system

Abstract

The system according to this embodiment aims to provide an optimal travel plan based on individual health data and travel preferences. [Solution] The system according to the embodiment comprises a reception unit, an analysis unit, a generation unit, a provision unit, and a management unit. The reception unit receives health data and travel preferences. The analysis unit analyzes the information entered by the reception unit. The generation unit generates an optimal travel plan based on the information analyzed by the analysis unit. The provision unit provides the travel plan generated by the generation unit. The management unit manages the user's health status during the trip in real time.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance 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, it has not been fully achieved to provide an optimal travel plan based on individual health data and travel preferences, and there is room for improvement.

[0005] The system according to the embodiment aims to provide an optimal travel plan based on individual health data and travel preferences.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, an analysis unit, a generation unit, a provision unit, and a management unit. The reception unit receives health data and travel preferences. The analysis unit analyzes the information entered by the reception unit. The generation unit generates an optimal travel plan based on the information analyzed by the analysis unit. The provision unit provides the travel plan generated by the generation unit. The management unit manages the user's health status in real time during the trip. [Effects of the Invention]

[0007] The system according to this embodiment can provide an optimal travel plan based on individual health data and travel preferences. [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 numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F controls communication between a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The wellness travel plan provision system according to an embodiment of the present invention is a system that uses generative AI to analyze individual health data and preferences and provides users with an optimal wellness travel plan. This system is a new form of travel service that combines technology and health awareness, and enables health management anywhere using 5G and satellite communication. For example, the generative AI analyzes the health data and travel preferences provided by the user and generates an optimal travel plan. It is also possible to adjust the support plan in real time according to changes in the user's health condition during the trip. This allows users to achieve relaxation while maintaining their health. First, the user inputs their health data and travel preferences. For example, the user can input their daily health condition and preferred travel style. This information is input into the generative AI. Next, the generative AI analyzes the input information and generates an optimal wellness travel plan for the user. Based on the user's health data and travel preferences, the generative AI suggests the optimal travel destination and activities. For example, it can suggest a relaxing hot spring resort for stressed business people, and a resort where healthy meals can be enjoyed for health-conscious middle-aged and elderly people. Furthermore, health management is possible in real time even during the trip using 5G and satellite communication. For example, if a user's health condition changes during a trip, the generating AI detects the change and provides an appropriate support plan. This allows users to enjoy their trip with peace of mind. This service provides the best travel experience for everyone seeking health and wellness. For instance, it offers effective relaxation and health management methods for business people who are stressed and have health problems due to their busy daily lives, and for middle-aged and elderly people who are highly conscious of health and wellness. It is also ideal for young people who are interested in technology and want to try new services. In this way, the wellness travel planner utilizing generating AI is a new form of travel service that allows users to maintain their health while achieving relaxation by analyzing their health data and preferences and providing the optimal travel plan. Thus, the wellness travel plan provision system can analyze the user's health data and preferences and provide the optimal wellness travel plan.

[0029] The wellness travel plan provision system according to this embodiment comprises a reception unit, an analysis unit, a generation unit, a provision unit, and a management unit. The reception unit receives the user's health data and travel preferences. For example, the reception unit allows the user to input their daily health status and preferred travel style. The reception unit can also use AI to convert the user's input data into a format that is easy to analyze. The analysis unit uses generation AI to analyze the information entered by the reception unit. For example, the analysis unit extracts data to generate an optimal travel plan based on the user's health data and travel preferences. The generation unit uses generation AI to generate an optimal travel plan based on the information analyzed by the analysis unit. For example, the generation unit suggests the optimal travel destination and activities according to the user's health status and travel preferences. The provision unit provides the travel plan generated by the generation unit to the user. For example, the provision unit can display the travel plan through a web application or a mobile application. The management unit uses 5G or satellite communication to manage the user's health status during travel in real time. The management department, for example, detects changes in the user's health status using a generated AI and provides an appropriate support plan. This allows the wellness travel plan provision system according to the embodiment to analyze the user's health data and preferences and provide the optimal wellness travel plan.

[0030] The reception desk inputs the user's health data and travel preferences. Specifically, users can input data on their daily health status, such as blood pressure, heart rate, weight, and sleep patterns. They can also select travel preferences, such as wanting to relax, be active, enjoy nature, or experience culture. The reception desk collects this data and converts it into a format that is easy to analyze using AI. For example, it standardizes the health data entered by the user and organizes travel preferences into categories. Furthermore, the reception desk can provide more accurate data by considering the user's past travel history and health data trends. This allows the reception desk to accurately understand the user's detailed health status and travel preferences and smoothly provide data to the analysis department.

[0031] The analysis unit uses a generation AI to analyze the information entered by the reception unit. Specifically, it extracts data to generate the optimal travel plan based on the user's health data and travel preferences. The generation AI analyzes the user's health data and, for example, if the user has high blood pressure, suggests a relaxing travel destination with less stress. It also analyzes travel preferences and, if the user wants to enjoy nature, selects a place rich in nature. Furthermore, the generation AI combines the user's health data and travel preferences to extract data to generate the optimal travel plan. For example, if the user wants to be active, it suggests a plan that includes activities such as hiking and cycling. This allows the analysis unit to quickly and accurately extract data to generate the optimal travel plan based on the user's health status and travel preferences.

[0032] The generation unit uses a generation AI to generate an optimal travel plan based on the information analyzed by the analysis unit. Specifically, it suggests the best travel destination and activities according to the user's health condition and travel preferences. The generation AI considers the user's health data; for example, it suggests a plan including active activities for users with a stable heart rate. It also considers travel preferences; if the user wants to relax, it suggests a plan including relaxation facilities such as spas and hot springs. Furthermore, the generation AI can generate a more accurate travel plan by considering the user's past travel history and health data trends. As a result, the generation unit can generate and provide the user with a travel plan that is optimal for their health condition and travel preferences.

[0033] The service provider delivers the travel plans generated by the generation unit to the user. Specifically, the travel plans can be displayed through web applications and mobile applications. Through the applications, users can review the generated travel plans and view detailed information. For example, detailed information about the travel destination, activity schedules, and accommodation information can be displayed. The service provider also provides a function that allows users to customize their travel plans. Through the applications, users can change parts of the travel plan or select additional activities. This allows the service provider to provide users with the most suitable travel plans and improve user satisfaction.

[0034] The management department uses 5G and satellite communication to monitor users' health status in real time during their trip. Specifically, if a user's health status changes, a generating AI detects the change and provides an appropriate support plan. For example, if a user's heart rate suddenly increases, the generating AI detects the change and notifies the user to rest. Also, if a user's health condition deteriorates, the generating AI notifies emergency contacts and guides them to an appropriate medical facility. Furthermore, the management department can continuously monitor users' health data and manage their health status in real time during their trip. This allows the management department to always be aware of the user's health status and ensure their safety and comfort during their trip.

[0035] The reception desk can analyze the user's past health data and travel history and suggest the optimal input format. For example, the reception desk can automatically suggest the optimal input format based on the user's past health data and travel preferences. The reception desk can also prioritize inputting frequently visited places and preferred activities based on the user's past travel history. Furthermore, the reception desk can analyze changes in the user's health data and provide the input format best suited to their current health condition. This improves input efficiency by suggesting the optimal input format based on past data. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's past health data into a generating AI and have the generating AI suggest the optimal input format.

[0036] The reception unit can filter health data input based on the user's current lifestyle and health status. For example, when a user inputs their current lifestyle, the reception unit can display only relevant health data to simplify input. The reception unit can also filter data based on the user's current health status to ensure only necessary data is entered. Furthermore, the reception unit can automatically adjust the priority of the data to be entered according to the user's lifestyle and health status. This simplifies data input by filtering data based on the user's current lifestyle and health status. Some or all of the above processing in the reception unit may be performed using AI or not. For example, the reception unit can input the user's lifestyle data into a generating AI and have the generating AI perform the filtering.

[0037] The reception desk can prioritize inputting highly relevant data when users enter health data, taking into account their geographical location. For example, if a user is in a specific region, the reception desk will prioritize inputting health data related to that region. The reception desk can also automatically suggest relevant health information based on the user's current location. Furthermore, if a user is traveling, the reception desk can prioritize inputting health information for their travel destination. This allows for the priority input of highly relevant data by considering geographical location. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's geographical location information into a generating AI and have the generating AI suggest highly relevant data.

[0038] The reception unit can analyze the user's social media activity and input relevant data when entering health data. For example, the reception unit can extract health-related information from the user's social media posts and use it as input data. The reception unit can also analyze the user's social media activity and automatically input data related to their health status. Furthermore, the reception unit can supplement the input of health data based on the user's social media activity history. This allows for the automatic input of relevant data by analyzing social media activity. Some or all of the above processing in the reception unit may be performed using AI or not. For example, the reception unit can input the user's social media data into a generating AI and have the generating AI extract relevant health data.

[0039] The analysis unit can adjust the level of detail of the analysis based on the importance of the health data during the analysis. For example, the analysis unit will perform a detailed analysis on important health data. It can also perform a simplified analysis on general health data. Furthermore, the analysis unit can adjust the level of detail of the analysis according to the user's health status. This allows for detailed analysis of important data by adjusting the level of detail based on the importance of the health data. Some or all of the above-described processes in the analysis unit may be performed using a generating AI, or they may be performed without a generating AI. For example, the analysis unit can input the user's health data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis based on importance.

[0040] The analysis unit can apply different analysis algorithms depending on the category of health data during analysis. For example, the analysis unit can apply a specific algorithm to heart rate data. It can also apply a different algorithm to blood pressure data. Furthermore, the analysis unit can select the optimal analysis algorithm according to the user's health data category. This improves the accuracy of the analysis by applying the most suitable analysis algorithm for each health data category. Some or all of the above-described processes in the analysis unit may be performed using a generative AI, or they may be performed without a generative AI. For example, the analysis unit can input the user's health data into a generative AI and have the generative AI apply the appropriate analysis algorithm based on the data category.

[0041] The analysis unit can determine the priority of analysis based on the timing of health data submission during the analysis process. For example, the analysis unit may prioritize the analysis of the most recent health data. It can also prioritize the analysis of health data within a period specified by the user. Furthermore, the analysis unit can determine the priority of analysis by considering fluctuations in the user's health status. This allows for the prioritization of analysis based on the timing of health data submission, thereby ensuring that the most recent data is analyzed first. Some or all of the above-described processes in the analysis unit may be performed using a generating AI, or they may be performed without a generating AI. For example, the analysis unit can input the user's health data into a generating AI and have the generating AI determine the priority of analysis based on the submission timing.

[0042] The analysis unit can adjust the order of analysis based on the relevance of health data during the analysis process. For example, the analysis unit may prioritize the analysis of highly relevant health data. It can also prioritize the analysis of data related to the user's health status. Furthermore, the analysis unit can automatically adjust the order of analysis based on the relevance of health data. This allows for the prioritization of highly relevant data by adjusting the order of analysis based on the relevance of health data. Some or all of the above-described processes in the analysis unit may be performed using a generative AI, or they may be performed without a generative AI. For example, the analysis unit can input the user's health data into a generative AI and have the generative AI perform the adjustment of the analysis order based on relevance.

[0043] The generation unit can adjust the level of detail generated based on the importance of the travel plan during generation. For example, the generation unit provides detailed information for important travel plans. It can also provide concise information for general travel plans. Furthermore, the generation unit can adjust the level of detail generated according to the user's travel purpose. This allows for the provision of detailed information for important plans by adjusting the level of detail based on the importance of the travel plan. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input the user's travel plan data into a generation AI and have the generation AI perform the adjustment of the level of detail based on importance.

[0044] The generation unit can apply different generation algorithms depending on the category of the travel plan during generation. For example, the generation unit can apply a specific generation algorithm to travel plans intended for relaxation. It can also apply a different generation algorithm to active travel plans. Furthermore, the generation unit can select the optimal generation algorithm according to the user's travel purpose. This improves the accuracy of generation by applying the optimal generation algorithm according to the category of the travel plan. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input the user's travel plan data into a generation AI and have the generation AI execute the application of a generation algorithm according to the category.

[0045] The generation unit can determine the generation priority based on the submission date of the travel plan during generation. For example, the generation unit may prioritize generating the most recent travel plan. It can also prioritize generating travel plans within a period specified by the user. Furthermore, the generation unit can determine the generation priority according to the user's travel purpose. This allows for the prioritization of the most recent plan by determining the generation priority based on the submission date of the travel plan. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input the user's travel plan data into a generation AI and have the generation AI determine the generation priority based on the submission date.

[0046] The generation unit can adjust the generation order based on the relevance of the travel plans during generation. For example, the generation unit can prioritize generating highly relevant travel plans. It can also prioritize generating plans related to the user's travel purpose. Furthermore, the generation unit can automatically adjust the generation order based on the relevance of the travel plans. This allows for the priority generation of highly relevant plans by adjusting the generation order based on the relevance of the travel plans. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input the user's travel plan data into a generation AI and have the generation AI perform the adjustment of the generation order based on relevance.

[0047] The service provider can select the optimal display method by referring to the user's past travel history at the time of service provision. For example, the service provider can provide the optimal display method based on the display method the user has preferred to use in the past. The service provider can also preferentially suggest a specific display method based on the user's past travel history. Furthermore, the service provider can analyze the user's travel history and select the display method with the highest visibility. This makes it possible to provide a user-friendly display by selecting the optimal display method based on past travel history. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input the user's travel history data into a generating AI and have the generating AI select the optimal display method.

[0048] The service provider can select the optimal display method at the time of delivery, taking into account the user's device information. For example, if the user is using a smartphone, the service provider can provide a display method that matches the screen size. Furthermore, if the user is using a tablet, the service provider can provide a display method optimized for a larger screen. In addition, if the user is using a smartwatch, the service provider can provide a concise and highly visible display method. This allows for a display optimized for the user's device by considering device information. Some or all of the above processing in the service provider may be performed using AI, or not. For example, the service provider can input the user's device information into a generating AI and have the generating AI select the optimal display method.

[0049] The management department can select the optimal management method by referring to the user's past health data during health management. For example, the management department can provide the optimal health management method based on the user's past health data. The management department can also preferentially suggest a specific management method based on the user's health history. Furthermore, the management department can analyze the user's health data and select the most effective management method. This enables effective health management by selecting the optimal management method based on past health data. Some or all of the above processes in the management department may be performed using AI or not. For example, the management department can input the user's past health data into a generating AI and have the generating AI select the optimal management method.

[0050] The management unit can customize the means of health management based on the user's current lifestyle. For example, the management unit can provide the optimal health management method based on the user's current lifestyle. The management unit can also customize the means of health management according to the user's lifestyle. Furthermore, the management unit can adjust the means of management based on the user's current health status. This allows for more appropriate health management by customizing the means of management based on the current lifestyle. Some or all of the above processes in the management unit may be performed using AI or not. For example, the management unit can input user lifestyle data into a generating AI and have the generating AI perform the customization of the means of management.

[0051] The management department can select the optimal management method when managing a user's health, taking into account the user's geographical location. For example, if the user is in a specific region, the management department can provide a health management method relevant to that region. The management department can also suggest the optimal health management method based on the user's current location. Furthermore, if the user is traveling, the management department can adjust the management method based on health information from their travel destination. This allows for the provision of highly relevant management methods by considering geographical location information. Some or all of the above processes in the management department may be performed using AI or not. For example, the management department can input the user's geographical location information into a generating AI and have the generating AI select the optimal management method.

[0052] The management department can analyze a user's social media activity and propose management methods during health management. For example, the management department can extract health-related information from a user's social media posts and use it as a management method. The management department can also analyze a user's social media activity and propose management methods related to their health status. Furthermore, the management department can supplement health management methods based on a user's social media activity history. In this way, by analyzing social media activity, it can provide relevant management methods. Some or all of the above processes in the management department may be performed using AI or not. For example, the management department can input a user's social media data into a generating AI and have the generating AI propose the optimal management method.

[0053] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0054] The reception desk can analyze the user's past health data and travel history to suggest the optimal input format. For example, it can automatically suggest the optimal input format based on the user's past health data and travel preferences. It can also prioritize frequently visited places and preferred activities based on the user's past travel history. Furthermore, it can analyze changes in the user's health data and provide the input format best suited to their current health condition. This improves input efficiency by suggesting the optimal input format based on past data. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's past health data into a generating AI and have the generating AI suggest the optimal input format.

[0055] The reception unit can filter health data input based on the user's current lifestyle and health status. For example, when a user inputs their current lifestyle, it can display only relevant health data to simplify input. It can also filter data based on the user's current health status to ensure only necessary data is entered. Furthermore, it can automatically adjust the priority of data to be entered according to the user's lifestyle and health status. This simplifies data input by filtering data based on the user's current lifestyle and health status. Some or all of the above processing in the reception unit may be performed using AI or not. For example, the reception unit can input the user's lifestyle data into a generating AI and have the generating AI perform the filtering.

[0056] The reception desk can prioritize inputting highly relevant data when users enter health data, taking into account their geographical location. For example, if a user is in a specific region, health data related to that region can be prioritized. It can also automatically suggest relevant health information based on the user's current location. Furthermore, if a user is traveling, health information for their travel destination can be prioritized. This allows for the priority input of highly relevant data by considering geographical location. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's geographical location information into a generating AI and have the generating AI suggest highly relevant data.

[0057] The analysis unit can adjust the level of detail of the analysis based on the importance of the health data during the analysis. For example, it can perform a detailed analysis on important health data, and a simplified analysis on general health data. Furthermore, it can adjust the level of detail of the analysis according to the user's health status. This allows for detailed analysis of important data by adjusting the level of detail of the analysis based on the importance of the health data. Some or all of the above processing in the analysis unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the analysis unit can input the user's health data into a generation AI and have the generation AI perform the adjustment of the level of detail of the analysis based on importance.

[0058] The analysis unit can apply different analysis algorithms depending on the category of health data during analysis. For example, a specific algorithm can be applied to heart rate data for analysis. A different algorithm can also be applied to blood pressure data for analysis. Furthermore, the optimal analysis algorithm can be selected according to the user's health data category. This improves the accuracy of the analysis by applying the most suitable analysis algorithm according to the health data category. Some or all of the above-described processes in the analysis unit may be performed using a generative AI, or they may be performed without a generative AI. For example, the analysis unit can input the user's health data into a generative AI and have the generative AI execute the application of the appropriate analysis algorithm based on the category.

[0059] The generation unit can adjust the level of detail generated based on the importance of the travel plan during generation. For example, it can provide detailed information for important travel plans, and concise information for general travel plans. Furthermore, it can adjust the level of detail generated according to the user's travel purpose. This allows for the provision of detailed information for important plans by adjusting the level of detail based on the importance of the travel plan. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input the user's travel plan data into a generation AI and have the generation AI perform the adjustment of the level of detail generated based on importance.

[0060] The following briefly describes the processing flow for example form 1.

[0061] Step 1: The reception desk inputs the user's health data and travel preferences. For example, the user can input their daily health status and preferred travel style. The reception desk can also use AI to convert the user's input data into a format that is easy to analyze. Step 2: The analysis unit uses a generation AI to analyze the information entered by the reception unit. For example, it extracts data to generate an optimal travel plan based on the user's health data and travel preferences. Step 3: The generation unit uses the generation AI to generate the optimal travel plan based on the information analyzed by the analysis unit. For example, it suggests the best travel destination and activities according to the user's health condition and travel preferences. Step 4: The providing unit provides the travel plan generated by the generating unit to the user. For example, the travel plan can be displayed through a web application or a mobile application. Step 5: The management department uses 5G and satellite communication to monitor the user's health status in real time during their trip. For example, if a user's health status changes, a generated AI detects the change and provides an appropriate support plan.

[0062] (Example of form 2) The wellness travel plan provision system according to an embodiment of the present invention is a system that uses generative AI to analyze individual health data and preferences and provides users with an optimal wellness travel plan. This system is a new form of travel service that combines technology and health awareness, and enables health management anywhere using 5G and satellite communication. For example, the generative AI analyzes the health data and travel preferences provided by the user and generates an optimal travel plan. It is also possible to adjust the support plan in real time according to changes in the user's health condition during the trip. This allows users to achieve relaxation while maintaining their health. First, the user inputs their health data and travel preferences. For example, the user can input their daily health condition and preferred travel style. This information is input into the generative AI. Next, the generative AI analyzes the input information and generates an optimal wellness travel plan for the user. Based on the user's health data and travel preferences, the generative AI suggests the optimal travel destination and activities. For example, it can suggest a relaxing hot spring resort for stressed business people, and a resort where healthy meals can be enjoyed for health-conscious middle-aged and elderly people. Furthermore, health management is possible in real time even during the trip using 5G and satellite communication. For example, if a user's health condition changes during a trip, the generating AI detects the change and provides an appropriate support plan. This allows users to enjoy their trip with peace of mind. This service provides the best travel experience for everyone seeking health and wellness. For instance, it offers effective relaxation and health management methods for business people who are stressed and have health problems due to their busy daily lives, and for middle-aged and elderly people who are highly conscious of health and wellness. It is also ideal for young people who are interested in technology and want to try new services. In this way, the wellness travel planner utilizing generating AI is a new form of travel service that allows users to maintain their health while achieving relaxation by analyzing their health data and preferences and providing the optimal travel plan. Thus, the wellness travel plan provision system can analyze the user's health data and preferences and provide the optimal wellness travel plan.

[0063] The wellness travel plan provision system according to this embodiment comprises a reception unit, an analysis unit, a generation unit, a provision unit, and a management unit. The reception unit receives the user's health data and travel preferences. For example, the reception unit allows the user to input their daily health status and preferred travel style. The reception unit can also use AI to convert the user's input data into a format that is easy to analyze. The analysis unit uses generation AI to analyze the information entered by the reception unit. For example, the analysis unit extracts data to generate an optimal travel plan based on the user's health data and travel preferences. The generation unit uses generation AI to generate an optimal travel plan based on the information analyzed by the analysis unit. For example, the generation unit suggests the optimal travel destination and activities according to the user's health status and travel preferences. The provision unit provides the travel plan generated by the generation unit to the user. For example, the provision unit can display the travel plan through a web application or a mobile application. The management unit uses 5G or satellite communication to manage the user's health status during travel in real time. The management department, for example, detects changes in the user's health status using a generated AI and provides an appropriate support plan. This allows the wellness travel plan provision system according to the embodiment to analyze the user's health data and preferences and provide the optimal wellness travel plan.

[0064] The reception desk inputs the user's health data and travel preferences. Specifically, users can input data on their daily health status, such as blood pressure, heart rate, weight, and sleep patterns. They can also select travel preferences, such as wanting to relax, be active, enjoy nature, or experience culture. The reception desk collects this data and converts it into a format that is easy to analyze using AI. For example, it standardizes the health data entered by the user and organizes travel preferences into categories. Furthermore, the reception desk can provide more accurate data by considering the user's past travel history and health data trends. This allows the reception desk to accurately understand the user's detailed health status and travel preferences and smoothly provide data to the analysis department.

[0065] The analysis unit uses a generation AI to analyze the information entered by the reception unit. Specifically, it extracts data to generate the optimal travel plan based on the user's health data and travel preferences. The generation AI analyzes the user's health data and, for example, if the user has high blood pressure, suggests a relaxing travel destination with less stress. It also analyzes travel preferences and, if the user wants to enjoy nature, selects a place rich in nature. Furthermore, the generation AI combines the user's health data and travel preferences to extract data to generate the optimal travel plan. For example, if the user wants to be active, it suggests a plan that includes activities such as hiking and cycling. This allows the analysis unit to quickly and accurately extract data to generate the optimal travel plan based on the user's health status and travel preferences.

[0066] The generation unit uses a generation AI to generate an optimal travel plan based on the information analyzed by the analysis unit. Specifically, it suggests the best travel destination and activities according to the user's health condition and travel preferences. The generation AI considers the user's health data; for example, it suggests a plan including active activities for users with a stable heart rate. It also considers travel preferences; if the user wants to relax, it suggests a plan including relaxation facilities such as spas and hot springs. Furthermore, the generation AI can generate a more accurate travel plan by considering the user's past travel history and health data trends. As a result, the generation unit can generate and provide the user with a travel plan that is optimal for their health condition and travel preferences.

[0067] The service provider delivers the travel plans generated by the generation unit to the user. Specifically, the travel plans can be displayed through web applications and mobile applications. Through the applications, users can review the generated travel plans and view detailed information. For example, detailed information about the travel destination, activity schedules, and accommodation information can be displayed. The service provider also provides a function that allows users to customize their travel plans. Through the applications, users can change parts of the travel plan or select additional activities. This allows the service provider to provide users with the most suitable travel plans and improve user satisfaction.

[0068] The management department uses 5G and satellite communication to monitor users' health status in real time during their trip. Specifically, if a user's health status changes, a generating AI detects the change and provides an appropriate support plan. For example, if a user's heart rate suddenly increases, the generating AI detects the change and notifies the user to rest. Also, if a user's health condition deteriorates, the generating AI notifies emergency contacts and guides them to an appropriate medical facility. Furthermore, the management department can continuously monitor users' health data and manage their health status in real time during their trip. This allows the management department to always be aware of the user's health status and ensure their safety and comfort during their trip.

[0069] The reception desk can estimate the user's emotions and adjust the input method for health data and travel preferences based on the estimated emotions. For example, if the user is stressed, the reception desk can provide a simple interface and minimize the input steps. If the user is relaxed, the reception desk can also provide detailed input options and suggest customizable input methods. Furthermore, if the user is in a hurry, the reception desk can prioritize voice input to allow for quick input of health data and travel preferences. This allows for more appropriate data entry by adjusting the input method according to 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 reception desk may be performed using AI or not. For example, the reception desk can input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0070] The reception desk can analyze the user's past health data and travel history and suggest the optimal input format. For example, the reception desk can automatically suggest the optimal input format based on the user's past health data and travel preferences. The reception desk can also prioritize inputting frequently visited places and preferred activities based on the user's past travel history. Furthermore, the reception desk can analyze changes in the user's health data and provide the input format best suited to their current health condition. This improves input efficiency by suggesting the optimal input format based on past data. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's past health data into a generating AI and have the generating AI suggest the optimal input format.

[0071] The reception unit can filter health data input based on the user's current lifestyle and health status. For example, when a user inputs their current lifestyle, the reception unit can display only relevant health data to simplify input. The reception unit can also filter data based on the user's current health status to ensure only necessary data is entered. Furthermore, the reception unit can automatically adjust the priority of the data to be entered according to the user's lifestyle and health status. This simplifies data input by filtering data based on the user's current lifestyle and health status. Some or all of the above processing in the reception unit may be performed using AI or not. For example, the reception unit can input the user's lifestyle data into a generating AI and have the generating AI perform the filtering.

[0072] The reception desk can estimate the user's emotions and prioritize the data to be entered based on the estimated emotions. For example, if the user is stressed, the reception desk may prioritize inputting only important data. If the user is relaxed, the reception desk may also prompt them to enter detailed data. Furthermore, if the user is in a hurry, the reception desk may prioritize inputting the most important data. This ensures that important data is entered preferentially by prioritizing data 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 may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk may input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0073] The reception desk can prioritize inputting highly relevant data when users enter health data, taking into account their geographical location. For example, if a user is in a specific region, the reception desk will prioritize inputting health data related to that region. The reception desk can also automatically suggest relevant health information based on the user's current location. Furthermore, if a user is traveling, the reception desk can prioritize inputting health information for their travel destination. This allows for the priority input of highly relevant data by considering geographical location. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's geographical location information into a generating AI and have the generating AI suggest highly relevant data.

[0074] The reception unit can analyze the user's social media activity and input relevant data when entering health data. For example, the reception unit can extract health-related information from the user's social media posts and use it as input data. The reception unit can also analyze the user's social media activity and automatically input data related to their health status. Furthermore, the reception unit can supplement the input of health data based on the user's social media activity history. This allows for the automatic input of relevant data by analyzing social media activity. Some or all of the above processing in the reception unit may be performed using AI or not. For example, the reception unit can input the user's social media data into a generating AI and have the generating AI extract relevant health data.

[0075] 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 relaxed, the analysis unit can provide detailed analysis results. If the user is in a hurry, the analysis unit can also provide concise analysis results that get straight to the point. Furthermore, if the user is stressed, the analysis unit can provide visually easy-to-understand analysis results. In this way, by adjusting the presentation of the analysis according to the user's emotions, more appropriate analysis results can be provided. 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 or without a generative AI. For example, the analysis unit can input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0076] The analysis unit can adjust the level of detail of the analysis based on the importance of the health data during the analysis. For example, the analysis unit will perform a detailed analysis on important health data. It can also perform a simplified analysis on general health data. Furthermore, the analysis unit can adjust the level of detail of the analysis according to the user's health status. This allows for detailed analysis of important data by adjusting the level of detail based on the importance of the health data. Some or all of the above-described processes in the analysis unit may be performed using a generating AI, or they may be performed without a generating AI. For example, the analysis unit can input the user's health data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis based on importance.

[0077] The analysis unit can apply different analysis algorithms depending on the category of health data during analysis. For example, the analysis unit can apply a specific algorithm to heart rate data. It can also apply a different algorithm to blood pressure data. Furthermore, the analysis unit can select the optimal analysis algorithm according to the user's health data category. This improves the accuracy of the analysis by applying the most suitable analysis algorithm for each health data category. Some or all of the above-described processes in the analysis unit may be performed using a generative AI, or they may be performed without a generative AI. For example, the analysis unit can input the user's health data into a generative AI and have the generative AI apply the appropriate analysis algorithm based on the data category.

[0078] 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 result. If the user is relaxed, the analysis unit can also provide a detailed analysis result. Furthermore, if the user is stressed, the analysis unit can provide a visually easy-to-understand analysis result. By adjusting the length of the analysis according to the user's emotions, more appropriate analysis results can be provided. 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 or without a generative AI. For example, the analysis unit can input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0079] The analysis unit can determine the priority of analysis based on the timing of health data submission during the analysis process. For example, the analysis unit may prioritize the analysis of the most recent health data. It can also prioritize the analysis of health data within a period specified by the user. Furthermore, the analysis unit can determine the priority of analysis by considering fluctuations in the user's health status. This allows for the prioritization of analysis based on the timing of health data submission, thereby ensuring that the most recent data is analyzed first. Some or all of the above-described processes in the analysis unit may be performed using a generating AI, or they may be performed without a generating AI. For example, the analysis unit can input the user's health data into a generating AI and have the generating AI determine the priority of analysis based on the submission timing.

[0080] The analysis unit can adjust the order of analysis based on the relevance of health data during the analysis process. For example, the analysis unit may prioritize the analysis of highly relevant health data. It can also prioritize the analysis of data related to the user's health status. Furthermore, the analysis unit can automatically adjust the order of analysis based on the relevance of health data. This allows for the prioritization of highly relevant data by adjusting the order of analysis based on the relevance of health data. Some or all of the above-described processes in the analysis unit may be performed using a generative AI, or they may be performed without a generative AI. For example, the analysis unit can input the user's health data into a generative AI and have the generative AI perform the adjustment of the analysis order based on relevance.

[0081] The generation unit can estimate the user's emotions and adjust the way the generated travel plan is presented based on the estimated emotions. For example, if the user is relaxed, the generation unit can provide a detailed travel plan. If the user is in a hurry, the generation unit can also provide a concise travel plan that gets straight to the point. Furthermore, if the user is stressed, the generation unit can provide a visually easy-to-understand travel plan. In this way, by adjusting the way the travel plan is presented according to the user's emotions, a more appropriate plan can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation 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 generation unit may be performed using the generation AI or not. For example, the generation unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0082] The generation unit can adjust the level of detail generated based on the importance of the travel plan during generation. For example, the generation unit provides detailed information for important travel plans. It can also provide concise information for general travel plans. Furthermore, the generation unit can adjust the level of detail generated according to the user's travel purpose. This allows for the provision of detailed information for important plans by adjusting the level of detail based on the importance of the travel plan. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input the user's travel plan data into a generation AI and have the generation AI perform the adjustment of the level of detail based on importance.

[0083] The generation unit can apply different generation algorithms depending on the category of the travel plan during generation. For example, the generation unit can apply a specific generation algorithm to travel plans intended for relaxation. It can also apply a different generation algorithm to active travel plans. Furthermore, the generation unit can select the optimal generation algorithm according to the user's travel purpose. This improves the accuracy of generation by applying the optimal generation algorithm according to the category of the travel plan. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input the user's travel plan data into a generation AI and have the generation AI execute the application of a generation algorithm according to the category.

[0084] The generation unit can estimate the user's emotions and adjust the length of the generated travel plan based on the estimated emotions. For example, if the user is in a hurry, the generation unit can provide a short, concise travel plan. If the user is relaxed, the generation unit can also provide a detailed travel plan. Furthermore, if the user is stressed, the generation unit can provide a visually easy-to-understand travel plan. By adjusting the length of the travel plan according to the user's emotions, a more appropriate plan can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using the generation AI or not. For example, the generation unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0085] The generation unit can determine the generation priority based on the submission date of the travel plan during generation. For example, the generation unit may prioritize generating the most recent travel plan. It can also prioritize generating travel plans within a period specified by the user. Furthermore, the generation unit can determine the generation priority according to the user's travel purpose. This allows for the prioritization of the most recent plan by determining the generation priority based on the submission date of the travel plan. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input the user's travel plan data into a generation AI and have the generation AI determine the generation priority based on the submission date.

[0086] The generation unit can adjust the generation order based on the relevance of the travel plans during generation. For example, the generation unit can prioritize generating highly relevant travel plans. It can also prioritize generating plans related to the user's travel purpose. Furthermore, the generation unit can automatically adjust the generation order based on the relevance of the travel plans. This allows for the priority generation of highly relevant plans by adjusting the generation order based on the relevance of the travel plans. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input the user's travel plan data into a generation AI and have the generation AI perform the adjustment of the generation order based on relevance.

[0087] The service provider can estimate the user's emotions and adjust how the travel plan is displayed based on the estimated emotions. For example, if the user is relaxed, the service provider can provide a detailed travel plan. If the user is in a hurry, the service provider can also provide a concise travel plan that gets straight to the point. Furthermore, if the user is stressed, the service provider can provide a visually easy-to-understand travel plan. By adjusting how the travel plan is displayed according to the user's emotions, a more appropriate display becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using generative AI or not. For example, the service provider can input the user's facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0088] The service provider can select the optimal display method by referring to the user's past travel history at the time of service provision. For example, the service provider can provide the optimal display method based on the display method the user has preferred to use in the past. The service provider can also preferentially suggest a specific display method based on the user's past travel history. Furthermore, the service provider can analyze the user's travel history and select the display method with the highest visibility. This makes it possible to provide a user-friendly display by selecting the optimal display method based on past travel history. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input the user's travel history data into a generating AI and have the generating AI select the optimal display method.

[0089] The service provider can estimate the user's emotions and prioritize the travel plans offered based on those emotions. For example, if the user is relaxed, the service provider may prioritize offering a detailed travel plan. If the user is in a hurry, the service provider may prioritize offering a concise travel plan that gets straight to the point. Furthermore, if the user is stressed, the service provider may prioritize offering a visually easy-to-understand travel plan. This allows the service provider to prioritize important travel plans by prioritizing them according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using or without generative AI. For example, the service provider can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0090] The service provider can select the optimal display method at the time of delivery, taking into account the user's device information. For example, if the user is using a smartphone, the service provider can provide a display method that matches the screen size. Furthermore, if the user is using a tablet, the service provider can provide a display method optimized for a larger screen. In addition, if the user is using a smartwatch, the service provider can provide a concise and highly visible display method. This allows for a display optimized for the user's device by considering device information. Some or all of the above processing in the service provider may be performed using AI, or not. For example, the service provider can input the user's device information into a generating AI and have the generating AI select the optimal display method.

[0091] The management unit can estimate the user's emotions and adjust the health management method based on the estimated emotions. For example, if the user is relaxed, the management unit can provide a detailed health management plan. If the user is in a hurry, the management unit can also provide a concise health management plan that gets straight to the point. Furthermore, if the user is stressed, the management unit can provide a visually easy-to-understand health management plan. This allows for more appropriate health management by adjusting the health management method according to 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 management unit may be performed using generative AI or not. For example, the management unit can input the user's facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0092] The management department can select the optimal management method by referring to the user's past health data during health management. For example, the management department can provide the optimal health management method based on the user's past health data. The management department can also preferentially suggest a specific management method based on the user's health history. Furthermore, the management department can analyze the user's health data and select the most effective management method. This enables effective health management by selecting the optimal management method based on past health data. Some or all of the above processes in the management department may be performed using AI or not. For example, the management department can input the user's past health data into a generating AI and have the generating AI select the optimal management method.

[0093] The management unit can customize the means of health management based on the user's current lifestyle. For example, the management unit can provide the optimal health management method based on the user's current lifestyle. The management unit can also customize the means of health management according to the user's lifestyle. Furthermore, the management unit can adjust the means of management based on the user's current health status. This allows for more appropriate health management by customizing the means of management based on the current lifestyle. Some or all of the above processes in the management unit may be performed using AI or not. For example, the management unit can input user lifestyle data into a generating AI and have the generating AI perform the customization of the means of management.

[0094] The management unit can estimate the user's emotions and determine the priority of health management based on those emotions. For example, if the user is relaxed, the management unit can prioritize providing a detailed health management plan. If the user is in a hurry, the management unit can also prioritize providing a concise health management plan that gets straight to the point. Furthermore, if the user is stressed, the management unit can prioritize providing a visually easy-to-understand health management plan. This allows for prioritizing important management by determining the priority of health management according to 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 management unit may be performed using generative AI or not. For example, the management unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0095] The management department can select the optimal management method when managing a user's health, taking into account the user's geographical location. For example, if the user is in a specific region, the management department can provide a health management method relevant to that region. The management department can also suggest the optimal health management method based on the user's current location. Furthermore, if the user is traveling, the management department can adjust the management method based on health information from their travel destination. This allows for the provision of highly relevant management methods by considering geographical location information. Some or all of the above processes in the management department may be performed using AI or not. For example, the management department can input the user's geographical location information into a generating AI and have the generating AI select the optimal management method.

[0096] The management department can analyze a user's social media activity and propose management methods during health management. For example, the management department can extract health-related information from a user's social media posts and use it as a management method. The management department can also analyze a user's social media activity and propose management methods related to their health status. Furthermore, the management department can supplement health management methods based on a user's social media activity history. In this way, by analyzing social media activity, it can provide relevant management methods. Some or all of the above processes in the management department may be performed using AI or not. For example, the management department can input a user's social media data into a generating AI and have the generating AI propose the optimal management method.

[0097] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0098] The reception desk can estimate the user's emotions and adjust the input method for health data and travel preferences based on the estimated emotions. For example, if the user is stressed, a simple interface can be provided to minimize the input steps. If the user is relaxed, detailed input options can be provided, and customizable input methods can be suggested. Furthermore, if the user is in a hurry, voice input can be prioritized to allow for quick input of health data and travel preferences. This allows for more appropriate data entry by adjusting the input method 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 reception desk may be performed using AI or not. For example, the reception desk can input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0099] The reception desk can analyze the user's past health data and travel history to suggest the optimal input format. For example, it can automatically suggest the optimal input format based on the user's past health data and travel preferences. It can also prioritize frequently visited places and preferred activities based on the user's past travel history. Furthermore, it can analyze changes in the user's health data and provide the input format best suited to their current health condition. This improves input efficiency by suggesting the optimal input format based on past data. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's past health data into a generating AI and have the generating AI suggest the optimal input format.

[0100] The reception unit can filter health data input based on the user's current lifestyle and health status. For example, when a user inputs their current lifestyle, it can display only relevant health data to simplify input. It can also filter data based on the user's current health status to ensure only necessary data is entered. Furthermore, it can automatically adjust the priority of data to be entered according to the user's lifestyle and health status. This simplifies data input by filtering data based on the user's current lifestyle and health status. Some or all of the above processing in the reception unit may be performed using AI or not. For example, the reception unit can input the user's lifestyle data into a generating AI and have the generating AI perform the filtering.

[0101] The reception desk can estimate the user's emotions and determine the priority of the data to be entered based on the estimated emotions. For example, if the user is stressed, it can prioritize inputting only important data. If the user is relaxed, it can prompt them to enter detailed data. Furthermore, if the user is in a hurry, it can prioritize inputting the most important data. In this way, important data can be prioritized by determining the data priority according to 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 reception desk may be performed using AI or not. For example, the reception desk can input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0102] The reception desk can prioritize inputting highly relevant data when users enter health data, taking into account their geographical location. For example, if a user is in a specific region, health data related to that region can be prioritized. It can also automatically suggest relevant health information based on the user's current location. Furthermore, if a user is traveling, health information for their travel destination can be prioritized. This allows for the priority input of highly relevant data by considering geographical location. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's geographical location information into a generating AI and have the generating AI suggest highly relevant data.

[0103] 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 relaxed, it can provide detailed analysis results. If the user is in a hurry, it can provide concise analysis results that get straight to the point. Furthermore, if the user is stressed, it can provide visually easy-to-understand analysis results. In this way, by adjusting the presentation of the analysis according to the user's emotions, more appropriate analysis results can be provided. 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 generative AI or not using generative AI. For example, the analysis unit can input the user's facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0104] The analysis unit can adjust the level of detail of the analysis based on the importance of the health data during the analysis. For example, it can perform a detailed analysis on important health data, and a simplified analysis on general health data. Furthermore, it can adjust the level of detail of the analysis according to the user's health status. This allows for detailed analysis of important data by adjusting the level of detail of the analysis based on the importance of the health data. Some or all of the above processing in the analysis unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the analysis unit can input the user's health data into a generation AI and have the generation AI perform the adjustment of the level of detail of the analysis based on importance.

[0105] The analysis unit can apply different analysis algorithms depending on the category of health data during analysis. For example, a specific algorithm can be applied to heart rate data for analysis. A different algorithm can also be applied to blood pressure data for analysis. Furthermore, the optimal analysis algorithm can be selected according to the user's health data category. This improves the accuracy of the analysis by applying the most suitable analysis algorithm according to the health data category. Some or all of the above-described processes in the analysis unit may be performed using a generative AI, or they may be performed without a generative AI. For example, the analysis unit can input the user's health data into a generative AI and have the generative AI execute the application of the appropriate analysis algorithm based on the category.

[0106] The generation unit can estimate the user's emotions and adjust the way the generated travel plan is presented based on the estimated emotions. For example, if the user is relaxed, it can provide a detailed travel plan. If the user is in a hurry, it can provide a concise travel plan that gets straight to the point. Furthermore, if the user is stressed, it can provide a visually easy-to-understand travel plan. In this way, by adjusting the way the travel plan is presented according to the user's emotions, a more appropriate plan can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above processing in the generation unit may be performed using the generation AI or not. For example, the generation unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0107] The generation unit can adjust the level of detail generated based on the importance of the travel plan during generation. For example, it can provide detailed information for important travel plans, and concise information for general travel plans. Furthermore, it can adjust the level of detail generated according to the user's travel purpose. This allows for the provision of detailed information for important plans by adjusting the level of detail based on the importance of the travel plan. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input the user's travel plan data into a generation AI and have the generation AI perform the adjustment of the level of detail generated based on importance.

[0108] The following briefly describes the processing flow for example form 2.

[0109] Step 1: The reception desk inputs the user's health data and travel preferences. For example, the user can input their daily health status and preferred travel style. The reception desk can also use AI to convert the user's input data into a format that is easy to analyze. Step 2: The analysis unit uses a generation AI to analyze the information entered by the reception unit. For example, it extracts data to generate an optimal travel plan based on the user's health data and travel preferences. Step 3: The generation unit uses the generation AI to generate the optimal travel plan based on the information analyzed by the analysis unit. For example, it suggests the best travel destination and activities according to the user's health condition and travel preferences. Step 4: The providing unit provides the travel plan generated by the generating unit to the user. For example, the travel plan can be displayed through a web application or a mobile application. Step 5: The management department uses 5G and satellite communication to monitor the user's health status in real time during their trip. For example, if a user's health status changes, a generated AI detects the change and provides an appropriate support plan.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] Each of the multiple elements described above, including the reception unit, analysis unit, generation unit, provision unit, and management unit, is implemented, for example, by at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the reception device 38 of the smart device 14, which inputs the user's health data and travel preferences. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which analyzes the input information using a generation AI. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which generates an optimal travel plan based on the analyzed information. The provision unit is implemented, for example, by the output device 40 of the smart device 14, which provides the generated travel plan to the user. The management unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which manages the user's health status during travel in real time using 5G or satellite communication. The correspondence between each unit and the devices and control units is not limited to the examples described above, and various changes are possible.

[0114] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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).

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.).

[0126] 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.

[0127] 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.

[0128] 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.

[0129] Each of the multiple elements described above, including the reception unit, analysis unit, generation unit, provision unit, and management unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the smart glasses 214, which inputs the user's health data and travel preferences. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which analyzes the input information using a generation AI. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which generates an optimal travel plan based on the analyzed information. The provision unit is implemented, for example, by the speaker 240 of the smart glasses 214, which provides the generated travel plan to the user. The management unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which manages the user's health status in real time using 5G or satellite communication. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0130] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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).

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.).

[0142] 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.

[0143] 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.

[0144] 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.

[0145] Each of the multiple elements described above, including the reception unit, analysis unit, generation unit, provision unit, and management unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the headset terminal 314, which inputs the user's health data and travel preferences. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which analyzes the input information using a generation AI. The generation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which generates an optimal travel plan based on the analyzed information. The provision unit is implemented by, for example, the display 343 of the headset terminal 314, which provides the generated travel plan to the user. The management unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which manages the user's health status in real time using 5G or satellite communication. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0146] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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).

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.).

[0159] 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.

[0160] 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.

[0161] 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.

[0162] Each of the multiple elements described above, including the reception unit, analysis unit, generation unit, provision unit, and management unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the robot 414, which inputs the user's health data and travel preferences. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which analyzes the input information using a generation AI. The generation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which generates an optimal travel plan based on the analyzed information. The provision unit is implemented by, for example, the speaker 240 of the robot 414, which provides the generated travel plan to the user. The management unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which manages the user's health status during travel in real time using 5G or satellite communication. The correspondence between each unit and the devices and control units is not limited to the examples described above, and various changes are possible.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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."

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] (Note 1) A reception area where you enter your health data and travel preferences, An analysis unit analyzes the information input by the reception unit, A generation unit generates an optimal travel plan based on the information analyzed by the analysis unit, A providing unit that provides the travel plan generated by the generation unit, It includes a management unit that manages the health status of travelers in real time. A system characterized by the following features. (Note 2) The aforementioned reception unit is It estimates the user's emotions and adjusts how health data and travel preferences are entered based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned reception unit is We analyze the user's past health data and travel history to suggest the optimal input format. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned reception unit is When entering health data, filtering is performed based on the user's current lifestyle and health status. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned reception unit is It estimates the user's emotions and determines the priority of input data based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reception unit is When entering health data, the system prioritizes inputting highly relevant data, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is When entering health data, the system analyzes the user's social media activity and inputs relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned analysis unit, It estimates the user's emotions and adjusts the way the analysis is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned analysis unit, During analysis, the level of detail of the analysis is adjusted based on the importance of the health data. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the category of health data. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned analysis unit, It estimates the user's emotions and adjusts the length of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, During analysis, the priority of analyses is determined based on when the health data was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, During analysis, the order of analysis is adjusted based on the relevance of the health data. The system described in Appendix 1, characterized by the features described herein. (Note 14) The generating unit is We estimate the user's emotions and adjust how the travel plan is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The generating unit is During generation, adjust the level of detail based on the importance of the travel plan. The system described in Appendix 1, characterized by the features described herein. (Note 16) The generating unit is During generation, different generation algorithms are applied depending on the category of the travel plan. The system described in Appendix 1, characterized by the features described herein. (Note 17) The generating unit is It estimates the user's emotions and adjusts the length of the travel plan generated based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The generating unit is During generation, the priority of generation is determined based on when the travel plan was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 19) The generating unit is During generation, the order of generation is adjusted based on the relevance of the travel plan. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned supply unit is, We estimate the user's emotions and adjust how travel plans are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned supply unit is, When providing the service, the system will refer to the user's past travel history to select the most suitable display method. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned supply unit is, It estimates the user's emotions and prioritizes the travel plans offered based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned supply unit is, When providing the service, the optimal display method is selected considering the user's device information. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned management department, It estimates the user's emotions and adjusts health management methods based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned management department, During health management, the system selects the optimal management method by referring to the user's past health data. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned management department, When managing health, the management methods are customized based on the user's current lifestyle. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned management department, It estimates the user's emotions and determines health management priorities based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned management department, When managing health information, the optimal management method is selected by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned management department, When managing health, we analyze users' social media activity and suggest management methods. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0182] 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 reception area where you enter your health data and travel preferences, An analysis unit analyzes the information input by the reception unit, A generation unit generates an optimal travel plan based on the information analyzed by the analysis unit, A providing unit that provides the travel plan generated by the generation unit, It includes a management unit that manages the health status of travelers in real time. A system characterized by the following features.

2. The aforementioned reception unit is It estimates the user's emotions and adjusts how health data and travel preferences are entered based on those estimated emotions. The system according to feature 1.

3. The aforementioned reception unit is We analyze the user's past health data and travel history to suggest the optimal input format. The system according to feature 1.

4. The aforementioned reception unit is When entering health data, filtering is performed based on the user's current lifestyle and health status. The system according to feature 1.

5. The aforementioned reception unit is It estimates the user's emotions and determines the priority of input data based on those estimated emotions. The system according to feature 1.

6. The aforementioned reception unit is When entering health data, the system prioritizes inputting highly relevant data, taking into account the user's geographical location. The system according to feature 1.

7. The aforementioned reception unit is When entering health data, the system analyzes the user's social media activity and inputs relevant data. The system according to feature 1.

8. The aforementioned analysis unit, It estimates the user's emotions and adjusts the way the analysis is presented based on those estimated emotions. The system according to feature 1.

Images