system

Abstract

The system according to this embodiment aims to comprehensively manage the health status during travel and propose optimal countermeasures. [Solution] The system according to the embodiment comprises a data collection unit, an analysis unit, and a proposal unit. The data collection unit collects data. The analysis unit analyzes the data collected by the data collection unit. The proposal unit proposes countermeasures based on the analysis results obtained by the analysis unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, the health condition during travel has not been comprehensively managed and optimal countermeasures have not been sufficiently proposed, leaving room for improvement.

[0005] The system according to the embodiment aims to comprehensively manage the health condition during travel and propose optimal countermeasures.

Means for Solving the Problems

[0006] The system according to the embodiment includes a collection unit, an analysis unit, and a proposal unit. The collection unit collects data. The analysis unit analyzes the data collected by the collection unit. The proposal unit proposes countermeasures based on the analysis results obtained by the analysis unit.

Effects of the Invention

[0007] The system according to this embodiment can comprehensively manage the health status during travel and propose optimal countermeasures. [Brief explanation of the drawing]

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

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

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

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

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

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

[0014] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface 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 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. Also, the reception device 38, the output device 40, and the camera 42 are 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 AI ​​system for comprehensively managing the health status of travelers according to an embodiment of the present invention is a system for comprehensively managing the health status of travelers. This system records environmental data such as weather, temperature, and humidity of the place of residence in daily life. In addition, starting a few days before travel, it records data such as water intake, meals, steps taken, and sleep duration, and the AI ​​analyzes and stores this data. At the travel destination, it also records data such as steps taken, temperature and humidity of the current location, sleep duration, and photos of food and drinks, and the AI ​​analyzes this data. This allows the system to check the impact on the user's health status and bring the user closer to their best condition. For example, in daily life, it records environmental data such as weather, temperature, and humidity of the place of residence. For example, temperature and humidity can be recorded in real time using the sensors of a smartphone. In addition, starting a few days before travel, it records data such as water intake, meals, steps taken, and sleep duration. The user inputs the amount of water intake, and meals are recorded by taking photos. Steps are recorded using the automatic measurement function of the smartphone, and sleep duration is recorded by activating the smartphone when going to bed and waking up. Next, the AI ​​analyzes and stores this data. AI analyzes recorded data to understand the user's current physical condition. For example, based on past data, it can calculate the user's average steps and sleep duration and use this to assess their physical condition. Even while traveling, data such as steps, current temperature, humidity, sleep duration, and photos of food and drinks are recorded. This data is automatically recorded using the smartphone's sensors and camera. For example, the temperature and humidity at the travel destination are recorded in real time by the smartphone's sensors, and photos of food and drinks are recorded when taken by the user. AI analyzes the data recorded at the travel destination to check its impact on the user's health. For example, if the temperature or humidity at the travel destination is high, it can assess the impact on the user's physical condition and suggest necessary countermeasures. It can also analyze photos of food and drinks and analyze their nutritional content to assess the user's dietary balance. In this way, AI comprehensively manages the user's health and helps the user get closer to their best condition.For example, if a user's health deteriorates during a trip, the AI ​​can suggest appropriate measures and support them in maintaining their health. This allows them to enjoy their trip in better condition. In this way, an AI system that comprehensively manages the health status of travelers can optimize the user's health.

[0029] The health management system according to this embodiment comprises a data collection unit, an analysis unit, and a proposal unit. The data collection unit collects data. For example, the data collection unit collects data such as the weather, temperature, humidity, water intake, meals, steps taken, and sleep duration of the user's place of residence. For example, the data collection unit can record temperature and humidity in real time using the sensors of a smartphone. The data collection unit can also record water intake entered by the user and meals by taking photos. The data collection unit can record steps taken using the automatic measurement function of a smartphone. The data collection unit can record sleep duration by activating the smartphone at bedtime and upon waking. The analysis unit analyzes the data collected by the data collection unit. For example, the analysis unit can analyze the recorded data to understand the user's current physical condition. For example, the analysis unit can calculate the user's average steps and sleep duration based on past data and evaluate their physical condition based on these. The proposal unit proposes countermeasures based on the analysis results obtained by the analysis unit. For example, the proposal unit can evaluate how high temperatures and humidity at a travel destination affect the user's physical condition and propose necessary countermeasures. The proposal unit can, for example, analyze photos of food and beverages and analyze their nutritional components to evaluate the user's dietary balance. This allows the health management system according to the embodiment to optimize the user's health status. Some or all of the above-described processes in the collection unit, analysis unit, and proposal unit may be performed using AI, for example, or without AI. For example, the collection unit can input data acquired by the smartphone's sensors into the AI ​​and have the AI ​​perform data analysis. The analysis unit can input the collected data into the AI ​​and have the AI ​​perform a health assessment. The proposal unit can input the analysis results into the AI ​​and have the AI ​​perform countermeasure suggestions.

[0030] The data collection unit collects data such as weather, temperature, humidity, water intake, meals, steps taken, and sleep duration in the user's place of residence. Specifically, it can record temperature and humidity in real time using the smartphone's sensors. This allows for accurate understanding of the user's surrounding environment. The data collection unit also provides a function for users to manually input water intake, and allows users to record meals by taking photos. These meal photos are used for analysis by the analysis unit, which will be described later. Furthermore, the data collection unit can record steps using the smartphone's automatic measurement function. This allows for understanding the user's daily exercise level. Sleep duration can be recorded by the user activating their smartphone when going to bed and waking up. This allows for detailed tracking of the user's sleep patterns. The data collection unit centrally manages this data and sends it to a cloud server, enabling collaboration with other departments to utilize the data. For example, the collected data is updated in real time and made accessible to the analysis and proposal departments. The data collection unit can also adjust the frequency and accuracy of data collection, enabling flexible responses to specific situations and conditions. This allows the data collection unit to collect data efficiently and effectively, improving the overall performance of the system.

[0031] The analysis department analyzes the data collected by the data collection department. Specifically, it analyzes the recorded data to understand the user's current physical condition. For example, it can calculate the user's average number of steps and sleep duration based on past data and use this to evaluate their physical condition. When using AI, the collected data can be input into the AI, and the AI ​​can perform the physical condition evaluation. The AI ​​can learn the user's data patterns using machine learning algorithms and detect abnormal patterns and health risks. For example, the AI ​​can analyze the user's step count data and warn of the risk of insufficient exercise if it is abnormally low compared to the user's normal activity level. It can also analyze sleep data and evaluate the quality and patterns of sleep to detect early signs of sleep disorders. Furthermore, the analysis department can statistically analyze the collected data to understand trends in the user's health status. For example, it can analyze long-term data to evaluate seasonal fluctuations in physical condition and the impact of specific lifestyle habits on health. This allows the analysis department to comprehensively evaluate the user's health status and identify individual health risks.

[0032] The proposal department proposes countermeasures based on the analysis results obtained by the analysis department. Specifically, it can evaluate how high temperatures and humidity at the travel destination affect the user's physical condition and propose necessary countermeasures. For example, if the temperature at the travel destination is high, it can suggest sufficient hydration and appropriate clothing. It can also analyze photos of food and drinks and analyze their nutritional components to evaluate the user's dietary balance. When using AI, the analysis results can be input into the AI, and the AI ​​can execute the proposed countermeasures. The AI ​​can automatically generate optimal countermeasures based on the user's health data and past proposal history. For example, the AI ​​can analyze the user's dietary data and, if the nutritional balance is skewed, can suggest specific ingredients and recipes. Also, if a lack of exercise is detected, it can suggest an exercise plan tailored to the user's lifestyle. Furthermore, the proposal department can collect user feedback and continuously improve the accuracy and effectiveness of the proposals. For example, it can collect user physical condition data after implementing the proposed countermeasures and evaluate the effectiveness of the proposals, which can then be reflected in future proposals. In this way, the proposal department can provide users with optimal health management measures and optimize their health status.

[0033] The data collection unit can collect data such as weather, temperature, humidity, water intake, meals, steps taken, and sleep duration in the user's place of residence. For example, the data collection unit can record temperature and humidity in real time using the sensor of a smartphone. For example, the data collection unit can record water intake entered by the user and meals by taking photos. For example, the data collection unit can record steps taken using the automatic step counting function of a smartphone. For example, the data collection unit can record sleep duration by activating the smartphone at bedtime and upon waking. By collecting various data from daily life, the user's health status can be understood in detail. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data acquired by the smartphone sensor into AI and have the AI ​​perform data collection.

[0034] The analysis unit can analyze the collected data and evaluate the user's physical condition. For example, the analysis unit can analyze recorded data to understand a physical condition baseline that closely reflects the user's current state. For example, the analysis unit can calculate the user's average number of steps and sleep duration based on past data and evaluate their physical condition based on this. In this way, the user's physical condition can be accurately evaluated by analyzing the collected data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the collected data into AI and have the AI ​​perform the physical condition evaluation.

[0035] The suggestion unit can propose measures to optimize the user's health based on the analysis results. For example, the suggestion unit can evaluate how high temperatures and humidity at a travel destination affect the user's physical condition and propose necessary countermeasures. For example, the suggestion unit can analyze photos of food and drinks and analyze their nutritional components to evaluate the user's dietary balance. This allows the suggestion unit to optimize the user's health by proposing appropriate measures based on the analysis results. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the analysis results into AI and have the AI ​​execute the suggested countermeasures.

[0036] The data collection unit can collect data such as the number of steps taken at the travel destination, the current temperature and humidity, sleep duration, and photos of food and drinks. For example, the data collection unit can record the temperature and humidity of the travel destination in real time using the smartphone's sensors. For example, the data collection unit can record photos of food and drinks taken by the user. For example, the data collection unit can record the number of steps taken at the travel destination using the smartphone's automatic measurement function. For example, the data collection unit can record sleep duration at the travel destination by activating the smartphone at bedtime and wake-up time. This allows for the collection of detailed data even while traveling, enabling a better understanding of one's health status during the trip. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data acquired by the smartphone's sensors into the AI ​​and have the AI ​​perform the data collection.

[0037] The analysis unit can analyze data collected at the travel destination and evaluate its impact on the user's health. For example, the analysis unit can evaluate how high temperatures and humidity at the travel destination affect the user's physical condition. For example, the analysis unit can analyze photos of food and drinks and analyze their nutritional content to evaluate the user's dietary balance. In this way, by analyzing data from the travel destination, the impact on the user's health during the trip can be evaluated. 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 data collected at the travel destination into AI and have the AI ​​perform the impact evaluation.

[0038] The suggestion unit can propose measures to optimize the user's health while traveling. For example, the suggestion unit can evaluate how high temperatures and humidity at the travel destination affect the user's physical condition and propose necessary countermeasures. For example, the suggestion unit can analyze photos of food and drinks and analyze their nutritional content to evaluate the user's dietary balance. This allows the suggestion unit to propose measures to optimize the user's health while traveling, thereby helping them maintain their health during their trip. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input data collected at the travel destination into AI and have the AI ​​execute the suggested countermeasures.

[0039] The data collection unit can analyze the user's past data collection history and select the optimal collection method. For example, the data collection unit can suggest the optimal collection method based on the data the user has frequently collected in the past. For example, the data collection unit can adjust the collection frequency based on the user's past data collection history. For example, the data collection unit can analyze the user's past data collection history and select the type of data to collect. This allows the optimal collection method to be selected by analyzing the past data collection history. Some or all of the above processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past data collection history into AI and have AI select the optimal collection method.

[0040] The data collection unit can filter data based on the user's current activity status and areas of interest during data collection. For example, if the user is exercising, the data collection unit can prioritize collecting data related to exercise. For example, if the user is eating, the data collection unit can prioritize collecting data related to eating. For example, if the user is resting, the data collection unit can prioritize collecting data related to resting. This allows for the collection of highly relevant data by filtering the data based on the user's activity status and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's activity status and areas of interest into the AI ​​and have the AI ​​perform the data filtering.

[0041] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information during data collection. For example, if the user is traveling, the data collection unit can prioritize the collection of environmental data such as temperature and humidity at the travel destination. For example, if the user is at home, the data collection unit can prioritize the collection of environmental data at home. For example, if the user is in a specific location, the data collection unit can prioritize the collection of data related to that location. In this way, by considering the user's geographical location information, highly relevant data can be prioritized. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into AI and have AI perform data collection.

[0042] The data collection unit can analyze the user's social media activity and collect relevant data during data collection. For example, the data collection unit can collect meal data from photos shared by the user on social media. For example, the data collection unit can collect location information from places where the user has checked in on social media. For example, the data collection unit can estimate the emotional state of a user from content posted on social media and collect relevant data. This allows for the efficient collection of relevant data by analyzing the user's social media activity. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media activity into AI and have the AI ​​perform data collection.

[0043] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit can perform a detailed analysis on important data. For example, the analysis unit can perform a simplified analysis on less important data. For example, the analysis unit can determine the priority of the analysis according to the importance of the data. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the data into the AI ​​and have the AI ​​adjust the level of detail of the analysis.

[0044] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply a nutritional component analysis algorithm to dietary data. For example, the analysis unit can apply an exercise amount analysis algorithm to step count data. For example, the analysis unit can apply a sleep quality analysis algorithm to sleep data. By applying the appropriate analysis algorithm according to the data category, highly accurate analysis becomes possible. 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 the data category into the AI ​​and have the AI ​​execute the application of the appropriate analysis algorithm.

[0045] The analysis unit can determine the priority of analysis based on the data collection timing during the analysis. For example, the analysis unit can prioritize the analysis of the most recent data. For example, the analysis unit can analyze the most recent data while referring to past data. For example, the analysis unit can adjust the priority of analysis according to the data collection timing. This allows for the prioritization of analysis of the most recent data by determining the priority of analysis based on the data collection timing. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data collection timing into the AI ​​and have the AI ​​perform the analysis priority determination.

[0046] The analysis unit can adjust the order of analysis based on the relevance of the data during the analysis. For example, the analysis unit can prioritize the analysis of highly relevant data. For example, the analysis unit can postpone the analysis of less relevant data. The analysis unit can adjust the order of analysis according to the relevance of the data. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance of the data into the AI ​​and have the AI ​​perform the adjustment of the order of analysis.

[0047] The proposal unit can adjust the level of detail in a proposal based on the importance of the countermeasure. For example, the proposal unit can provide detailed proposals for important countermeasures. For example, the proposal unit can provide simplified proposals for less important countermeasures. The proposal unit can also determine the priority of proposals according to the importance of the countermeasures. This allows for efficient proposals by adjusting the level of detail based on the importance of the countermeasures. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input the importance of the countermeasures into the AI ​​and have the AI ​​adjust the level of detail of the proposals.

[0048] The proposal unit can apply different proposal algorithms depending on the category of the countermeasure when making a proposal. For example, for a countermeasure related to diet, the proposal unit can apply a nutritional balance proposal algorithm. For example, for a countermeasure related to exercise, the proposal unit can apply an exercise volume proposal algorithm. For example, for a countermeasure related to sleep, the proposal unit can apply a sleep improvement proposal algorithm. By applying the appropriate proposal algorithm according to the category of the countermeasure, highly accurate proposals can be made. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without using AI. For example, the proposal unit can input the category of the countermeasure into the AI ​​and have the AI ​​execute the application of the appropriate proposal algorithm.

[0049] The proposal department can determine the priority of proposals based on the implementation timing of the measures at the time of proposal. For example, the proposal department can prioritize proposals for measures that should be implemented in the immediate future. For example, the proposal department can postpone proposals for measures that should be implemented in the long term. For example, the proposal department can adjust the priority of proposals according to the implementation timing of the measures. This makes it possible to implement measures at the appropriate time by determining the priority of proposals based on the implementation timing of the measures. Some or all of the above processing in the proposal department may be performed using AI, for example, or not using AI. For example, the proposal department can input the implementation timing of the measures into the AI ​​and have the AI ​​perform the determination of the proposal priority.

[0050] The proposal unit can adjust the order of proposals based on the relevance of the countermeasures when making a proposal. For example, the proposal unit can prioritize proposing countermeasures that are highly relevant. For example, the proposal unit can postpone proposing countermeasures that are less relevant. For example, the proposal unit can adjust the order of proposals according to the relevance of the countermeasures. This makes it possible to make efficient proposals by adjusting the order of proposals based on the relevance of the countermeasures. Some or all of the above processing in the proposal unit may be performed using AI, for example, or not using AI. For example, the proposal unit can input the relevance of the countermeasures into the AI ​​and have the AI ​​perform the adjustment of the order of proposals.

[0051] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0052] The data collection unit can monitor the user's health status in real time and issue alerts if an abnormality is detected. For example, if the user's heart rate increases rapidly, the data collection unit can issue an alert and encourage the user to rest. Also, if the user's blood pressure shows an abnormal value, the data collection unit can issue an alert and recommend that the user seek medical attention. Furthermore, if the user's body temperature rises, the data collection unit can issue an alert and suggest appropriate measures. This allows for real-time monitoring of the user's health status and a rapid response when an abnormality occurs. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can input the user's health data into AI and have the AI ​​perform abnormality detection and alert generation.

[0053] The analysis unit can analyze users' health data and predict future health risks. For example, it can predict a user's risk of developing high blood pressure in the future based on past data. It can also analyze a user's diet and exercise habits to predict their future risk of obesity. Furthermore, it can analyze a user's sleep patterns to predict their future risk of sleep disorders. This allows for proactive identification of users' health risks and the implementation of preventative measures. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user health data into AI and have the AI ​​perform risk predictions.

[0054] The suggestion unit can propose a personalized exercise plan based on the user's health status. For example, it can suggest appropriate exercise intensity and duration according to the user's fitness level and health condition. It can also suggest specific exercise menus, such as strength training or aerobic exercise, according to the user's goals. Furthermore, it can adjust the timing and frequency of exercise to fit the user's schedule. This allows the unit to provide an optimal exercise plan for the user's health condition and support the maintenance of their health. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's health data into AI and have the AI ​​execute the exercise plan proposal.

[0055] The data collection unit can analyze the user's past data collection history and select the optimal collection method. For example, the data collection unit can suggest the optimal collection method based on the data the user has frequently collected in the past. For example, the data collection unit can adjust the collection frequency based on the user's past data collection history. For example, the data collection unit can analyze the user's past data collection history and select the type of data to collect. This allows the optimal collection method to be selected by analyzing the past data collection history. Some or all of the above processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past data collection history into AI and have AI select the optimal collection method.

[0056] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit can perform a detailed analysis on important data. For example, the analysis unit can perform a simplified analysis on less important data. For example, the analysis unit can determine the priority of the analysis according to the importance of the data. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the data into the AI ​​and have the AI ​​adjust the level of detail of the analysis.

[0057] The proposal unit can adjust the level of detail in a proposal based on the importance of the countermeasure. For example, the proposal unit can provide detailed proposals for important countermeasures. For example, the proposal unit can provide simplified proposals for less important countermeasures. The proposal unit can also determine the priority of proposals according to the importance of the countermeasures. This allows for efficient proposals by adjusting the level of detail based on the importance of the countermeasures. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input the importance of the countermeasures into the AI ​​and have the AI ​​adjust the level of detail of the proposals.

[0058] The proposal department can determine the priority of proposals based on the implementation timing of the measures at the time of proposal. For example, the proposal department can prioritize proposals for measures that should be implemented in the immediate future. For example, the proposal department can postpone proposals for measures that should be implemented in the long term. For example, the proposal department can adjust the priority of proposals according to the implementation timing of the measures. This makes it possible to implement measures at the appropriate time by determining the priority of proposals based on the implementation timing of the measures. Some or all of the above processing in the proposal department may be performed using AI, for example, or not using AI. For example, the proposal department can input the implementation timing of the measures into the AI ​​and have the AI ​​perform the determination of the proposal priority.

[0059] The following briefly describes the processing flow for example form 1.

[0060] Step 1: The data collection unit collects data. For example, it collects data such as the weather, temperature, humidity, water intake, meals, steps taken, and sleep duration in the user's area of ​​residence. The data collection unit uses the smartphone's sensors to record temperature and humidity in real time, the user inputs water intake, meals are recorded by taking photos, steps are recorded using the smartphone's automatic measurement function, and sleep duration is recorded by activating the smartphone at bedtime and upon waking. Step 2: The analysis unit analyzes the data collected by the data collection unit. For example, it analyzes the recorded data to understand the user's current physical condition. Based on past data, it calculates the user's average number of steps and sleep duration, and uses this to evaluate their physical condition. Step 3: The proposal department proposes countermeasures based on the analysis results obtained by the analysis department. For example, if the temperature and humidity at the travel destination are high, the department evaluates how this will affect the user's physical condition and proposes necessary countermeasures. By analyzing photos of food and drinks and analyzing their nutritional content, the department evaluates the user's dietary balance.

[0061] (Example of form 2) The AI ​​system for comprehensively managing the health status of travelers according to an embodiment of the present invention is a system for comprehensively managing the health status of travelers. This system records environmental data such as weather, temperature, and humidity of the place of residence in daily life. In addition, starting a few days before travel, it records data such as water intake, meals, steps taken, and sleep duration, and the AI ​​analyzes and stores this data. At the travel destination, it also records data such as steps taken, temperature and humidity of the current location, sleep duration, and photos of food and drinks, and the AI ​​analyzes this data. This allows the system to check the impact on the user's health status and bring the user closer to their best condition. For example, in daily life, it records environmental data such as weather, temperature, and humidity of the place of residence. For example, temperature and humidity can be recorded in real time using the sensors of a smartphone. In addition, starting a few days before travel, it records data such as water intake, meals, steps taken, and sleep duration. The user inputs the amount of water intake, and meals are recorded by taking photos. Steps are recorded using the automatic measurement function of the smartphone, and sleep duration is recorded by activating the smartphone when going to bed and waking up. Next, the AI ​​analyzes and stores this data. AI analyzes recorded data to understand the user's current physical condition. For example, based on past data, it can calculate the user's average steps and sleep duration and use this to assess their physical condition. Even while traveling, data such as steps, current temperature, humidity, sleep duration, and photos of food and drinks are recorded. This data is automatically recorded using the smartphone's sensors and camera. For example, the temperature and humidity at the travel destination are recorded in real time by the smartphone's sensors, and photos of food and drinks are recorded when taken by the user. AI analyzes the data recorded at the travel destination to check its impact on the user's health. For example, if the temperature or humidity at the travel destination is high, it can assess the impact on the user's physical condition and suggest necessary countermeasures. It can also analyze photos of food and drinks and analyze their nutritional content to assess the user's dietary balance. In this way, AI comprehensively manages the user's health and helps the user get closer to their best condition.For example, if a user's health deteriorates during a trip, the AI ​​can suggest appropriate measures and support them in maintaining their health. This allows them to enjoy their trip in better condition. In this way, an AI system that comprehensively manages the health status of travelers can optimize the user's health.

[0062] The health management system according to this embodiment comprises a data collection unit, an analysis unit, and a proposal unit. The data collection unit collects data. For example, the data collection unit collects data such as the weather, temperature, humidity, water intake, meals, steps taken, and sleep duration of the user's place of residence. For example, the data collection unit can record temperature and humidity in real time using the sensors of a smartphone. The data collection unit can also record water intake entered by the user and meals by taking photos. The data collection unit can record steps taken using the automatic measurement function of a smartphone. The data collection unit can record sleep duration by activating the smartphone at bedtime and upon waking. The analysis unit analyzes the data collected by the data collection unit. For example, the analysis unit can analyze the recorded data to understand the user's current physical condition. For example, the analysis unit can calculate the user's average steps and sleep duration based on past data and evaluate their physical condition based on these. The proposal unit proposes countermeasures based on the analysis results obtained by the analysis unit. For example, the proposal unit can evaluate how high temperatures and humidity at a travel destination affect the user's physical condition and propose necessary countermeasures. The proposal unit can, for example, analyze photos of food and beverages and analyze their nutritional components to evaluate the user's dietary balance. This allows the health management system according to the embodiment to optimize the user's health status. Some or all of the above-described processes in the collection unit, analysis unit, and proposal unit may be performed using AI, for example, or without AI. For example, the collection unit can input data acquired by the smartphone's sensors into the AI ​​and have the AI ​​perform data analysis. The analysis unit can input the collected data into the AI ​​and have the AI ​​perform a health assessment. The proposal unit can input the analysis results into the AI ​​and have the AI ​​perform countermeasure suggestions.

[0063] The data collection unit collects data such as weather, temperature, humidity, water intake, meals, steps taken, and sleep duration in the user's place of residence. Specifically, it can record temperature and humidity in real time using the smartphone's sensors. This allows for accurate understanding of the user's surrounding environment. The data collection unit also provides a function for users to manually input water intake, and allows users to record meals by taking photos. These meal photos are used for analysis by the analysis unit, which will be described later. Furthermore, the data collection unit can record steps using the smartphone's automatic measurement function. This allows for understanding the user's daily exercise level. Sleep duration can be recorded by the user activating their smartphone when going to bed and waking up. This allows for detailed tracking of the user's sleep patterns. The data collection unit centrally manages this data and sends it to a cloud server, enabling collaboration with other departments to utilize the data. For example, the collected data is updated in real time and made accessible to the analysis and proposal departments. The data collection unit can also adjust the frequency and accuracy of data collection, enabling flexible responses to specific situations and conditions. This allows the data collection unit to collect data efficiently and effectively, improving the overall performance of the system.

[0064] The analysis department analyzes the data collected by the data collection department. Specifically, it analyzes the recorded data to understand the user's current physical condition. For example, it can calculate the user's average number of steps and sleep duration based on past data and use this to evaluate their physical condition. When using AI, the collected data can be input into the AI, and the AI ​​can perform the physical condition evaluation. The AI ​​can learn the user's data patterns using machine learning algorithms and detect abnormal patterns and health risks. For example, the AI ​​can analyze the user's step count data and warn of the risk of insufficient exercise if it is abnormally low compared to the user's normal activity level. It can also analyze sleep data and evaluate the quality and patterns of sleep to detect early signs of sleep disorders. Furthermore, the analysis department can statistically analyze the collected data to understand trends in the user's health status. For example, it can analyze long-term data to evaluate seasonal fluctuations in physical condition and the impact of specific lifestyle habits on health. This allows the analysis department to comprehensively evaluate the user's health status and identify individual health risks.

[0065] The proposal department proposes countermeasures based on the analysis results obtained by the analysis department. Specifically, it can evaluate how high temperatures and humidity at the travel destination affect the user's physical condition and propose necessary countermeasures. For example, if the temperature at the travel destination is high, it can suggest sufficient hydration and appropriate clothing. It can also analyze photos of food and drinks and analyze their nutritional components to evaluate the user's dietary balance. When using AI, the analysis results can be input into the AI, and the AI ​​can execute the proposed countermeasures. The AI ​​can automatically generate optimal countermeasures based on the user's health data and past proposal history. For example, the AI ​​can analyze the user's dietary data and, if the nutritional balance is skewed, can suggest specific ingredients and recipes. Also, if a lack of exercise is detected, it can suggest an exercise plan tailored to the user's lifestyle. Furthermore, the proposal department can collect user feedback and continuously improve the accuracy and effectiveness of the proposals. For example, it can collect user physical condition data after implementing the proposed countermeasures and evaluate the effectiveness of the proposals, which can then be reflected in future proposals. In this way, the proposal department can provide users with optimal health management measures and optimize their health status.

[0066] The data collection unit can collect data such as weather, temperature, humidity, water intake, meals, steps taken, and sleep duration in the user's place of residence. For example, the data collection unit can record temperature and humidity in real time using the sensor of a smartphone. For example, the data collection unit can record water intake entered by the user and meals by taking photos. For example, the data collection unit can record steps taken using the automatic step counting function of a smartphone. For example, the data collection unit can record sleep duration by activating the smartphone at bedtime and upon waking. By collecting various data from daily life, the user's health status can be understood in detail. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data acquired by the smartphone sensor into AI and have the AI ​​perform data collection.

[0067] The analysis unit can analyze the collected data and evaluate the user's physical condition. For example, the analysis unit can analyze recorded data to understand a physical condition baseline that closely reflects the user's current state. For example, the analysis unit can calculate the user's average number of steps and sleep duration based on past data and evaluate their physical condition based on this. In this way, the user's physical condition can be accurately evaluated by analyzing the collected data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the collected data into AI and have the AI ​​perform the physical condition evaluation.

[0068] The suggestion unit can propose measures to optimize the user's health based on the analysis results. For example, the suggestion unit can evaluate how high temperatures and humidity at a travel destination affect the user's physical condition and propose necessary countermeasures. For example, the suggestion unit can analyze photos of food and drinks and analyze their nutritional components to evaluate the user's dietary balance. This allows the suggestion unit to optimize the user's health by proposing appropriate measures based on the analysis results. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the analysis results into AI and have the AI ​​execute the suggested countermeasures.

[0069] The data collection unit can collect data such as the number of steps taken at the travel destination, the current temperature and humidity, sleep duration, and photos of food and drinks. For example, the data collection unit can record the temperature and humidity of the travel destination in real time using the smartphone's sensors. For example, the data collection unit can record photos of food and drinks taken by the user. For example, the data collection unit can record the number of steps taken at the travel destination using the smartphone's automatic measurement function. For example, the data collection unit can record sleep duration at the travel destination by activating the smartphone at bedtime and wake-up time. This allows for the collection of detailed data even while traveling, enabling a better understanding of one's health status during the trip. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data acquired by the smartphone's sensors into the AI ​​and have the AI ​​perform the data collection.

[0070] The analysis unit can analyze data collected at the travel destination and evaluate its impact on the user's health. For example, the analysis unit can evaluate how high temperatures and humidity at the travel destination affect the user's physical condition. For example, the analysis unit can analyze photos of food and drinks and analyze their nutritional content to evaluate the user's dietary balance. In this way, by analyzing data from the travel destination, the impact on the user's health during the trip can be evaluated. 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 data collected at the travel destination into AI and have the AI ​​perform the impact evaluation.

[0071] The suggestion unit can propose measures to optimize the user's health while traveling. For example, the suggestion unit can evaluate how high temperatures and humidity at the travel destination affect the user's physical condition and propose necessary countermeasures. For example, the suggestion unit can analyze photos of food and drinks and analyze their nutritional content to evaluate the user's dietary balance. This allows the suggestion unit to propose measures to optimize the user's health while traveling, thereby helping them maintain their health during their trip. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input data collected at the travel destination into AI and have the AI ​​execute the suggested countermeasures.

[0072] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can reduce the frequency of data collection to lessen the user's burden. For example, if the user is relaxed, the data collection unit can increase the collection frequency to collect more detailed data. For example, if the user is in a hurry, the data collection unit can prioritize collecting only important data. This reduces the user's burden by adjusting the timing of data collection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into an AI and have the AI ​​adjust the timing of data collection.

[0073] The data collection unit can analyze the user's past data collection history and select the optimal collection method. For example, the data collection unit can suggest the optimal collection method based on the data the user has frequently collected in the past. For example, the data collection unit can adjust the collection frequency based on the user's past data collection history. For example, the data collection unit can analyze the user's past data collection history and select the type of data to collect. This allows the optimal collection method to be selected by analyzing the past data collection history. Some or all of the above processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past data collection history into AI and have AI select the optimal collection method.

[0074] The data collection unit can filter data based on the user's current activity status and areas of interest during data collection. For example, if the user is exercising, the data collection unit can prioritize collecting data related to exercise. For example, if the user is eating, the data collection unit can prioritize collecting data related to eating. For example, if the user is resting, the data collection unit can prioritize collecting data related to resting. This allows for the collection of highly relevant data by filtering the data based on the user's activity status and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's activity status and areas of interest into the AI ​​and have the AI ​​perform the data filtering.

[0075] The data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated user emotions. For example, if the user is stressed, the data collection unit can prioritize collecting data related to stress reduction. For example, if the user is relaxed, the data collection unit can prioritize collecting data to maintain that relaxed state. For example, if the user is in a hurry, the data collection unit can prioritize collecting data to address the urgent situation. In this way, important data can be collected preferentially by determining the priority of data 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 data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input user emotion data into an AI and have the AI ​​perform data prioritization.

[0076] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information during data collection. For example, if the user is traveling, the data collection unit can prioritize the collection of environmental data such as temperature and humidity at the travel destination. For example, if the user is at home, the data collection unit can prioritize the collection of environmental data at home. For example, if the user is in a specific location, the data collection unit can prioritize the collection of data related to that location. In this way, by considering the user's geographical location information, highly relevant data can be prioritized. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into AI and have AI perform data collection.

[0077] The data collection unit can analyze the user's social media activity and collect relevant data during data collection. For example, the data collection unit can collect meal data from photos shared by the user on social media. For example, the data collection unit can collect location information from places where the user has checked in on social media. For example, the data collection unit can estimate the emotional state of a user from content posted on social media and collect relevant data. This allows for the efficient collection of relevant data by analyzing the user's social media activity. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media activity into AI and have the AI ​​perform data collection.

[0078] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is tense, the analysis unit can provide simple and easy-to-understand analysis results. For example, if the user is relaxed, the analysis unit can provide detailed analysis results. For example, if the user is in a hurry, the analysis unit can provide concise analysis results that get straight to the point. In this way, by adjusting the presentation of the analysis according to the user's emotions, the analysis results can be provided that are easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input user emotion data into AI and have the AI ​​adjust the presentation of the analysis.

[0079] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit can perform a detailed analysis on important data. For example, the analysis unit can perform a simplified analysis on less important data. For example, the analysis unit can determine the priority of the analysis according to the importance of the data. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the data into the AI ​​and have the AI ​​adjust the level of detail of the analysis.

[0080] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply a nutritional component analysis algorithm to dietary data. For example, the analysis unit can apply an exercise amount analysis algorithm to step count data. For example, the analysis unit can apply a sleep quality analysis algorithm to sleep data. By applying the appropriate analysis algorithm according to the data category, highly accurate analysis becomes possible. 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 the data category into the AI ​​and have the AI ​​execute the application of the appropriate analysis algorithm.

[0081] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, the analysis unit can provide a short, concise analysis. For example, if the user is relaxed, the analysis unit can provide a detailed analysis. For example, if the user is excited, the analysis unit can provide an analysis with visually stimulating effects. By adjusting the length of the analysis according to the user's emotions, the system can provide the user with the most optimal analysis results. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input user emotion data into an AI and have the AI ​​adjust the length of the analysis.

[0082] The analysis unit can determine the priority of analysis based on the data collection timing during the analysis. For example, the analysis unit can prioritize the analysis of the most recent data. For example, the analysis unit can analyze the most recent data while referring to past data. For example, the analysis unit can adjust the priority of analysis according to the data collection timing. This allows for the prioritization of analysis of the most recent data by determining the priority of analysis based on the data collection timing. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data collection timing into the AI ​​and have the AI ​​perform the analysis priority determination.

[0083] The analysis unit can adjust the order of analysis based on the relevance of the data during the analysis. For example, the analysis unit can prioritize the analysis of highly relevant data. For example, the analysis unit can postpone the analysis of less relevant data. The analysis unit can adjust the order of analysis according to the relevance of the data. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance of the data into the AI ​​and have the AI ​​perform the adjustment of the order of analysis.

[0084] The suggestion unit can estimate the user's emotions and adjust the way suggestions are presented based on those emotions. For example, if the user is nervous, the suggestion unit can provide simple and easily understandable suggestions. If the user is relaxed, the suggestion unit can provide detailed suggestions. If the user is in a hurry, the suggestion unit can provide concise suggestions that get straight to the point. By adjusting the way suggestions are presented according to the user's emotions, suggestions that are easy for the user to understand can be provided. Emotion estimation is achieved using an emotion estimation function, such as 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 suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into AI and have the AI ​​adjust the way suggestions are presented.

[0085] The proposal unit can adjust the level of detail in a proposal based on the importance of the countermeasure. For example, the proposal unit can provide detailed proposals for important countermeasures. For example, the proposal unit can provide simplified proposals for less important countermeasures. The proposal unit can also determine the priority of proposals according to the importance of the countermeasures. This allows for efficient proposals by adjusting the level of detail based on the importance of the countermeasures. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input the importance of the countermeasures into the AI ​​and have the AI ​​adjust the level of detail of the proposals.

[0086] The proposal unit can apply different proposal algorithms depending on the category of the countermeasure when making a proposal. For example, for a countermeasure related to diet, the proposal unit can apply a nutritional balance proposal algorithm. For example, for a countermeasure related to exercise, the proposal unit can apply an exercise volume proposal algorithm. For example, for a countermeasure related to sleep, the proposal unit can apply a sleep improvement proposal algorithm. By applying the appropriate proposal algorithm according to the category of the countermeasure, highly accurate proposals can be made. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without using AI. For example, the proposal unit can input the category of the countermeasure into the AI ​​and have the AI ​​execute the application of the appropriate proposal algorithm.

[0087] The suggestion unit can estimate the user's emotions and adjust the length of suggestions based on the estimated emotions. For example, if the user is in a hurry, the suggestion unit can provide short, concise suggestions. For example, if the user is relaxed, the suggestion unit can provide detailed suggestions. For example, if the user is excited, the suggestion unit can provide suggestions with visually stimulating effects. By adjusting the length of suggestions according to the user's emotions, the system can provide the most suitable suggestions for the user. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into an AI and have the AI ​​adjust the length of suggestions.

[0088] The proposal department can determine the priority of proposals based on the implementation timing of the measures at the time of proposal. For example, the proposal department can prioritize proposals for measures that should be implemented in the immediate future. For example, the proposal department can postpone proposals for measures that should be implemented in the long term. For example, the proposal department can adjust the priority of proposals according to the implementation timing of the measures. This makes it possible to implement measures at the appropriate time by determining the priority of proposals based on the implementation timing of the measures. Some or all of the above processing in the proposal department may be performed using AI, for example, or not using AI. For example, the proposal department can input the implementation timing of the measures into the AI ​​and have the AI ​​perform the determination of the proposal priority.

[0089] The proposal unit can adjust the order of proposals based on the relevance of the countermeasures when making a proposal. For example, the proposal unit can prioritize proposing countermeasures that are highly relevant. For example, the proposal unit can postpone proposing countermeasures that are less relevant. For example, the proposal unit can adjust the order of proposals according to the relevance of the countermeasures. This makes it possible to make efficient proposals by adjusting the order of proposals based on the relevance of the countermeasures. Some or all of the above processing in the proposal unit may be performed using AI, for example, or not using AI. For example, the proposal unit can input the relevance of the countermeasures into the AI ​​and have the AI ​​perform the adjustment of the order of proposals.

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

[0091] The data collection unit can monitor the user's health status in real time and issue alerts if an abnormality is detected. For example, if the user's heart rate increases rapidly, the data collection unit can issue an alert and encourage the user to rest. Also, if the user's blood pressure shows an abnormal value, the data collection unit can issue an alert and recommend that the user seek medical attention. Furthermore, if the user's body temperature rises, the data collection unit can issue an alert and suggest appropriate measures. This allows for real-time monitoring of the user's health status and a rapid response when an abnormality occurs. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can input the user's health data into AI and have the AI ​​perform abnormality detection and alert generation.

[0092] The analysis unit can analyze users' health data and predict future health risks. For example, it can predict a user's risk of developing high blood pressure in the future based on past data. It can also analyze a user's diet and exercise habits to predict their future risk of obesity. Furthermore, it can analyze a user's sleep patterns to predict their future risk of sleep disorders. This allows for proactive identification of users' health risks and the implementation of preventative measures. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user health data into AI and have the AI ​​perform risk predictions.

[0093] The suggestion unit can propose a personalized exercise plan based on the user's health status. For example, it can suggest appropriate exercise intensity and duration according to the user's fitness level and health condition. It can also suggest specific exercise menus, such as strength training or aerobic exercise, according to the user's goals. Furthermore, it can adjust the timing and frequency of exercise to fit the user's schedule. This allows the unit to provide an optimal exercise plan for the user's health condition and support the maintenance of their health. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's health data into AI and have the AI ​​execute the exercise plan proposal.

[0094] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can reduce the frequency of data collection to lessen the user's burden. For example, if the user is relaxed, the data collection unit can increase the collection frequency to collect more detailed data. For example, if the user is in a hurry, the data collection unit can prioritize collecting only important data. This reduces the user's burden by adjusting the timing of data collection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into an AI and have the AI ​​adjust the timing of data collection.

[0095] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is tense, the analysis unit can provide simple and easy-to-understand analysis results. For example, if the user is relaxed, the analysis unit can provide detailed analysis results. For example, if the user is in a hurry, the analysis unit can provide concise analysis results that get straight to the point. In this way, by adjusting the presentation of the analysis according to the user's emotions, the analysis results can be provided that are easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input user emotion data into AI and have the AI ​​adjust the presentation of the analysis.

[0096] The suggestion unit can estimate the user's emotions and adjust the way suggestions are presented based on those emotions. For example, if the user is nervous, the suggestion unit can provide simple and easily understandable suggestions. If the user is relaxed, the suggestion unit can provide detailed suggestions. If the user is in a hurry, the suggestion unit can provide concise suggestions that get straight to the point. By adjusting the way suggestions are presented according to the user's emotions, suggestions that are easy for the user to understand can be provided. Emotion estimation is achieved using an emotion estimation function, such as 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 suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into AI and have the AI ​​adjust the way suggestions are presented.

[0097] The data collection unit can analyze the user's past data collection history and select the optimal collection method. For example, the data collection unit can suggest the optimal collection method based on the data the user has frequently collected in the past. For example, the data collection unit can adjust the collection frequency based on the user's past data collection history. For example, the data collection unit can analyze the user's past data collection history and select the type of data to collect. This allows the optimal collection method to be selected by analyzing the past data collection history. Some or all of the above processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past data collection history into AI and have AI select the optimal collection method.

[0098] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit can perform a detailed analysis on important data. For example, the analysis unit can perform a simplified analysis on less important data. For example, the analysis unit can determine the priority of the analysis according to the importance of the data. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the data into the AI ​​and have the AI ​​adjust the level of detail of the analysis.

[0099] The proposal unit can adjust the level of detail in a proposal based on the importance of the countermeasure. For example, the proposal unit can provide detailed proposals for important countermeasures. For example, the proposal unit can provide simplified proposals for less important countermeasures. The proposal unit can also determine the priority of proposals according to the importance of the countermeasures. This allows for efficient proposals by adjusting the level of detail based on the importance of the countermeasures. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input the importance of the countermeasures into the AI ​​and have the AI ​​adjust the level of detail of the proposals.

[0100] The proposal department can determine the priority of proposals based on the implementation timing of the measures at the time of proposal. For example, the proposal department can prioritize proposals for measures that should be implemented in the immediate future. For example, the proposal department can postpone proposals for measures that should be implemented in the long term. For example, the proposal department can adjust the priority of proposals according to the implementation timing of the measures. This makes it possible to implement measures at the appropriate time by determining the priority of proposals based on the implementation timing of the measures. Some or all of the above processing in the proposal department may be performed using AI, for example, or not using AI. For example, the proposal department can input the implementation timing of the measures into the AI ​​and have the AI ​​perform the determination of the proposal priority.

[0101] The following briefly describes the processing flow for example form 2.

[0102] Step 1: The data collection unit collects data. For example, it collects data such as the weather, temperature, humidity, water intake, meals, steps taken, and sleep duration in the user's area of ​​residence. The data collection unit uses the smartphone's sensors to record temperature and humidity in real time, the user inputs water intake, meals are recorded by taking photos, steps are recorded using the smartphone's automatic measurement function, and sleep duration is recorded by activating the smartphone at bedtime and upon waking. Step 2: The analysis unit analyzes the data collected by the data collection unit. For example, it analyzes the recorded data to understand the user's current physical condition. Based on past data, it calculates the user's average number of steps and sleep duration, and uses this to evaluate their physical condition. Step 3: The proposal department proposes countermeasures based on the analysis results obtained by the analysis department. For example, if the temperature and humidity at the travel destination are high, the department evaluates how this will affect the user's physical condition and proposes necessary countermeasures. By analyzing photos of food and drinks and analyzing their nutritional content, the department evaluates the user's dietary balance.

[0103] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating 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.

[0104] 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.

[0105] 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.

[0106] Each of the multiple elements described above, including the data collection unit, analysis unit, and proposal unit, is implemented by at least one of the smart device 14 and the data processing unit 12. For example, the data collection unit is implemented by recording temperature and humidity in real time using the sensors of the smart device 14, and by the user inputting water intake and photos of meals. The analysis unit analyzes the data collected by the specific processing unit 290 of the data processing unit 12 to understand the user's physical condition. The proposal unit proposes countermeasures based on the analysis results by the specific processing unit 290 of the data processing unit 12. Each element of the data collection unit, analysis unit, and proposal unit may also be implemented by the control unit 46A of the smart device 14. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

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

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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).

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.).

[0119] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating 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.

[0120] 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.

[0121] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is 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.

[0122] Each of the multiple elements described above, including the data collection unit, analysis unit, and proposal unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit is implemented by recording temperature and humidity in real time using the sensors of the smart glasses 214, and by the user inputting water intake and photos of meals. The analysis unit analyzes the data collected by the specific processing unit 290 of the data processing unit 12 to understand the user's physical condition. The proposal unit proposes countermeasures based on the analysis results by the specific processing unit 290 of the data processing unit 12. Each element of the data collection unit, analysis unit, and proposal unit may also be implemented by the control unit 46A of the smart glasses 214. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various changes are possible.

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

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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).

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.).

[0135] 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.

[0136] 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.

[0137] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is 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.

[0138] Each of the multiple elements described above, including the data collection unit, analysis unit, and proposal unit, is implemented by at least one of the headset terminal 314 and the data processing unit 12. For example, the data collection unit is implemented by recording temperature and humidity in real time using the sensors of the headset terminal 314, and by the user inputting water intake and photos of meals. The analysis unit analyzes the data collected by the specific processing unit 290 of the data processing unit 12 to understand the user's physical condition. The proposal unit proposes countermeasures based on the analysis results by the specific processing unit 290 of the data processing unit 12. Each element of the data collection unit, analysis unit, and proposal unit may also be implemented by the control unit 46A of the headset terminal 314. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

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

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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).

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.).

[0152] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the 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.

[0153] 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.

[0154] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is 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.

[0155] Each of the multiple elements described above, including the data collection unit, analysis unit, and proposal unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the data collection unit is implemented by recording temperature and humidity in real time using the sensors of the robot 414, and by the user inputting water intake and photos of meals. The analysis unit analyzes the data collected by the specific processing unit 290 of the data processing unit 12 to understand the user's physical condition. The proposal unit proposes countermeasures based on the analysis results by the specific processing unit 290 of the data processing unit 12. Each element of the data collection unit, analysis unit, and proposal unit may also be implemented by the control unit 46A of the robot 414. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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."

[0162] 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.

[0163] 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.

[0164] 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.

[0165] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] (Note 1) A data collection unit that collects data, An analysis unit analyzes the data collected by the aforementioned collection unit, The system includes a proposal unit that proposes countermeasures based on the analysis results obtained by the analysis unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is The system collects data such as weather, temperature, humidity, water intake, diet, steps taken, and sleep duration in the area of ​​residence. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit is The collected data is analyzed to evaluate the user's physical condition. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned proposal section is, Based on the analysis results, we propose measures to optimize the user's health status. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned collection unit is The system collects data such as the number of steps taken at the travel destination, the current temperature and humidity, sleep duration, and photos of food and drinks. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned analysis unit is We analyze data collected at travel destinations to evaluate its impact on users' health. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned proposal section is, We propose measures to optimize your health while traveling. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is Analyze the user's past data collection history and select the optimal collection method. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is When collecting data, filtering is performed based on the user's current activities and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is During data collection, the system prioritizes the collection of highly relevant data, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned collection unit is During data collection, the system analyzes users' social media activity and collects relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit is It estimates the user's emotions and adjusts the way the analysis is presented based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit is During analysis, adjust the level of detail based on the importance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit is During analysis, different analytical algorithms are applied depending on the data category. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit is It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit is During analysis, prioritize the analysis based on when the data was collected. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned analysis unit is During analysis, adjust the order of analysis based on the relevance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned proposal section is, It estimates the user's emotions and adjusts the way suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned proposal section is, When making a proposal, adjust the level of detail based on the importance of the proposed measures. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned proposal section is, When making a proposal, different proposal algorithms are applied depending on the category of the countermeasure. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned proposal section is, It estimates the user's emotions and adjusts the length of the suggestion based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned proposal section is, When making a proposal, prioritize the proposals based on the timing of their implementation. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned proposal section is, When making proposals, adjust the order of the proposals based on their relevance. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0175] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A data collection unit that collects data, An analysis unit analyzes the data collected by the aforementioned collection unit, The system includes a proposal unit that proposes countermeasures based on the analysis results obtained by the analysis unit. A system characterized by the following features.

2. The aforementioned collection unit is The system collects data such as weather, temperature, humidity, water intake, diet, steps taken, and sleep duration in the area of ​​residence. The system according to feature 1.

3. The aforementioned analysis unit is The collected data is analyzed to evaluate the user's physical condition. The system according to feature 1.

4. The aforementioned proposal section is, Based on the analysis results, we propose measures to optimize the user's health status. The system according to feature 1.

5. The aforementioned collection unit is The system collects data such as the number of steps taken at the travel destination, the current temperature and humidity, sleep duration, and photos of food and drinks. The system according to feature 1.

6. The aforementioned analysis unit is We analyze data collected at travel destinations to evaluate its impact on users' health. The system according to feature 1.

7. The aforementioned proposal section is, We propose measures to optimize your health while traveling. The system according to feature 1.

8. The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system according to feature 1.

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