate support can be provided. Some or all of the above-mentioned processing in the life support unit may be performed, for example, using AI, or may be performed without using AI. For example, the life support unit can input the subject's current living situation data into a generation AI and cause the generation AI to customize the means of support.

[0087] The life support unit can estimate the subject's emotions and determine support priorities based on the estimated emotions. The life support unit estimates the subject's emotions using, for example, facial expression recognition or voice analysis. For example, the life support unit can analyze the subject's facial expression data to estimate emotions. The life support unit can also analyze the subject's voice data to estimate emotions. Furthermore, the life support unit can determine support priorities based on the estimated emotions. For example, if the subject is feeling stressed, the life support unit can provide support that prioritizes stress reduction. Also, if the subject is relaxed, the life support unit can provide support that attracts the subject's attention. In this way, by determining support priorities based on emotions, more appropriate support can be provided. Some or all of the above-described processing in the life support unit may be performed using, for example, AI, or may be performed without using AI. For example, the life support unit can input the subject's emotion data into a generation AI and have the generation AI determine the support priorities.

[0088] The life support unit can provide appropriate support by taking into account the geographical location information of the subject when providing life support. The life support unit, for example, acquires the geographical location information of the subject using GPS data or address information and takes it into account when providing life support. For example, if the subject is at home, the life support unit can provide support that can be used at home. Also, if the subject is out, the life support unit can provide support that can be used while away from home. Furthermore, if the subject is in a specific facility, the life support unit can provide support that can be used at that facility. In this way, optimal support can be provided by taking into account the geographical location information. Some or all of the above-described processing in the life support unit may be performed, for example, using AI, or may be performed without using AI. For example, the life support unit can input geographical location information data to a generation AI and cause the generation AI to provide appropriate support.

[0089] The life support unit can analyze the social media activity of the subject and provide relevant support during life support. The life support unit, for example, analyzes the social media activity of the subject and takes it into consideration during life support. For example, if the subject posts about health on social media, the life support unit can provide support related to that content. Furthermore, if the subject posts about a particular hobby on social media, the life support unit can provide support related to that hobby. Furthermore, if the subject posts about stress on social media, the life support unit can provide support related to stress reduction. In this way, relevant support can be provided by analyzing social media activity. Some or all of the above-mentioned processing in the life support unit may be performed, for example, using AI, or may be performed without using AI. For example, the life support unit can input social media activity data into a generation AI and cause the generation AI to provide relevant support. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, analysis unit, meal provision unit, and life support unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects health data of the subject using the camera 42 and microphone 38B of the smart device 14 and transmits the data to the data processing device 12 via the control unit 46A. The analysis unit, realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes the collected data and evaluates the subject's health condition. The meal provision unit, realized, for example, by the specific processing unit 290 of the data processing device 12, provides appropriate meals based on the evaluated health condition. The life support unit, realized, for example, by the control unit 46A of the smart device 14, provides information and services to support the subject's daily life. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, meal provision unit, and life support unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects health data of the subject using the camera 42 and microphone 238 of the smart glasses 214 and transmits the data to the data processing device 12 via the control unit 46A. The analysis unit, realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes the collected data and evaluates the subject's health condition. The meal provision unit, realized, for example, by the specific processing unit 290 of the data processing device 12, provides appropriate meals based on the evaluated health condition. The life support unit, realized, for example, by the control unit 46A of the smart glasses 214, provides information and services to support the subject's daily life. === Hard Collateral 1-3 === Each of the multiple elements, including the collection unit, analysis unit, meal provision unit, and life support unit, described above, is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the collection unit collects health data of the subject using the camera 42 and microphone 238 of the headset-type terminal 314 and transmits the data to the data processing device 12 via the control unit 46A. The analysis unit, realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes the collected data and evaluates the subject's health condition. The meal provision unit, realized, for example, by the specific processing unit 290 of the data processing device 12, provides appropriate meals based on the evaluated health condition. The life support unit, realized, for example, by the control unit 46A of the headset-type terminal 314, provides information and services to support the subject's daily life. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, analysis unit, meal provision unit, and life support unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects health data of the subject using the camera 42 and microphone 238 of the robot 414 and transmits the data to the data processing device 12 via the control unit 46A. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data to evaluate the subject's health condition. The meal provision unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and provides appropriate meals based on the evaluated health condition. The life support unit is realized, for example, by the control unit 46A of the robot 414, and provides information and services to support the subject's daily life.

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

[0091] The health management system may further include an exercise provider that provides an exercise program based on the subject's health condition. The exercise provider provides an exercise program appropriate for the subject based on the health condition evaluated by the analysis unit. For example, the exercise provider may provide a program of light stretching or walking based on the subject's heart rate and physical fitness level. The exercise provider may also provide the subject with a program of strength training or aerobic exercise. Furthermore, the exercise provider may track the subject's progress and adjust the exercise program based on the subject's exercise history. This allows the subject to maintain their health by providing an exercise program appropriate to their health condition.

[0092] When collecting the subject's health data, the collection unit can also collect data on the subject's living environment. For example, the collection unit can collect data on the temperature, humidity, air quality, and other aspects of the subject's living environment. The collection unit can also collect data on the subject's lifestyle habits and eating patterns. Furthermore, the collection unit can collect data on the subject's stress level and sleep quality. Thus, by collecting the subject's living environment data, it is possible to understand the subject's health condition in more detail.

[0093] The analysis unit can take into account the subject's genetic information when analyzing the collected data. For example, the analysis unit can evaluate the subject's risk of a particular disease based on the subject's genetic information. The analysis unit can also suggest a diet and exercise program appropriate for the subject based on the genetic information. Furthermore, the analysis unit can evaluate the subject's drug responsiveness based on the genetic information and provide appropriate medical support. This makes it possible to provide more personalized health management by taking genetic information into account.

[0094] The meal provision unit can customize the method of providing meals according to the subject's health condition. For example, if the subject cooks at home, the meal provision unit can provide easy-to-cook recipes. In addition, if the subject is out and about, the meal provision unit can also provide meals that are easy to carry. Furthermore, if the subject is in a specific facility, the meal provision unit can also provide meals that can be provided at that facility. In this way, by customizing the method of providing meals according to the subject's health condition, more appropriate meals can be provided.

[0095] The life support unit can estimate the subject's emotions and provide a relaxation program based on the estimated emotions. For example, if the subject is feeling stressed, the life support unit can provide music or videos that have a relaxing effect. Also, if the subject is relaxed, the life support unit can provide interesting content. Furthermore, the life support unit can provide meditation or deep breathing programs according to the subject's emotions. In this way, more appropriate support can be provided by providing a relaxation program based on emotions.

[0096] The collection unit can estimate the subject's emotions and adjust the timing of data collection based on the estimated emotions. For example, if the subject is feeling stressed, the collection unit can delay the timing of data collection to reduce the burden on the subject. Also, if the subject is relaxed, the collection unit can advance the timing of data collection to collect detailed data. Furthermore, the collection unit can adjust the frequency of data collection according to the subject's emotions. In this way, by adjusting the timing of data collection based on emotions, detailed data can be collected while reducing the burden on the subject.

[0097] The collection unit can analyze the subject's past health data and select the appropriate timing for data collection. For example, the collection unit can concentrate data collection during a specific time period based on the past health data. The collection unit can also prioritize the collection of specific vital signs based on the past health data. Furthermore, the collection unit can analyze the past health data and select the most effective timing for data collection. In this way, the optimal timing for data collection can be selected by analyzing the past health data.

[0098] When collecting data, the collection unit can prioritize data based on the subject's current activity status. For example, when the subject is exercising, the collection unit can prioritize collecting data related to exercise. When the subject is resting, the collection unit can also collect data related to a relaxed state. Furthermore, when the subject is working, the collection unit can prioritize collecting data related to stress levels. Thus, by prioritizing data based on the subject's current activity status, highly relevant data can be collected.

[0099] The collection unit can estimate the subject's emotion and determine the type of data to collect based on the estimated emotion. For example, if the subject is feeling stressed, the collection unit can prioritize collecting data related to the stress level. Also, if the subject is relaxed, the collection unit can prioritize collecting data related to the relaxed state. Furthermore, the collection unit can adjust the type of data to collect depending on the subject's emotion. In this way, by determining the type of data to collect based on the emotion, important data can be collected preferentially.

[0100] When collecting data, the collection unit can adjust the data collection method by taking into account the geographical location information of the subject. For example, if the subject is in a hospital, the collection unit can prioritize collecting medical-related data. Also, if the subject is at home, the collection unit can prioritize collecting data related to daily life. Furthermore, if the subject is out, the collection unit can prioritize collecting data related to movement. In this way, by taking into account the geographical location information, highly relevant data can be collected preferentially.

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

[0102] Step 1: The collection unit collects the subject's health data, including vital signs, blood test results, exercise data, heart rate, blood pressure, body temperature, number of steps, amount of exercise, sleep patterns, stress levels, etc. Step 2: The analysis unit analyzes the data collected by the collection unit and evaluates the health status. The analysis is performed using statistical analysis and machine learning algorithms, and health risks are assessed based on data on heart rate, body temperature fluctuations, blood pressure, and blood sugar levels. Step 3: The meal provision unit provides a meal tailored to the subject based on the health status assessed by the analysis unit. The meal may be a low-calorie meal, a meal containing specific nutrients, or a menu that differs based on the subject's dietary history. Step 4: The life support unit supports the subject's daily life based on the health status assessed by the analysis unit. It creates and distributes images and audio that interest the subject, and provides services by automatically linking with necessary support facilities and providing daily walks.

[0103] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0104] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). 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 (e.g., still image data or video data). 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 one or more data formats of voice data, text data, image data, etc. 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 may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0105] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0106] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0107] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0108] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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).

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0121] 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.

[0122] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

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

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

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

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

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

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

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

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

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

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

[0133] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 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 identification processing unit 290 using these models.

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

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

[0136] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

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

[0138] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0139] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0140] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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).

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification 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 the same process as the identification processing unit 290 using these models.

[0151] 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.

[0152] 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.

[0153] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0154] 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.

[0155] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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).

[0160] 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 indicated, and when they approach the ideal, a state of pleasure is indicated. 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 indicated, and when they approach the ideal, a state of pleasure is indicated. 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.

[0161] 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."

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] [Explanation of symbols]

[0175] 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 collection unit that collects health data of subjects; an analysis unit that analyzes the data collected by the collection unit and evaluates the health state; a meal providing unit that provides a meal suited to the subject based on the health condition evaluated by the analysis unit; a life support unit that supports the subject's daily life based on the health condition evaluated by the analysis unit. A system characterized by:

2. The collecting unit Collecting subject movement or vital sign data 2. The system of claim 1.

3. The analysis unit Analyze the collected data and evaluate your health status 2. The system of claim 1.

4. The meal provision unit includes: Providing low-calorie or nutrient-specific meals depending on your health condition 2. The system of claim 1.

5. The life support department Create and distribute images and audio that interest the target person, and sense their needs through conversation to provide daily walks or services by automatically connecting with necessary support facilities.

2. The system of claim 1.

6. The collecting unit Estimate the subject's emotions and adjust the frequency of data collection based on the estimated emotions.

2. The system of claim 1.

7. The collecting unit Analyze the subjects' past health data and select the appropriate data collection method 2. The system of claim 1.

8. The collecting unit When collecting data, filter it based on the subject's current activity status.

2. The system of claim 1.

9. The collecting unit Estimate the emotions of the target audience and prioritize the data to be collected based on the estimated emotions.

2. The system of claim 1.

10. The collecting unit When collecting data, prioritize collecting the most relevant data by taking into account the geographic location of the subjects.

2. The system of claim 1.

Images