system

Abstract

The system according to this embodiment aims to analyze the user's dietary habits and exercise data, predict future health risks, and provide appropriate countermeasures. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a determination unit, a distribution unit, and a provision unit. The collection unit collects the user's daily diet and exercise data. The analysis unit analyzes the data collected by the collection unit. The determination unit determines the risk of future illnesses based on the data analyzed by the analysis unit. The distribution unit distributes the risk information determined by the determination unit in the form of an email newsletter. The provision unit provides the necessary amount of exercise and nutrients based on the information distributed by the distribution unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that future health risks have not been sufficiently predicted based on the eating habits and exercise data of individual users, and appropriate countermeasures have not been provided.

[0005] The system according to the embodiment aims to analyze the eating habits and exercise data of users, predict future health risks, and provide appropriate countermeasures.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, a determination unit, a distribution unit, and a provision unit. The data collection unit collects the user's daily diet and exercise data. The analysis unit analyzes the data collected by the data collection unit. The determination unit determines the risk of future illnesses based on the data analyzed by the analysis unit. The distribution unit distributes the risk information determined by the determination unit in the form of an email newsletter. The provision unit provides the necessary amount of exercise and nutrients based on the information distributed by the distribution unit. [Effects of the Invention]

[0007] The system according to this embodiment can analyze the user's dietary habits and exercise data, predict future health risks, and provide appropriate countermeasures. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10]This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the 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, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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), etc.

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

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

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

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The health management system according to an embodiment of the present invention is a system that analyzes dietary and exercise data registered on a smartphone and delivers information on the expected future risk of disease in the form of an email newsletter. The health management system works by having the user register their daily dietary and exercise data on their smartphone. Next, an AI analyzes this data and determines the expected future risk of disease. The results are delivered to the user in the form of an email newsletter. This newsletter includes articles on the experiences of others who have become ill and their exercise efforts. It also provides information such as the number of steps needed to burn off the required amount of exercise, methods for strength training, and links to websites where users can purchase supplements to replenish any missing nutrients. This allows users to improve their lifestyle from a young age and lead a healthy life. For example, a user registers their daily dietary and exercise data on their smartphone. For instance, they input what they ate for breakfast and their daily exercise amount. This data is analyzed by the AI. Next, the AI ​​analyzes the registered data and determines the expected future risk of disease. For example, it may determine that a continued unbalanced diet and lack of exercise increase the risk of myocardial infarction, stroke, and renal failure. The results are delivered to the user in the form of an email newsletter. This email newsletter includes articles on the experiences of others who have fallen ill and their exercise regimens. For example, it features cases of people with similar diets who developed myocardial infarction, and cases of people who regained their health through exercise. It also provides information on the number of steps needed to achieve the necessary amount of exercise and methods for strength training. For instance, it recommends walking 8,000 steps a day and strength training 2-3 times a week. Furthermore, it provides links to websites where users can purchase supplements to replenish any deficient nutrients. For example, if iron or dietary fiber is deficient, links to websites where these supplements can be purchased are provided. This system allows users to improve their lifestyle from a young age and live a healthy life. For example, by registering daily dietary and exercise data and supplementing the necessary amount of exercise and nutrients based on the results of AI analysis, it is possible to reduce the risk of future illness. This helps build a foundation for living a healthy life in the era of 100-year lifespans. In this way, the health management system can support users in leading a healthy life.

[0029] The health management system according to this embodiment comprises a data collection unit, an analysis unit, a judgment unit, a distribution unit, and a provision unit. The data collection unit collects the user's daily eating habits and exercise data. For example, the user can input the contents of their meals, the type of exercise, and the duration of exercise using a smartphone app. The data collection unit can also automatically acquire data from wearable devices. For example, the data collection unit collects heart rate and step count data from a smartwatch. Furthermore, the data collection unit can integrate and manage data manually entered by the user. The analysis unit analyzes the data collected by the data collection unit. For example, the analysis unit performs statistical analysis of the data to understand the user's eating habits and exercise patterns. The analysis unit can also use machine learning algorithms to determine the risk of diseases expected in the future. For example, the analysis unit predicts the risk of myocardial infarction, stroke, diabetes, etc., based on past data. The judgment unit determines the risk of diseases expected in the future based on the data analyzed by the analysis unit. For example, the judgment unit determines that a biased diet or lack of exercise will increase the risk of certain diseases. For example, the judgment unit detects excesses or deficiencies of specific nutrients from the user's dietary data and determines the risk based on the results. The distribution unit distributes the risk information determined by the judgment unit in the form of an email newsletter. The distribution unit, for example, sets the email format and regularly distributes the risk information to the user. The distribution unit can also adjust the distribution frequency according to the user's preferences. For example, the distribution unit can distribute once a week. The provision unit provides the necessary amount of exercise and nutrients based on the information distributed by the distribution unit. The provision unit, for example, suggests a daily target number of steps and specific strength training exercises. The provision unit can also provide links to websites where users can purchase supplements to replenish any missing nutrients. For example, the provision unit provides links to websites where users can purchase vitamin D supplements or protein powder. In this way, the health management system according to the embodiment can support the user's healthy lifestyle.

[0030] The data collection unit collects data on the user's daily eating habits and exercise. For example, the user can input details of their meals, type of exercise, and duration of exercise using a smartphone app. Specifically, the user can take photos of their meals within the app and input the ingredients and quantities to record calorie and nutrient intake. Regarding exercise, the user can input details of the type and duration of exercise, such as running, walking, or gym training. Furthermore, the data collection unit can automatically acquire data from wearable devices. For example, the unit can collect heart rate and step count data from a smartwatch. The smartwatch monitors the user's heart rate in real time, recording heart rate fluctuations during exercise and resting heart rate. Step count data is also important for understanding the user's daily activity level, and walking distance and calories burned are also calculated. In addition, the data collection unit can integrate and manage data manually entered by the user. For example, if a user manually enters details of their meals, that data is integrated with exercise data acquired from wearable devices to help understand their overall health status. This allows the data collection unit to centrally manage diverse data related to the user's diet and exercise habits and provide it to the analysis and judgment units.

[0031] The analysis unit analyzes the data collected by the data collection unit. For example, the analysis unit performs statistical analysis of the data to understand the user's eating habits and exercise habits. Specifically, based on the collected data, it calculates the user's average calorie intake, nutrient balance, exercise frequency, and exercise intensity. The analysis unit can also use machine learning algorithms to determine the risk of future diseases. For example, based on past data, the analysis unit predicts the risk of myocardial infarction, stroke, diabetes, etc. The machine learning algorithm considers the user's eating habits, exercise habits, genetic factors, etc., to identify high-risk patterns. Furthermore, the analysis unit can compare the user's data with that of other users to detect abnormal patterns and signs of risk early. For example, if excessive or insufficient intake of a particular nutrient, or lack of exercise, continues, the analysis unit will rate the risk highly and notify the judgment unit. In this way, the analysis unit can analyze the user's health status in detail and provide important information for predicting future risks.

[0032] The assessment unit determines the risk of future diseases based on the data analyzed by the analysis unit. For example, the assessment unit can determine that a biased diet or lack of exercise increases the risk of certain diseases. Specifically, the assessment unit detects excesses or deficiencies of specific nutrients from the user's dietary data and determines the risk based on the results. For example, it can determine that a persistent vitamin D deficiency increases the risk of osteoporosis and issue a warning to the user. The assessment unit can also determine, based on exercise data, that a persistent lack of exercise increases the risk of cardiovascular disease. Furthermore, the assessment unit comprehensively evaluates multiple risk factors based on the results of machine learning algorithms provided by the analysis unit and provides specific risk information to the user. For example, if dietary improvements or exercise habits need to be reviewed, it provides specific advice. In this way, the assessment unit can accurately determine the user's health risks and provide information to take appropriate measures.

[0033] The distribution department distributes risk information determined by the assessment department in the form of an email newsletter. For example, the distribution department sets the email format and regularly delivers risk information to users. Specifically, the distribution department creates emails that clearly summarize the user's health status and risk information and delivers them once a week. The distribution department can also adjust the delivery frequency according to the user's preferences. For example, if the user wishes, it is possible to deliver emails once a month or only when a specific risk increases. Furthermore, the distribution department can customize the content of the emails and provide different advice and information to each user. For example, if a deficiency in a specific nutrient is determined, information on foods and supplements containing that nutrient will be provided. In addition, the distribution department can monitor the email open rate and click-through rate and analyze user interest and responses to improve the content of the deliveries. In this way, the distribution department can provide users with appropriate risk information in a timely manner and support their health management.

[0034] The service provider provides the necessary exercise and nutrients based on the information distributed by the distribution provider. For example, the service provider might suggest a daily step goal or specific strength training exercises. Specifically, the service provider creates a customized exercise plan considering the user's current exercise habits and health condition. For example, it might suggest three aerobic exercise sessions per week or strength training menus to train specific muscle groups. The service provider can also provide links to websites where users can purchase supplements to replenish any missing nutrients. For example, it might provide links to websites where users can purchase vitamin D supplements or protein powder, making it easy for users to replenish the necessary nutrients. Furthermore, the service provider can collect user feedback and continuously improve the accuracy and effectiveness of its suggestions. For example, it can provide feedback on the results of users executing the suggested exercise plan and adjust the next suggestion based on that data. The service provider can also track whether users have purchased the suggested supplements and make future suggestions based on their purchase history. In this way, the service provider can provide users with individually customized exercise and nutrient suggestions to support a healthy lifestyle.

[0035] The data collection unit allows users to register their daily eating habits and exercise data. For example, users can input details of their meals, types of exercise, and exercise duration using a smartphone app. The data collection unit can also automatically acquire data from wearable devices. For example, it can collect heart rate and step count data from a smartwatch. Furthermore, the data collection unit can integrate and manage data manually entered by users. This enables data collection by allowing users to register their daily eating habits and exercise data. Some or all of the above-described processes in the data collection unit may be performed using AI or not. For example, the data collection unit can input data entered into a smartphone app into an AI, and have the AI ​​perform data collection and management.

[0036] The analysis unit can analyze registered data and determine that a continued unbalanced diet and lack of exercise increase the risk of myocardial infarction, stroke, kidney failure, and other diseases. For example, the analysis unit can perform statistical analysis of the data to understand the user's eating habits and exercise patterns. The analysis unit can also use machine learning algorithms to determine the risk of diseases that are expected in the future. For example, the analysis unit can predict the risk of myocardial infarction, stroke, diabetes, and other diseases based on past data. This allows the analysis unit to inform the user of the risk by determining that a continued unbalanced diet and lack of exercise increase the risk of myocardial infarction, stroke, kidney failure, and other diseases. Some or all of the above processing in the analysis unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the analysis unit can input registered data into a generative AI and have the generative AI perform data analysis and risk determination.

[0037] The distribution unit can deliver the judgment results to users in the form of an email newsletter. For example, the distribution unit can set the email format and regularly deliver risk information to users. The distribution unit can also adjust the delivery frequency according to the user's preferences. For example, the distribution unit can deliver once a week. This allows users to receive risk information by delivering the judgment results in the form of an email newsletter. Some or all of the above processing in the distribution unit may be performed using AI or not. For example, the distribution unit can input the judgment results into AI and have the AI ​​execute the delivery in the form of an email newsletter.

[0038] The distribution department can send out email newsletters containing articles about the experiences of others who have become ill or their exercise routines. For example, the distribution department can introduce the experiences of others who have become ill, providing users with useful information. The distribution department can also introduce exercise routines and success stories. For example, the distribution department could introduce the case of someone who suffered a myocardial infarction despite having a similar diet, or the case of someone who regained their health by incorporating exercise. In this way, by distributing email newsletters containing articles about the experiences of others who have become ill or their exercise routines, the distribution department can provide users with useful information. Some or all of the above processing in the distribution department may be performed using AI or not. For example, the distribution department can input articles about the experiences of others who have become ill or their exercise routines into an AI and have the AI ​​execute the distribution in email newsletter format.

[0039] The service provider can provide information such as the number of steps needed to achieve the required amount of exercise, methods for strength training, and links to websites where users can purchase supplements to replenish any missing nutrients. For example, the service provider can suggest a daily target number of steps and specific strength training exercises. The service provider can also provide links to websites where users can purchase supplements to replenish any missing nutrients. For example, the service provider can provide links to websites where users can purchase vitamin D supplements or protein powder. In this way, by providing information such as the number of steps needed to achieve the required amount of exercise, methods for strength training, and links to websites where users can purchase supplements to replenish any missing nutrients, the service provider can provide users with concrete means to lead a healthy life. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input user data into AI and have the AI ​​suggest the required amount of exercise and nutrients.

[0040] The data collection unit can analyze the user's past eating habits and exercise data and select the optimal data collection method. For example, the data collection unit can suggest the optimal data collection frequency based on data previously entered by the user. The data collection unit can also concentrate data collection during specific time periods based on the user's past data. Furthermore, the data collection unit can analyze the user's past data and customize the data collection method. This allows for efficient data collection by selecting the optimal method through analysis of the user's past data. Some or all of the above processes in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's past data into AI and have the AI ​​select the optimal data collection method.

[0041] The data collection unit can filter the collected dietary and exercise data based on the user's current health status and lifestyle. For example, the data collection unit can consider the user's current health status and collect only the necessary data. The data collection unit can also select the types of data to collect based on the user's lifestyle. Furthermore, the data collection unit can filter the collected data according to the user's health status and lifestyle. This allows for efficient data management by filtering the data based on the user's current health status and lifestyle, thereby collecting only the necessary data. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's health status and lifestyle data into AI and have the AI ​​perform the data filtering.

[0042] The data collection unit can prioritize the collection of highly relevant data when collecting dietary and exercise data, taking into account the user's geographical location. For example, if the user is in a specific region, the data collection unit will prioritize the collection of data related to that region. The data collection unit can also determine the optimal collection timing based on the user's geographical location. Furthermore, the data collection unit can collect highly relevant data while considering the user's location. This allows for efficient data management by prioritizing the collection of highly relevant data while considering the user's geographical location. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's geographical location into AI and have the AI ​​perform the collection of highly relevant data.

[0043] The data collection unit can analyze a user's social media activity and collect relevant data when collecting dietary and exercise data. For example, the data collection unit can analyze a user's social media posts and collect data related to diet and exercise. The data collection unit can also identify health-related interests from a user's social media activity and collect relevant data. Furthermore, the data collection unit can select the types of data to collect based on the user's social media activity. This allows for more accurate data analysis by collecting relevant data through the analysis of the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's social media data into an AI and have the AI ​​collect relevant data.

[0044] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit can perform a detailed analysis on important data and a simplified analysis on less important data. The analysis unit can also determine the priority of the analysis according to the importance of the data. Furthermore, the analysis unit can apply multiple analysis methods to important data. This allows for efficient data analysis by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input the importance of the data into the AI ​​and have the AI ​​perform the adjustment of the level of detail of the analysis.

[0045] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply a nutritional balance analysis algorithm to dietary data. It can also apply an exercise effect analysis algorithm to exercise data. Furthermore, it can apply a risk assessment analysis algorithm to health status data. By applying different analysis algorithms depending on the data category, more accurate data analysis becomes possible. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input the data category into the AI ​​and have the AI ​​execute the application of different analysis algorithms.

[0046] The analysis unit can determine the priority of analysis based on the data submission date during the analysis process. For example, the analysis unit can prioritize the analysis of the most recent data and postpone the analysis of older data. The analysis unit can also adjust the analysis schedule based on the data submission date. Furthermore, the analysis unit can prioritize the analysis of data with upcoming submission dates and provide rapid feedback. This enables efficient data analysis by determining the priority of analysis based on the data submission date. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input the data submission date into the AI ​​and have the AI ​​determine the analysis priority.

[0047] The analysis unit can adjust the order of analysis based on the relevance of the data during the analysis process. For example, the analysis unit can prioritize the analysis of highly relevant data and postpone the analysis of less relevant data. The analysis unit can also determine the order of analysis based on the relevance of the data. Furthermore, the analysis unit can perform detailed analysis based on highly relevant data. This allows for efficient data analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input the relevance of the data into the AI ​​and have the AI ​​adjust the order of analysis.

[0048] The judgment unit can improve the accuracy of its judgment by considering the interrelationships between data during the judgment process. For example, the judgment unit can improve the accuracy of its judgment by considering the interrelationships between dietary data and exercise data. It can also improve the accuracy of its judgment by considering the interrelationships between health status data and lifestyle data. Furthermore, the judgment unit can improve the accuracy of its risk assessment based on the interrelationships of the data. In this way, the accuracy of the judgment can be improved by considering the interrelationships of the data. Some or all of the above processing in the judgment unit may be performed using AI or not. For example, the judgment unit can input the interrelationships of the data into the AI ​​and have the AI ​​perform the improvement of the judgment accuracy.

[0049] The judgment unit can perform judgments by considering the attribute information of the data submitter. For example, the judgment unit can improve the accuracy of the judgment by considering the user's age and gender. The judgment unit can also improve the accuracy of the judgment by considering the user's occupation and lifestyle. Furthermore, the judgment unit can improve the accuracy of the judgment by considering the user's health status and past medical history. In this way, the accuracy of the judgment can be improved by considering the attribute information of the data submitter. Some or all of the above processing in the judgment unit may be performed using AI or not. For example, the judgment unit can input the user's attribute information into AI and have the AI ​​perform the improvement of the judgment accuracy.

[0050] The determination unit can perform determinations while considering the geographical distribution of the data. For example, the determination unit can improve the accuracy of the determination by considering the health risks of the user's place of residence. The determination unit can also consider region-specific risks based on the geographical distribution. Furthermore, the determination unit can improve the accuracy of the determination by considering environmental factors of the user's place of residence. This makes it possible to perform determinations that take region-specific risks into account by considering the geographical distribution of the data. Some or all of the above processing in the determination unit may be performed using AI or not. For example, the determination unit can input the user's geographical distribution data into AI and have the AI ​​perform the task of improving the accuracy of the determination.

[0051] The judgment unit can improve the accuracy of its judgment by referring to relevant literature during the judgment process. For example, the judgment unit can improve the accuracy of its judgment based on relevant literature. The judgment unit can also improve the accuracy of its judgment by referring to the latest research results. Furthermore, the judgment unit can improve the accuracy of its risk assessment based on relevant literature. Thus, the accuracy of the judgment can be improved by referring to relevant literature. Some or all of the above processing in the judgment unit may be performed using AI or not. For example, the judgment unit can input relevant literature data into AI and have the AI ​​perform the improvement of the judgment accuracy.

[0052] The distribution unit can optimize the current content by referring to past distribution data during distribution. For example, the distribution unit can analyze past distribution data and prioritize the distribution of content that users are most interested in. The distribution unit can also redistribute articles that received a good response from users based on past distribution data. Furthermore, the distribution unit can customize the current content based on past distribution data. In this way, by referring to past distribution data, the current content can be optimized and useful information can be provided to users. Some or all of the above processing in the distribution unit may be performed using AI or not. For example, the distribution unit can input past distribution data into AI and have the AI ​​perform the optimization of the distribution content.

[0053] The distribution unit can apply different distribution methods to each data category during distribution. For example, the distribution unit can distribute articles about health in the form of a detailed email newsletter. It can also distribute articles about exercise in video format. Furthermore, it can distribute articles about nutrition in infographic format. By applying different distribution methods to each data category, the distribution unit can provide information in the format that is most suitable for the user. Some or all of the above processing in the distribution unit may be performed using AI or not. For example, the distribution unit can input data categories into the AI ​​and have the AI ​​apply different distribution methods.

[0054] The distribution unit can analyze changes in distribution based on the data submission timing during distribution. For example, the distribution unit can analyze changes in the content of distribution based on the data submission timing. The distribution unit can also prioritize the distribution of data with upcoming submission dates and provide rapid feedback. Furthermore, the distribution unit can adjust the distribution schedule based on the data submission timing. This enables efficient distribution by analyzing changes in distribution based on the data submission timing. Some or all of the above processes in the distribution unit may be performed using AI or not. For example, the distribution unit can input the data submission timing into the AI ​​and have the AI ​​perform the analysis of changes in the content of distribution.

[0055] The distribution unit can analyze the content to be distributed by referring to relevant market data at the time of distribution. For example, the distribution unit can optimize the content to be distributed based on relevant market data. The distribution unit can also refer to market data and prioritize the distribution of content that is of high interest to users. Furthermore, the distribution unit can customize the content to be distributed based on market data. This allows the distribution unit to prioritize the distribution of content that is of high interest to users by referring to relevant market data. Some or all of the above processing in the distribution unit may be performed using AI or not. For example, the distribution unit can input relevant market data into AI and have the AI ​​perform the analysis of the content to be distributed.

[0056] The data delivery unit can improve the accuracy of data delivery by considering the interrelationships between data. For example, the delivery unit can improve the accuracy of data delivery by considering the interrelationships between dietary data and exercise data. It can also improve the accuracy of data delivery by considering the interrelationships between health status data and lifestyle data. Furthermore, the delivery unit can improve the accuracy of risk assessment based on the interrelationships of data. In this way, the accuracy of data delivery can be improved by considering the interrelationships of data. Some or all of the above processing in the delivery unit may be performed using AI or not. For example, the delivery unit can input the interrelationships of data into AI and have AI perform the improvement of delivery accuracy.

[0057] The data provisioning unit can provide data while considering the attribute information of the data submitter. For example, the provisioning unit can improve the accuracy of the provision by considering the user's age and gender. It can also improve the accuracy of the provision by considering the user's occupation and lifestyle. Furthermore, it can improve the accuracy of the provision by considering the user's health status and past medical history. In this way, the accuracy of the provision can be improved by considering the attribute information of the data submitter. Some or all of the above processing in the provisioning unit may be performed using AI or not. For example, the provisioning unit can input user attribute information into AI and have the AI ​​perform the task of improving the accuracy of the provision.

[0058] The data delivery unit can consider the geographical distribution of the data when providing it. For example, the delivery unit can consider the health risks of the user's place of residence to improve the accuracy of the delivery. The delivery unit can also consider region-specific risks based on the geographical distribution. Furthermore, the delivery unit can consider environmental factors of the user's place of residence to improve the accuracy of the delivery. This makes it possible to provide data that takes region-specific risks into account by considering the geographical distribution of the data. Some or all of the above processing in the delivery unit may be performed using AI or not. For example, the delivery unit can input the user's geographical distribution data into AI and have the AI ​​perform the task of improving the accuracy of the delivery.

[0059] The data provider can improve the accuracy of its data provision by referring to relevant literature at the time of provision. For example, the data provider can improve the accuracy of its data provision based on relevant literature. The data provider can also improve the accuracy of its data provision by referring to the latest research results. Furthermore, the data provider can improve the accuracy of its risk assessment based on relevant literature. In this way, the accuracy of the data provision can be improved by referring to relevant literature. Some or all of the above processing in the data provider may be performed using AI or not. For example, the data provider can input relevant literature data into AI and have the AI ​​perform the task of improving the accuracy of the data provision.

[0060] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0061] A health management system can take into account the user's geographical location to provide advice based on region-specific health risks and the availability of local ingredients. For example, if a user lives at high altitude, it can suggest foods and exercises that help with oxygen supply. If a user lives near the coast, it can suggest nutritionally balanced meals utilizing seafood. Furthermore, if a user lives in an urban area, it can provide advice on coping with urban-specific stress and addressing lack of exercise. This allows for the provision of specific health management methods tailored to the user's place of residence.

[0062] A health management system can analyze a user's social media activity to identify their health interests and tailor advice accordingly. For example, if a user shows interest in a particular diet on social media, it can suggest a meal plan related to that diet. Similarly, if a user is interested in a specific exercise, it can provide a training plan related to that exercise. Furthermore, if a user frequently shares health-related articles or posts, it can offer advice based on that content. This allows for the provision of specific health management methods tailored to the user's interests and concerns.

[0063] A health management system can analyze a user's past health data, predict future health risks, and provide advice. For example, if past data indicates a high risk of a specific disease, it can suggest dietary and exercise plans to mitigate that risk. If past data indicates a deficiency in a specific nutrient, it can provide a meal plan to supplement that nutrient. Furthermore, if past data indicates a persistent lack of exercise, it can provide specific advice to improve exercise habits. This allows for the provision of specific health management methods based on the user's past health data.

[0064] A health management system can analyze a user's lifestyle data and provide health advice tailored to specific events and seasons. For example, if a user tends to have irregular eating habits during the New Year holidays, the system can suggest a meal management plan suited to that period. Similarly, if a user tends to be less active during the summer, the system can provide an exercise plan that takes heat countermeasures into account. Furthermore, if a user plans to participate in a specific event (e.g., a marathon), the system can provide a training plan for that event. This allows for the provision of specific health management methods tailored to the user's lifestyle and events.

[0065] A health management system can compare a user's health data with other users, assess their relative health status, and provide advice. For example, if a user exercises less than other users of the same age, it can suggest an exercise plan to bridge that gap. If a user is deficient in a particular nutrient compared to other users of the same gender, it can provide a meal plan to supplement that nutrient. Furthermore, if a user has a higher health risk compared to other users living in the same area, it can provide specific advice to mitigate that risk. This allows for a relative assessment of a user's health status and provides specific health management methods.

[0066] A health management system can analyze a user's health data and visualize their progress toward specific health goals. For example, if a user aims to lose weight, the system can display daily weight changes in a graph, visualizing their progress toward achieving that goal. Similarly, if a user aims to increase muscle strength, the system can display training results numerically and graphically. Furthermore, if a user aims to increase their intake of a specific nutrient, the system can visualize changes in that intake. By visualizing the user's progress toward their health goals, this system can help maintain their motivation.

[0067] The following briefly describes the processing flow for example form 1.

[0068] Step 1: The data collection unit collects the user's daily eating habits and exercise data. For example, the user can input details of their meals, type of exercise, and exercise time using a smartphone app. The data collection unit can also automatically acquire data from wearable devices. For example, the unit can collect heart rate and step count data from a smartwatch. Furthermore, the data collection unit can integrate and manage data manually entered by the user. Step 2: The analysis unit analyzes the data collected by the data collection unit. For example, the analysis unit performs statistical analysis of the data to understand the user's eating habits and exercise routines. The analysis unit can also use machine learning algorithms to determine the risk of future diseases. For example, the analysis unit can predict the risk of myocardial infarction, stroke, diabetes, etc., based on past data. Step 3: The judgment unit determines the risk of future diseases based on the data analyzed by the analysis unit. For example, the judgment unit determines that a biased diet or lack of exercise will increase the risk of certain diseases. For example, the judgment unit detects excesses or deficiencies of specific nutrients from the user's dietary data and determines the risk based on the results. Step 4: The distribution unit distributes the risk information determined by the assessment unit in the form of an email newsletter. The distribution unit, for example, sets the email format and regularly distributes risk information to users. The distribution unit can also adjust the distribution frequency according to the user's preferences. For example, the distribution unit can distribute once a week. Step 5: The provider provides the necessary exercise and nutrients based on the information distributed by the delivery department. For example, the provider might suggest a daily step goal or specific strength training exercises. The provider can also provide links to websites where supplements can be purchased to replenish any missing nutrients. For example, the provider might provide links to websites where vitamin D supplements or protein powder can be purchased.

[0069] (Example of form 2) The health management system according to an embodiment of the present invention is a system that analyzes dietary and exercise data registered on a smartphone and delivers information on the expected future risk of disease in the form of an email newsletter. The health management system works by having the user register their daily dietary and exercise data on their smartphone. Next, an AI analyzes this data and determines the expected future risk of disease. The results are delivered to the user in the form of an email newsletter. This newsletter includes articles on the experiences of others who have become ill and their exercise efforts. It also provides information such as the number of steps needed to burn off the required amount of exercise, methods for strength training, and links to websites where users can purchase supplements to replenish any missing nutrients. This allows users to improve their lifestyle from a young age and lead a healthy life. For example, a user registers their daily dietary and exercise data on their smartphone. For instance, they input what they ate for breakfast and their daily exercise amount. This data is analyzed by the AI. Next, the AI ​​analyzes the registered data and determines the expected future risk of disease. For example, it may determine that a continued unbalanced diet and lack of exercise increase the risk of myocardial infarction, stroke, and renal failure. The results are delivered to the user in the form of an email newsletter. This email newsletter includes articles on the experiences of others who have fallen ill and their exercise regimens. For example, it features cases of people with similar diets who developed myocardial infarction, and cases of people who regained their health through exercise. It also provides information on the number of steps needed to achieve the necessary amount of exercise and methods for strength training. For instance, it recommends walking 8,000 steps a day and strength training 2-3 times a week. Furthermore, it provides links to websites where users can purchase supplements to replenish any deficient nutrients. For example, if iron or dietary fiber is deficient, links to websites where these supplements can be purchased are provided. This system allows users to improve their lifestyle from a young age and live a healthy life. For example, by registering daily dietary and exercise data and supplementing the necessary amount of exercise and nutrients based on the results of AI analysis, it is possible to reduce the risk of future illness. This helps build a foundation for living a healthy life in the era of 100-year lifespans. In this way, the health management system can support users in leading a healthy life.

[0070] The health management system according to this embodiment comprises a data collection unit, an analysis unit, a judgment unit, a distribution unit, and a provision unit. The data collection unit collects the user's daily eating habits and exercise data. For example, the user can input the contents of their meals, the type of exercise, and the duration of exercise using a smartphone app. The data collection unit can also automatically acquire data from wearable devices. For example, the data collection unit collects heart rate and step count data from a smartwatch. Furthermore, the data collection unit can integrate and manage data manually entered by the user. The analysis unit analyzes the data collected by the data collection unit. For example, the analysis unit performs statistical analysis of the data to understand the user's eating habits and exercise patterns. The analysis unit can also use machine learning algorithms to determine the risk of diseases expected in the future. For example, the analysis unit predicts the risk of myocardial infarction, stroke, diabetes, etc., based on past data. The judgment unit determines the risk of diseases expected in the future based on the data analyzed by the analysis unit. For example, the judgment unit determines that a biased diet or lack of exercise will increase the risk of certain diseases. For example, the judgment unit detects excesses or deficiencies of specific nutrients from the user's dietary data and determines the risk based on the results. The distribution unit distributes the risk information determined by the judgment unit in the form of an email newsletter. The distribution unit, for example, sets the email format and regularly distributes the risk information to the user. The distribution unit can also adjust the distribution frequency according to the user's preferences. For example, the distribution unit can distribute once a week. The provision unit provides the necessary amount of exercise and nutrients based on the information distributed by the distribution unit. The provision unit, for example, suggests a daily target number of steps and specific strength training exercises. The provision unit can also provide links to websites where users can purchase supplements to replenish any missing nutrients. For example, the provision unit provides links to websites where users can purchase vitamin D supplements or protein powder. In this way, the health management system according to the embodiment can support the user's healthy lifestyle.

[0071] The data collection unit collects data on the user's daily eating habits and exercise. For example, the user can input details of their meals, type of exercise, and duration of exercise using a smartphone app. Specifically, the user can take photos of their meals within the app and input the ingredients and quantities to record calorie and nutrient intake. Regarding exercise, the user can input details of the type and duration of exercise, such as running, walking, or gym training. Furthermore, the data collection unit can automatically acquire data from wearable devices. For example, the unit can collect heart rate and step count data from a smartwatch. The smartwatch monitors the user's heart rate in real time, recording heart rate fluctuations during exercise and resting heart rate. Step count data is also important for understanding the user's daily activity level, and walking distance and calories burned are also calculated. In addition, the data collection unit can integrate and manage data manually entered by the user. For example, if a user manually enters details of their meals, that data is integrated with exercise data acquired from wearable devices to help understand their overall health status. This allows the data collection unit to centrally manage diverse data related to the user's diet and exercise habits and provide it to the analysis and judgment units.

[0072] The analysis unit analyzes the data collected by the data collection unit. For example, the analysis unit performs statistical analysis of the data to understand the user's eating habits and exercise habits. Specifically, based on the collected data, it calculates the user's average calorie intake, nutrient balance, exercise frequency, and exercise intensity. The analysis unit can also use machine learning algorithms to determine the risk of future diseases. For example, based on past data, the analysis unit predicts the risk of myocardial infarction, stroke, diabetes, etc. The machine learning algorithm considers the user's eating habits, exercise habits, genetic factors, etc., to identify high-risk patterns. Furthermore, the analysis unit can compare the user's data with that of other users to detect abnormal patterns and signs of risk early. For example, if excessive or insufficient intake of a particular nutrient, or lack of exercise, continues, the analysis unit will rate the risk highly and notify the judgment unit. In this way, the analysis unit can analyze the user's health status in detail and provide important information for predicting future risks.

[0073] The assessment unit determines the risk of future diseases based on the data analyzed by the analysis unit. For example, the assessment unit can determine that a biased diet or lack of exercise increases the risk of certain diseases. Specifically, the assessment unit detects excesses or deficiencies of specific nutrients from the user's dietary data and determines the risk based on the results. For example, it can determine that a persistent vitamin D deficiency increases the risk of osteoporosis and issue a warning to the user. The assessment unit can also determine, based on exercise data, that a persistent lack of exercise increases the risk of cardiovascular disease. Furthermore, the assessment unit comprehensively evaluates multiple risk factors based on the results of machine learning algorithms provided by the analysis unit and provides specific risk information to the user. For example, if dietary improvements or exercise habits need to be reviewed, it provides specific advice. In this way, the assessment unit can accurately determine the user's health risks and provide information to take appropriate measures.

[0074] The distribution department distributes risk information determined by the assessment department in the form of an email newsletter. For example, the distribution department sets the email format and regularly delivers risk information to users. Specifically, the distribution department creates emails that clearly summarize the user's health status and risk information and delivers them once a week. The distribution department can also adjust the delivery frequency according to the user's preferences. For example, if the user wishes, it is possible to deliver emails once a month or only when a specific risk increases. Furthermore, the distribution department can customize the content of the emails and provide different advice and information to each user. For example, if a deficiency in a specific nutrient is determined, information on foods and supplements containing that nutrient will be provided. In addition, the distribution department can monitor the email open rate and click-through rate and analyze user interest and responses to improve the content of the deliveries. In this way, the distribution department can provide users with appropriate risk information in a timely manner and support their health management.

[0075] The service provider provides the necessary exercise and nutrients based on the information distributed by the distribution provider. For example, the service provider might suggest a daily step goal or specific strength training exercises. Specifically, the service provider creates a customized exercise plan considering the user's current exercise habits and health condition. For example, it might suggest three aerobic exercise sessions per week or strength training menus to train specific muscle groups. The service provider can also provide links to websites where users can purchase supplements to replenish any missing nutrients. For example, it might provide links to websites where users can purchase vitamin D supplements or protein powder, making it easy for users to replenish the necessary nutrients. Furthermore, the service provider can collect user feedback and continuously improve the accuracy and effectiveness of its suggestions. For example, it can provide feedback on the results of users executing the suggested exercise plan and adjust the next suggestion based on that data. The service provider can also track whether users have purchased the suggested supplements and make future suggestions based on their purchase history. In this way, the service provider can provide users with individually customized exercise and nutrient suggestions to support a healthy lifestyle.

[0076] The data collection unit allows users to register their daily eating habits and exercise data. For example, users can input details of their meals, types of exercise, and exercise duration using a smartphone app. The data collection unit can also automatically acquire data from wearable devices. For example, it can collect heart rate and step count data from a smartwatch. Furthermore, the data collection unit can integrate and manage data manually entered by users. This enables data collection by allowing users to register their daily eating habits and exercise data. Some or all of the above-described processes in the data collection unit may be performed using AI or not. For example, the data collection unit can input data entered into a smartphone app into an AI, and have the AI ​​perform data collection and management.

[0077] The analysis unit can analyze registered data and determine that a continued unbalanced diet and lack of exercise increase the risk of myocardial infarction, stroke, kidney failure, and other diseases. For example, the analysis unit can perform statistical analysis of the data to understand the user's eating habits and exercise patterns. The analysis unit can also use machine learning algorithms to determine the risk of diseases that are expected in the future. For example, the analysis unit can predict the risk of myocardial infarction, stroke, diabetes, and other diseases based on past data. This allows the analysis unit to inform the user of the risk by determining that a continued unbalanced diet and lack of exercise increase the risk of myocardial infarction, stroke, kidney failure, and other diseases. Some or all of the above processing in the analysis unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the analysis unit can input registered data into a generative AI and have the generative AI perform data analysis and risk determination.

[0078] The distribution unit can deliver the judgment results to users in the form of an email newsletter. For example, the distribution unit can set the email format and regularly deliver risk information to users. The distribution unit can also adjust the delivery frequency according to the user's preferences. For example, the distribution unit can deliver once a week. This allows users to receive risk information by delivering the judgment results in the form of an email newsletter. Some or all of the above processing in the distribution unit may be performed using AI or not. For example, the distribution unit can input the judgment results into AI and have the AI ​​execute the delivery in the form of an email newsletter.

[0079] The distribution department can send out email newsletters containing articles about the experiences of others who have become ill or their exercise routines. For example, the distribution department can introduce the experiences of others who have become ill, providing users with useful information. The distribution department can also introduce exercise routines and success stories. For example, the distribution department could introduce the case of someone who suffered a myocardial infarction despite having a similar diet, or the case of someone who regained their health by incorporating exercise. In this way, by distributing email newsletters containing articles about the experiences of others who have become ill or their exercise routines, the distribution department can provide users with useful information. Some or all of the above processing in the distribution department may be performed using AI or not. For example, the distribution department can input articles about the experiences of others who have become ill or their exercise routines into an AI and have the AI ​​execute the distribution in email newsletter format.

[0080] The service provider can provide information such as the number of steps needed to achieve the required amount of exercise, methods for strength training, and links to websites where users can purchase supplements to replenish any missing nutrients. For example, the service provider can suggest a daily target number of steps and specific strength training exercises. The service provider can also provide links to websites where users can purchase supplements to replenish any missing nutrients. For example, the service provider can provide links to websites where users can purchase vitamin D supplements or protein powder. In this way, by providing information such as the number of steps needed to achieve the required amount of exercise, methods for strength training, and links to websites where users can purchase supplements to replenish any missing nutrients, the service provider can provide users with concrete means to lead a healthy life. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input user data into AI and have the AI ​​suggest the required amount of exercise and nutrients.

[0081] The data collection unit can estimate the user's emotions and adjust the timing of data collection for diet and exercise based on the estimated emotions. For example, if the user is stressed, the data collection unit can reduce the collection timing to lessen the burden. Conversely, if the user is relaxed, the data collection unit can collect detailed data to obtain more accurate information. Furthermore, if the user is in a hurry, the data collection unit can perform simplified data collection to quickly obtain information. By adjusting the collection timing based on the user's emotions, the burden on the user can be reduced and more accurate data can be collected. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's emotion data into a generative AI and have the generative AI perform emotion estimation and adjustment of collection timing.

[0082] The data collection unit can analyze the user's past eating habits and exercise data and select the optimal data collection method. For example, the data collection unit can suggest the optimal data collection frequency based on data previously entered by the user. The data collection unit can also concentrate data collection during specific time periods based on the user's past data. Furthermore, the data collection unit can analyze the user's past data and customize the data collection method. This allows for efficient data collection by selecting the optimal method through analysis of the user's past data. Some or all of the above processes in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's past data into AI and have the AI ​​select the optimal data collection method.

[0083] The data collection unit can filter the collected dietary and exercise data based on the user's current health status and lifestyle. For example, the data collection unit can consider the user's current health status and collect only the necessary data. The data collection unit can also select the types of data to collect based on the user's lifestyle. Furthermore, the data collection unit can filter the collected data according to the user's health status and lifestyle. This allows for efficient data management by filtering the data based on the user's current health status and lifestyle, thereby collecting only the necessary data. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's health status and lifestyle data into AI and have the AI ​​perform the data filtering.

[0084] The data collection unit can estimate the user's emotions and prioritize the data to be collected based on the estimated emotions. For example, if the user is stressed, the data collection unit will prioritize collecting only important data. If the user is relaxed, the data collection unit can prioritize collecting detailed data. Furthermore, if the user is in a hurry, the data collection unit can prioritize collecting simplified data. This allows for efficient data management by prioritizing data based on the user's emotions, thereby prioritizing the collection of important data. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation and data prioritization.

[0085] The data collection unit can prioritize the collection of highly relevant data when collecting dietary and exercise data, taking into account the user's geographical location. For example, if the user is in a specific region, the data collection unit will prioritize the collection of data related to that region. The data collection unit can also determine the optimal collection timing based on the user's geographical location. Furthermore, the data collection unit can collect highly relevant data while considering the user's location. This allows for efficient data management by prioritizing the collection of highly relevant data while considering the user's geographical location. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's geographical location into AI and have the AI ​​perform the collection of highly relevant data.

[0086] The data collection unit can analyze a user's social media activity and collect relevant data when collecting dietary and exercise data. For example, the data collection unit can analyze a user's social media posts and collect data related to diet and exercise. The data collection unit can also identify health-related interests from a user's social media activity and collect relevant data. Furthermore, the data collection unit can select the types of data to collect based on the user's social media activity. This allows for more accurate data analysis by collecting relevant data through the analysis of the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's social media data into an AI and have the AI ​​collect relevant data.

[0087] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is tense, the analysis unit can provide simple and easy-to-understand analysis results. If the user is relaxed, the analysis unit can also provide detailed analysis results. Furthermore, if the user is in a hurry, the analysis unit can provide concise analysis results. In this way, by adjusting the presentation of the analysis based on the user's emotions, it is possible to provide analysis results that are easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation and adjustment of the presentation of the analysis.

[0088] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit can perform a detailed analysis on important data and a simplified analysis on less important data. The analysis unit can also determine the priority of the analysis according to the importance of the data. Furthermore, the analysis unit can apply multiple analysis methods to important data. This allows for efficient data analysis by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input the importance of the data into the AI ​​and have the AI ​​perform the adjustment of the level of detail of the analysis.

[0089] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply a nutritional balance analysis algorithm to dietary data. It can also apply an exercise effect analysis algorithm to exercise data. Furthermore, it can apply a risk assessment analysis algorithm to health status data. By applying different analysis algorithms depending on the data category, more accurate data analysis becomes possible. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input the data category into the AI ​​and have the AI ​​execute the application of different analysis algorithms.

[0090] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, the analysis unit can provide a short, concise analysis. If the user is relaxed, the analysis unit can provide a detailed analysis. Furthermore, if the user is excited, the analysis unit can provide a visually stimulating analysis. By adjusting the length of the analysis based on the user's emotions, the analysis unit can provide an analysis of an appropriate length for the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation and adjustment of the analysis length.

[0091] The analysis unit can determine the priority of analysis based on the data submission date during the analysis process. For example, the analysis unit can prioritize the analysis of the most recent data and postpone the analysis of older data. The analysis unit can also adjust the analysis schedule based on the data submission date. Furthermore, the analysis unit can prioritize the analysis of data with upcoming submission dates and provide rapid feedback. This enables efficient data analysis by determining the priority of analysis based on the data submission date. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input the data submission date into the AI ​​and have the AI ​​determine the analysis priority.

[0092] The analysis unit can adjust the order of analysis based on the relevance of the data during the analysis process. For example, the analysis unit can prioritize the analysis of highly relevant data and postpone the analysis of less relevant data. The analysis unit can also determine the order of analysis based on the relevance of the data. Furthermore, the analysis unit can perform detailed analysis based on highly relevant data. This allows for efficient data analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input the relevance of the data into the AI ​​and have the AI ​​adjust the order of analysis.

[0093] The judgment unit can estimate the user's emotions and adjust the judgment criteria based on the estimated emotions. For example, if the user is tense, the judgment unit may relax strict criteria. If the user is relaxed, the judgment unit may apply detailed criteria. Furthermore, if the user is in a hurry, the judgment unit may apply simplified criteria. By adjusting the judgment criteria based on the user's emotions, the judgment unit can provide an appropriate judgment result for the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the judgment unit may be performed using AI or not. For example, the judgment unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation and adjustment of judgment criteria.

[0094] The judgment unit can improve the accuracy of its judgment by considering the interrelationships between data during the judgment process. For example, the judgment unit can improve the accuracy of its judgment by considering the interrelationships between dietary data and exercise data. It can also improve the accuracy of its judgment by considering the interrelationships between health status data and lifestyle data. Furthermore, the judgment unit can improve the accuracy of its risk assessment based on the interrelationships of the data. In this way, the accuracy of the judgment can be improved by considering the interrelationships of the data. Some or all of the above processing in the judgment unit may be performed using AI or not. For example, the judgment unit can input the interrelationships of the data into the AI ​​and have the AI ​​perform the improvement of the judgment accuracy.

[0095] The judgment unit can perform judgments by considering the attribute information of the data submitter. For example, the judgment unit can improve the accuracy of the judgment by considering the user's age and gender. The judgment unit can also improve the accuracy of the judgment by considering the user's occupation and lifestyle. Furthermore, the judgment unit can improve the accuracy of the judgment by considering the user's health status and past medical history. In this way, the accuracy of the judgment can be improved by considering the attribute information of the data submitter. Some or all of the above processing in the judgment unit may be performed using AI or not. For example, the judgment unit can input the user's attribute information into AI and have the AI ​​perform the improvement of the judgment accuracy.

[0096] The judgment unit can estimate the user's emotions and adjust the order in which the judgment results are displayed based on the estimated emotions. For example, if the user is nervous, the judgment unit can display important results first. If the user is relaxed, the judgment unit can display detailed results sequentially. Furthermore, if the user is in a hurry, the judgment unit can display concise results first. In this way, by adjusting the display order of the judgment results based on the user's emotions, the system can provide results that are easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the judgment unit may be performed using AI or not. For example, the judgment unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation and adjustment of the result display order.

[0097] The determination unit can perform determinations while considering the geographical distribution of the data. For example, the determination unit can improve the accuracy of the determination by considering the health risks of the user's place of residence. The determination unit can also consider region-specific risks based on the geographical distribution. Furthermore, the determination unit can improve the accuracy of the determination by considering environmental factors of the user's place of residence. This makes it possible to perform determinations that take region-specific risks into account by considering the geographical distribution of the data. Some or all of the above processing in the determination unit may be performed using AI or not. For example, the determination unit can input the user's geographical distribution data into AI and have the AI ​​perform the task of improving the accuracy of the determination.

[0098] The judgment unit can improve the accuracy of its judgment by referring to relevant literature during the judgment process. For example, the judgment unit can improve the accuracy of its judgment based on relevant literature. The judgment unit can also improve the accuracy of its judgment by referring to the latest research results. Furthermore, the judgment unit can improve the accuracy of its risk assessment based on relevant literature. Thus, the accuracy of the judgment can be improved by referring to relevant literature. Some or all of the above processing in the judgment unit may be performed using AI or not. For example, the judgment unit can input relevant literature data into AI and have the AI ​​perform the improvement of the judgment accuracy.

[0099] The delivery unit can estimate the user's emotions and adjust the display method of the delivery based on the estimated emotions. For example, if the user is stressed, the delivery unit can provide a simple and highly visible display method. If the user is relaxed, the delivery unit can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the delivery unit can provide a concise display method. In this way, by adjusting the display method of the delivery based on the user's emotions, a highly visible display method can be provided to the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the delivery unit may be performed using AI or not. For example, the delivery unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation and adjustment of the display method.

[0100] The distribution unit can optimize the current content by referring to past distribution data during distribution. For example, the distribution unit can analyze past distribution data and prioritize the distribution of content that users are most interested in. The distribution unit can also redistribute articles that received a good response from users based on past distribution data. Furthermore, the distribution unit can customize the current content based on past distribution data. In this way, by referring to past distribution data, the current content can be optimized and useful information can be provided to users. Some or all of the above processing in the distribution unit may be performed using AI or not. For example, the distribution unit can input past distribution data into AI and have the AI ​​perform the optimization of the distribution content.

[0101] The distribution unit can apply different distribution methods to each data category during distribution. For example, the distribution unit can distribute articles about health in the form of a detailed email newsletter. It can also distribute articles about exercise in video format. Furthermore, it can distribute articles about nutrition in infographic format. By applying different distribution methods to each data category, the distribution unit can provide information in the format that is most suitable for the user. Some or all of the above processing in the distribution unit may be performed using AI or not. For example, the distribution unit can input data categories into the AI ​​and have the AI ​​apply different distribution methods.

[0102] The delivery unit can estimate the user's emotions and adjust the importance of deliveries based on those emotions. For example, if the user is stressed, the delivery unit will prioritize delivering important information. If the user is relaxed, the delivery unit can deliver deliveries containing detailed information. Furthermore, if the user is in a hurry, the delivery unit can prioritize delivering concise information. In this way, by adjusting the importance of deliveries based on the user's emotions, the delivery unit can prioritize providing information that is important to the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the delivery unit may be performed using AI or not. For example, the delivery unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation and adjust the importance of deliveries.

[0103] The distribution unit can analyze changes in distribution based on the data submission timing during distribution. For example, the distribution unit can analyze changes in the content of distribution based on the data submission timing. The distribution unit can also prioritize the distribution of data with upcoming submission dates and provide rapid feedback. Furthermore, the distribution unit can adjust the distribution schedule based on the data submission timing. This enables efficient distribution by analyzing changes in distribution based on the data submission timing. Some or all of the above processes in the distribution unit may be performed using AI or not. For example, the distribution unit can input the data submission timing into the AI ​​and have the AI ​​perform the analysis of changes in the content of distribution.

[0104] The distribution unit can analyze the content to be distributed by referring to relevant market data at the time of distribution. For example, the distribution unit can optimize the content to be distributed based on relevant market data. The distribution unit can also refer to market data and prioritize the distribution of content that is of high interest to users. Furthermore, the distribution unit can customize the content to be distributed based on market data. This allows the distribution unit to prioritize the distribution of content that is of high interest to users by referring to relevant market data. Some or all of the above processing in the distribution unit may be performed using AI or not. For example, the distribution unit can input relevant market data into AI and have the AI ​​perform the analysis of the content to be distributed.

[0105] The service provider can estimate the user's emotions and prioritize the content to be provided based on those emotions. For example, if the user is stressed, the service provider can prioritize providing important information. If the user is relaxed, the service provider can provide detailed information. Furthermore, if the user is in a hurry, the service provider can prioritize providing concise information. In this way, by prioritizing the content to be provided based on the user's emotions, the service provider can prioritize providing information that is important to the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input user emotion data into a generative AI and have the generative AI perform emotion estimation and determine the priority of the content to be provided.

[0106] The data delivery unit can improve the accuracy of data delivery by considering the interrelationships between data. For example, the delivery unit can improve the accuracy of data delivery by considering the interrelationships between dietary data and exercise data. It can also improve the accuracy of data delivery by considering the interrelationships between health status data and lifestyle data. Furthermore, the delivery unit can improve the accuracy of risk assessment based on the interrelationships of data. In this way, the accuracy of data delivery can be improved by considering the interrelationships of data. Some or all of the above processing in the delivery unit may be performed using AI or not. For example, the delivery unit can input the interrelationships of data into AI and have AI perform the improvement of delivery accuracy.

[0107] The data provisioning unit can provide data while considering the attribute information of the data submitter. For example, the provisioning unit can improve the accuracy of the provision by considering the user's age and gender. It can also improve the accuracy of the provision by considering the user's occupation and lifestyle. Furthermore, it can improve the accuracy of the provision by considering the user's health status and past medical history. In this way, the accuracy of the provision can be improved by considering the attribute information of the data submitter. Some or all of the above processing in the provisioning unit may be performed using AI or not. For example, the provisioning unit can input user attribute information into AI and have the AI ​​perform the task of improving the accuracy of the provision.

[0108] The service provider can estimate the user's emotions and adjust the display method of the content based on the estimated emotions. For example, if the user is nervous, the service provider can provide a simple and highly visible display method. If the user is relaxed, the service provider can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the service provider can provide a concise display method. In this way, by adjusting the display method of the content based on the user's emotions, a highly visible display method can be provided to the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input user emotion data into a generative AI and have the generative AI perform emotion estimation and adjustment of the display method.

[0109] The data delivery unit can consider the geographical distribution of the data when providing it. For example, the delivery unit can consider the health risks of the user's place of residence to improve the accuracy of the delivery. The delivery unit can also consider region-specific risks based on the geographical distribution. Furthermore, the delivery unit can consider environmental factors of the user's place of residence to improve the accuracy of the delivery. This makes it possible to provide data that takes region-specific risks into account by considering the geographical distribution of the data. Some or all of the above processing in the delivery unit may be performed using AI or not. For example, the delivery unit can input the user's geographical distribution data into AI and have the AI ​​perform the task of improving the accuracy of the delivery.

[0110] The data provider can improve the accuracy of its data provision by referring to relevant literature at the time of provision. For example, the data provider can improve the accuracy of its data provision based on relevant literature. The data provider can also improve the accuracy of its data provision by referring to the latest research results. Furthermore, the data provider can improve the accuracy of its risk assessment based on relevant literature. In this way, the accuracy of the data provision can be improved by referring to relevant literature. Some or all of the above processing in the data provider may be performed using AI or not. For example, the data provider can input relevant literature data into AI and have the AI ​​perform the task of improving the accuracy of the data provision.

[0111] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0112] A health management system can estimate a user's emotions and customize diet and exercise advice based on those emotions. For example, if a user is stressed, it can suggest relaxing meals and light exercise. If the user is relaxed, it can suggest more vigorous exercise and nutritionally balanced meals. Furthermore, if the user is in a hurry, it can suggest quick and effective exercises and easy-to-prepare healthy meals. This enhances the effectiveness of health management by providing appropriate advice tailored to the user's emotions.

[0113] A health management system can take into account the user's geographical location to provide advice based on region-specific health risks and the availability of local ingredients. For example, if a user lives at high altitude, it can suggest foods and exercises that help with oxygen supply. If a user lives near the coast, it can suggest nutritionally balanced meals utilizing seafood. Furthermore, if a user lives in an urban area, it can provide advice on coping with urban-specific stress and addressing lack of exercise. This allows for the provision of specific health management methods tailored to the user's place of residence.

[0114] A health management system can analyze a user's social media activity to identify their health interests and tailor advice accordingly. For example, if a user shows interest in a particular diet on social media, it can suggest a meal plan related to that diet. Similarly, if a user is interested in a specific exercise, it can provide a training plan related to that exercise. Furthermore, if a user frequently shares health-related articles or posts, it can offer advice based on that content. This allows for the provision of specific health management methods tailored to the user's interests and concerns.

[0115] A health management system can analyze a user's past health data, predict future health risks, and provide advice. For example, if past data indicates a high risk of a specific disease, it can suggest dietary and exercise plans to mitigate that risk. If past data indicates a deficiency in a specific nutrient, it can provide a meal plan to supplement that nutrient. Furthermore, if past data indicates a persistent lack of exercise, it can provide specific advice to improve exercise habits. This allows for the provision of specific health management methods based on the user's past health data.

[0116] A health management system can estimate a user's emotions and adjust how health risks are communicated based on those emotions. For example, if a user is stressed, risk information can be presented in a simple, easy-to-read format. If the user is relaxed, detailed risk information can be provided. Furthermore, if the user is in a hurry, concise risk information can be quickly communicated. This improves the ease with which risk information is received by providing appropriate notifications tailored to the user's emotions.

[0117] A health management system can analyze a user's lifestyle data and provide health advice tailored to specific events and seasons. For example, if a user tends to have irregular eating habits during the New Year holidays, the system can suggest a meal management plan suited to that period. Similarly, if a user tends to be less active during the summer, the system can provide an exercise plan that takes heat countermeasures into account. Furthermore, if a user plans to participate in a specific event (e.g., a marathon), the system can provide a training plan for that event. This allows for the provision of specific health management methods tailored to the user's lifestyle and events.

[0118] A health management system can estimate a user's emotions and prioritize health advice based on those emotions. For example, if a user is stressed, it can prioritize advice that is effective in reducing stress. If a user is relaxed, it can provide advice that aligns with long-term health goals. Furthermore, if a user is in a hurry, it can prioritize advice that can be implemented in a short amount of time. This enhances the effectiveness of health management by providing appropriate advice tailored to the user's emotions.

[0119] A health management system can compare a user's health data with other users, assess their relative health status, and provide advice. For example, if a user exercises less than other users of the same age, it can suggest an exercise plan to bridge that gap. If a user is deficient in a particular nutrient compared to other users of the same gender, it can provide a meal plan to supplement that nutrient. Furthermore, if a user has a higher health risk compared to other users living in the same area, it can provide specific advice to mitigate that risk. This allows for a relative assessment of a user's health status and provides specific health management methods.

[0120] A health management system can estimate a user's emotions and adjust the way health advice is presented based on those emotions. For example, if a user is stressed, it can provide simple, easy-to-understand advice. If a user is relaxed, it can provide advice with more detailed information. Furthermore, if a user is in a hurry, it can provide concise, quick advice. This improves the ease with which advice is received by providing advice in an appropriate way that suits the user's emotions.

[0121] A health management system can analyze a user's health data and visualize their progress toward specific health goals. For example, if a user aims to lose weight, the system can display daily weight changes in a graph, visualizing their progress toward achieving that goal. Similarly, if a user aims to increase muscle strength, the system can display training results numerically and graphically. Furthermore, if a user aims to increase their intake of a specific nutrient, the system can visualize changes in that intake. By visualizing the user's progress toward their health goals, this system can help maintain their motivation.

[0122] The following briefly describes the processing flow for example form 2.

[0123] Step 1: The data collection unit collects the user's daily eating habits and exercise data. For example, the user can input details of their meals, type of exercise, and exercise time using a smartphone app. The data collection unit can also automatically acquire data from wearable devices. For example, the unit can collect heart rate and step count data from a smartwatch. Furthermore, the data collection unit can integrate and manage data manually entered by the user. Step 2: The analysis unit analyzes the data collected by the data collection unit. For example, the analysis unit performs statistical analysis of the data to understand the user's eating habits and exercise routines. The analysis unit can also use machine learning algorithms to determine the risk of future diseases. For example, the analysis unit can predict the risk of myocardial infarction, stroke, diabetes, etc., based on past data. Step 3: The judgment unit determines the risk of future diseases based on the data analyzed by the analysis unit. For example, the judgment unit determines that a biased diet or lack of exercise will increase the risk of certain diseases. For example, the judgment unit detects excesses or deficiencies of specific nutrients from the user's dietary data and determines the risk based on the results. Step 4: The distribution unit distributes the risk information determined by the assessment unit in the form of an email newsletter. The distribution unit, for example, sets the email format and regularly distributes risk information to users. The distribution unit can also adjust the distribution frequency according to the user's preferences. For example, the distribution unit can distribute once a week. Step 5: The provider provides the necessary exercise and nutrients based on the information distributed by the delivery department. For example, the provider might suggest a daily step goal or specific strength training exercises. The provider can also provide links to websites where supplements can be purchased to replenish any missing nutrients. For example, the provider might provide links to websites where vitamin D supplements or protein powder can be purchased.

[0124] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0125] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, 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), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0126] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0127] Each of the multiple elements described above, including the collection unit, analysis unit, determination unit, distribution unit, and provision unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit is implemented by the computer 36 and receiving device 38 of the smart device 14 and collects the user's daily diet and exercise data. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the collected data. The determination unit is implemented by the identification processing unit 290 of the data processing unit 12 and determines the risk of future diseases based on the analysis results. The distribution unit is implemented by the communication I / F 44 of the smart device 14 and distributes the determination results in the form of an email newsletter. The provision unit is implemented by the output device 40 of the smart device 14 and provides the necessary amount of exercise and nutrients. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0128] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0129] As shown in Figure 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.

[0130] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0131] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0132] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0133] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0134] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0135] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0136] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0137] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0138] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0139] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0140] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0141] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0142] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0143] Each of the multiple elements described above, including the data collection unit, analysis unit, determination unit, distribution unit, and provision unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit is implemented by the computer 36 and microphone 238 of the smart glasses 214 and collects the user's daily diet and exercise data. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the collected data. The determination unit is implemented by the identification processing unit 290 of the data processing unit 12 and determines the risk of future diseases based on the analysis results. The distribution unit is implemented by the communication I / F 44 of the smart glasses 214 and distributes the determination results in the form of an email newsletter. The provision unit is implemented by the speaker 240 of the smart glasses 214 and provides the necessary amount of exercise and nutrients. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0144] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0145] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0146] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0147] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0148] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0149] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0150] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0151] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0152] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0153] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0154] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0155] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0156] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0157] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0158] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0159] Each of the multiple elements described above, including the collection unit, analysis unit, determination unit, distribution unit, and provision unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit is implemented by the computer 36 and microphone 238 of the headset terminal 314 and collects the user's daily diet and exercise data. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the collected data. The determination unit is implemented by the identification processing unit 290 of the data processing unit 12 and determines the risk of future diseases based on the analysis results. The distribution unit is implemented by the communication I / F 44 of the headset terminal 314 and distributes the determination results in the form of an email newsletter. The provision unit is implemented by the display 343 of the headset terminal 314 and provides the necessary exercise amount and nutrients. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0160] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0161] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0162] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0163] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0164] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0165] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0166] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0167] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0168] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0169] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0170] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0171] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0172] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0173] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0174] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0175] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0176] Each of the multiple elements described above, including the data collection unit, analysis unit, determination unit, distribution unit, and provision unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the data collection unit is implemented by the robot 414's computer 36 and microphone 238 to collect the user's daily diet and exercise data. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 to analyze the collected data. The determination unit is implemented by the identification processing unit 290 of the data processing unit 12 to determine the risk of future illnesses based on the analysis results. The distribution unit is implemented by the robot 414's communication I / F 44 to distribute the determination results in email newsletter format. The provision unit is implemented by the robot 414's speaker 240 to provide the necessary exercise and nutrients. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0177] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0178] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0179] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0180] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0181] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0182] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0183] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0184] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0185] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0186] 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.

[0187] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0188] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0189] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0190] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0191] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0192] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0193] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0194] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0195] (Note 1) A data collection unit that collects data on the user's daily eating habits and exercise, An analysis unit analyzes the data collected by the aforementioned collection unit, A determination unit that determines the future risk of disease based on the data analyzed by the analysis unit, A distribution unit that distributes the risk information determined by the aforementioned determination unit in the form of an email newsletter, The system includes a supply unit that provides the necessary amount of exercise and nutrients based on the information distributed by the aforementioned distribution unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is Users register their daily eating habits and exercise data. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, The registered data is analyzed to determine if a poor diet or lack of exercise continues, increasing the risk of myocardial infarction, stroke, kidney failure, and other related conditions. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned distribution unit, The results of the assessment will be delivered to users via email newsletter. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned distribution unit, We distribute a newsletter that includes articles about the experiences of others who have become ill and their efforts in exercise. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned supply unit is, We provide links to websites where you can purchase supplements to meet your required exercise needs, including the number of steps needed and methods for strength training, as well as information on supplements to replenish any missing nutrients. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is The system estimates the user's emotions and adjusts the timing of collecting dietary and exercise data based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is We analyze the user's past eating habits and exercise data to select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When collecting dietary and exercise data, filtering is performed based on the user's current health status and lifestyle. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting dietary and exercise data, the system prioritizes collecting highly relevant data by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is When collecting dietary and exercise data, we analyze users' social media activity and collect relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, adjust the level of detail based on the importance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the data category. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, the priority of analyses is determined based on the timing of data submission. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, adjust the order of analysis based on the relevance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 19) The determination unit, The system estimates the user's emotions and adjusts the criteria for judgment based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The determination unit, When making a judgment, the accuracy of the judgment is improved by considering the interrelationships between the data. The system described in Appendix 1, characterized by the features described herein. (Note 21) The determination unit, When making a decision, the attribute information of the data submitter is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 22) The determination unit, It estimates the user's emotions and adjusts the order in which the judgment results are displayed based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The determination unit, When making a decision, the geographical distribution of the data is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 24) The determination unit, When making a judgment, we refer to relevant literature to improve the accuracy of the judgment. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned distribution unit, It estimates user sentiment and adjusts how deliverables are displayed based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned distribution unit, During broadcasting, the current broadcast content is optimized by referring to past broadcasting data. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned distribution unit, When distributing data, different distribution methods are applied to each data category. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned distribution unit, It estimates user sentiment and adjusts the importance of delivery based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned distribution unit, At the time of distribution, analyze changes in distribution based on the timing of data submission. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned distribution unit, When distributing data, we analyze the content of the distribution by referring to relevant market data. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned supply unit is, It estimates the user's emotions and prioritizes the content to be provided based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned supply unit is, When providing data, we improve the accuracy of the data by considering the interrelationships between the data. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned supply unit is, When providing data, the data will be provided while taking into account the attribute information of the data submitter. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned supply unit is, It estimates the user's emotions and adjusts how the content is displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned supply unit is, When providing data, we will take into account its geographical distribution. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned supply unit is, When providing data, we refer to relevant literature to improve the accuracy of the data. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0196] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A data collection unit that collects data on the user's daily eating habits and exercise, An analysis unit analyzes the data collected by the aforementioned collection unit, A determination unit that determines the future risk of disease based on the data analyzed by the analysis unit, A distribution unit that distributes the risk information determined by the aforementioned determination unit in the form of an email newsletter, The system includes a supply unit that provides the necessary amount of exercise and nutrients based on the information distributed by the aforementioned distribution unit. A system characterized by the following features.

2. The aforementioned collection unit is Users register their daily eating habits and exercise data. The system according to feature 1.

3. The aforementioned analysis unit, The registered data is analyzed to determine if a poor diet or lack of exercise continues, increasing the risk of myocardial infarction, stroke, kidney failure, and other related conditions. The system according to feature 1.

4. The aforementioned distribution unit, The results of the assessment will be delivered to users via email newsletter. The system according to feature 1.

5. The aforementioned distribution unit, We distribute a newsletter that includes articles about the experiences of others who have become ill and their efforts in exercise. The system according to feature 1.

6. The aforementioned supply unit is, We provide links to websites where you can purchase supplements to meet your required exercise needs, including the number of steps needed and methods for strength training, as well as information on supplements to replenish any missing nutrients. The system according to feature 1.

7. The aforementioned collection unit is The system estimates the user's emotions and adjusts the timing of collecting dietary and exercise data based on those estimated emotions. The system according to feature 1.

8. The aforementioned collection unit is We analyze the user's past eating habits and exercise data to select the optimal data collection method. The system according to feature 1.

9. The aforementioned collection unit is When collecting dietary and exercise data, filtering is performed based on the user's current health status and lifestyle. The system according to feature 1.

10. The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system according to feature 1.

Images