system

Abstract

The system according to this embodiment aims to create customized travel plans based on the user's purpose and wishes, and to match travelers with families living in remote areas. [Solution] The system according to the embodiment comprises a reception unit, a generation unit, and a matching unit. The reception unit receives input to understand the user's purpose and wishes. The generation unit creates a travel plan based on the information received by the reception unit. The matching unit matches travelers with families living in remote areas based on the travel plan created by the generation unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that a customized travel plan based on the purpose and wish of a user has not been sufficiently created, and a family living in a hidden place and a traveler have not been sufficiently matched.

[0005] The system according to an embodiment aims to create a customized travel plan based on the purpose and wish of a user, and match a family living in a hidden place and a traveler.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, a generation unit, and a matching unit. The reception unit receives input to understand the user's purpose and wishes. The generation unit creates a travel plan based on the information received by the reception unit. The matching unit matches travelers with families living in remote areas based on the travel plan created by the generation unit. [Effects of the Invention]

[0007] The system according to this embodiment can create customized travel plans based on the user's purpose and wishes, and can match travelers with families living in remote areas. [Brief explanation of the drawing]

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

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

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

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

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

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

[0014] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between a plurality of computers. Examples of communication standards applicable to the communication I / F include wireless communication standards including 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 three or more matters are expressed by connecting them with "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 travel plan creation system according to an embodiment of the present invention is a system that uses AI to match travelers with families living in remote areas to create completely customized travel plans in order to understand the user's purpose and desires and provide a unique local experience. In this system, the AI ​​understands the user's purpose and desires, and based on the user's purpose and desires, the AI ​​matches travelers with families living in remote areas, thereby enabling the user to obtain a unique local experience. Furthermore, as a measure for health management, a GPS-based emergency alert system is linked to notify the AI ​​of the user's location information and to respond immediately if a problem occurs. In addition, the AI ​​automatically checks the user's health status (physical condition, forgotten medication, etc.) at set times, and if new symptoms appear, the AI ​​searches for an appropriate medical institution and sets a visit time. As a measure for peace of mind, the AI ​​provides the latest information that the user should know, such as epidemics, weather, and local news during the trip. Furthermore, a 24-hour AI support center is set up so that users can consult and inquire at any time, reducing language difficulties and anxiety in new places. In addition, the AI ​​manages important information such as emergency contacts and travel information and provides it when necessary. This system targets elderly individuals who wish to travel alone, aiming to address the challenge of planning and executing travel independently, despite the existing demand for senior-friendly travel services. AI understands the user's purpose and desires from the outset, creating customized travel plans that enable enjoyable solo travel for seniors. Furthermore, the AI ​​matches travelers with local families, providing local experiences that allow seniors to spend time with local families rather than alone, directly learning about local life and culture. This system addresses the expanding senior travel market and the increasing demand for travel to remote locations. Advances in AI enable accurate understanding of user needs and the provision of customized travel plans. Additionally, the growing demand among travelers for individual local experiences and unique interactions allows for improved accuracy in the AI-powered matching system.The vision of this system is to improve the quality of life (QOL) of seniors by providing them with the most complementary and fulfilling travel experiences possible, promoting interaction with locals and cultural understanding, thereby making trips to remote areas more meaningful. Furthermore, by supporting solo travelers so they can enjoy themselves with peace of mind, the system aims to help them pursue self-realization and a sense of fulfillment in life by gaining new insights and personal growth through rich local experiences and interactions. To achieve this, the travel plan creation system uses AI to understand the user's purpose and desires, and to provide unique local experiences by matching travelers with families living in remote areas, creating completely customized travel plans.

[0029] The travel plan creation system according to this embodiment comprises a reception unit, a generation unit, and a matching unit. The reception unit receives input to understand the user's purpose and desires. The reception unit provides an interface for the user to input, for example, the purpose of the trip, budget, desired activities, etc. The reception unit can receive user information by methods such as voice input, text input, and touch input. The generation unit creates a travel plan based on the information received by the reception unit. The generation unit generates the optimal travel plan for the user's purpose and desires, for example, using AI. The generation unit analyzes the user's input information and proposes the optimal travel destination and activities, for example. The generation unit provides a customized travel plan, for example, taking into account the user's past travel history and current living situation. The matching unit matches travelers with families living in remote areas based on the travel plan created by the generation unit. The matching unit selects families that match the user's preferences, for example, using AI. The matching unit proposes the optimal family, for example, taking into account the user's emotions and past travel history. The matching unit matches the optimal family based on the user's current living situation and geographical location information, for example. This allows the travel planning system to match travelers with families living in remote areas based on the user's purpose and preferences.

[0030] The reception desk accepts input to understand the user's purpose and preferences. For example, it provides an interface for users to input their travel purpose, budget, and desired activities. Specifically, the reception desk provides web forms and mobile applications designed for intuitive user operation. These interfaces include input elements such as dropdown menus, checkboxes, and radio buttons to allow users to easily enter travel details. Furthermore, voice input functionality allows users to communicate their preferences verbally. For example, if a user voice-inputs "I want to relax at a beach resort," the system converts this into text and sends it to the generation unit for analysis. Text input provides a text box where users can freely describe their preferences, allowing for detailed requests. Touch input allows users to directly select their desired locations on a map using the touchscreen of their smartphone or tablet. This enables the reception desk to accommodate diverse user input methods and collect information accurately and quickly. Additionally, the reception desk analyzes user input in real time and presents additional questions as needed to gather more detailed information. For example, if a user selects "hiking" as their "activity," the system displays additional questions such as "difficulty level of hiking" and "desired hiking trail length." This allows the reception department to understand the user's specific needs and provide accurate information to the generation department.

[0031] The generation unit creates travel plans based on information received by the reception unit. For example, the generation unit uses AI to generate travel plans that are best suited to the user's purpose and desires. Specifically, the generation unit uses natural language processing technology to analyze the user's input information and understand their travel purpose and desires. For example, if a user inputs "I want to relax," the generation unit analyzes the keyword "relax" and suggests relaxing travel destinations such as beach resorts or hot springs. It also considers the user's budget information and selects the best accommodations and activities within that budget. Furthermore, the generation unit provides customized travel plans by considering past travel history and current living circumstances. For example, based on data on places the user has visited and activities they have participated in in the past, it suggests new travel destinations and activities the user has not yet visited. The generation unit can use AI machine learning algorithms to learn the user's preferences and tendencies and generate more personalized travel plans. For example, if a user has previously enjoyed "adventure" or "outdoor" related travel, the generation unit considers this tendency and includes activities such as "mountain climbing" or "rafting" in the next travel plan. Furthermore, the generation unit can utilize real-time updated information to incorporate the latest travel destination and event information. For example, it can acquire information on festivals and events held in a specific region and incorporate it into the user's travel plan. This allows the generation unit to provide the optimal and most up-to-date travel plan based on the user's purpose and preferences.

[0032] The matching unit matches travelers with families living in remote areas based on travel plans created by the generation unit. Specifically, the matching unit uses AI to select families that match the user's preferences. For example, if a user wishes to "experience local culture," the matching unit will select a family that has a deep understanding of the region's culture and traditions. The matching unit also considers the user's emotions and past travel history to suggest the most suitable families. For example, if a user has previously enjoyed "homestays" or "interacting with local families," the matching unit will consider this preference and select a family that can provide a similar experience on the next trip. It also matches the most suitable families based on the user's current living situation and geographical location. For example, it prioritizes selecting families in areas easily accessible from where the user currently lives. Furthermore, the matching unit also considers the families' wishes and conditions to achieve a mutually satisfying match. For example, if a family wishes for a "traveler who can speak English," the matching unit will select a user who meets that condition. In this way, the matching unit can provide the best possible match for both users and families, improving the quality of the travel experience. Furthermore, the matching unit collects feedback after matching and uses it as data to improve the accuracy of future matches. For example, based on feedback from both users and families, the matching algorithm is improved to achieve more accurate matches. The matching unit also provides tools and platforms to support communication between users and families, facilitating smooth interactions. In this way, the matching unit can provide a highly satisfying travel experience for both users and families.

[0033] The Emergency Department can integrate a GPS-based emergency alert system to notify the AI ​​of the user's location and respond when a problem occurs. For example, the Emergency Department can grasp the user's location in real time and respond quickly when an emergency occurs. For example, the Emergency Department can notify the nearest medical facility or emergency contact based on the user's location. For example, the Emergency Department can provide the optimal evacuation route based on the user's location. This allows the Emergency Department to grasp the user's location in real time and respond quickly when a problem occurs. Some or all of the above processes in the Emergency Department may be performed using AI or not. For example, the Emergency Department can input the user's location into the AI ​​and have the AI ​​execute emergency response measures.

[0034] The health management department can automatically check the user's health status at set times using AI. The health management department can periodically check the user's health status, such as body temperature, blood pressure, and heart rate. The health management department can also propose appropriate countermeasures based on the user's health status. The health management department can also propose a visit to a medical institution based on the user's health status. This allows the health management department to regularly check the user's health status and take appropriate action. Some or all of the above processes in the health management department may be performed using AI or not. For example, the health management department can input the user's health status into the AI ​​and have the AI ​​perform the health status check.

[0035] The medical search unit can use AI to search for appropriate medical institutions and set visit times when new symptoms appear. For example, the medical search unit searches for the most suitable medical institution based on the user's symptoms. For example, the medical search unit sets visit times at medical institutions based on the user's symptoms. For example, the medical search unit makes reservations at medical institutions based on the user's symptoms. This allows the medical search unit to quickly search for appropriate medical institutions and set visit times when new symptoms appear. Some or all of the above processes in the medical search unit may be performed using AI or not. For example, the medical search unit can input the user's symptoms into the AI ​​and have the AI ​​perform the search for medical institutions and set visit times.

[0036] The information provision unit can use AI to provide users with the latest information they need to know, such as epidemics, weather, and local news during their trip. For example, the information provision unit can provide information on epidemics in the user's current location and travel destination. For example, the information provision unit can provide information on weather in the user's current location and travel destination. For example, the information provision unit can provide local news in the user's current location and travel destination. In this way, the information provision unit can provide users with the latest information they need during their trip. Some or all of the above processing in the information provision unit may be performed using AI or not. For example, the information provision unit can input information on the user's current location and travel destination into the AI ​​and have the AI ​​perform the task of providing the latest information.

[0037] The support department can establish a 24-hour AI support center, allowing users to consult and inquire at any time. For example, the support department can provide 24-hour support if a user encounters a problem while traveling. For example, the support department can provide 24-hour support if a user has questions or concerns while traveling. For example, the support department can provide 24-hour support if an emergency occurs while a user is traveling. This allows users to enjoy their trip with peace of mind, as they can consult and inquire at any time. Some or all of the above processes in the support department may be performed using AI or not. For example, the support department can input the user's inquiry into the AI ​​and have the AI ​​provide 24-hour support.

[0038] The Information Management Department can use AI to manage and provide important information, such as emergency contacts and travel information, when necessary. For example, the Information Management Department can manage and provide users' emergency contacts when needed. For example, the Information Management Department can manage and provide users' travel information when needed. For example, the Information Management Department can manage users' health information when needed. This allows the Information Management Department to quickly provide necessary information in emergencies. Some or all of the above processes in the Information Management Department may be performed using AI or not. For example, the Information Management Department can input users' emergency contacts and travel information into the AI ​​and have the AI ​​manage and provide the information.

[0039] The reception desk can analyze the user's past travel history and suggest the optimal input method. For example, the reception desk can automatically display as suggestions the purposes and preferences the user has frequently entered in the past. For example, the reception desk can prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. For example, the reception desk can predict and suggest preferences related to specific seasons or events based on the user's past travel history. In this way, the reception desk can streamline the input process by suggesting the optimal input method based on the user's past travel history. Some or all of the above processes in the reception desk may be performed using AI or not. For example, the reception desk can input the user's past travel history data into a generating AI and have the generating AI suggest the optimal input method.

[0040] The reception desk can filter the user's current lifestyle and areas of interest during input. For example, the reception desk can suggest relevant travel plans based on the user's current lifestyle (work, family, etc.). For example, the reception desk can suggest the optimal travel plan based on the user's areas of interest (culture, nature, activities, etc.). For example, the reception desk can suggest a reasonable travel plan based on the user's current health and physical condition. In this way, the reception desk can suggest more appropriate travel plans by filtering based on the user's current lifestyle and areas of interest. 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 data on the user's lifestyle and areas of interest into a generating AI and have the generating AI perform the filtering.

[0041] The reception desk can prioritize retrieving highly relevant information by considering the user's geographical location during input. For example, the reception desk can suggest nearby tourist attractions and activities based on the user's current location. For example, the reception desk can automatically set the optimal departure point and destination based on the user's geographical location. For example, if the user is interested in a particular region, the reception desk will prioritize displaying information related to that region. In this way, the reception desk can propose a more appropriate travel plan by prioritizing the retrieval of highly relevant information by considering the user's 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 retrieve highly relevant information.

[0042] The reception desk can analyze the user's social media activity and obtain relevant information during input. For example, the reception desk can suggest the optimal travel plan based on the travel destinations and activities the user has shared on social media. For example, the reception desk can analyze the content of the user's social media posts and provide information related to topics of interest. For example, the reception desk can suggest a travel plan by referring to the travel destinations of influencers and friends the user follows. In this way, the reception desk can suggest a more appropriate travel plan by analyzing the user's social media activity. 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 social media activity data into a generating AI and have the generating AI perform the task of obtaining relevant information.

[0043] The generation unit can propose the optimal travel plan by referring to the user's past travel history when generating a travel plan. For example, the generation unit can propose similar travel plans based on places and experiences the user has visited in the past. For example, the generation unit can create a plan that includes the user's preferred activities and sightseeing spots from the user's past travel history. For example, the generation unit can propose the optimal plan by considering places and activities the user has avoided in the past. In this way, the generation unit can propose a more appropriate travel plan by referring to the user's past travel history. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input the user's past travel history data into a generation AI and have the generation AI perform the task of proposing the optimal plan.

[0044] The generation unit can customize travel plans based on the user's current living situation when generating them. For example, the generation unit can suggest a manageable schedule based on the user's current work and family circumstances. For example, the generation unit can create a plan that includes appropriate activities based on the user's current health and physical condition. For example, the generation unit can provide a customized travel plan based on the user's current interests. In this way, the generation unit can provide a more appropriate travel plan by customizing it based on the user's current living situation. Some or all of the above processes in the generation unit may be performed using AI or not. For example, the generation unit can input the user's living situation data into a generation AI and have the generation AI perform the plan customization.

[0045] The generation unit can propose the optimal travel plan by considering the user's geographical location when generating a travel plan. For example, the generation unit can suggest nearby tourist attractions and activities based on the user's current location. For example, the generation unit can automatically set the optimal departure point and destination based on the user's geographical location. For example, if the user is interested in a particular region, the generation unit can prioritize displaying information related to that region. In this way, the generation unit can provide a more appropriate travel plan by proposing the optimal plan by considering the user's geographical location. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input the user's geographical location information into a generation AI and have the generation AI perform the task of proposing the optimal plan.

[0046] The generation unit can analyze the user's social media activity and customize the travel plan when generating it. For example, the generation unit can suggest the optimal travel plan based on the travel destinations and activities the user has shared on social media. For example, the generation unit can analyze the content of the user's social media posts and provide information related to themes of interest. For example, the generation unit can suggest a travel plan by referring to the travel destinations of influencers and friends the user follows. In this way, the generation unit can provide a more appropriate travel plan by analyzing the user's social media activity. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input the user's social media activity data into a generation AI and have the generation AI perform the plan customization.

[0047] The matching unit can suggest the most suitable host family by referring to the user's past travel history during the matching process. For example, the matching unit can suggest similar host families based on places and experiences the user has visited in the past. For example, the matching unit can create a plan that includes preferred host families from the user's past travel history. For example, the matching unit can suggest the most suitable host family by considering places and host families the user has avoided in the past. In this way, the matching unit can suggest a more appropriate host family by referring to the user's past travel history. Some or all of the above processes in the matching unit may be performed using AI or not. For example, the matching unit can input the user's past travel history data into a generating AI and have the generating AI perform the task of suggesting the most suitable host family.

[0048] The matching unit can customize a household based on the user's current living situation during the matching process. For example, the matching unit can select a household that proposes a manageable schedule based on the user's current work and family circumstances. For example, the matching unit can select an appropriate household based on the user's current health and physical condition. For example, the matching unit can propose a customized household based on the user's current interests and concerns. In this way, the matching unit can propose a more appropriate household by customizing it based on the user's current living situation. Some or all of the above processes in the matching unit may be performed using AI or not. For example, the matching unit can input the user's living situation data into a generating AI and have the generating AI perform the household customization.

[0049] The matching unit can suggest the most suitable home by considering the user's geographical location during the matching process. For example, the matching unit can suggest nearby homes based on the user's current location. For example, the matching unit can automatically set the most suitable home based on the user's geographical location. For example, if the user is interested in a particular area, the matching unit can prioritize displaying homes related to that area. In this way, the matching unit can match the user with a more suitable home by suggesting the most suitable home by considering the user's geographical location. Some or all of the above processes in the matching unit may be performed using AI or not. For example, the matching unit can input the user's geographical location into a generating AI and have the generating AI perform the task of suggesting the most suitable home.

[0050] The matching unit can analyze the user's social media activity and customize the chosen home during the matching process. For example, the matching unit can suggest the most suitable home based on travel destinations and activities shared by the user on social media. For example, the matching unit can analyze the user's social media posts and provide homes related to themes of interest. For example, the matching unit can suggest homes based on travel destinations shared by influencers and friends the user follows. In this way, the matching unit can suggest more appropriate homes by analyzing the user's social media activity. Some or all of the above processes in the matching unit may be performed using AI or not. For example, the matching unit can input the user's social media activity data into a generating AI and have the generating AI perform the home customization.

[0051] The Emergency Department can propose the most appropriate response when an emergency alert is triggered, by referring to the user's past health history. For example, the Emergency Department can propose the most appropriate response based on health problems the user has experienced in the past. For example, the Emergency Department can provide a response to a specific symptom based on the user's past health history. For example, the Emergency Department can analyze the user's past health history and propose a quick response. This allows the Emergency Department to respond more appropriately by referring to the user's past health history. Some or all of the above processes in the Emergency Department may be performed using AI or not. For example, the Emergency Department can input the user's past health history data into a generating AI and have the generating AI propose the most appropriate response.

[0052] The Emergency Unit can acquire the user's current location information in real time during an emergency alert and propose the most appropriate response. For example, the Emergency Unit can suggest the nearest medical facility based on the user's current location. For example, the Emergency Unit can provide the optimal evacuation route based on the user's current location information. For example, if the user is on the move, the Emergency Unit can update the location information in real time and propose the most appropriate response. This allows the Emergency Unit to respond more appropriately by acquiring the user's current location information in real time. Some or all of the above processes in the Emergency Unit may be performed using AI or not. For example, the Emergency Unit can input the user's current location information into a generating AI and have the generating AI propose the most appropriate response.

[0053] The Emergency Department can propose the most appropriate response when an emergency alert is issued, taking into account the user's geographical location. For example, the Emergency Department may suggest the nearest medical facility based on the user's current location. For example, the Emergency Department may provide the optimal evacuation route based on the user's geographical location. For example, if the user is in a specific area, the Emergency Department may prioritize displaying information relevant to that area. This allows the Emergency Department to respond more appropriately by considering the user's geographical location. Some or all of the above processing in the Emergency Department may be performed using AI or not. For example, the Emergency Department can input the user's geographical location into a generating AI and have the generating AI propose the most appropriate response.

[0054] The Health Management Department can suggest the optimal health check method by referring to the user's past health history when checking their health status. For example, the Health Management Department can suggest the optimal check method based on the health problems the user has experienced in the past. For example, the Health Management Department can provide a check method for a specific symptom based on the user's past health history. For example, the Health Management Department can analyze the user's past health history and suggest a quick check method. This allows the Health Management Department to provide more appropriate health management by referring to the user's past health history. Some or all of the above processes in the Health Management Department may be performed using AI or not. For example, the Health Management Department can input the user's past health history data into a generating AI and have the generating AI suggest the optimal check method.

[0055] The Health Management Department can acquire the user's current location information in real time when checking their health status and propose the most suitable check-up method. For example, the Health Management Department can suggest the nearest medical institution based on the user's current location. For example, the Health Management Department can provide the most suitable check-up method based on the user's current location information. For example, if the user is on the move, the Health Management Department can update the location information in real time and propose the most suitable check-up method. This allows the Health Management Department to provide more appropriate health management by acquiring the user's current location information in real time. Some or all of the above processes in the Health Management Department may be performed using AI or not. For example, the Health Management Department can input the user's current location information into a generating AI and have the generating AI propose the most suitable check-up method.

[0056] The Health Management Department can propose the most suitable health check method when checking a user's health status, taking into account the user's geographical location. For example, the Health Management Department can suggest the nearest medical institution based on the user's current location. For example, the Health Management Department can provide the most suitable health check method based on the user's geographical location. For example, if the user is in a specific region, the Health Management Department can prioritize displaying information related to that region. This allows the Health Management Department to provide more appropriate health management by taking the user's geographical location into consideration. Some or all of the above processes in the Health Management Department may be performed using AI or not. For example, the Health Management Department can input the user's geographical location information into a generating AI and have the generating AI propose the most suitable health check method.

[0057] The medical search unit can suggest the most suitable medical institution by referring to the user's past health history when searching for a medical institution. For example, the medical search unit can suggest the most suitable medical institution based on the medical institutions the user has visited in the past. For example, the medical search unit can provide medical institutions that can treat specific symptoms based on the user's past health history. For example, the medical search unit can analyze the user's past health history and suggest medical institutions that can provide a quick response. In this way, the medical search unit can suggest more appropriate medical institutions by referring to the user's past health history. Some or all of the above processes in the medical search unit may be performed using AI or not. For example, the medical search unit can input the user's past health history data into a generating AI and have the generating AI perform the task of suggesting the most suitable medical institutions.

[0058] The medical search unit can acquire the user's current location information in real time when searching for a medical institution and suggest the most suitable medical institution. For example, the medical search unit suggests the nearest medical institution based on the user's current location. For example, the medical search unit provides the most suitable medical institution based on the user's current location information. For example, if the user is on the move, the medical search unit updates the location information in real time and suggests the most suitable medical institution. As a result, the medical search unit can suggest a more appropriate medical institution by acquiring the user's current location information in real time. Some or all of the above processing in the medical search unit may be performed using AI or not. For example, the medical search unit can input the user's current location information into a generating AI and have the generating AI suggest the most suitable medical institution.

[0059] The medical search unit can suggest the most suitable medical institution when a user searches for a medical institution, taking into account the user's geographical location. For example, the medical search unit suggests the nearest medical institution based on the user's current location. For example, the medical search unit provides the most suitable medical institution based on the user's geographical location. For example, if the user is in a specific region, the medical search unit prioritizes displaying medical institutions related to that region. In this way, the medical search unit can suggest a more appropriate medical institution by taking into account the user's geographical location. Some or all of the above processing in the medical search unit may be performed using AI or not. For example, the medical search unit can input the user's geographical location information into a generating AI and have the generating AI suggest the most suitable medical institution.

[0060] The information provision unit can provide optimal information by referring to the user's past travel history when providing information. For example, the information provision unit can provide relevant information based on places and experiences the user has visited in the past. For example, the information provision unit can provide information on preferred activities and tourist spots from the user's past travel history. For example, the information provision unit can provide optimal information by considering places and activities the user has avoided in the past. In this way, the information provision unit can provide more appropriate information by referring to the user's past travel history. Some or all of the above processing in the information provision unit may be performed using AI or not. For example, the information provision unit can input the user's past travel history data into a generating AI and have the generating AI perform the task of providing optimal information.

[0061] The information provision unit can customize information based on the user's current living situation when providing it. For example, the information provision unit can provide relevant information based on the user's current work and family situation. For example, the information provision unit can provide appropriate information based on the user's current health and physical condition. For example, the information provision unit can provide customized information based on the user's current interests and concerns. In this way, the information provision unit can provide more appropriate information by customizing it based on the user's current living situation. Some or all of the above processing in the information provision unit may be performed using AI or not. For example, the information provision unit can input the user's living situation data into a generating AI and have the generating AI perform the information customization.

[0062] The information provision unit can provide optimal information by considering the user's geographical location when providing information. For example, the information provision unit can provide information about nearby tourist attractions and activities based on the user's current location. For example, the information provision unit can provide information about optimal starting points and destinations based on the user's geographical location. For example, if the user is interested in a particular region, the information provision unit can prioritize displaying information related to that region. In this way, the information provision unit can provide more appropriate information by considering the user's geographical location. Some or all of the above processing in the information provision unit may be performed using AI or not. For example, the information provision unit can input the user's geographical location information into a generating AI and have the generating AI perform the task of providing optimal information.

[0063] The information provision unit can analyze a user's social media activity and provide relevant information when providing information. For example, the information provision unit can provide relevant information based on travel destinations and activities shared by the user on social media. For example, the information provision unit can analyze the content of a user's social media posts and provide information related to topics of interest. For example, the information provision unit can provide information by referring to travel destinations of influencers and friends that the user follows. In this way, the information provision unit can provide more appropriate information by analyzing the user's social media activity. Some or all of the above processing in the information provision unit may be performed using AI or not. For example, the information provision unit can input the user's social media activity data into a generating AI and have the generating AI perform the provision of relevant information.

[0064] The support department can provide optimal support by referring to the user's past inquiry history when providing support. For example, the support department can provide relevant support based on the content of past inquiries the user has made. For example, the support department can provide support for a specific problem based on the user's past inquiry history. For example, the support department can analyze the user's past inquiry history and provide support that can be responded to quickly. In this way, the support department can provide more appropriate support by referring to the user's past inquiry history. Some or all of the above processes in the support department may be performed using AI or not. For example, the support department can input the user's past inquiry history data into a generating AI and have the generating AI perform the task of providing optimal support.

[0065] The support unit can acquire the user's current location information in real time when providing support, enabling it to provide optimal support. For example, the support unit can suggest the nearest support center based on the user's current location. For example, the support unit can provide optimal support based on the user's current location information. For example, if the user is on the move, the support unit can update the location information in real time to provide optimal support. This allows the support unit to provide more appropriate support by acquiring the user's current location information in real time. Some or all of the above processes in the support unit may be performed using AI or not. For example, the support unit can input the user's current location information into a generating AI and have the generating AI perform the task of providing optimal support.

[0066] The support unit can provide optimal support by considering the user's geographical location when providing support. For example, the support unit can suggest the nearest support center based on the user's current location. For example, the support unit can provide optimal support based on the user's geographical location. For example, if the user is in a specific region, the support unit can prioritize displaying support related to that region. In this way, the support unit can provide more appropriate support by considering the user's geographical location. Some or all of the above processes in the support unit may be performed using AI or not. For example, the support unit can input the user's geographical location information into a generating AI and have the generating AI perform the task of providing optimal support.

[0067] The information management unit can manage optimal information by referring to the user's past travel history during information management. For example, the information management unit manages relevant information based on places and experiences the user has visited in the past. For example, the information management unit manages information on preferred activities and tourist spots from the user's past travel history. For example, the information management unit manages optimal information by considering places and activities the user has avoided in the past. In this way, the information management unit can manage information more appropriately by referring to the user's past travel history. Some or all of the above processing in the information management unit may be performed using AI or not. For example, the information management unit can input the user's past travel history data into a generating AI and have the generating AI perform the management of optimal information.

[0068] The Information Management Department can customize information based on the user's current living situation when managing information. For example, the Information Management Department manages relevant information based on the user's current work and family situation. For example, the Information Management Department manages appropriate information based on the user's current health status and physical condition. For example, the Information Management Department manages customized information based on the user's current interests and concerns. This allows the Information Management Department to provide more appropriate information management by customizing information based on the user's current living situation. Some or all of the above processes in the Information Management Department may be performed using AI or not. For example, the Information Management Department can input user living situation data into a generating AI and have the generating AI perform the information customization.

[0069] The information management unit can manage information optimally by considering the user's geographical location information during information management. For example, the information management unit manages relevant information based on the user's current location. For example, the information management unit provides optimal information based on the user's geographical location information. For example, if the user is in a specific region, the information management unit prioritizes displaying information related to that region. This allows the information management unit to manage information more appropriately by considering the user's geographical location information. Some or all of the above processes in the information management unit may be performed using AI or not. For example, the information management unit can input the user's geographical location information into a generating AI and have the generating AI perform the management of optimal information.

[0070] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0071] The reception desk can analyze the user's past travel history and suggest the optimal input method. For example, the reception desk can automatically display as suggestions the purposes and preferences the user has frequently entered in the past. For example, the reception desk can prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. For example, the reception desk can predict and suggest preferences related to specific seasons or events based on the user's past travel history. In this way, the reception desk can streamline the input process by suggesting the optimal input method based on the user's past travel history. Some or all of the above processes in the reception desk may be performed using AI or not. For example, the reception desk can input the user's past travel history data into a generating AI and have the generating AI suggest the optimal input method.

[0072] The generation unit can propose the optimal travel plan by referring to the user's past travel history when generating a travel plan. For example, the generation unit can propose similar travel plans based on places and experiences the user has visited in the past. For example, the generation unit can create a plan that includes the user's preferred activities and sightseeing spots from the user's past travel history. For example, the generation unit can propose the optimal plan by considering places and activities the user has avoided in the past. In this way, the generation unit can propose a more appropriate travel plan by referring to the user's past travel history. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input the user's past travel history data into a generation AI and have the generation AI perform the task of proposing the optimal plan.

[0073] The matching unit can suggest the most suitable host family by referring to the user's past travel history during the matching process. For example, the matching unit can suggest similar host families based on places and experiences the user has visited in the past. For example, the matching unit can create a plan that includes preferred host families from the user's past travel history. For example, the matching unit can suggest the most suitable host family by considering places and host families the user has avoided in the past. In this way, the matching unit can suggest a more appropriate host family by referring to the user's past travel history. Some or all of the above processes in the matching unit may be performed using AI or not. For example, the matching unit can input the user's past travel history data into a generating AI and have the generating AI perform the task of suggesting the most suitable host family.

[0074] The Emergency Department can propose the most appropriate response when an emergency alert is triggered, by referring to the user's past health history. For example, the Emergency Department can propose the most appropriate response based on health problems the user has experienced in the past. For example, the Emergency Department can provide a response to a specific symptom based on the user's past health history. For example, the Emergency Department can analyze the user's past health history and propose a quick response. This allows the Emergency Department to respond more appropriately by referring to the user's past health history. Some or all of the above processes in the Emergency Department may be performed using AI or not. For example, the Emergency Department can input the user's past health history data into a generating AI and have the generating AI propose the most appropriate response.

[0075] The medical search unit can suggest the most suitable medical institution by referring to the user's past health history when searching for a medical institution. For example, the medical search unit can suggest the most suitable medical institution based on the medical institutions the user has visited in the past. For example, the medical search unit can provide medical institutions that can treat specific symptoms based on the user's past health history. For example, the medical search unit can analyze the user's past health history and suggest medical institutions that can provide a quick response. In this way, the medical search unit can suggest more appropriate medical institutions by referring to the user's past health history. Some or all of the above processes in the medical search unit may be performed using AI or not. For example, the medical search unit can input the user's past health history data into a generating AI and have the generating AI perform the task of suggesting the most suitable medical institutions.

[0076] The following briefly describes the processing flow for example form 1.

[0077] Step 1: The reception desk accepts input to understand the user's purpose and preferences. The reception desk provides an interface for the user to input information such as the purpose of their trip, budget, and desired activities. The reception desk can accept user information through methods such as voice input, text input, and touch input. Step 2: The generation unit creates a travel plan based on the information received by the reception unit. The generation unit generates the optimal travel plan for the user's purpose and preferences, for example, by using AI. The generation unit analyzes the user's input information and suggests the most suitable travel destinations and activities. The generation unit provides a customized travel plan, for example, by considering past travel history and current living situation. Step 3: The matching unit matches travelers with families living in remote areas based on the travel plans created by the generation unit. The matching unit, for example, uses AI to select families that match the user's preferences. The matching unit, for example, considers the user's emotions and past travel history to suggest the most suitable family. The matching unit, for example, matches the user with the most suitable family based on their current living situation and geographical location information.

[0078] (Example of form 2) The travel plan creation system according to an embodiment of the present invention is a system that uses AI to match travelers with families living in remote areas to create completely customized travel plans in order to understand the user's purpose and desires and provide a unique local experience. In this system, the AI ​​understands the user's purpose and desires, and based on the user's purpose and desires, the AI ​​matches travelers with families living in remote areas, thereby enabling the user to obtain a unique local experience. Furthermore, as a measure for health management, a GPS-based emergency alert system is linked to notify the AI ​​of the user's location information and to respond immediately if a problem occurs. In addition, the AI ​​automatically checks the user's health status (physical condition, forgotten medication, etc.) at set times, and if new symptoms appear, the AI ​​searches for an appropriate medical institution and sets a visit time. As a measure for peace of mind, the AI ​​provides the latest information that the user should know, such as epidemics, weather, and local news during the trip. Furthermore, a 24-hour AI support center is set up so that users can consult and inquire at any time, reducing language difficulties and anxiety in new places. In addition, the AI ​​manages important information such as emergency contacts and travel information and provides it when necessary. This system targets elderly individuals who wish to travel alone, aiming to address the challenge of planning and executing travel independently, despite the existing demand for senior-friendly travel services. AI understands the user's purpose and desires from the outset, creating customized travel plans that enable enjoyable solo travel for seniors. Furthermore, the AI ​​matches travelers with local families, providing local experiences that allow seniors to spend time with local families rather than alone, directly learning about local life and culture. This system addresses the expanding senior travel market and the increasing demand for travel to remote locations. Advances in AI enable accurate understanding of user needs and the provision of customized travel plans. Additionally, the growing demand among travelers for individual local experiences and unique interactions allows for improved accuracy in the AI-powered matching system.The vision of this system is to improve the quality of life (QOL) of seniors by providing them with the most complementary and fulfilling travel experiences possible, promoting interaction with locals and cultural understanding, thereby making trips to remote areas more meaningful. Furthermore, by supporting solo travelers so they can enjoy themselves with peace of mind, the system aims to help them pursue self-realization and a sense of fulfillment in life by gaining new insights and personal growth through rich local experiences and interactions. To achieve this, the travel plan creation system uses AI to understand the user's purpose and desires, and to provide unique local experiences by matching travelers with families living in remote areas, creating completely customized travel plans.

[0079] The travel plan creation system according to this embodiment comprises a reception unit, a generation unit, and a matching unit. The reception unit receives input to understand the user's purpose and desires. The reception unit provides an interface for the user to input, for example, the purpose of the trip, budget, desired activities, etc. The reception unit can receive user information by methods such as voice input, text input, and touch input. The generation unit creates a travel plan based on the information received by the reception unit. The generation unit generates the optimal travel plan for the user's purpose and desires, for example, using AI. The generation unit analyzes the user's input information and proposes the optimal travel destination and activities, for example. The generation unit provides a customized travel plan, for example, taking into account the user's past travel history and current living situation. The matching unit matches travelers with families living in remote areas based on the travel plan created by the generation unit. The matching unit selects families that match the user's preferences, for example, using AI. The matching unit proposes the optimal family, for example, taking into account the user's emotions and past travel history. The matching unit matches the optimal family based on the user's current living situation and geographical location information, for example. This allows the travel planning system to match travelers with families living in remote areas based on the user's purpose and preferences.

[0080] The reception desk accepts input to understand the user's purpose and preferences. For example, it provides an interface for users to input their travel purpose, budget, and desired activities. Specifically, the reception desk provides web forms and mobile applications designed for intuitive user operation. These interfaces include input elements such as dropdown menus, checkboxes, and radio buttons to allow users to easily enter travel details. Furthermore, voice input functionality allows users to communicate their preferences verbally. For example, if a user voice-inputs "I want to relax at a beach resort," the system converts this into text and sends it to the generation unit for analysis. Text input provides a text box where users can freely describe their preferences, allowing for detailed requests. Touch input allows users to directly select their desired locations on a map using the touchscreen of their smartphone or tablet. This enables the reception desk to accommodate diverse user input methods and collect information accurately and quickly. Additionally, the reception desk analyzes user input in real time and presents additional questions as needed to gather more detailed information. For example, if a user selects "hiking" as their "activity," the system displays additional questions such as "difficulty level of hiking" and "desired hiking trail length." This allows the reception department to understand the user's specific needs and provide accurate information to the generation department.

[0081] The generation unit creates travel plans based on information received by the reception unit. For example, the generation unit uses AI to generate travel plans that are best suited to the user's purpose and desires. Specifically, the generation unit uses natural language processing technology to analyze the user's input information and understand their travel purpose and desires. For example, if a user inputs "I want to relax," the generation unit analyzes the keyword "relax" and suggests relaxing travel destinations such as beach resorts or hot springs. It also considers the user's budget information and selects the best accommodations and activities within that budget. Furthermore, the generation unit provides customized travel plans by considering past travel history and current living circumstances. For example, based on data on places the user has visited and activities they have participated in in the past, it suggests new travel destinations and activities the user has not yet visited. The generation unit can use AI machine learning algorithms to learn the user's preferences and tendencies and generate more personalized travel plans. For example, if a user has previously enjoyed "adventure" or "outdoor" related travel, the generation unit considers this tendency and includes activities such as "mountain climbing" or "rafting" in the next travel plan. Furthermore, the generation unit can utilize real-time updated information to incorporate the latest travel destination and event information. For example, it can acquire information on festivals and events held in a specific region and incorporate it into the user's travel plan. This allows the generation unit to provide the optimal and most up-to-date travel plan based on the user's purpose and preferences.

[0082] The matching unit matches travelers with families living in remote areas based on travel plans created by the generation unit. Specifically, the matching unit uses AI to select families that match the user's preferences. For example, if a user wishes to "experience local culture," the matching unit will select a family that has a deep understanding of the region's culture and traditions. The matching unit also considers the user's emotions and past travel history to suggest the most suitable families. For example, if a user has previously enjoyed "homestays" or "interacting with local families," the matching unit will consider this preference and select a family that can provide a similar experience on the next trip. It also matches the most suitable families based on the user's current living situation and geographical location. For example, it prioritizes selecting families in areas easily accessible from where the user currently lives. Furthermore, the matching unit also considers the families' wishes and conditions to achieve a mutually satisfying match. For example, if a family wishes for a "traveler who can speak English," the matching unit will select a user who meets that condition. In this way, the matching unit can provide the best possible match for both users and families, improving the quality of the travel experience. Furthermore, the matching unit collects feedback after matching and uses it as data to improve the accuracy of future matches. For example, based on feedback from both users and families, the matching algorithm is improved to achieve more accurate matches. The matching unit also provides tools and platforms to support communication between users and families, facilitating smooth interactions. In this way, the matching unit can provide a highly satisfying travel experience for both users and families.

[0083] The Emergency Department can integrate a GPS-based emergency alert system to notify the AI ​​of the user's location and respond when a problem occurs. For example, the Emergency Department can grasp the user's location in real time and respond quickly when an emergency occurs. For example, the Emergency Department can notify the nearest medical facility or emergency contact based on the user's location. For example, the Emergency Department can provide the optimal evacuation route based on the user's location. This allows the Emergency Department to grasp the user's location in real time and respond quickly when a problem occurs. Some or all of the above processes in the Emergency Department may be performed using AI or not. For example, the Emergency Department can input the user's location into the AI ​​and have the AI ​​execute emergency response measures.

[0084] The health management department can automatically check the user's health status at set times using AI. The health management department can periodically check the user's health status, such as body temperature, blood pressure, and heart rate. The health management department can also propose appropriate countermeasures based on the user's health status. The health management department can also propose a visit to a medical institution based on the user's health status. This allows the health management department to regularly check the user's health status and take appropriate action. Some or all of the above processes in the health management department may be performed using AI or not. For example, the health management department can input the user's health status into the AI ​​and have the AI ​​perform the health status check.

[0085] The medical search unit can use AI to search for appropriate medical institutions and set visit times when new symptoms appear. For example, the medical search unit searches for the most suitable medical institution based on the user's symptoms. For example, the medical search unit sets visit times at medical institutions based on the user's symptoms. For example, the medical search unit makes reservations at medical institutions based on the user's symptoms. This allows the medical search unit to quickly search for appropriate medical institutions and set visit times when new symptoms appear. Some or all of the above processes in the medical search unit may be performed using AI or not. For example, the medical search unit can input the user's symptoms into the AI ​​and have the AI ​​perform the search for medical institutions and set visit times.

[0086] The information provision unit can use AI to provide users with the latest information they need to know, such as epidemics, weather, and local news during their trip. For example, the information provision unit can provide information on epidemics in the user's current location and travel destination. For example, the information provision unit can provide information on weather in the user's current location and travel destination. For example, the information provision unit can provide local news in the user's current location and travel destination. In this way, the information provision unit can provide users with the latest information they need during their trip. Some or all of the above processing in the information provision unit may be performed using AI or not. For example, the information provision unit can input information on the user's current location and travel destination into the AI ​​and have the AI ​​perform the task of providing the latest information.

[0087] The support department can establish a 24-hour AI support center, allowing users to consult and inquire at any time. For example, the support department can provide 24-hour support if a user encounters a problem while traveling. For example, the support department can provide 24-hour support if a user has questions or concerns while traveling. For example, the support department can provide 24-hour support if an emergency occurs while a user is traveling. This allows users to enjoy their trip with peace of mind, as they can consult and inquire at any time. Some or all of the above processes in the support department may be performed using AI or not. For example, the support department can input the user's inquiry into the AI ​​and have the AI ​​provide 24-hour support.

[0088] The Information Management Department can use AI to manage and provide important information, such as emergency contacts and travel information, when necessary. For example, the Information Management Department can manage and provide users' emergency contacts when needed. For example, the Information Management Department can manage and provide users' travel information when needed. For example, the Information Management Department can manage users' health information when needed. This allows the Information Management Department to quickly provide necessary information in emergencies. Some or all of the above processes in the Information Management Department may be performed using AI or not. For example, the Information Management Department can input users' emergency contacts and travel information into the AI ​​and have the AI ​​manage and provide the information.

[0089] The reception desk can estimate the user's emotions and adjust the priority of input content 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. For example, if the user is relaxed, the reception desk can provide detailed input options and suggest a customizable input method. For example, if the user is in a hurry, the reception desk can prioritize voice input to allow for quick input of purpose and wishes. This allows the reception desk to create a more appropriate travel plan by adjusting the priority of input content 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 user emotion data into a generative AI and have the generative AI perform emotion estimation and adjustment of input content priority.

[0090] The reception desk can analyze the user's past travel history and suggest the optimal input method. For example, the reception desk can automatically display as suggestions the purposes and preferences the user has frequently entered in the past. For example, the reception desk can prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. For example, the reception desk can predict and suggest preferences related to specific seasons or events based on the user's past travel history. In this way, the reception desk can streamline the input process by suggesting the optimal input method based on the user's past travel history. Some or all of the above processes in the reception desk may be performed using AI or not. For example, the reception desk can input the user's past travel history data into a generating AI and have the generating AI suggest the optimal input method.

[0091] The reception desk can filter the user's current lifestyle and areas of interest during input. For example, the reception desk can suggest relevant travel plans based on the user's current lifestyle (work, family, etc.). For example, the reception desk can suggest the optimal travel plan based on the user's areas of interest (culture, nature, activities, etc.). For example, the reception desk can suggest a reasonable travel plan based on the user's current health and physical condition. In this way, the reception desk can suggest more appropriate travel plans by filtering based on the user's current lifestyle and areas of interest. 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 data on the user's lifestyle and areas of interest into a generating AI and have the generating AI perform the filtering.

[0092] The reception unit can estimate the user's emotions and adjust how the input content is displayed based on the estimated emotions. For example, if the user is tense, the reception unit can provide an interface with calming colors to reduce visual stress. For example, if the user is having fun, the reception unit can provide an interface with bright colors to make the input process enjoyable. For example, if the user is tired, the reception unit can provide a simple and highly visible interface to facilitate the input process. In this way, the reception unit can provide a more comfortable input environment by adjusting how the input content is displayed 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 unit may be performed using AI or not. For example, the reception unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation and adjustment of the display method.

[0093] The reception desk can prioritize retrieving highly relevant information by considering the user's geographical location during input. For example, the reception desk can suggest nearby tourist attractions and activities based on the user's current location. For example, the reception desk can automatically set the optimal departure point and destination based on the user's geographical location. For example, if the user is interested in a particular region, the reception desk will prioritize displaying information related to that region. In this way, the reception desk can propose a more appropriate travel plan by prioritizing the retrieval of highly relevant information by considering the user's 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 retrieve highly relevant information.

[0094] The reception desk can analyze the user's social media activity and obtain relevant information during input. For example, the reception desk can suggest the optimal travel plan based on the travel destinations and activities the user has shared on social media. For example, the reception desk can analyze the content of the user's social media posts and provide information related to topics of interest. For example, the reception desk can suggest a travel plan by referring to the travel destinations of influencers and friends the user follows. In this way, the reception desk can suggest a more appropriate travel plan by analyzing the user's social media activity. 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 social media activity data into a generating AI and have the generating AI perform the task of obtaining relevant information.

[0095] The generation unit can estimate the user's emotions and adjust the level of detail in the travel plan based on the estimated emotions. For example, if the user is relaxed, the generation unit will provide a detailed travel plan and suggest various options. If the user is in a hurry, the generation unit will provide a concise and to-the-point travel plan. If the user is excited, the generation unit will provide a visually appealing plan and emphasize entertainment elements. In this way, the generation unit can provide a more appropriate travel plan by adjusting the level of detail in the travel plan according to the user's emotions. 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 AI or not. For example, the generation unit can input user emotion data into the generation AI and have the generation AI perform emotion estimation and adjustment of the level of detail in the travel plan.

[0096] The generation unit can propose the optimal travel plan by referring to the user's past travel history when generating a travel plan. For example, the generation unit can propose similar travel plans based on places and experiences the user has visited in the past. For example, the generation unit can create a plan that includes the user's preferred activities and sightseeing spots from the user's past travel history. For example, the generation unit can propose the optimal plan by considering places and activities the user has avoided in the past. In this way, the generation unit can propose a more appropriate travel plan by referring to the user's past travel history. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input the user's past travel history data into a generation AI and have the generation AI perform the task of proposing the optimal plan.

[0097] The generation unit can customize travel plans based on the user's current living situation when generating them. For example, the generation unit can suggest a manageable schedule based on the user's current work and family circumstances. For example, the generation unit can create a plan that includes appropriate activities based on the user's current health and physical condition. For example, the generation unit can provide a customized travel plan based on the user's current interests. In this way, the generation unit can provide a more appropriate travel plan by customizing it based on the user's current living situation. Some or all of the above processes in the generation unit may be performed using AI or not. For example, the generation unit can input the user's living situation data into a generation AI and have the generation AI perform the plan customization.

[0098] The generation unit can estimate the user's emotions and determine the priority of travel plans based on the estimated emotions. For example, if the user is relaxed, the generation unit will prioritize suggesting relaxing activities. If the user is excited, the generation unit will prioritize suggesting adventurous activities. If the user is tired, the generation unit will prioritize suggesting refreshing activities. In this way, the generation unit can provide a more appropriate travel plan by prioritizing travel plans according to the user's emotions. 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 AI or not. For example, the generation unit can input user emotion data into a generation AI and have the generation AI perform emotion estimation and travel plan priority determination.

[0099] The generation unit can propose the optimal travel plan by considering the user's geographical location when generating a travel plan. For example, the generation unit can suggest nearby tourist attractions and activities based on the user's current location. For example, the generation unit can automatically set the optimal departure point and destination based on the user's geographical location. For example, if the user is interested in a particular region, the generation unit can prioritize displaying information related to that region. In this way, the generation unit can provide a more appropriate travel plan by proposing the optimal plan by considering the user's geographical location. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input the user's geographical location information into a generation AI and have the generation AI perform the task of proposing the optimal plan.

[0100] The generation unit can analyze the user's social media activity and customize the travel plan when generating it. For example, the generation unit can suggest the optimal travel plan based on the travel destinations and activities the user has shared on social media. For example, the generation unit can analyze the content of the user's social media posts and provide information related to themes of interest. For example, the generation unit can suggest a travel plan by referring to the travel destinations of influencers and friends the user follows. In this way, the generation unit can provide a more appropriate travel plan by analyzing the user's social media activity. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input the user's social media activity data into a generation AI and have the generation AI perform the plan customization.

[0101] The matching unit can estimate the user's emotions and adjust the matching criteria based on the estimated emotions. For example, if the user is relaxed, the matching unit will prioritize matching them with relaxing homes. If the user is excited, the matching unit will prioritize matching them with active homes. If the user is tired, the matching unit will prioritize matching them with quiet and calm homes. In this way, the matching unit can match the user with a more appropriate home by adjusting the matching criteria 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 matching unit may be performed using AI or not. For example, the matching unit can input the user's emotion data into a generative AI and have the generative AI perform emotion estimation and adjustment of the matching criteria.

[0102] The matching unit can suggest the most suitable host family by referring to the user's past travel history during the matching process. For example, the matching unit can suggest similar host families based on places and experiences the user has visited in the past. For example, the matching unit can create a plan that includes preferred host families from the user's past travel history. For example, the matching unit can suggest the most suitable host family by considering places and host families the user has avoided in the past. In this way, the matching unit can suggest a more appropriate host family by referring to the user's past travel history. Some or all of the above processes in the matching unit may be performed using AI or not. For example, the matching unit can input the user's past travel history data into a generating AI and have the generating AI perform the task of suggesting the most suitable host family.

[0103] The matching unit can customize a household based on the user's current living situation during the matching process. For example, the matching unit can select a household that proposes a manageable schedule based on the user's current work and family circumstances. For example, the matching unit can select an appropriate household based on the user's current health and physical condition. For example, the matching unit can propose a customized household based on the user's current interests and concerns. In this way, the matching unit can propose a more appropriate household by customizing it based on the user's current living situation. Some or all of the above processes in the matching unit may be performed using AI or not. For example, the matching unit can input the user's living situation data into a generating AI and have the generating AI perform the household customization.

[0104] The matching unit can estimate the user's emotions and determine matching priorities based on the estimated emotions. For example, if the user is relaxed, the matching unit will prioritize matching with relaxing homes. If the user is excited, the matching unit will prioritize matching with active homes. If the user is tired, the matching unit will prioritize matching with quiet and calm homes. In this way, the matching unit can match with a more appropriate home by determining matching priorities 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 matching unit may be performed using AI or not. For example, the matching unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation and determination of matching priorities.

[0105] The matching unit can suggest the most suitable home by considering the user's geographical location during the matching process. For example, the matching unit can suggest nearby homes based on the user's current location. For example, the matching unit can automatically set the most suitable home based on the user's geographical location. For example, if the user is interested in a particular area, the matching unit can prioritize displaying homes related to that area. In this way, the matching unit can match the user with a more suitable home by suggesting the most suitable home by considering the user's geographical location. Some or all of the above processes in the matching unit may be performed using AI or not. For example, the matching unit can input the user's geographical location into a generating AI and have the generating AI perform the task of suggesting the most suitable home.

[0106] The matching unit can analyze the user's social media activity and customize the chosen home during the matching process. For example, the matching unit can suggest the most suitable home based on travel destinations and activities shared by the user on social media. For example, the matching unit can analyze the user's social media posts and provide homes related to themes of interest. For example, the matching unit can suggest homes based on travel destinations shared by influencers and friends the user follows. In this way, the matching unit can suggest more appropriate homes by analyzing the user's social media activity. Some or all of the above processes in the matching unit may be performed using AI or not. For example, the matching unit can input the user's social media activity data into a generating AI and have the generating AI perform the home customization.

[0107] The Emergency Unit can estimate the user's emotions and adjust the priority of emergency alerts based on the estimated emotions. For example, if the user is feeling anxious, the Emergency Unit will quickly display an emergency alert. For example, if the user is relaxed, the Emergency Unit will display only the minimum necessary alerts. For example, if the user is excited, the Emergency Unit will provide detailed alert information. This allows the Emergency Unit to respond more appropriately by adjusting the priority of emergency alerts 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 Emergency Unit may be performed using AI or not. For example, the Emergency Unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation and adjust the priority of emergency alerts.

[0108] The Emergency Department can propose the most appropriate response when an emergency alert is triggered, by referring to the user's past health history. For example, the Emergency Department can propose the most appropriate response based on health problems the user has experienced in the past. For example, the Emergency Department can provide a response to a specific symptom based on the user's past health history. For example, the Emergency Department can analyze the user's past health history and propose a quick response. This allows the Emergency Department to respond more appropriately by referring to the user's past health history. Some or all of the above processes in the Emergency Department may be performed using AI or not. For example, the Emergency Department can input the user's past health history data into a generating AI and have the generating AI propose the most appropriate response.

[0109] The Emergency Unit can acquire the user's current location information in real time during an emergency alert and propose the most appropriate response. For example, the Emergency Unit can suggest the nearest medical facility based on the user's current location. For example, the Emergency Unit can provide the optimal evacuation route based on the user's current location information. For example, if the user is on the move, the Emergency Unit can update the location information in real time and propose the most appropriate response. This allows the Emergency Unit to respond more appropriately by acquiring the user's current location information in real time. Some or all of the above processes in the Emergency Unit may be performed using AI or not. For example, the Emergency Unit can input the user's current location information into a generating AI and have the generating AI propose the most appropriate response.

[0110] The Emergency Unit can estimate the user's emotions and adjust how emergency alerts are displayed based on the estimated emotions. For example, if the user is stressed, the Emergency Unit provides a simple and highly visible display. If the user is relaxed, the Emergency Unit provides a display that includes detailed information. If the user is in a hurry, the Emergency Unit provides a display that gets straight to the point. This allows the Emergency Unit to respond more appropriately by adjusting how emergency alerts are displayed according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the Emergency Unit may be performed using AI or not. For example, the Emergency Unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation and adjust how emergency alerts are displayed.

[0111] The Emergency Department can propose the most appropriate response when an emergency alert is issued, taking into account the user's geographical location. For example, the Emergency Department may suggest the nearest medical facility based on the user's current location. For example, the Emergency Department may provide the optimal evacuation route based on the user's geographical location. For example, if the user is in a specific area, the Emergency Department may prioritize displaying information relevant to that area. This allows the Emergency Department to respond more appropriately by considering the user's geographical location. Some or all of the above processing in the Emergency Department may be performed using AI or not. For example, the Emergency Department can input the user's geographical location into a generating AI and have the generating AI propose the most appropriate response.

[0112] The health management unit can estimate the user's emotions and adjust the frequency of health checks based on the estimated emotions. For example, if the user is feeling anxious, the health management unit will check their health more frequently. If the user is relaxed, the health management unit will check their health to the minimum necessary frequency. If the user is excited, the health management unit will perform a more detailed health check. This allows the health management unit to provide more appropriate health management by adjusting the frequency of health checks 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 health management unit may be performed using AI or not. For example, the health management unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation and adjustment of the health check frequency.

[0113] The Health Management Department can suggest the optimal health check method by referring to the user's past health history when checking their health status. For example, the Health Management Department can suggest the optimal check method based on the health problems the user has experienced in the past. For example, the Health Management Department can provide a check method for a specific symptom based on the user's past health history. For example, the Health Management Department can analyze the user's past health history and suggest a quick check method. This allows the Health Management Department to provide more appropriate health management by referring to the user's past health history. Some or all of the above processes in the Health Management Department may be performed using AI or not. For example, the Health Management Department can input the user's past health history data into a generating AI and have the generating AI suggest the optimal check method.

[0114] The Health Management Department can acquire the user's current location information in real time when checking their health status and propose the most suitable check-up method. For example, the Health Management Department can suggest the nearest medical institution based on the user's current location. For example, the Health Management Department can provide the most suitable check-up method based on the user's current location information. For example, if the user is on the move, the Health Management Department can update the location information in real time and propose the most suitable check-up method. This allows the Health Management Department to provide more appropriate health management by acquiring the user's current location information in real time. Some or all of the above processes in the Health Management Department may be performed using AI or not. For example, the Health Management Department can input the user's current location information into a generating AI and have the generating AI propose the most suitable check-up method.

[0115] The health management department can estimate the user's emotions and adjust the method of checking their health status based on the estimated emotions. For example, if the user is stressed, the health management department provides a simple and highly visible method of checking their health status. For example, if the user is relaxed, the health management department provides a method of checking their health status that includes detailed information. For example, if the user is in a hurry, the health management department provides a concise method of checking their health status. This allows the health management department to provide more appropriate health management by adjusting the method of checking their health status 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 health management department may be performed using AI or not. For example, the health management department can input user emotion data into a generative AI and have the generative AI perform emotion estimation and adjustment of the health status checking method.

[0116] The Health Management Department can propose the most suitable health check method when checking a user's health status, taking into account the user's geographical location. For example, the Health Management Department can suggest the nearest medical institution based on the user's current location. For example, the Health Management Department can provide the most suitable health check method based on the user's geographical location. For example, if the user is in a specific region, the Health Management Department can prioritize displaying information related to that region. This allows the Health Management Department to provide more appropriate health management by taking the user's geographical location into consideration. Some or all of the above processes in the Health Management Department may be performed using AI or not. For example, the Health Management Department can input the user's geographical location information into a generating AI and have the generating AI propose the most suitable health check method.

[0117] The medical search unit can estimate the user's emotions and adjust the search criteria for medical institutions based on the estimated emotions. For example, if the user is feeling anxious, the medical search unit will prioritize searching for medical institutions that can respond quickly. For example, if the user is relaxed, the medical search unit will search for medical institutions that contain detailed information. For example, if the user is excited, the medical search unit will search for medical institutions that are visually appealing. In this way, the medical search unit can find more appropriate medical institutions by adjusting the search criteria for medical institutions 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 medical search unit may be performed using AI or not. For example, the medical search unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation and adjustment of medical institution search criteria.

[0118] The medical search unit can suggest the most suitable medical institution by referring to the user's past health history when searching for a medical institution. For example, the medical search unit can suggest the most suitable medical institution based on the medical institutions the user has visited in the past. For example, the medical search unit can provide medical institutions that can treat specific symptoms based on the user's past health history. For example, the medical search unit can analyze the user's past health history and suggest medical institutions that can provide a quick response. In this way, the medical search unit can suggest more appropriate medical institutions by referring to the user's past health history. Some or all of the above processes in the medical search unit may be performed using AI or not. For example, the medical search unit can input the user's past health history data into a generating AI and have the generating AI perform the task of suggesting the most suitable medical institutions.

[0119] The medical search unit can acquire the user's current location information in real time when searching for a medical institution and suggest the most suitable medical institution. For example, the medical search unit suggests the nearest medical institution based on the user's current location. For example, the medical search unit provides the most suitable medical institution based on the user's current location information. For example, if the user is on the move, the medical search unit updates the location information in real time and suggests the most suitable medical institution. As a result, the medical search unit can suggest a more appropriate medical institution by acquiring the user's current location information in real time. Some or all of the above processing in the medical search unit may be performed using AI or not. For example, the medical search unit can input the user's current location information into a generating AI and have the generating AI suggest the most suitable medical institution.

[0120] The medical search unit can estimate the user's emotions and adjust how the search results for medical institutions are displayed based on the estimated emotions. For example, if the user is nervous, the medical search unit provides a simple and highly visible display method. If the user is relaxed, the medical search unit provides a display method that includes detailed information. If the user is in a hurry, the medical search unit provides a display method that gets straight to the point. In this way, the medical search unit can suggest more appropriate medical institutions by adjusting how the search results for medical institutions are displayed 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 medical search unit may be performed using AI or not. For example, the medical search unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation and adjust how the search results for medical institutions are displayed.

[0121] The medical search unit can suggest the most suitable medical institution when a user searches for a medical institution, taking into account the user's geographical location. For example, the medical search unit suggests the nearest medical institution based on the user's current location. For example, the medical search unit provides the most suitable medical institution based on the user's geographical location. For example, if the user is in a specific region, the medical search unit prioritizes displaying medical institutions related to that region. In this way, the medical search unit can suggest a more appropriate medical institution by taking into account the user's geographical location. Some or all of the above processing in the medical search unit may be performed using AI or not. For example, the medical search unit can input the user's geographical location information into a generating AI and have the generating AI suggest the most suitable medical institution.

[0122] The information provider can estimate the user's emotions and adjust the priority of the information it provides based on the estimated emotions. For example, if the user is feeling anxious, the information provider will prioritize providing reassuring information. For example, if the user is relaxed, the information provider will provide detailed information. For example, if the user is excited, the information provider will provide visually appealing information. In this way, the information provider can provide more appropriate information by adjusting the priority of information 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 information provider may be performed using AI or not. For example, the information provider can input user emotion data into a generative AI and have the generative AI perform emotion estimation and adjust the priority of information.

[0123] The information provision unit can provide optimal information by referring to the user's past travel history when providing information. For example, the information provision unit can provide relevant information based on places and experiences the user has visited in the past. For example, the information provision unit can provide information on preferred activities and tourist spots from the user's past travel history. For example, the information provision unit can provide optimal information by considering places and activities the user has avoided in the past. In this way, the information provision unit can provide more appropriate information by referring to the user's past travel history. Some or all of the above processing in the information provision unit may be performed using AI or not. For example, the information provision unit can input the user's past travel history data into a generating AI and have the generating AI perform the task of providing optimal information.

[0124] The information provision unit can customize information based on the user's current living situation when providing it. For example, the information provision unit can provide relevant information based on the user's current work and family situation. For example, the information provision unit can provide appropriate information based on the user's current health and physical condition. For example, the information provision unit can provide customized information based on the user's current interests and concerns. In this way, the information provision unit can provide more appropriate information by customizing it based on the user's current living situation. Some or all of the above processing in the information provision unit may be performed using AI or not. For example, the information provision unit can input the user's living situation data into a generating AI and have the generating AI perform the information customization.

[0125] The information provider can estimate the user's emotions and adjust the way information is displayed based on the estimated emotions. For example, if the user is nervous, the information provider can provide a simple and highly visible display method. For example, if the user is relaxed, the information provider can provide a display method that includes detailed information. For example, if the user is in a hurry, the information provider can provide a display method that gets straight to the point. In this way, the information provider can provide more appropriate information by adjusting the way information is displayed according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the information provider may be performed using AI or not using AI. For example, the information provider can input user emotion data into a generative AI and have the generative AI perform emotion estimation and adjustment of the information display method.

[0126] The information provision unit can provide optimal information by considering the user's geographical location when providing information. For example, the information provision unit can provide information about nearby tourist attractions and activities based on the user's current location. For example, the information provision unit can provide information about optimal starting points and destinations based on the user's geographical location. For example, if the user is interested in a particular region, the information provision unit can prioritize displaying information related to that region. In this way, the information provision unit can provide more appropriate information by considering the user's geographical location. Some or all of the above processing in the information provision unit may be performed using AI or not. For example, the information provision unit can input the user's geographical location information into a generating AI and have the generating AI perform the task of providing optimal information.

[0127] The information provision unit can analyze a user's social media activity and provide relevant information when providing information. For example, the information provision unit can provide relevant information based on travel destinations and activities shared by the user on social media. For example, the information provision unit can analyze the content of a user's social media posts and provide information related to topics of interest. For example, the information provision unit can provide information by referring to travel destinations of influencers and friends that the user follows. In this way, the information provision unit can provide more appropriate information by analyzing the user's social media activity. Some or all of the above processing in the information provision unit may be performed using AI or not. For example, the information provision unit can input the user's social media activity data into a generating AI and have the generating AI perform the provision of relevant information.

[0128] The support unit can estimate the user's emotions and adjust the priority of support based on the estimated emotions. For example, if the user is feeling anxious, the support unit will prioritize providing support that can be addressed quickly. For example, if the user is relaxed, the support unit will provide detailed support. For example, if the user is excited, the support unit will provide visually appealing support. In this way, the support unit can provide more appropriate support by adjusting the priority of support 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 support unit may be performed using AI or not. For example, the support unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation and adjust the priority of support.

[0129] The support department can provide optimal support by referring to the user's past inquiry history when providing support. For example, the support department can provide relevant support based on the content of past inquiries the user has made. For example, the support department can provide support for a specific problem based on the user's past inquiry history. For example, the support department can analyze the user's past inquiry history and provide support that can be responded to quickly. In this way, the support department can provide more appropriate support by referring to the user's past inquiry history. Some or all of the above processes in the support department may be performed using AI or not. For example, the support department can input the user's past inquiry history data into a generating AI and have the generating AI perform the task of providing optimal support.

[0130] The support unit can acquire the user's current location information in real time when providing support, enabling it to provide optimal support. For example, the support unit can suggest the nearest support center based on the user's current location. For example, the support unit can provide optimal support based on the user's current location information. For example, if the user is on the move, the support unit can update the location information in real time to provide optimal support. This allows the support unit to provide more appropriate support by acquiring the user's current location information in real time. Some or all of the above processes in the support unit may be performed using AI or not. For example, the support unit can input the user's current location information into a generating AI and have the generating AI perform the task of providing optimal support.

[0131] The support unit can estimate the user's emotions and adjust the display method of support content based on the estimated emotions. For example, if the user is nervous, the support unit provides a simple and highly visible display method. For example, if the user is relaxed, the support unit provides a display method that includes detailed information. For example, if the user is in a hurry, the support unit provides a display method that gets straight to the point. In this way, the support unit can provide more appropriate support by adjusting the display method of support content 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 support unit may be performed using AI or not. For example, the support unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation and adjustment of the display method of support content.

[0132] The support unit can provide optimal support by considering the user's geographical location when providing support. For example, the support unit can suggest the nearest support center based on the user's current location. For example, the support unit can provide optimal support based on the user's geographical location. For example, if the user is in a specific region, the support unit can prioritize displaying support related to that region. In this way, the support unit can provide more appropriate support by considering the user's geographical location. Some or all of the above processes in the support unit may be performed using AI or not. For example, the support unit can input the user's geographical location information into a generating AI and have the generating AI perform the task of providing optimal support.

[0133] The information management unit can estimate the user's emotions and adjust the priority of the information it manages based on the estimated emotions. For example, if the user is feeling anxious, the information management unit will prioritize managing reassuring information. For example, if the user is relaxed, the information management unit will manage detailed information. For example, if the user is excited, the information management unit will manage visually appealing information. This allows the information management unit to manage information more appropriately by adjusting the priority of information 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 information management unit may be performed using AI or not. For example, the information management unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation and adjust the priority of information.

[0134] The information management unit can manage optimal information by referring to the user's past travel history during information management. For example, the information management unit manages relevant information based on places and experiences the user has visited in the past. For example, the information management unit manages information on preferred activities and tourist spots from the user's past travel history. For example, the information management unit manages optimal information by considering places and activities the user has avoided in the past. In this way, the information management unit can manage information more appropriately by referring to the user's past travel history. Some or all of the above processing in the information management unit may be performed using AI or not. For example, the information management unit can input the user's past travel history data into a generating AI and have the generating AI perform the management of optimal information.

[0135] The Information Management Department can customize information based on the user's current living situation when managing information. For example, the Information Management Department manages relevant information based on the user's current work and family situation. For example, the Information Management Department manages appropriate information based on the user's current health status and physical condition. For example, the Information Management Department manages customized information based on the user's current interests and concerns. This allows the Information Management Department to provide more appropriate information management by customizing information based on the user's current living situation. Some or all of the above processes in the Information Management Department may be performed using AI or not. For example, the Information Management Department can input user living situation data into a generating AI and have the generating AI perform the information customization.

[0136] The information management unit can estimate the user's emotions and adjust the way information is displayed based on the estimated emotions. For example, if the user is tense, the information management unit provides a simple and highly visible display method. For example, if the user is relaxed, the information management unit provides a display method that includes detailed information. For example, if the user is in a hurry, the information management unit provides a display method that gets straight to the point. This allows the information management unit to provide more appropriate information management by adjusting the way information is displayed 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 information management unit may be performed using AI or not. For example, the information management unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation and adjustment of the information display method.

[0137] The information management unit can manage information optimally by considering the user's geographical location information during information management. For example, the information management unit manages relevant information based on the user's current location. For example, the information management unit provides optimal information based on the user's geographical location information. For example, if the user is in a specific region, the information management unit prioritizes displaying information related to that region. This allows the information management unit to manage information more appropriately by considering the user's geographical location information. Some or all of the above processes in the information management unit may be performed using AI or not. For example, the information management unit can input the user's geographical location information into a generating AI and have the generating AI perform the management of optimal information.

[0138] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0139] The reception desk can estimate the user's emotions and adjust the priority of input content 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. For example, if the user is relaxed, the reception desk can provide detailed input options and suggest a customizable input method. For example, if the user is in a hurry, the reception desk can prioritize voice input to allow for quick input of purpose and wishes. This allows the reception desk to create a more appropriate travel plan by adjusting the priority of input content 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 user emotion data into a generative AI and have the generative AI perform emotion estimation and adjustment of input content priority.

[0140] The generation unit can estimate the user's emotions and adjust the level of detail in the travel plan based on the estimated emotions. For example, if the user is relaxed, the generation unit will provide a detailed travel plan and suggest various options. If the user is in a hurry, the generation unit will provide a concise and to-the-point travel plan. If the user is excited, the generation unit will provide a visually appealing plan and emphasize entertainment elements. In this way, the generation unit can provide a more appropriate travel plan by adjusting the level of detail in the travel plan according to the user's emotions. 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 AI or not. For example, the generation unit can input user emotion data into the generation AI and have the generation AI perform emotion estimation and adjustment of the level of detail in the travel plan.

[0141] The matching unit can estimate the user's emotions and adjust the matching criteria based on the estimated emotions. For example, if the user is relaxed, the matching unit will prioritize matching them with relaxing homes. If the user is excited, the matching unit will prioritize matching them with active homes. If the user is tired, the matching unit will prioritize matching them with quiet and calm homes. In this way, the matching unit can match the user with a more appropriate home by adjusting the matching criteria 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 matching unit may be performed using AI or not. For example, the matching unit can input the user's emotion data into a generative AI and have the generative AI perform emotion estimation and adjustment of the matching criteria.

[0142] The Emergency Unit can estimate the user's emotions and adjust the priority of emergency alerts based on the estimated emotions. For example, if the user is feeling anxious, the Emergency Unit will quickly display an emergency alert. For example, if the user is relaxed, the Emergency Unit will display only the minimum necessary alerts. For example, if the user is excited, the Emergency Unit will provide detailed alert information. This allows the Emergency Unit to respond more appropriately by adjusting the priority of emergency alerts 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 Emergency Unit may be performed using AI or not. For example, the Emergency Unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation and adjust the priority of emergency alerts.

[0143] The health management unit can estimate the user's emotions and adjust the frequency of health checks based on the estimated emotions. For example, if the user is feeling anxious, the health management unit will check their health more frequently. If the user is relaxed, the health management unit will check their health to the minimum necessary frequency. If the user is excited, the health management unit will perform a more detailed health check. This allows the health management unit to provide more appropriate health management by adjusting the frequency of health checks 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 health management unit may be performed using AI or not. For example, the health management unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation and adjustment of the health check frequency.

[0144] The reception desk can analyze the user's past travel history and suggest the optimal input method. For example, the reception desk can automatically display as suggestions the purposes and preferences the user has frequently entered in the past. For example, the reception desk can prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. For example, the reception desk can predict and suggest preferences related to specific seasons or events based on the user's past travel history. In this way, the reception desk can streamline the input process by suggesting the optimal input method based on the user's past travel history. Some or all of the above processes in the reception desk may be performed using AI or not. For example, the reception desk can input the user's past travel history data into a generating AI and have the generating AI suggest the optimal input method.

[0145] The generation unit can propose the optimal travel plan by referring to the user's past travel history when generating a travel plan. For example, the generation unit can propose similar travel plans based on places and experiences the user has visited in the past. For example, the generation unit can create a plan that includes the user's preferred activities and sightseeing spots from the user's past travel history. For example, the generation unit can propose the optimal plan by considering places and activities the user has avoided in the past. In this way, the generation unit can propose a more appropriate travel plan by referring to the user's past travel history. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input the user's past travel history data into a generation AI and have the generation AI perform the task of proposing the optimal plan.

[0146] The matching unit can suggest the most suitable host family by referring to the user's past travel history during the matching process. For example, the matching unit can suggest similar host families based on places and experiences the user has visited in the past. For example, the matching unit can create a plan that includes preferred host families from the user's past travel history. For example, the matching unit can suggest the most suitable host family by considering places and host families the user has avoided in the past. In this way, the matching unit can suggest a more appropriate host family by referring to the user's past travel history. Some or all of the above processes in the matching unit may be performed using AI or not. For example, the matching unit can input the user's past travel history data into a generating AI and have the generating AI perform the task of suggesting the most suitable host family.

[0147] The Emergency Department can propose the most appropriate response when an emergency alert is triggered, by referring to the user's past health history. For example, the Emergency Department can propose the most appropriate response based on health problems the user has experienced in the past. For example, the Emergency Department can provide a response to a specific symptom based on the user's past health history. For example, the Emergency Department can analyze the user's past health history and propose a quick response. This allows the Emergency Department to respond more appropriately by referring to the user's past health history. Some or all of the above processes in the Emergency Department may be performed using AI or not. For example, the Emergency Department can input the user's past health history data into a generating AI and have the generating AI propose the most appropriate response.

[0148] The medical search unit can suggest the most suitable medical institution by referring to the user's past health history when searching for a medical institution. For example, the medical search unit can suggest the most suitable medical institution based on the medical institutions the user has visited in the past. For example, the medical search unit can provide medical institutions that can treat specific symptoms based on the user's past health history. For example, the medical search unit can analyze the user's past health history and suggest medical institutions that can provide a quick response. In this way, the medical search unit can suggest more appropriate medical institutions by referring to the user's past health history. Some or all of the above processes in the medical search unit may be performed using AI or not. For example, the medical search unit can input the user's past health history data into a generating AI and have the generating AI perform the task of suggesting the most suitable medical institutions.

[0149] The following briefly describes the processing flow for example form 2.

[0150] Step 1: The reception desk accepts input to understand the user's purpose and preferences. The reception desk provides an interface for the user to input information such as the purpose of their trip, budget, and desired activities. The reception desk can accept user information through methods such as voice input, text input, and touch input. Step 2: The generation unit creates a travel plan based on the information received by the reception unit. The generation unit generates the optimal travel plan for the user's purpose and preferences, for example, by using AI. The generation unit analyzes the user's input information and suggests the most suitable travel destinations and activities. The generation unit provides a customized travel plan, for example, by considering past travel history and current living situation. Step 3: The matching unit matches travelers with families living in remote areas based on the travel plans created by the generation unit. The matching unit, for example, uses AI to select families that match the user's preferences. The matching unit, for example, considers the user's emotions and past travel history to suggest the most suitable family. The matching unit, for example, matches the user with the most suitable family based on their current living situation and geographical location information.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] Each of the multiple elements mentioned above, including the reception unit, generation unit, matching unit, emergency unit, health management unit, medical search unit, information provision unit, support unit, and information management unit, is implemented by, for example, at least one of the smart device 14 and the data processing unit 12. For example, the reception unit receives the user's purpose and wishes using the reception device 38 of the smart device 14. The generation unit creates a travel plan using the specific processing unit 290 of the data processing unit 12. The matching unit matches families with travelers using the specific processing unit 290 of the data processing unit 12. The emergency unit uses the GPS function of the smart device 14 to grasp the user's location information in real time and respond to emergencies. The health management unit checks the user's health status using the sensors of the smart device 14. The medical search unit searches for an appropriate medical institution and sets a visit time using the specific processing unit 290 of the data processing unit 12. The information provision unit provides the latest information during the trip using the specific processing unit 290 of the data processing unit 12. The support unit provides 24-hour support using the communication I / F 44 of the smart device 14. The information management unit manages emergency contact information and travel information using the specific processing unit 290 of the data processing device 12, and provides it when necessary. The correspondence between each unit and the devices and control units is not limited to the example described above, and various changes are possible.

[0155] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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).

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.).

[0167] 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.

[0168] 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.

[0169] 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.

[0170] Each of the multiple elements mentioned above, including the reception unit, generation unit, matching unit, emergency unit, health management unit, medical search unit, information provision unit, support unit, and information management unit, is implemented by, for example, at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit receives the user's purpose and wishes using the microphone 238 of the smart glasses 214. The generation unit creates a travel plan using the specific processing unit 290 of the data processing unit 12. The matching unit matches families with travelers using the specific processing unit 290 of the data processing unit 12. The emergency unit uses the GPS function of the smart glasses 214 to grasp the user's location information in real time and respond to emergencies. The health management unit checks the user's health status using the sensors of the smart glasses 214. The medical search unit searches for an appropriate medical institution and sets a visit time using the specific processing unit 290 of the data processing unit 12. The information provision unit provides the latest information during the trip using the specific processing unit 290 of the data processing unit 12. The support unit provides 24-hour support using the communication interface 44 of the smart glasses 214. The information management unit manages emergency contact information and travel information using the specific processing unit 290 of the data processing device 12 and provides it when necessary. The correspondence between each unit and the devices and control units is not limited to the example described above and can be modified in various ways.

[0171] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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).

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.).

[0183] 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.

[0184] 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.

[0185] 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.

[0186] Each of the multiple elements mentioned above, including the reception unit, generation unit, matching unit, emergency unit, health management unit, medical search unit, information provision unit, support unit, and information 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 receives the user's purpose and wishes using the microphone 238 of the headset terminal 314. The generation unit creates a travel plan using the specific processing unit 290 of the data processing unit 12. The matching unit matches families with travelers using the specific processing unit 290 of the data processing unit 12. The emergency unit uses the GPS function of the headset terminal 314 to grasp the user's location information in real time and respond to emergencies. The health management unit checks the user's health status using the sensors of the headset terminal 314. The medical search unit searches for an appropriate medical institution and sets a visit time using the specific processing unit 290 of the data processing unit 12. The information provision unit provides the latest information during the trip using the specific processing unit 290 of the data processing unit 12. The support unit provides 24-hour support using the communication interface 44 of the headset terminal 314. The information management unit manages emergency contact information and travel information using the specific processing unit 290 of the data processing device 12 and provides it when necessary. The correspondence between each unit and the devices and control units is not limited to the example described above and can be modified in various ways.

[0187] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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).

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.).

[0200] 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.

[0201] 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.

[0202] 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.

[0203] Each of the multiple elements described above, including the reception unit, generation unit, matching unit, emergency unit, health management unit, medical search unit, information provision unit, support unit, and information 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 receives the user's purpose and wishes using the microphone 238 of the robot 414. The generation unit creates a travel plan using the specific processing unit 290 of the data processing unit 12. The matching unit matches families with travelers using the specific processing unit 290 of the data processing unit 12. The emergency unit uses the GPS function of the robot 414 to grasp the user's location information in real time and respond to emergencies. The health management unit checks the user's health status using the sensors of the robot 414. The medical search unit searches for an appropriate medical institution and sets a visit time using the specific processing unit 290 of the data processing unit 12. The information provision unit provides the latest information during the trip using the specific processing unit 290 of the data processing unit 12. The support unit provides 24-hour support using the communication I / F 44 of the robot 414. The information management unit manages emergency contact information and travel information using the specific processing unit 290 of the data processing device 12, and provides it when necessary. The correspondence between each unit and the devices and control units is not limited to the example described above, and various changes are possible.

[0204] 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.

[0205] 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.

[0206] 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.

[0207] 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.

[0208] 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.

[0209] 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."

[0210] 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.

[0211] 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.

[0212] 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.

[0213] 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.

[0214] 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.

[0215] 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.

[0216] 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.

[0217] 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.

[0218] 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.

[0219] 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.

[0220] 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.

[0221] 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.

[0222] (Note 1) A reception area that accepts input to understand the user's purpose and wishes, A generation unit that creates a travel plan based on the information received by the reception unit, The system includes a matching unit that matches travelers with families living in remote areas based on the travel plans created by the generation unit. A system characterized by the following features. (Note 2) By integrating a GPS-based emergency alert system, It notifies the AI ​​of the user's location and includes an emergency unit that responds when problems occur. The system described in Appendix 1, characterized by the features described herein. (Note 3) It features a health management unit where AI automatically checks the user's health status at set times. The system described in Appendix 1, characterized by the features described herein. (Note 4) The system includes a medical search unit that uses AI to find appropriate medical facilities and schedule visit times when new symptoms appear. The system described in Appendix 1, characterized by the features described herein. (Note 5) AI provides information on epidemics, weather, and local news during travel. It has an information department that provides users with the latest information they need to know. The system described in Appendix 1, characterized by the features described herein. (Note 6) We have established a 24-hour AI support center. We have a support department that you can consult or inquire about at any time. The system described in Appendix 1, characterized by the features described herein. (Note 7) AI can provide emergency contact information, travel information, etc. It has an information management department that manages important information and provides it when necessary. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is It estimates the user's emotions and adjusts the priority of input content based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is It analyzes the user's past travel history and suggests the optimal input method. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is When inputting data, filtering is performed based on the user's current lifestyle and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is It estimates the user's emotions and adjusts how the input content is displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is When inputting data, the system prioritizes retrieving highly relevant information by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned reception unit is During input, the system analyzes the user's social media activity and retrieves relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 14) The generating unit is The system estimates the user's emotions and adjusts the level of detail in the travel plan based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The generating unit is When generating a travel plan, the system refers to the user's past travel history to suggest the most suitable plan. The system described in Appendix 1, characterized by the features described herein. (Note 16) The generating unit is When generating a travel plan, customize the plan based on the user's current living situation. 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 prioritizes travel plans based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The generating unit is When generating travel plans, the system takes the user's geographical location into consideration to suggest the most suitable plan. The system described in Appendix 1, characterized by the features described herein. (Note 19) The generating unit is When generating travel plans, the system analyzes the user's social media activity to customize the plan. The system described in Appendix 1, characterized by the features described herein. (Note 20) The matching unit is It estimates the user's emotions and adjusts the matching criteria based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The matching unit is During the matching process, we refer to the user's past travel history to suggest the most suitable host family. The system described in Appendix 1, characterized by the features described herein. (Note 22) The matching unit is During the matching process, the home is customized based on the user's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 23) The matching unit is The system estimates the user's emotions and determines matching priorities based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The matching unit is When matching users, we consider their geographical location to suggest the most suitable home. The system described in Appendix 1, characterized by the features described herein. (Note 25) The matching unit is During the matching process, the system analyzes the user's social media activity to customize the home. The system described in Appendix 1, characterized by the features described herein. (Note 26) The emergency unit is, It estimates the user's emotions and adjusts the priority of emergency alerts based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 27) The emergency unit is, During an emergency alert, the system will refer to the user's past health history to suggest the most appropriate course of action. The system described in Appendix 2, characterized by the features described herein. (Note 28) The emergency unit is, During an emergency alert, the system acquires the user's current location in real time and suggests the most appropriate response. The system described in Appendix 2, characterized by the features described herein. (Note 29) The emergency unit is, It estimates the user's emotions and adjusts how emergency alerts are displayed based on those emotions. The system described in Appendix 2, characterized by the features described herein. (Note 30) The emergency unit is, When an emergency alert is triggered, the system will suggest the optimal response based on the user's geographical location. The system described in Appendix 2, characterized by the features described herein. (Note 31) The aforementioned health management department, The system estimates the user's emotions and adjusts the frequency of health checks based on those emotions. The system described in Appendix 3, characterized by the features described herein. (Note 32) The aforementioned health management department, When checking a user's health status, the system will refer to the user's past health history to suggest the most suitable check method. The system described in Appendix 3, characterized by the features described herein. (Note 33) The aforementioned health management department, During health status checks, the system acquires the user's current location information in real time and suggests the most suitable check method. The system described in Appendix 3, characterized by the features described herein. (Note 34) The aforementioned health management department, The system estimates the user's emotions and adjusts the health check method based on the estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 35) The aforementioned health management department, When checking your health status, we will suggest the optimal check method considering the user's geographical location. The system described in Appendix 3, characterized by the features described herein. (Note 36) The aforementioned medical search unit, We estimate user sentiment and adjust the search criteria for healthcare providers based on the estimated user sentiment. The system described in Appendix 4, characterized by the features described herein. (Note 37) The aforementioned medical search unit, When searching for a medical institution, the system suggests the most suitable institution by referencing the user's past health history. The system described in Appendix 4, characterized by the features described herein. (Note 38) The aforementioned medical search unit, When searching for medical facilities, the system obtains the user's current location information in real time and suggests the most suitable medical facilities. The system described in Appendix 4, characterized by the features described herein. (Note 39) The aforementioned medical search unit, The system estimates the user's emotions and adjusts how search results for healthcare institutions are displayed based on those estimated emotions. The system described in Appendix 4, characterized by the features described herein. (Note 40) The aforementioned medical search unit, When searching for a medical institution, propose the optimal medical institution considering the geographical location information of the user The system according to appended note 4, characterized in that it is such. (Appended note 41) The information providing unit Estimates the user's emotion and adjusts the priority of the information provided based on the estimated user's emotion The system according to appended note 5, characterized in that it is such. (Appended note 42) The information providing unit When providing information, refer to the user's past travel history and provide optimal information The system according to appended note 5, characterized in that it is such. (Appended note 43) The information providing unit When providing information, customize the information based on the user's current living situation The system according to appended note 5, characterized in that it is such. (Appended note 44) The information providing unit Estimates the user's emotion and adjusts the display method of the information based on the estimated user's emotion The system according to appended note 5, characterized in that it is such. (Appended note 45) The information providing unit When providing information, consider the geographical location information of the user and provide optimal information The system according to appended note 5, characterized in that it is such. (Appended note 46) The information providing unit When providing information, analyze the user's social media activities and provide relevant information The system according to appended note 5, characterized in that it is such. (Appended note 47) The support unit Estimates the user's emotion and adjusts the priority of the support content based on the estimated user's emotion The system according to appended note 6, characterized in that it is such. (Appended note 48) The support unit When providing support, refer to the user's past inquiry history to provide optimal support The system according to appended note 6, characterized by this (Appended note 49) The support unit When providing support, acquire the user's current location information in real time and provide optimal support The system according to appended note 6, characterized by this (Appended note 50) The support unit Estimate the user's emotion and adjust the display method of the support content based on the estimated user emotion The system according to appended note 6, characterized by this (Appended note 51) The support unit When providing support, consider the user's geographical location information and provide optimal support The system according to appended note 6, characterized by this (Appended note 52) The information management unit Estimate the user's emotion and adjust the priority of the information to be managed based on the estimated user emotion The system according to appended note 7, characterized by this (Appended note 53) The information management unit When managing information, refer to the user's past travel history to manage optimal information The system according to appended note 7, characterized by this (Appended note 54) The information management unit When managing information, customize the information based on the user's current living situation The system according to appended note 7, characterized by this (Appended note 55) The information management unit Estimate the user's emotion and adjust the display method of the information based on the estimated user emotion The system according to appended note 7, characterized by this (Appended note 56) The information management unit When managing information, we manage the information optimally by taking into account the user's geographical location. The system described in Appendix 7, characterized by the features described herein. [Explanation of Symbols]

[0223] 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 that accepts input to understand the user's purpose and wishes, A generation unit that creates a travel plan based on the information received by the reception unit, The system includes a matching unit that matches travelers with families living in remote areas based on the travel plans created by the generation unit. A system characterized by the following features.

2. By integrating a GPS-based emergency alert system, It notifies the AI ​​of the user's location and includes an emergency unit that responds when problems occur. The system according to feature 1.

3. It features a health management unit where AI automatically checks the user's health status at set times. The system according to feature 1.

4. The system includes a medical search unit where AI searches for an appropriate medical institution and sets an appointment time if new symptoms appear. The system according to feature 1.

5. AI provides information on epidemics, weather, and local news during travel. It has an information department that provides users with the latest information they need to know. The system according to feature 1.

6. We have established a 24-hour AI support center. We have a support department that you can consult or inquire about at any time. The system according to feature 1.

7. AI can collect emergency contact information, travel information, etc. It has an information management department that manages important information and provides it when necessary. The system according to feature 1.

8. The aforementioned reception unit is It estimates the user's emotions and adjusts the priority of input content based on the estimated user emotions. The system according to feature 1.

9. The aforementioned reception unit is It analyzes the user's past travel history and suggests the optimal input method. The system according to feature 1.

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