system

Abstract

The system according to this embodiment aims to optimize the user's health status by comprehensively managing environmental information and health data. [Solution] The system according to the embodiment comprises a recording unit, an analysis unit, a checking unit, and a providing unit. The recording unit records environmental information. The analysis unit analyzes the information recorded by the recording unit. The checking unit checks the user's health status based on the analysis results obtained by the analysis unit. The providing unit provides the user with the most suitable information based on the results obtained by the checking unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance as a 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 environmental information and health data are not sufficiently managed integratively to optimize the health state of a user.

[0005] The system according to the embodiment aims to integratively manage environmental information and health data and optimize the health state of a user.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a recording unit, an analysis unit, a checking unit, and a provisioning unit. The recording unit records environmental information. The analysis unit analyzes the information recorded by the recording unit. The checking unit checks the user's health status based on the analysis results obtained by the analysis unit. The provisioning unit provides the user with the most suitable information based on the results obtained by the checking unit. [Effects of the Invention]

[0007] The system according to this embodiment can integrate and manage environmental information and health data, and optimize the user's health status. [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).

[0019] The smart device 14 comprises a computer 36, a reception 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 reception 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 information-integrated management AI system according to an embodiment of the present invention is an application that records environmental information such as weather, temperature, and humidity in the user's place of residence on a daily basis, and records health information such as water intake, meals, steps taken, and sleep duration a few days before a trip. This application stores and analyzes this information in the AI ​​to understand the user's current physical condition. Even at the travel destination, it checks the impact on the user's health by analyzing conditions such as steps taken, temperature, humidity, and sleep duration at the current location, as well as analyzing the nutritional components of meals captured in photos. This provides an information-integrated management AI system that can bring the user closer to their best condition. The purpose of this system is to provide the user with optimal information to enjoy their trip. For example, the user records environmental information such as weather, temperature, and humidity in their place of residence on a daily basis. Next, a few days before a trip, they record health information such as water intake, meals, steps taken, and sleep duration. This information is stored and analyzed in the application's AI. At the travel destination, it analyzes conditions such as steps taken, temperature, humidity, and sleep duration at the current location, as well as analyzing the nutritional components of meals captured in photos. This allows the system to check the impact on the user's health and provide information to help them get closer to their best condition. For example, it can analyze how meals at a travel destination affect the user's health and provide appropriate advice. The goal of this system is to provide users with the best possible information to enjoy their trip. For instance, it can advise on appropriate clothing and activities based on information such as weather, temperature, and humidity at the travel destination. It can also provide advice to optimize the user's health based on information about meals and exercise at the travel destination. In this way, the integrated information management AI system can efficiently manage the user's health and provide optimal information.

[0029] The information-integrated management AI system according to this embodiment comprises a recording unit, an analysis unit, a checking unit, and a provision unit. The recording unit records environmental information. Environmental information includes, but is not limited to, weather, temperature, humidity, and air quality. For example, the recording unit can record environmental information such as the weather, temperature, and humidity of the user's place of residence. The recording unit can also record health information such as water intake, meals, steps taken, and sleep duration starting several days before a trip. For example, the recording unit records the amount of water the user has consumed, the contents of their meals, the number of steps taken, and the sleep duration. The analysis unit analyzes the information recorded by the recording unit. For example, the analysis unit can analyze the recorded environmental information and health information. The analysis unit uses data analysis methods and algorithms to analyze the recorded information in detail. For example, the analysis unit analyzes environmental information such as weather, temperature, and humidity to evaluate its impact on the user's health. The analysis unit also analyzes health information such as water intake, meals, steps taken, and sleep duration to evaluate the user's health. The checking unit checks the user's health status based on the analysis results obtained by the analysis unit. The checking unit evaluates the user's health status in detail, for example, by measuring vital signs or calculating a health score. The checking unit comprehensively evaluates the user's health status and proposes necessary countermeasures. The providing unit provides the user with optimal information based on the results obtained by the checking unit. The providing unit can, for example, provide health advice or lifestyle suggestions. The providing unit provides information to optimize the user's health status and supports the user in maintaining their health. For example, the providing unit advises the user on appropriate clothing and behavior. The providing unit also provides advice to optimize the user's health status based on information about meals and exercise at the travel destination. As a result, the information-integrated management type AI system according to this embodiment can efficiently manage the user's health status and provide optimal information.

[0030] The recording unit records environmental information. This includes, but is not limited to, weather, temperature, humidity, and air quality. For example, the recording unit can record environmental information such as the weather, temperature, and humidity of the user's place of residence. It can also record health information such as water intake, meals, steps taken, and sleep duration starting several days before a trip. For example, the recording unit can record the amount of water consumed, the content of meals, steps taken, and sleep duration. The recording unit works in conjunction with various sensors and devices to collect this information. For example, it can automatically record the user's steps, heart rate, and sleep patterns using a smartwatch or fitness tracker. Users can also manually input meal details and water intake through a smartphone application. Furthermore, the recording unit can automatically import weather forecasts and air quality data obtained from the internet. This allows the recording unit to centrally manage diverse data related to the user's living environment and health status, and maintain detailed records. The recorded data is securely stored on a cloud server and shared with other systems and departments as needed. This allows the recording unit to efficiently collect information necessary for user health management and enrich the system's overall database.

[0031] The analysis unit analyzes the information recorded by the recording unit. For example, the analysis unit can analyze recorded environmental and health information. The analysis unit uses data analysis methods and algorithms to analyze the recorded information in detail. For example, the analysis unit analyzes environmental information such as weather, temperature, and humidity to evaluate its impact on the user's health. The analysis unit also analyzes health information such as water intake, meals, steps taken, and sleep duration to evaluate the user's health. Specifically, the analysis unit uses machine learning algorithms to analyze the correlation between the user's health data and environmental data. For example, it evaluates the impact of changes in temperature and humidity on the user's sleep patterns and proposes appropriate countermeasures. It also analyzes the balance between water intake and exercise to evaluate whether the user is adequately hydrated. Furthermore, based on past data, the analysis unit can predict trends in the user's health status and assess future risks. For example, it analyzes past data on diet and exercise to predict future weight gain and lifestyle-related disease risks. As a result, the analysis unit can comprehensively evaluate the user's health status and provide information to take appropriate measures.

[0032] The checking unit checks the user's health status based on the analysis results obtained by the analysis unit. The checking unit evaluates the user's health status in detail, for example, by measuring vital signs and calculating health scores. The checking unit comprehensively evaluates the user's health status and proposes necessary countermeasures. Specifically, the checking unit periodically measures the user's vital signs such as heart rate, blood pressure, and body temperature, and calculates a health score. The health score is a numerical representation of the user's health status, allowing the user to easily understand their own health condition. In addition, the checking unit creates a detailed report on the user's health status based on the data provided by the analysis unit. The report includes the evaluation results of the user's health status, areas that need improvement, and recommended countermeasures. Furthermore, the checking unit can continuously monitor the user's health status and issue an immediate warning if an abnormality is detected. For example, if the heart rate is abnormally high or blood pressure rises sharply, the unit will issue a warning to the user and urge them to take appropriate measures. In this way, the checking unit can evaluate the user's health status in detail and support a quick and appropriate response.

[0033] The service provider provides users with the most relevant information based on the results obtained by the checking service provider. For example, the service provider can offer health advice and lifestyle suggestions. The service provider provides information to optimize the user's health and supports the user in maintaining their health. For example, the service provider advises users on appropriate clothing and behavior. The service provider also provides advice to optimize the user's health based on information about meals and exercise at the travel destination. Specifically, the service provider proposes meal plans and exercise programs tailored to the user's health. For example, it recommends a balanced diet and moderate exercise to help the user maintain their health while traveling. The service provider can also suggest stress management and relaxation methods tailored to the user's health. Furthermore, the service provider can collect user feedback and continuously improve the accuracy and effectiveness of the information it provides. For example, by recording the results of users following the advice provided and evaluating its effectiveness, more effective advice can be provided. The service provider can also incorporate the latest research and information on users' health and always provide the most up-to-date information. This allows the service provider to provide users with the most relevant information and support them in maintaining their health.

[0034] The recording unit can record environmental information such as weather, temperature, and humidity of the place of residence. For example, the recording unit can record weather information of the place of residence. The recording unit can also record temperature information of the place of residence. The recording unit can also record humidity information of the place of residence. By recording environmental information of the place of residence, the user's health status can be understood more accurately. Place of residence includes, but is not limited to, cities, regions, and addresses. Some or all of the above processing in the recording unit may be performed using, for example, AI, or without using AI. For example, the recording unit can input weather information of the place of residence into a generating AI and have the generating AI record the weather information.

[0035] The recording unit can record health information such as fluid intake, meals, steps taken, and sleep duration starting several days before a trip. For example, the recording unit can record the amount of fluid the user has consumed starting several days before the trip. The recording unit can also record the user's meals starting several days before the trip. The recording unit can also record the user's steps taken starting several days before the trip. The recording unit can also record the user's sleep duration starting several days before the trip. This makes it easier to manage one's health during a trip by recording health information before the trip. Several days before the trip includes, for example, three days before, one week before, etc., but is not limited to such examples. Health information includes, for example, fluid intake, meals, steps taken, and sleep duration, etc., but is not limited to such examples. Some or all of the above processing in the recording unit may be performed using, for example, AI, or not using AI. For example, the recording unit can input health information from several days before the trip into a generating AI and have the generating AI record the health information.

[0036] The analysis unit can analyze recorded environmental and health information. For example, the analysis unit can analyze recorded weather information. For example, the analysis unit can also analyze recorded temperature information. For example, the analysis unit can also analyze recorded humidity information. For example, the analysis unit can also analyze recorded water intake. For example, the analysis unit can also analyze recorded meal content. For example, the analysis unit can also analyze recorded step count. For example, the analysis unit can also analyze recorded sleep duration. This allows for an accurate assessment of the user's health status by analyzing the recorded information. Environmental information includes, but is not limited to, weather, temperature, humidity, and air quality. Health information includes, but is not limited to, water intake, meal content, step count, and sleep duration. Some or all of the above-described processes in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input recorded environmental and health information into a generating AI and have the generating AI perform the analysis of the information.

[0037] The checking unit can check the user's health status based on the analysis results. For example, the checking unit can measure the user's vital signs based on the analysis results. The checking unit can also calculate the user's health score based on the analysis results. For example, the checking unit can comprehensively evaluate the user's health status based on the analysis results. This improves the user's health management by checking their health status based on the analysis results. Checking the health status includes, but is not limited to, measuring vital signs and calculating a health score. Some or all of the above-described processes in the checking unit may be performed using, for example, AI, or not using AI. For example, the checking unit can input the analysis results into a generating AI and have the generating AI perform the health status check.

[0038] The information provider can provide the user with the most suitable information based on the check results. For example, the information provider can provide the user with health advice based on the check results. For example, the information provider can also provide the user with suggestions for lifestyle habits based on the check results. For example, the information provider can also provide the user with advice on appropriate clothing and behavior based on the check results. In this way, the user's health can be optimized by providing the most suitable information based on the check results. The most suitable information includes, but is not limited to, health advice and suggestions for lifestyle habits. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the information provider can input the check results into a generating AI and have the generating AI perform the task of providing the most suitable information.

[0039] The service provider can advise on appropriate clothing and actions based on information such as weather, temperature, and humidity at the travel destination. For example, the service provider can advise the user on appropriate clothing based on weather information at the travel destination. For example, the service provider can also advise the user on appropriate actions based on temperature information at the travel destination. For example, the service provider can also advise the user on appropriate actions based on humidity information at the travel destination. In this way, by providing appropriate advice based on environmental information at the travel destination, it is possible to support the user's comfortable trip. Appropriate clothing and actions include, but are not limited to, examples of clothing types and specific examples of actions. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input weather information at the travel destination into a generating AI and have the generating AI generate advice on appropriate clothing and actions.

[0040] The service provider can provide advice to optimize the user's health based on information about meals and exercise at the travel destination. For example, the service provider can suggest healthy meals to the user based on information about meals at the travel destination. For example, the service provider can also suggest appropriate exercise to the user based on information about exercise at the travel destination. For example, the service provider can also suggest nutritionally balanced meals to the user based on information about meals at the travel destination. In this way, the user's health can be optimized by providing health advice based on information about meals and exercise at the travel destination. Advice to optimize health includes, but is not limited to, meal suggestions and exercise recommendations. Some or all of the above processing in the service provider may be performed using, for example, AI, or not using AI. For example, the service provider can input meal information from the travel destination into a generating AI and have the generating AI generate healthy meal suggestions.

[0041] The recording unit can optimize the recording frequency by referring to the user's past health data during recording. For example, the recording unit can set the optimal recording frequency based on data that the user has frequently recorded in the past. The recording unit can also predict when the user's health status is likely to change and increase the recording frequency at that time. For example, the recording unit can adjust the recording frequency to specific time periods based on the user's past data. This enables efficient data recording by optimizing the recording frequency by referring to past health data. Past health data includes, but is not limited to, data from the past year or specific health indicators. Optimizing the recording frequency includes, but is not limited to, frequency adjustments based on data importance. Some or all of the above processing in the recording unit may be performed using, for example, AI, or not using AI. For example, the recording unit can input the user's past health data into a generating AI and have the generating AI perform the optimization of the recording frequency.

[0042] The recording unit can adjust the recording timing based on the user's daily rhythm. For example, the recording unit can start recording immediately after the user wakes up in the morning. The recording unit can also record before the user goes to bed. The recording unit can also record according to the user's meal times. By adjusting the recording timing based on the daily rhythm, it becomes possible to record data that is tailored to the user's lifestyle. Daily rhythm includes, but is not limited to, sleep patterns and daily activity times. Adjusting the recording timing includes, but is not limited to, recording during specific time periods. Some or all of the above processing in the recording unit may be performed using, for example, AI, or not using AI. For example, the recording unit can input the user's daily rhythm data into a generating AI and have the generating AI perform the adjustment of the recording timing.

[0043] The recording unit can prioritize recording highly relevant environmental information based on the user's geographical location information during recording. For example, if the user is traveling, the recording unit will prioritize recording the weather, temperature, and humidity of that area. For example, if the user is at home, the recording unit can prioritize recording environmental information of their place of residence. For example, if the user is on the move, the recording unit can prioritize recording environmental information of their destination. This enables data recording tailored to the user's situation by recording highly relevant environmental information based on geographical location information. Geographical location information includes, but is not limited to, GPS data and address information. Highly relevant environmental information includes, but is not limited to, weather information and air quality information of the current location. Some or all of the above processing in the recording unit may be performed using, for example, AI, or not using AI. For example, the recording unit can input the user's geographical location information into a generating AI and have the generating AI perform the recording of highly relevant environmental information.

[0044] The recording unit can analyze the user's social media activity and record relevant health information during recording. For example, the recording unit can record nutritional information based on meal information shared by the user on social media. The recording unit can also record steps and exercise volume based on exercise information shared by the user on social media. The recording unit can also record sleep duration based on sleep information shared by the user on social media. This enables data recording that is tailored to the user's behavior by recording health information based on social media activity. Social media activity includes, but is not limited to, posts and the number of likes. Relevant health information includes, but is not limited to, stress levels and exercise volume. Some or all of the above processing in the recording unit may be performed using, for example, AI, or not using AI. For example, the recording unit can input the user's social media activity data into a generating AI and have the generating AI record the relevant health information.

[0045] The analysis unit can adjust the level of detail of the analysis based on the importance of the recorded information during the analysis. For example, the analysis unit can perform a detailed analysis on information of high importance. For example, the analysis unit can perform a simplified analysis on information of low importance. For example, the analysis unit can perform an analysis with an appropriate level of detail on information of moderate importance. This allows for efficient data analysis by adjusting the level of detail of the analysis based on the importance of the information. The importance of information includes, but is not limited to, data reliability and impact. Adjusting the level of detail of the analysis includes, but is not limited to, conditions for performing a detailed analysis. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input the importance data of the recorded information into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0046] The analysis unit can apply different analytical methods depending on the category of information during analysis. For example, the analysis unit can apply a meteorological data analysis method to environmental information. For example, the analysis unit can also apply a health data analysis method to health information. For example, the analysis unit can also apply a nutritional component analysis method to dietary information. By applying different analytical methods depending on the category of information, more accurate data analysis becomes possible. Categories of information include, but are not limited to, health information and environmental information. Different analytical methods include, but are not limited to, statistical analysis and machine learning. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input information category data into a generating AI and have the generating AI execute the application of different analytical methods.

[0047] The analysis unit can determine the priority of analysis based on the timing of information submission during the analysis process. For example, the analysis unit may prioritize the analysis of the most recent information. The analysis unit may also postpone the analysis of older information. The analysis unit may also prioritize the analysis of information submitted during a specific period. This enables efficient data analysis by determining the priority of analysis based on the timing of information submission. The timing of information submission includes, but is not limited to, the submission date and time. Determining the priority of analysis includes, but is not limited to, prioritizing based on submission timing. Some or all of the above processes in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input information submission timing data into a generating AI and have the generating AI perform the determination of analysis priorities.

[0048] The analysis unit can adjust the order of analysis based on the relevance of the information during the analysis process. For example, the analysis unit may prioritize the analysis of highly relevant information. For example, the analysis unit may postpone the analysis of less relevant information. For example, the analysis unit may prioritize the analysis of information related to a specific category. This allows for efficient data analysis by adjusting the order of analysis based on the relevance of the information. The relevance of the information includes, but is not limited to, data correlation and relevance. Adjusting the order of analysis includes, but is not limited to, ordering based on relevance. Some or all of the above processes in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input information relevance data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0049] The checking unit can improve the accuracy of the check by referring to past health data during the check. For example, the checking unit can accurately check the user's current health status based on the user's past health data. The checking unit can also predict specific health risks from the user's past health data and reflect them in the check. For example, the checking unit can analyze the user's past health data and optimize the check criteria. This makes it possible to evaluate the health status more accurately by improving the accuracy of the check by referring to past health data. Past health data includes, but is not limited to, data from the past year or specific health indicators. Improving the accuracy of the check includes, but is not limited to, methods for comparing data or techniques for improving accuracy. Some or all of the above processing in the checking unit may be performed using, for example, AI, or not using AI. For example, the checking unit can input past health data into a generating AI and have the generating AI perform the improvement of the check accuracy.

[0050] The checking unit can evaluate the user's health status while considering the user's attribute information. For example, the checking unit evaluates the health status based on attribute information such as the user's age, gender, and weight. The checking unit can also evaluate the health status while considering attribute information such as the user's lifestyle and occupation. The checking unit can also evaluate health risks based on the user's genetic information. This enables more personalized health management by evaluating the health status while considering the user's attribute information. Attribute information includes, but is not limited to, age, gender, and occupation. Evaluating the health status includes, but is not limited to, measuring vital signs and calculating health scores. Some or all of the above-described processes in the checking unit may be performed using, for example, AI, or without AI. For example, the checking unit can input the user's attribute information into a generating AI and have the generating AI perform the health status evaluation.

[0051] The checking unit can evaluate health status while considering geographical distribution during the check. For example, the checking unit can evaluate health status while considering the health risks of the area where the user resides. For example, the checking unit can also evaluate health status while considering the health risks of the area the user is traveling to. For example, the checking unit can also evaluate health status while considering the health risks of the area the user is traveling to. This makes it possible to evaluate health status that reflects region-specific health risks by considering geographical distribution. Geographical distribution includes, but is not limited to, regional health risks and geographical characteristics. Evaluating health status includes, but is not limited to, measuring vital signs and calculating health scores. Some or all of the above processing in the checking unit may be performed using, for example, AI, or not using AI. For example, the checking unit can input geographical distribution data into a generating AI and have the generating AI perform the health status evaluation.

[0052] The checking unit can improve the accuracy of its checks by referring to relevant literature during the check process. For example, the checking unit can update its check criteria by referring to the latest medical literature. The checking unit can also improve the accuracy of its checks by referring to relevant research papers. The checking unit can also optimize its check criteria by referring to expert opinions. This makes it possible to perform health assessments that reflect the latest knowledge by improving the accuracy of checks by referring to relevant literature. Relevant literature includes, but is not limited to, medical papers and research reports. Improving the accuracy of checks includes, but is not limited to, methods for comparing data and techniques for improving accuracy. Some or all of the above processing in the checking unit may be performed using, for example, AI, or not using AI. For example, the checking unit can input relevant literature data into a generating AI and have the generating AI perform the improvement of check accuracy.

[0053] The information provider can adjust the level of detail provided based on the importance of the information at the time of provision. For example, the provider can provide detailed information for highly important information. For example, the provider can provide simplified information for less important information. For example, the provider can provide information with an appropriate level of detail for moderately important information. This allows for efficient information provision by adjusting the level of detail based on the importance of the information. The importance of information includes, but is not limited to, data reliability and impact. Adjusting the level of detail includes, but is not limited to, adjusting the level of detail based on importance. Some or all of the above processing in the information provider may be performed using, for example, AI, or not using AI. For example, the information provider can input information importance data into a generating AI and have the generating AI perform the adjustment of the level of detail of the provision.

[0054] The information delivery unit can apply different delivery methods depending on the category of information at the time of delivery. For example, the delivery unit can apply a delivery method based on weather data to environmental information. For example, the delivery unit can also apply a delivery method based on health data to health information. For example, the delivery unit can also apply a delivery method based on nutritional component data to dietary information. By applying different delivery methods depending on the category of information, it becomes possible to provide more appropriate information. Information categories include, but are not limited to, health information and environmental information. Some or all of the processing described above in the delivery unit may be performed using, for example, AI, or not using AI. For example, the delivery unit can input information category data into a generating AI and have the generating AI execute the application of different delivery methods.

[0055] The information provider can determine the priority of information provision based on the submission date at the time of provision. For example, the provider may prioritize providing the most recent information. The provider may also postpone providing older information. The provider may also prioritize providing information submitted during a specific period. This enables efficient information provision by determining the priority of information provision based on the submission date. The submission date of information includes, but is not limited to, the submission date and time. Determining the priority of information provision includes, but is not limited to, prioritizing based on the submission date. Some or all of the above processing in the information provider may be performed using, for example, AI, or not using AI. For example, the information provider can input information submission date data into a generating AI and have the generating AI perform the determination of the priority of information provision.

[0056] The information provider can adjust the order of information delivery based on the relevance of the information. For example, the provider may prioritize providing highly relevant information. For example, the provider may postpone providing less relevant information. For example, the provider may prioritize providing information related to a specific category. This allows for efficient information delivery by adjusting the order of delivery based on the relevance of the information. The relevance of the information includes, but is not limited to, data correlation and relevance. Adjusting the order of delivery includes, but is not limited to, ordering based on relevance. Some or all of the above processing in the information provider may be performed using, for example, AI, or not using AI. For example, the information provider can input information relevance data into a generating AI and have the generating AI perform the adjustment of the order of delivery.

[0057] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0058] The recording unit can optimize health management at travel destinations by referring to the user's past travel data. For example, it can identify health risks that the user should pay particular attention to based on health information from past trips. It can also analyze eating and exercise patterns from past trips and suggest an optimal health management plan. It can also optimize sleep management during travel by referring to sleep patterns from past trips. This allows for more effective health management of the user by utilizing past travel data. Past travel data includes, but is not limited to, travel data from the past year or data from specific travel destinations. Some or all of the above processing in the recording unit may be performed using AI or not. For example, the recording unit can input past travel data into a generating AI and have the generating AI perform the optimization of health management.

[0059] The checking unit can predict health risks during travel by referring to the user's past health data. For example, it can identify health risks that are likely to occur during travel based on past health data. It can also predict health risks under specific environmental conditions from past health data. It can also analyze past health data to optimize a health management plan during travel. This allows users to predict health risks during travel and take appropriate measures by utilizing past health data. Past health data includes, but is not limited to, data from the past year or specific health indicators. Some or all of the above processing in the checking unit may be performed using AI or not. For example, the checking unit can input past health data into a generating AI and have the generating AI perform health risk predictions.

[0060] The recording unit can analyze the user's social media activity and record information useful for health management during travel. For example, it can record nutritional information based on meal information shared by the user on social media. It can also record steps and exercise volume based on exercise information shared by the user on social media. It can also record sleep duration based on sleep information shared by the user on social media. This allows for data recording tailored to the user's behavior by recording health information based on social media activity. Social media activity includes, but is not limited to, posts and the number of likes. Relevant health information includes, but is not limited to, stress levels and exercise volume. Some or all of the above processing in the recording unit may be performed using AI or not. For example, the recording unit can input the user's social media activity data into a generating AI and have the generating AI record the relevant health information.

[0061] The checking unit can assess health risks at a travel destination based on the user's geographical location information. For example, if the user is at a travel destination, the assessment can take into account the health risks of that area. If the user is at home, the assessment can also take into account the health risks of their place of residence. If the user is on the move, the assessment can also take into account the health risks of their destination. This enables health management tailored to the user's situation by assessing health risks based on geographical location information. Geographical location information includes, but is not limited to, GPS data and address information. Health risks include, but are not limited to, regional health risks and geographical characteristics. Some or all of the above processing in the checking unit may be performed using AI or not. For example, the checking unit can input geographical location information into a generating AI and have the generating AI perform the health risk assessment.

[0062] The service provider can suggest activities at a travel destination by referring to the user's past travel data. For example, it can suggest activities that the user will enjoy based on activity information from past trips. It can also analyze the user's preferences and interests from past trips and suggest the most suitable activities. It can also refer to feedback from past trips and suggest activities that the user will be satisfied with. In this way, past travel data can be used to provide activities that match the user's preferences. Past travel data includes, but is not limited to, travel data from the past year or data from a specific travel destination. Some or all of the processing described above in the service provider may be performed using AI or not. For example, the service provider can input past travel data into a generating AI and have the generating AI perform activity suggestions.

[0063] The following briefly describes the processing flow for example form 1.

[0064] Step 1: The recording unit records environmental information. Environmental information includes weather, temperature, humidity, and air quality. The recording unit can record environmental information such as the weather, temperature, and humidity of the place of residence. It can also record health information such as water intake, meals, steps taken, and sleep duration starting several days before a trip. For example, the recording unit records the amount of water the user has consumed, the content of their meals, steps taken, and sleep duration. Step 2: The analysis unit analyzes the information recorded by the recording unit. The analysis unit can analyze the recorded environmental and health information. It uses data analysis methods and algorithms to analyze the recorded information in detail. For example, it analyzes environmental information such as weather, temperature, and humidity to evaluate its impact on the user's health. It also analyzes health information such as water intake, meals, steps taken, and sleep duration to evaluate the user's health. Step 3: The checking unit checks the user's health status based on the analysis results obtained by the analysis unit. The checking unit evaluates the user's health status in detail using vital sign measurements and health score calculation methods. It comprehensively evaluates the user's health status and proposes necessary countermeasures. Step 4: The provision unit provides the user with the most relevant information based on the results obtained by the checking unit. The provision unit can offer health advice and lifestyle suggestions. It provides information to optimize the user's health condition and supports the user in maintaining their health. For example, it can advise on appropriate clothing and behavior, and provide advice to optimize the user's health condition based on information on diet and exercise at their travel destination.

[0065] (Example of form 2) The information-integrated management AI system according to an embodiment of the present invention is an application that records environmental information such as weather, temperature, and humidity in the user's place of residence on a daily basis, and records health information such as water intake, meals, steps taken, and sleep duration a few days before a trip. This application stores and analyzes this information in the AI ​​to understand the user's current physical condition. Even at the travel destination, it checks the impact on the user's health by analyzing conditions such as steps taken, temperature, humidity, and sleep duration at the current location, as well as analyzing the nutritional components of meals captured in photos. This provides an information-integrated management AI system that can bring the user closer to their best condition. The purpose of this system is to provide the user with optimal information to enjoy their trip. For example, the user records environmental information such as weather, temperature, and humidity in their place of residence on a daily basis. Next, a few days before a trip, they record health information such as water intake, meals, steps taken, and sleep duration. This information is stored and analyzed in the application's AI. At the travel destination, it analyzes conditions such as steps taken, temperature, humidity, and sleep duration at the current location, as well as analyzing the nutritional components of meals captured in photos. This allows the system to check the impact on the user's health and provide information to help them get closer to their best condition. For example, it can analyze how meals at a travel destination affect the user's health and provide appropriate advice. The goal of this system is to provide users with the best possible information to enjoy their trip. For instance, it can advise on appropriate clothing and activities based on information such as weather, temperature, and humidity at the travel destination. It can also provide advice to optimize the user's health based on information about meals and exercise at the travel destination. In this way, the integrated information management AI system can efficiently manage the user's health and provide optimal information.

[0066] The information-integrated management AI system according to this embodiment comprises a recording unit, an analysis unit, a checking unit, and a provision unit. The recording unit records environmental information. Environmental information includes, but is not limited to, weather, temperature, humidity, and air quality. For example, the recording unit can record environmental information such as the weather, temperature, and humidity of the user's place of residence. The recording unit can also record health information such as water intake, meals, steps taken, and sleep duration starting several days before a trip. For example, the recording unit records the amount of water the user has consumed, the contents of their meals, the number of steps taken, and the sleep duration. The analysis unit analyzes the information recorded by the recording unit. For example, the analysis unit can analyze the recorded environmental information and health information. The analysis unit uses data analysis methods and algorithms to analyze the recorded information in detail. For example, the analysis unit analyzes environmental information such as weather, temperature, and humidity to evaluate its impact on the user's health. The analysis unit also analyzes health information such as water intake, meals, steps taken, and sleep duration to evaluate the user's health. The checking unit checks the user's health status based on the analysis results obtained by the analysis unit. The checking unit evaluates the user's health status in detail, for example, by measuring vital signs or calculating a health score. The checking unit comprehensively evaluates the user's health status and proposes necessary countermeasures. The providing unit provides the user with optimal information based on the results obtained by the checking unit. The providing unit can, for example, provide health advice or lifestyle suggestions. The providing unit provides information to optimize the user's health status and supports the user in maintaining their health. For example, the providing unit advises the user on appropriate clothing and behavior. The providing unit also provides advice to optimize the user's health status based on information about meals and exercise at the travel destination. As a result, the information-integrated management type AI system according to this embodiment can efficiently manage the user's health status and provide optimal information.

[0067] The recording unit records environmental information. This includes, but is not limited to, weather, temperature, humidity, and air quality. For example, the recording unit can record environmental information such as the weather, temperature, and humidity of the user's place of residence. It can also record health information such as water intake, meals, steps taken, and sleep duration starting several days before a trip. For example, the recording unit can record the amount of water consumed, the content of meals, steps taken, and sleep duration. The recording unit works in conjunction with various sensors and devices to collect this information. For example, it can automatically record the user's steps, heart rate, and sleep patterns using a smartwatch or fitness tracker. Users can also manually input meal details and water intake through a smartphone application. Furthermore, the recording unit can automatically import weather forecasts and air quality data obtained from the internet. This allows the recording unit to centrally manage diverse data related to the user's living environment and health status, and maintain detailed records. The recorded data is securely stored on a cloud server and shared with other systems and departments as needed. This allows the recording unit to efficiently collect information necessary for user health management and enrich the system's overall database.

[0068] The analysis unit analyzes the information recorded by the recording unit. For example, the analysis unit can analyze recorded environmental and health information. The analysis unit uses data analysis methods and algorithms to analyze the recorded information in detail. For example, the analysis unit analyzes environmental information such as weather, temperature, and humidity to evaluate its impact on the user's health. The analysis unit also analyzes health information such as water intake, meals, steps taken, and sleep duration to evaluate the user's health. Specifically, the analysis unit uses machine learning algorithms to analyze the correlation between the user's health data and environmental data. For example, it evaluates the impact of changes in temperature and humidity on the user's sleep patterns and proposes appropriate countermeasures. It also analyzes the balance between water intake and exercise to evaluate whether the user is adequately hydrated. Furthermore, based on past data, the analysis unit can predict trends in the user's health status and assess future risks. For example, it analyzes past data on diet and exercise to predict future weight gain and lifestyle-related disease risks. As a result, the analysis unit can comprehensively evaluate the user's health status and provide information to take appropriate measures.

[0069] The checking unit checks the user's health status based on the analysis results obtained by the analysis unit. The checking unit evaluates the user's health status in detail, for example, by measuring vital signs and calculating health scores. The checking unit comprehensively evaluates the user's health status and proposes necessary countermeasures. Specifically, the checking unit periodically measures the user's vital signs such as heart rate, blood pressure, and body temperature, and calculates a health score. The health score is a numerical representation of the user's health status, allowing the user to easily understand their own health condition. In addition, the checking unit creates a detailed report on the user's health status based on the data provided by the analysis unit. The report includes the evaluation results of the user's health status, areas that need improvement, and recommended countermeasures. Furthermore, the checking unit can continuously monitor the user's health status and issue an immediate warning if an abnormality is detected. For example, if the heart rate is abnormally high or blood pressure rises sharply, the unit will issue a warning to the user and urge them to take appropriate measures. In this way, the checking unit can evaluate the user's health status in detail and support a quick and appropriate response.

[0070] The service provider provides users with the most relevant information based on the results obtained by the checking service provider. For example, the service provider can offer health advice and lifestyle suggestions. The service provider provides information to optimize the user's health and supports the user in maintaining their health. For example, the service provider advises users on appropriate clothing and behavior. The service provider also provides advice to optimize the user's health based on information about meals and exercise at the travel destination. Specifically, the service provider proposes meal plans and exercise programs tailored to the user's health. For example, it recommends a balanced diet and moderate exercise to help the user maintain their health while traveling. The service provider can also suggest stress management and relaxation methods tailored to the user's health. Furthermore, the service provider can collect user feedback and continuously improve the accuracy and effectiveness of the information it provides. For example, by recording the results of users following the advice provided and evaluating its effectiveness, more effective advice can be provided. The service provider can also incorporate the latest research and information on users' health and always provide the most up-to-date information. This allows the service provider to provide users with the most relevant information and support them in maintaining their health.

[0071] The recording unit can record environmental information such as weather, temperature, and humidity of the place of residence. For example, the recording unit can record weather information of the place of residence. The recording unit can also record temperature information of the place of residence. The recording unit can also record humidity information of the place of residence. By recording environmental information of the place of residence, the user's health status can be understood more accurately. Place of residence includes, but is not limited to, cities, regions, and addresses. Some or all of the above processing in the recording unit may be performed using, for example, AI, or without using AI. For example, the recording unit can input weather information of the place of residence into a generating AI and have the generating AI record the weather information.

[0072] The recording unit can record health information such as fluid intake, meals, steps taken, and sleep duration starting several days before a trip. For example, the recording unit can record the amount of fluid the user has consumed starting several days before the trip. The recording unit can also record the user's meals starting several days before the trip. The recording unit can also record the user's steps taken starting several days before the trip. The recording unit can also record the user's sleep duration starting several days before the trip. This makes it easier to manage one's health during a trip by recording health information before the trip. Several days before the trip includes, for example, three days before, one week before, etc., but is not limited to such examples. Health information includes, for example, fluid intake, meals, steps taken, and sleep duration, etc., but is not limited to such examples. Some or all of the above processing in the recording unit may be performed using, for example, AI, or not using AI. For example, the recording unit can input health information from several days before the trip into a generating AI and have the generating AI record the health information.

[0073] The analysis unit can analyze recorded environmental and health information. For example, the analysis unit can analyze recorded weather information. For example, the analysis unit can also analyze recorded temperature information. For example, the analysis unit can also analyze recorded humidity information. For example, the analysis unit can also analyze recorded water intake. For example, the analysis unit can also analyze recorded meal content. For example, the analysis unit can also analyze recorded step count. For example, the analysis unit can also analyze recorded sleep duration. This allows for an accurate assessment of the user's health status by analyzing the recorded information. Environmental information includes, but is not limited to, weather, temperature, humidity, and air quality. Health information includes, but is not limited to, water intake, meal content, step count, and sleep duration. Some or all of the above-described processes in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input recorded environmental and health information into a generating AI and have the generating AI perform the analysis of the information.

[0074] The checking unit can check the user's health status based on the analysis results. For example, the checking unit can measure the user's vital signs based on the analysis results. The checking unit can also calculate the user's health score based on the analysis results. For example, the checking unit can comprehensively evaluate the user's health status based on the analysis results. This improves the user's health management by checking their health status based on the analysis results. Checking the health status includes, but is not limited to, measuring vital signs and calculating a health score. Some or all of the above-described processes in the checking unit may be performed using, for example, AI, or not using AI. For example, the checking unit can input the analysis results into a generating AI and have the generating AI perform the health status check.

[0075] The information provider can provide the user with the most suitable information based on the check results. For example, the information provider can provide the user with health advice based on the check results. For example, the information provider can also provide the user with suggestions for lifestyle habits based on the check results. For example, the information provider can also provide the user with advice on appropriate clothing and behavior based on the check results. In this way, the user's health can be optimized by providing the most suitable information based on the check results. The most suitable information includes, but is not limited to, health advice and suggestions for lifestyle habits. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the information provider can input the check results into a generating AI and have the generating AI perform the task of providing the most suitable information.

[0076] The service provider can advise on appropriate clothing and actions based on information such as weather, temperature, and humidity at the travel destination. For example, the service provider can advise the user on appropriate clothing based on weather information at the travel destination. For example, the service provider can also advise the user on appropriate actions based on temperature information at the travel destination. For example, the service provider can also advise the user on appropriate actions based on humidity information at the travel destination. In this way, by providing appropriate advice based on environmental information at the travel destination, it is possible to support the user's comfortable trip. Appropriate clothing and actions include, but are not limited to, examples of clothing types and specific examples of actions. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input weather information at the travel destination into a generating AI and have the generating AI generate advice on appropriate clothing and actions.

[0077] The service provider can provide advice to optimize the user's health based on information about meals and exercise at the travel destination. For example, the service provider can suggest healthy meals to the user based on information about meals at the travel destination. For example, the service provider can also suggest appropriate exercise to the user based on information about exercise at the travel destination. For example, the service provider can also suggest nutritionally balanced meals to the user based on information about meals at the travel destination. In this way, the user's health can be optimized by providing health advice based on information about meals and exercise at the travel destination. Advice to optimize health includes, but is not limited to, meal suggestions and exercise recommendations. Some or all of the above processing in the service provider may be performed using, for example, AI, or not using AI. For example, the service provider can input meal information from the travel destination into a generating AI and have the generating AI generate healthy meal suggestions.

[0078] The recording unit can estimate the user's emotions and adjust the type of information recorded based on the estimated emotions. For example, if the user is stressed, the recording unit may prioritize recording relaxing environmental information. If the user is relaxed, the recording unit may also record detailed health information. If the user is tired, the recording unit may record only basic information. This allows for the recording of more appropriate information by adjusting the type of information recorded based on the user's emotions. 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 recording unit may be performed using AI or not. For example, the recording unit can input user emotion data into a generative AI and have the generative AI adjust the type of information to be recorded.

[0079] The recording unit can optimize the recording frequency by referring to the user's past health data during recording. For example, the recording unit can set the optimal recording frequency based on data that the user has frequently recorded in the past. The recording unit can also predict when the user's health status is likely to change and increase the recording frequency at that time. For example, the recording unit can adjust the recording frequency to specific time periods based on the user's past data. This enables efficient data recording by optimizing the recording frequency by referring to past health data. Past health data includes, but is not limited to, data from the past year or specific health indicators. Optimizing the recording frequency includes, but is not limited to, frequency adjustments based on data importance. Some or all of the above processing in the recording unit may be performed using, for example, AI, or not using AI. For example, the recording unit can input the user's past health data into a generating AI and have the generating AI perform the optimization of the recording frequency.

[0080] The recording unit can adjust the recording timing based on the user's daily rhythm. For example, the recording unit can start recording immediately after the user wakes up in the morning. The recording unit can also record before the user goes to bed. The recording unit can also record according to the user's meal times. By adjusting the recording timing based on the daily rhythm, it becomes possible to record data that is tailored to the user's lifestyle. Daily rhythm includes, but is not limited to, sleep patterns and daily activity times. Adjusting the recording timing includes, but is not limited to, recording during specific time periods. Some or all of the above processing in the recording unit may be performed using, for example, AI, or not using AI. For example, the recording unit can input the user's daily rhythm data into a generating AI and have the generating AI perform the adjustment of the recording timing.

[0081] The recording unit can estimate the user's emotions and determine the priority of information to record based on the estimated emotions. For example, if the user is stressed, the recording unit may prioritize recording information that has a relaxing effect. For example, if the user is relaxed, the recording unit may prioritize recording detailed health information. For example, if the user is tired, the recording unit may prioritize recording simple information. In this way, important information can be prioritized by determining the priority of information to record based on the user's emotions. 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 recording unit may be performed using AI or not using AI. For example, the recording unit can input user emotion data into a generative AI and have the generative AI determine the priority of information to record.

[0082] The recording unit can prioritize recording highly relevant environmental information based on the user's geographical location information during recording. For example, if the user is traveling, the recording unit will prioritize recording the weather, temperature, and humidity of that area. For example, if the user is at home, the recording unit can prioritize recording environmental information of their place of residence. For example, if the user is on the move, the recording unit can prioritize recording environmental information of their destination. This enables data recording tailored to the user's situation by recording highly relevant environmental information based on geographical location information. Geographical location information includes, but is not limited to, GPS data and address information. Highly relevant environmental information includes, but is not limited to, weather information and air quality information of the current location. Some or all of the above processing in the recording unit may be performed using, for example, AI, or not using AI. For example, the recording unit can input the user's geographical location information into a generating AI and have the generating AI perform the recording of highly relevant environmental information.

[0083] The recording unit can analyze the user's social media activity and record relevant health information during recording. For example, the recording unit can record nutritional information based on meal information shared by the user on social media. The recording unit can also record steps and exercise volume based on exercise information shared by the user on social media. The recording unit can also record sleep duration based on sleep information shared by the user on social media. This enables data recording that is tailored to the user's behavior by recording health information based on social media activity. Social media activity includes, but is not limited to, posts and the number of likes. Relevant health information includes, but is not limited to, stress levels and exercise volume. Some or all of the above processing in the recording unit may be performed using, for example, AI, or not using AI. For example, the recording unit can input the user's social media activity data into a generating AI and have the generating AI record the relevant health information.

[0084] The analysis unit can estimate the user's emotions and adjust the analysis algorithm based on the estimated emotions. For example, if the user is stressed, the analysis unit can apply an analysis algorithm that is effective in reducing stress. For example, if the user is relaxed, the analysis unit can also apply an algorithm that analyzes detailed health information. For example, if the user is tired, the analysis unit can also apply a simpler analysis algorithm. By adjusting the analysis algorithm based on the user's emotions, more appropriate analysis becomes possible. 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, for example, or without AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI adjust the analysis algorithm.

[0085] The analysis unit can adjust the level of detail of the analysis based on the importance of the recorded information during the analysis. For example, the analysis unit can perform a detailed analysis on information of high importance. For example, the analysis unit can perform a simplified analysis on information of low importance. For example, the analysis unit can perform an analysis with an appropriate level of detail on information of moderate importance. This allows for efficient data analysis by adjusting the level of detail of the analysis based on the importance of the information. The importance of information includes, but is not limited to, data reliability and impact. Adjusting the level of detail of the analysis includes, but is not limited to, conditions for performing a detailed analysis. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input the importance data of the recorded information into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0086] The analysis unit can apply different analytical methods depending on the category of information during analysis. For example, the analysis unit can apply a meteorological data analysis method to environmental information. For example, the analysis unit can also apply a health data analysis method to health information. For example, the analysis unit can also apply a nutritional component analysis method to dietary information. By applying different analytical methods depending on the category of information, more accurate data analysis becomes possible. Categories of information include, but are not limited to, health information and environmental information. Different analytical methods include, but are not limited to, statistical analysis and machine learning. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input information category data into a generating AI and have the generating AI execute the application of different analytical methods.

[0087] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is nervous, the analysis unit can provide a simple and highly visible display method. For example, if the user is relaxed, the analysis unit can also provide a display method that includes detailed information. For example, if the user is in a hurry, the analysis unit can also provide a display method that gets straight to the point. By adjusting the display method based on the user's emotions, it becomes possible to provide more appropriate information. 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, for example, or not using AI. For example, the analysis unit can input user emotion data into the generative AI and have the generative AI adjust the display method of the analysis results.

[0088] The analysis unit can determine the priority of analysis based on the timing of information submission during the analysis process. For example, the analysis unit may prioritize the analysis of the most recent information. The analysis unit may also postpone the analysis of older information. The analysis unit may also prioritize the analysis of information submitted during a specific period. This enables efficient data analysis by determining the priority of analysis based on the timing of information submission. The timing of information submission includes, but is not limited to, the submission date and time. Determining the priority of analysis includes, but is not limited to, prioritizing based on submission timing. Some or all of the above processes in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input information submission timing data into a generating AI and have the generating AI perform the determination of analysis priorities.

[0089] The analysis unit can adjust the order of analysis based on the relevance of the information during the analysis process. For example, the analysis unit may prioritize the analysis of highly relevant information. For example, the analysis unit may postpone the analysis of less relevant information. For example, the analysis unit may prioritize the analysis of information related to a specific category. This allows for efficient data analysis by adjusting the order of analysis based on the relevance of the information. The relevance of the information includes, but is not limited to, data correlation and relevance. Adjusting the order of analysis includes, but is not limited to, ordering based on relevance. Some or all of the above processes in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input information relevance data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0090] The checking unit can estimate the user's emotions and adjust the health status check criteria based on the estimated user emotions. For example, if the user is stressed, the checking unit will apply check criteria that focus on stress reduction. For example, if the user is relaxed, the checking unit may also apply detailed health status check criteria. For example, if the user is tired, the checking unit may also apply simplified check criteria. This allows for a more accurate assessment of health status by adjusting the check criteria based on the user's emotions. 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 checking unit may be performed using AI or not using AI. For example, the checking unit can input user emotion data into a generative AI and have the generative AI adjust the health status check criteria.

[0091] The checking unit can improve the accuracy of the check by referring to past health data during the check. For example, the checking unit can accurately check the user's current health status based on the user's past health data. The checking unit can also predict specific health risks from the user's past health data and reflect them in the check. For example, the checking unit can analyze the user's past health data and optimize the check criteria. This makes it possible to evaluate the health status more accurately by improving the accuracy of the check by referring to past health data. Past health data includes, but is not limited to, data from the past year or specific health indicators. Improving the accuracy of the check includes, but is not limited to, methods for comparing data or techniques for improving accuracy. Some or all of the above processing in the checking unit may be performed using, for example, AI, or not using AI. For example, the checking unit can input past health data into a generating AI and have the generating AI perform the improvement of the check accuracy.

[0092] The checking unit can evaluate the user's health status while considering the user's attribute information. For example, the checking unit evaluates the health status based on attribute information such as the user's age, gender, and weight. The checking unit can also evaluate the health status while considering attribute information such as the user's lifestyle and occupation. The checking unit can also evaluate health risks based on the user's genetic information. This enables more personalized health management by evaluating the health status while considering the user's attribute information. Attribute information includes, but is not limited to, age, gender, and occupation. Evaluating the health status includes, but is not limited to, measuring vital signs and calculating health scores. Some or all of the above-described processes in the checking unit may be performed using, for example, AI, or without AI. For example, the checking unit can input the user's attribute information into a generating AI and have the generating AI perform the health status evaluation.

[0093] The checking unit can estimate the user's emotions and adjust the display order of the check results based on the estimated emotions. For example, if the user is nervous, the checking unit can display important results first. If the user is relaxed, the checking unit can also display detailed results in order. If the user is in a hurry, the checking unit can also display results that summarize the key points first. By adjusting the display order based on the user's emotions, it becomes possible to provide more appropriate information. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. The 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 checking unit may be performed using AI, or not using AI. For example, the checking unit can input user emotion data into the generative AI and have the generative AI adjust the display order of the check results.

[0094] The checking unit can evaluate health status while considering geographical distribution during the check. For example, the checking unit can evaluate health status while considering the health risks of the area where the user resides. For example, the checking unit can also evaluate health status while considering the health risks of the area the user is traveling to. For example, the checking unit can also evaluate health status while considering the health risks of the area the user is traveling to. This makes it possible to evaluate health status that reflects region-specific health risks by considering geographical distribution. Geographical distribution includes, but is not limited to, regional health risks and geographical characteristics. Evaluating health status includes, but is not limited to, measuring vital signs and calculating health scores. Some or all of the above processing in the checking unit may be performed using, for example, AI, or not using AI. For example, the checking unit can input geographical distribution data into a generating AI and have the generating AI perform the health status evaluation.

[0095] The checking unit can improve the accuracy of its checks by referring to relevant literature during the check process. For example, the checking unit can update its check criteria by referring to the latest medical literature. The checking unit can also improve the accuracy of its checks by referring to relevant research papers. The checking unit can also optimize its check criteria by referring to expert opinions. This makes it possible to perform health assessments that reflect the latest knowledge by improving the accuracy of checks by referring to relevant literature. Relevant literature includes, but is not limited to, medical papers and research reports. Improving the accuracy of checks includes, but is not limited to, methods for comparing data and techniques for improving accuracy. Some or all of the above processing in the checking unit may be performed using, for example, AI, or not using AI. For example, the checking unit can input relevant literature data into a generating AI and have the generating AI perform the improvement of check accuracy.

[0096] The information provider can estimate the user's emotions and adjust the way the information is presented based on the estimated emotions. For example, if the user is tense, the information provider will present the information in a calm manner. For example, if the user is relaxed, the information provider may also present detailed information. For example, if the user is in a hurry, the information provider may also present the information in a concise manner. By adjusting the way the information is presented based on the user's emotions, more appropriate information can be provided. 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 information provider may be performed using AI, for example, or not using AI. For example, the information provider can input user emotion data into a generative AI and have the generative AI adjust the way the information is presented.

[0097] The information provider can adjust the level of detail provided based on the importance of the information at the time of provision. For example, the provider can provide detailed information for highly important information. For example, the provider can provide simplified information for less important information. For example, the provider can provide information with an appropriate level of detail for moderately important information. This allows for efficient information provision by adjusting the level of detail based on the importance of the information. The importance of information includes, but is not limited to, data reliability and impact. Adjusting the level of detail includes, but is not limited to, adjusting the level of detail based on importance. Some or all of the above processing in the information provider may be performed using, for example, AI, or not using AI. For example, the information provider can input information importance data into a generating AI and have the generating AI perform the adjustment of the level of detail of the provision.

[0098] The information delivery unit can apply different delivery methods depending on the category of information at the time of delivery. For example, the delivery unit can apply a delivery method based on weather data to environmental information. For example, the delivery unit can also apply a delivery method based on health data to health information. For example, the delivery unit can also apply a delivery method based on nutritional component data to dietary information. By applying different delivery methods depending on the category of information, it becomes possible to provide more appropriate information. Information categories include, but are not limited to, health information and environmental information. Some or all of the processing described above in the delivery unit may be performed using, for example, AI, or not using AI. For example, the delivery unit can input information category data into a generating AI and have the generating AI execute the application of different delivery methods.

[0099] The information provider can estimate the user's emotions and determine the priority of the information to be provided based on the estimated emotions. For example, if the user is stressed, the information provider may prioritize providing information that has a relaxing effect. For example, if the user is relaxed, the information provider may prioritize providing detailed health information. For example, if the user is in a hurry, the information provider may prioritize providing concise information. This makes it possible to provide more appropriate information by prioritizing information based on the user's emotions. 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 information provider may be performed using AI, for example, or not using AI. For example, the information provider can input user emotion data into a generative AI and have the generative AI perform the determination of information prioritization.

[0100] The information provider can determine the priority of information provision based on the submission date at the time of provision. For example, the provider may prioritize providing the most recent information. The provider may also postpone providing older information. The provider may also prioritize providing information submitted during a specific period. This enables efficient information provision by determining the priority of information provision based on the submission date. The submission date of information includes, but is not limited to, the submission date and time. Determining the priority of information provision includes, but is not limited to, prioritizing based on the submission date. Some or all of the above processing in the information provider may be performed using, for example, AI, or not using AI. For example, the information provider can input information submission date data into a generating AI and have the generating AI perform the determination of the priority of information provision.

[0101] The information provider can adjust the order of information delivery based on the relevance of the information. For example, the provider may prioritize providing highly relevant information. For example, the provider may postpone providing less relevant information. For example, the provider may prioritize providing information related to a specific category. This allows for efficient information delivery by adjusting the order of delivery based on the relevance of the information. The relevance of the information includes, but is not limited to, data correlation and relevance. Adjusting the order of delivery includes, but is not limited to, ordering based on relevance. Some or all of the above processing in the information provider may be performed using, for example, AI, or not using AI. For example, the information provider can input information relevance data into a generating AI and have the generating AI perform the adjustment of the order of delivery.

[0102] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0103] An integrated information management AI system can estimate a user's emotions and suggest activities at their travel destination based on those emotions. For example, if a user is stressed, it can suggest relaxing tourist spots and activities. If a user is excited, it can suggest adventure or sports activities. If a user is tired, it can suggest places suitable for rest and relaxation. This allows for improved travel satisfaction by providing an optimal travel plan based on the user's emotions. Emotion estimation is achieved 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 processing described above in the service unit may be performed using AI or not. For example, the service unit can input user emotion data into a generative AI and have the generative AI suggest activities at the travel destination.

[0104] The recording unit can optimize health management at travel destinations by referring to the user's past travel data. For example, it can identify health risks that the user should pay particular attention to based on health information from past trips. It can also analyze eating and exercise patterns from past trips and suggest an optimal health management plan. It can also optimize sleep management during travel by referring to sleep patterns from past trips. This allows for more effective health management of the user by utilizing past travel data. Past travel data includes, but is not limited to, travel data from the past year or data from specific travel destinations. Some or all of the above processing in the recording unit may be performed using AI or not. For example, the recording unit can input past travel data into a generating AI and have the generating AI perform the optimization of health management.

[0105] The analysis unit can estimate the user's emotions and suggest meals at the travel destination based on those emotions. For example, if the user is stressed, it can suggest meals that promote relaxation. If the user is excited, it can suggest meals suitable for energy replenishment. If the user is tired, it can suggest meals that promote recovery. This allows for support of health management during travel by suggesting optimal meals based on the user's emotions. Emotion estimation is achieved 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 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 make meal suggestions.

[0106] The checking unit can predict health risks during travel by referring to the user's past health data. For example, it can identify health risks that are likely to occur during travel based on past health data. It can also predict health risks under specific environmental conditions from past health data. It can also analyze past health data to optimize a health management plan during travel. This allows users to predict health risks during travel and take appropriate measures by utilizing past health data. Past health data includes, but is not limited to, data from the past year or specific health indicators. Some or all of the above processing in the checking unit may be performed using AI or not. For example, the checking unit can input past health data into a generating AI and have the generating AI perform health risk predictions.

[0107] The service provider can estimate the user's emotions and propose an exercise plan for their trip based on those emotions. For example, if the user is stressed, it can suggest relaxing exercises. If the user is excited, it can suggest exercises that help them release energy. If the user is tired, it can suggest light exercise or stretching. This allows the service provider to support the user's health management during their trip by providing an optimal exercise plan based on their emotions. Emotion estimation is achieved 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 processing described above 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 propose an exercise plan.

[0108] The recording unit can analyze the user's social media activity and record information useful for health management during travel. For example, it can record nutritional information based on meal information shared by the user on social media. It can also record steps and exercise volume based on exercise information shared by the user on social media. It can also record sleep duration based on sleep information shared by the user on social media. This allows for data recording tailored to the user's behavior by recording health information based on social media activity. Social media activity includes, but is not limited to, posts and the number of likes. Relevant health information includes, but is not limited to, stress levels and exercise volume. Some or all of the above processing in the recording unit may be performed using AI or not. For example, the recording unit can input the user's social media activity data into a generating AI and have the generating AI record the relevant health information.

[0109] The analysis unit can estimate the user's emotions and manage risks at the travel destination based on those estimated emotions. For example, if the user is stressed, it can apply risk management techniques that are effective in reducing stress. If the user is excited, it can also apply risk management techniques that help calm the excitement. If the user is tired, it can also apply risk management techniques that are effective in relieving fatigue. In this way, safety during travel can be improved by managing risks based on the user's emotions. Emotion estimation is achieved 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 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 adjustments to risk management.

[0110] The checking unit can assess health risks at a travel destination based on the user's geographical location information. For example, if the user is at a travel destination, the assessment can take into account the health risks of that area. If the user is at home, the assessment can also take into account the health risks of their place of residence. If the user is on the move, the assessment can also take into account the health risks of their destination. This enables health management tailored to the user's situation by assessing health risks based on geographical location information. Geographical location information includes, but is not limited to, GPS data and address information. Health risks include, but are not limited to, regional health risks and geographical characteristics. Some or all of the above processing in the checking unit may be performed using AI or not. For example, the checking unit can input geographical location information into a generating AI and have the generating AI perform the health risk assessment.

[0111] The service provider can estimate the user's emotions and propose communication plans for the travel destination based on those estimated emotions. For example, if the user is feeling stressed, it can suggest relaxing communication methods. If the user is excited, it can suggest communication methods that allow them to release energy. If the user is tired, it can suggest communication in a quiet environment. By providing the optimal communication plan based on the user's emotions, it can facilitate smoother interpersonal relationships during travel. Emotion estimation is achieved 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 processing described above 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 propose communication plans.

[0112] The service provider can suggest activities at a travel destination by referring to the user's past travel data. For example, it can suggest activities that the user will enjoy based on activity information from past trips. It can also analyze the user's preferences and interests from past trips and suggest the most suitable activities. It can also refer to feedback from past trips and suggest activities that the user will be satisfied with. In this way, past travel data can be used to provide activities that match the user's preferences. Past travel data includes, but is not limited to, travel data from the past year or data from a specific travel destination. Some or all of the processing described above in the service provider may be performed using AI or not. For example, the service provider can input past travel data into a generating AI and have the generating AI perform activity suggestions.

[0113] The following briefly describes the processing flow for example form 2.

[0114] Step 1: The recording unit records environmental information. Environmental information includes weather, temperature, humidity, and air quality. The recording unit can record environmental information such as the weather, temperature, and humidity of the place of residence. It can also record health information such as water intake, meals, steps taken, and sleep duration starting several days before a trip. For example, the recording unit records the amount of water the user has consumed, the content of their meals, steps taken, and sleep duration. Step 2: The analysis unit analyzes the information recorded by the recording unit. The analysis unit can analyze the recorded environmental and health information. It uses data analysis methods and algorithms to analyze the recorded information in detail. For example, it analyzes environmental information such as weather, temperature, and humidity to evaluate its impact on the user's health. It also analyzes health information such as water intake, meals, steps taken, and sleep duration to evaluate the user's health. Step 3: The checking unit checks the user's health status based on the analysis results obtained by the analysis unit. The checking unit evaluates the user's health status in detail using vital sign measurements and health score calculation methods. It comprehensively evaluates the user's health status and proposes necessary countermeasures. Step 4: The provision unit provides the user with the most relevant information based on the results obtained by the checking unit. The provision unit can offer health advice and lifestyle suggestions. It provides information to optimize the user's health condition and supports the user in maintaining their health. For example, it can advise on appropriate clothing and behavior, and provide advice to optimize the user's health condition based on information on diet and exercise at their travel destination.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] Each of the multiple elements described above, including the recording unit, analysis unit, checking unit, and provisioning unit, is implemented in at least one of the smart device 14 and the data processing device 12. For example, the recording unit is implemented by the control unit 46A of the smart device 14 and records the user's environmental information and health information. The analysis unit is implemented by the specific processing unit 290 of the data processing device 12 and analyzes the recorded information in detail. The checking unit is implemented by the specific processing unit 290 of the data processing device 12 and checks the user's health status based on the analysis results. The provisioning unit is implemented by the control unit 46A of the smart device 14 and provides the user with the most suitable information. 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.

[0119] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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).

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.).

[0131] 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.

[0132] 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.

[0133] 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.

[0134] Each of the multiple elements described above, including the recording unit, analysis unit, checking unit, and providing unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the recording unit is implemented by the control unit 46A of the smart glasses 214 and records the user's environmental information and health information. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the recorded information in detail. The checking unit is implemented by the specific processing unit 290 of the data processing unit 12 and checks the user's health status based on the analysis results. The providing unit is implemented by the control unit 46A of the smart glasses 214 and provides the user with the most suitable information. 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.

[0135] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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).

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.).

[0147] 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.

[0148] 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.

[0149] 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.

[0150] Each of the multiple elements described above, including the recording unit, analysis unit, checking unit, and provisioning unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the recording unit is implemented by the control unit 46A of the headset terminal 314 and records the user's environmental information and health information. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the recorded information in detail. The checking unit is implemented by the specific processing unit 290 of the data processing unit 12 and checks the user's health status based on the analysis results. The provisioning unit is implemented by the control unit 46A of the headset terminal 314 and provides the user with the most suitable information. 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.

[0151] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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).

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.).

[0164] 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.

[0165] 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.

[0166] 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.

[0167] Each of the multiple elements described above, including the recording unit, analysis unit, checking unit, and providing unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the recording unit is implemented by the control unit 46A of the robot 414 and records the user's environmental information and health information. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the recorded information in detail. The checking unit is implemented by the specific processing unit 290 of the data processing unit 12 and checks the user's health status based on the analysis results. The providing unit is implemented by the control unit 46A of the robot 414 and provides the user with the most suitable information. 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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."

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] (Note 1) A recording unit for recording environmental information, An analysis unit that analyzes the information recorded by the recording unit, A checking unit that checks the user's health status based on the analysis results obtained by the aforementioned analysis unit, The system includes a providing unit that provides the user with the most suitable information based on the results obtained by the checking unit. A system characterized by the following features. (Note 2) The aforementioned recording unit is Record environmental information such as weather, temperature, and humidity in your place of residence. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned recording unit is Record health information such as water intake, meals, steps taken, and sleep duration starting a few days before your trip. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned analysis unit is Analyze recorded environmental and health information. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned checking unit is Check the user's health status based on the analysis results. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned supply unit is, Provide users with the most relevant information based on the check results. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned supply unit is, Based on information such as the weather, temperature, and humidity at your travel destination, we provide advice on appropriate clothing and activities. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned supply unit is, Based on information about meals and exercise during travel, the service provides advice to optimize the user's health. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned recording unit is It estimates the user's emotions and adjusts the type of information recorded based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned recording unit is During recording, the frequency of recording is optimized by referencing the user's past health data. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned recording unit is During recording, the recording timing is adjusted based on the user's daily routine. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned recording unit is It estimates the user's emotions and determines the priority of information to record based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned recording unit is During recording, the system prioritizes recording highly relevant environmental information based on the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned recording unit is During recording, the system analyzes the user's social media activity and records relevant health information. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit is It estimates the user's emotions and adjusts the analysis algorithm based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit is During analysis, adjust the level of detail based on the importance of the recorded information. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit is During analysis, different analytical methods are applied depending on the category of information. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit is It estimates the user's emotions and adjusts how the analysis results are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned analysis unit is During the analysis, prioritize the analysis based on when the information was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned analysis unit is During analysis, adjust the order of analysis based on the relevance of the information. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned checking unit is The system estimates the user's emotions and adjusts the health check criteria based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned checking unit is During the check-up, past health data is referenced to improve the accuracy of the check. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned checking unit is During the check, the health status is evaluated taking into account the user's attribute information. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned checking unit is The system estimates the user's emotions and adjusts the display order of the check results based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned checking unit is During the check-up, health status is assessed taking geographical distribution into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned checking unit is During the check, refer to relevant literature to improve the accuracy of the check. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned supply unit is, It estimates the user's emotions and adjusts how the information provided is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned supply unit is, When providing information, adjust the level of detail based on its importance. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned supply unit is, When providing information, different delivery methods will be applied depending on the category of information. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned supply unit is, It estimates the user's emotions and prioritizes the information provided based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned supply unit is, When providing information, we will determine the priority of provision based on when the information was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned supply unit is, When providing information, the order of provision will be adjusted based on the relevance of the information. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0187] 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 recording unit for recording environmental information, An analysis unit that analyzes the information recorded by the recording unit, A checking unit that checks the user's health status based on the analysis results obtained by the aforementioned analysis unit, The system includes a providing unit that provides the user with the most suitable information based on the results obtained by the checking unit. A system characterized by the following features.

2. The recording unit is, Record environmental information such as weather, temperature, and humidity in your place of residence. The system according to feature 1.

3. The recording unit is, Record health information such as water intake, meals, steps taken, and sleep duration starting a few days before your trip. The system according to feature 1.

4. The aforementioned analysis unit is Analyze recorded environmental and health information. The system according to feature 1.

5. The aforementioned checking unit is Check the user's health status based on the analysis results. The system according to feature 1.

6. The aforementioned supply unit is, Provide users with the most relevant information based on the check results. The system according to feature 1.

7. The aforementioned supply unit is, Based on information such as the weather, temperature, and humidity at your travel destination, we provide advice on appropriate clothing and activities. The system according to feature 1.

8. The aforementioned supply unit is, Based on information about meals and exercise during travel, the service provides advice to optimize the user's health. The system according to feature 1.

Images