system

Abstract

The system according to this embodiment aims to provide users considering relocating to rural areas with information about the local living environment and community in advance. [Solution] The system according to the embodiment comprises a reception unit, a generation unit, a chat unit, a video unit, a matching unit, and a proposal unit. The reception unit receives information input. The generation unit generates experience content based on the information received by the reception unit. The chat unit provides live chat based on the experience content generated by the generation unit. The video unit provides 360-degree video based on the experience content generated by the generation unit. The matching unit provides matching services based on the experience content generated by the generation unit. The proposal unit proposes relocation plans based on the experience content generated by the generation unit.

Description

Technical Field

[0006] , , , ,

[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 performed by at least one processor, the method including 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, when considering moving to a local area, it is difficult to grasp in advance information on the living environment of the area and the local community, and there is a problem that it is difficult to make a decision to move.

[0005] The system according to the embodiment aims to provide in advance information on the living environment of the area and the local community to a user who is considering moving to a local area.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, a generation unit, a chat unit, a video unit, a matching unit, and a proposal unit. The reception unit receives information input. The generation unit generates experience content based on the information received by the reception unit. The chat unit provides live chat based on the experience content generated by the generation unit. The video unit provides 360-degree video based on the experience content generated by the generation unit. The matching unit provides matching services based on the experience content generated by the generation unit. The proposal unit proposes relocation plans based on the experience content generated by the generation unit. [Effects of the Invention]

[0007] The system according to this embodiment can provide users considering relocating to a rural area with information about the local living environment and community in advance. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

[0019] The smart device 14 comprises a computer 36, a 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 local city experience system according to an embodiment of the present invention is a system that enables users to experience life in a local city online in advance by utilizing generative AI. This system begins with the user inputting information to understand the local city's living environment, facilities, and how to participate in the local community. Based on each user's feedback, the generative AI provides a personalized online local city experience. This experience includes introductions to local products, live chats with locals, and 360-degree videos of the surrounding scenery, allowing users to experience the charm of the region in detail. The system also provides a matching service with locals based on the individual user's aptitude, hobbies, and lifestyle, and the AI ​​further analyzes the user's reactions and responses to propose an optimal relocation plan. As a result, users can acquire concrete regional information in one place before actually considering relocation and also gain opportunities to interact with local residents. For example, a user inputs, "I want to know about the living environment of XX city." This information is input into the generative AI. Next, the generative AI analyzes the input information and provides a personalized online local city experience. Based on the user's feedback, the generative AI generates the most suitable experience for that user. For example, if a user enters "I want to know about the local products of XX city," a video introducing those products will be generated. The generated experience will include introductions to local products, live chats with locals, and 360-degree videos of the surrounding scenery. For example, if a user enters "I want to see the scenery of XX city," a 360-degree video of that area will be generated. In addition, users can directly interact with locals through live chats. Furthermore, a matching service with locals is provided based on the individual user's aptitude, hobbies, and lifestyle. For example, if a user enters "I want to know about hobby activities in XX city," the system will match them with locals who share that hobby. Finally, the AI ​​analyzes the user's reactions and responses to propose the optimal relocation plan. For example, if a user enters "I want to move to XX city," the system will generate a relocation plan that is best suited to that user. This allows users to obtain concrete regional information before actually considering relocation and also provides opportunities to interact with local residents.This system leverages the power of generative AI to provide "a personalized rural relocation experience" for each individual, bridging the information gap between urban and rural areas and contributing to the formation of a balanced society. Specifically, we aim to help users find rural relocation destinations that suit their lifestyles and envision new lifestyles, thereby contributing to the resolution of the major social challenge of revitalizing rural cities. This rural city experience system will allow users to experience rural life online in advance and obtain specific regional information before relocating.

[0029] The local city experience system according to this embodiment comprises a reception unit, a generation unit, a chat unit, a video unit, a matching unit, and a proposal unit. The reception unit receives information input. For example, the user inputs information to understand in advance the living environment and facilities of the local city, and how to participate in the local community. The generation unit generates experience content based on the information received by the reception unit. The generation unit uses a generation AI to generate experience content based on user feedback. For example, if the user inputs "I want to know about the local products of XX city," an introductory video of those local products is generated. The generated experience content includes introductions to local products, live chats with local people, and 360-degree videos of the surrounding scenery. The chat unit provides live chat based on the experience content generated by the generation unit. For example, if the user inputs "I want to see the scenery of XX city," a 360-degree video of that area is generated. In addition, the user can directly interact with local people through live chat. The video unit provides 360-degree videos based on the experience content generated by the generation unit. For example, if a user inputs "I want to see the scenery of XX city," a 360-degree video of that area is generated. The matching unit provides a matching service based on the experience content generated by the generation unit. For example, if a user inputs "I want to know about hobby activities in XX city," the system matches them with local people who share that hobby. The proposal unit proposes a relocation plan based on the experience content generated by the generation unit. The proposal unit uses generation AI to analyze the user's reactions and responses and proposes the optimal relocation plan. For example, if a user inputs "I want to move to XX city," the system generates a relocation plan that is optimal for that user. As a result, the local city experience system according to this embodiment allows users to experience life in a local city online in advance and obtain specific local information before relocating.

[0030] The reception department inputs information. For example, it inputs information that allows users to understand in advance the living environment and facilities of a local city, as well as how to participate in the local community. Specifically, users can input detailed information about areas of interest, activities of interest, and lifestyles through a dedicated interface. For example, if a user inputs "I want to know about the educational environment in XX city," the reception department receives that information and passes it on to the next processing step. Similarly, if a user inputs "I want to know about medical facilities in XX city," that information is also received. The reception department organizes the user's input and stores it in a database so that the generation department can process it efficiently. Furthermore, the reception department also has a function to automatically complete relevant information based on the user's input. For example, if a user inputs "I want to know about transportation options in XX city," the reception department automatically collects information about public transportation and major transportation routes in that area and prepares it to pass on to the generation department. This allows the reception department to quickly and accurately collect the information the user needs and smoothly pass it on to the next processing step.

[0031] The generation unit generates experience content based on information received by the reception unit. The generation unit uses a generation AI to generate experience content based on user feedback. Specifically, the generation AI analyzes the information entered by the user and generates the most suitable experience content for that information. For example, if a user enters "I want to know about local specialties of XX city," the generation AI will refer to a database of local specialties in that region and generate introductory videos and related information about the specialties. The generation AI uses natural language processing technology to understand the user's input and generate appropriate content. In addition, the generation AI can provide more personalized experience content by considering the user's past feedback and behavioral history. For example, if a user previously entered "I want to know about tourist spots in XX city," the generation AI will use that information to suggest new information and events related to tourist spots. Furthermore, the generation unit can update the generated experience content in real time and respond flexibly to user requests. As a result, the generation unit can always provide users with the latest and most optimal experience content and effectively convey the charm of local cities.

[0032] The chat section provides live chat based on the experience content generated by the generation section. Specifically, if a user enters "I want to see the scenery of XX city," a 360-degree video of that area will be generated. Furthermore, users can interact directly with locals through live chat. The chat section uses generation AI to produce appropriate responses to user questions and comments. For example, if a user asks "What are some recommended restaurants in XX city?", the generation AI will refer to restaurant information in the area and recommend a suitable restaurant. The chat section also includes translation and speech recognition functions to facilitate communication between users and locals. This allows users who speak different languages ​​to communicate smoothly. In addition, the chat section can continuously improve the quality of the experience content by collecting user feedback and providing it to the generation section. This allows the chat section to provide users with a real-time, interactive experience, enabling them to gain a deeper understanding of the charm of local cities.

[0033] The video unit provides 360-degree videos based on the experience content generated by the generation unit. Specifically, if a user inputs "I want to see the scenery of XX city," a 360-degree video of that area will be generated. The video unit uses high-resolution cameras and drones to film the scenery and landmarks of local cities, providing users with immersive footage. For example, if a user inputs "I want to see the cherry blossom spots in XX city," the video unit will provide a 360-degree video of the cherry blossom spots in that area. Furthermore, the video unit can use generation AI to customize the video content based on the user's interests. For example, if a user inputs "I want to learn about the historical buildings in XX city," the generation AI will collect information about the historical buildings in that area and generate a relevant video. In addition, the video unit can embed information points and links within the video so that users can interactively acquire information while watching the video. This allows users to delve deeper into information that interests them while watching the video. In this way, the video unit can provide users with an immersive video experience and effectively convey the charm of local cities.

[0034] The matching unit provides matching services based on the experience content generated by the generation unit. Specifically, if a user enters "I want to know about hobby activities in XX city," the unit will match them with local people who share that hobby. The matching unit uses generation AI to make optimal matches based on the user's interests. For example, if a user enters "I want to find hiking partners in XX city," the generation AI will search for hiking groups and individuals in that area and introduce them to the user. The matching unit can also provide more personalized matches by considering the user's profile information and past activity history. For example, if a user enters "I want to participate in a cooking class in XX city," the generation AI will search for cooking classes and related events in that area and suggest them to the user. Furthermore, the matching unit provides chat and event participation functions to support communication between users. This allows users to interact directly with local people and share hobbies and interests. In this way, the matching unit provides users with opportunities to build new relationships and communities in regional cities and experience the charm of these cities more deeply.

[0035] The proposal unit proposes relocation plans based on the experience content generated by the generation unit. The proposal unit uses generation AI to analyze user reactions and responses to propose the optimal relocation plan. Specifically, if a user inputs "I want to move to XX city," the optimal relocation plan for that user is generated. The generation AI considers the user's lifestyle and desired conditions to propose the best housing, jobs, educational institutions, etc. For example, if a user inputs "I want to find a job in XX city," the generation AI searches for job postings in that area and proposes jobs suitable for the user. Also, if a user inputs "I want to know about the childcare environment in XX city," the generation AI provides information on educational institutions and childcare support facilities in that area. Furthermore, the proposal unit can continuously improve the relocation plan based on user feedback. For example, if a user provides feedback on the proposed plan saying "I would prefer a place with a richer natural environment," the generation AI generates a new plan based on that information. In this way, the proposal unit can provide users with the optimal relocation plan and support their relocation to regional cities.

[0036] The suggestion function can analyze user responses and answers to propose the optimal relocation plan. For example, if a user inputs "I want to move to XX city," the suggestion function will generate the optimal relocation plan for that user. The suggestion function uses a generation AI to analyze user responses and answers and propose the optimal relocation plan. For example, if a user inputs "I want to move to XX city," the optimal relocation plan for that user will be generated. This allows the system to provide the optimal relocation plan based on user responses.

[0037] The generation unit can generate experience content based on user feedback. For example, if a user inputs "I want to know about the local specialties of XX city," the generation unit will generate an introductory video about those specialties. The generation unit uses a generation AI to generate experience content based on user feedback. For example, if a user inputs "I want to know about the local specialties of XX city," an introductory video about those specialties will be generated. This allows for the provision of personalized experience content based on user feedback.

[0038] The chat function can provide live chat with local people. For example, if a user enters "I want to see the scenery of XX city," the chat function will generate a 360-degree video of that area. Furthermore, through live chat with local people, users can interact directly with them. The chat function uses a generation AI to provide live chat with local people. For example, if a user enters "I want to see the scenery of XX city," a 360-degree video of that area will be generated. Furthermore, through live chat with local people, users can interact directly with them. This allows users to interact directly with local people.

[0039] The video function can provide 360-degree videos of the surrounding scenery. For example, if a user inputs "I want to see the scenery of XX city," the video function will generate a 360-degree video of that area. The video function uses generation AI to provide 360-degree videos of the surrounding scenery. For example, if a user inputs "I want to see the scenery of XX city," a 360-degree video of that area will be generated. This allows users to experience the local scenery in 360-degree video.

[0040] The matching function can provide a matching service with local people based on each user's aptitude, hobbies, and lifestyle. For example, if a user enters "I want to know about hobby activities in XX city," the matching function will match them with local people who share that hobby. The matching function uses a generative AI to provide a matching service with local people based on each user's aptitude, hobbies, and lifestyle. For example, if a user enters "I want to know about hobby activities in XX city," the matching function will match them with local people who share that hobby. This ensures that users are appropriately matched with local people.

[0041] The reception desk can analyze the user's past input history and suggest the optimal input method. For example, it can automatically display information that the user has frequently entered in the past as a suggestion. It can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception desk can predict and suggest information that the user will use at specific times based on their past input history. This allows the system to suggest the optimal input method based on the user's past input history.

[0042] The input field can dynamically change input fields based on the user's current interests and lifestyle when they enter information. For example, the input field can automatically display relevant input fields based on the user's recent searches. It can also suggest appropriate input fields based on the user's lifestyle (e.g., raising children, working). Furthermore, the input field can customize input fields based on the user's interests (e.g., hobbies, travel). This allows the system to provide input fields tailored to the user's interests and lifestyle.

[0043] The reception system can prioritize inputting highly relevant information by considering the user's geographical location during data entry. For example, it can prioritize inputting information about locations close to the user's current location. Furthermore, if the user is interested in a particular region, it can prioritize inputting information related to that region. Additionally, if the user is traveling, it can prioritize inputting information about their travel destination. This allows the system to provide input information based on the user's geographical location.

[0044] The reception desk can analyze the user's social media activity during information entry and automatically input relevant information. For example, the reception desk can automatically display relevant input fields based on information the user has shared on social media. It can also analyze the user's social media posts and suggest appropriate input fields. Furthermore, the reception desk can consider the user's social media friendships and prompt them to input relevant information. This allows the system to provide input information based on the user's social media activity.

[0045] The generation unit can generate optimal experience content by referring to the user's past feedback during the experience content generation process. For example, the generation unit can generate similar experience content based on experiences that the user has previously given high ratings to. Furthermore, the generation unit can analyze the user's past feedback and generate experience content that reflects improvements. In addition, the generation unit can generate related experience content based on themes the user has shown interest in in the past. This allows the system to provide experience content that is based on the user's past feedback.

[0046] The generation unit can apply different generation algorithms depending on the user's lifestyle and hobbies when generating experience content. For example, if the user enjoys the outdoors, the generation unit can generate experience content related to nature and activities. If the user prefers indoor activities, the generation unit can also generate experience content related to culture and history. Furthermore, if the user is traveling with family, the generation unit can generate family-friendly experience content. This allows the system to provide experience content tailored to the user's lifestyle and hobbies.

[0047] The generation unit can prioritize experience content based on the user's submission timing when generating the experience content. For example, if the user is in a hurry, the generation unit will prioritize providing the most important information. Conversely, if the user has ample time, the generation unit can also provide more detailed information. Furthermore, if the user has set a specific deadline, the generation unit can provide experience content tailored to that deadline. This allows for the provision of experience content based on the user's submission timing.

[0048] The generation unit can adjust the order of experience content based on user relevance when generating experience content. For example, if a user is interested in a particular theme, the generation unit will prioritize providing experience content related to that theme. The generation unit can also sequentially provide relevant experience content based on what the user has shown interest in in the past. Furthermore, the generation unit can customize the order of experience content based on the user's interests. This allows for the provision of experience content based on user relevance.

[0049] The chat function can suggest the most suitable topics during live chats by referencing the user's past chat history. For example, it can suggest related topics based on topics the user has discussed in the past. It can also analyze the user's past chat history and suggest topics that might interest them. Furthermore, it can suggest similar topics based on topics the user has previously given high ratings to. This allows the chat function to provide topics tailored to the user's past chat history.

[0050] The chat function can customize chat content during live chats based on the user's life circumstances. For example, if the user is raising children, the chat function can offer topics related to childcare. It can also offer work-related topics if the user is at work, and travel-related topics if the user is traveling. This allows the chat content to be tailored to the user's situation.

[0051] The chat function can prioritize providing relevant topics during live chats, taking into account the user's geographical location. For example, it can prioritize topics related to the user's current location. It can also provide topics related to a specific region if the user is interested in that region. Furthermore, if the user is traveling, it can prioritize topics related to their travel destination. This allows for the provision of topics based on the user's geographical location.

[0052] The chat function can analyze a user's social media activity during live chats and suggest relevant topics. For example, it can provide relevant topics based on information shared by the user on social media. It can also analyze the content of a user's social media posts and suggest appropriate topics. Furthermore, it can consider the user's social media friendships when suggesting relevant topics. This allows the chat function to provide topics based on the user's social media activity.

[0053] The video department can suggest the most suitable 360-degree videos by referencing the user's past viewing history. For example, it can suggest similar videos based on videos the user has watched in the past. It can also analyze the user's past viewing history and suggest videos that might interest them. Furthermore, it can suggest related videos based on videos the user has previously rated highly. This allows the system to provide videos tailored to the user's past viewing history.

[0054] The video department can customize the content of 360-degree videos based on the user's lifestyle. For example, if the user is raising children, the video department can provide videos related to childcare. It can also provide videos related to work if the user is working, and videos related to travel if the user is traveling. This allows for the provision of video content tailored to the user's lifestyle.

[0055] The video section can prioritize providing highly relevant videos when delivering 360-degree videos, taking into account the user's geographical location. For example, it can prioritize videos of locations close to the user's current location. It can also provide videos related to a specific region if the user is interested in that region. Furthermore, if the user is traveling, it can prioritize videos of their travel destination. This allows for the provision of videos based on the user's geographical location.

[0056] The video department can analyze users' social media activity and suggest relevant videos when providing 360-degree videos. For example, the video department can provide relevant videos based on information shared by users on social media. It can also analyze the content of users' social media posts and suggest appropriate videos. Furthermore, the video department can consider users' social media friendships when suggesting relevant videos. This allows for the provision of videos based on users' social media activity.

[0057] The matching unit can suggest the most suitable match by referring to the user's past matching history when providing the matching service. For example, the matching unit can suggest similar matches based on matches that the user has previously given high ratings to. Furthermore, the matching unit can analyze the user's past matching history and suggest matches that might interest them. In addition, the matching unit can suggest related matches based on themes the user has shown interest in in the past. This allows the system to provide matches based on the user's past matching history.

[0058] The matching unit can customize the matching content based on the user's lifestyle when providing the matching service. For example, if the user is raising children, the matching unit can provide matching related to childcare. It can also provide matching related to work if the user is working. Furthermore, if the user is traveling, the matching unit can provide matching related to travel. This allows the system to provide matching content tailored to the user's lifestyle.

[0059] The matching unit can prioritize providing highly relevant matches by considering the user's geographical location when providing matching services. For example, the matching unit can prioritize matches in locations close to the user's current location. Furthermore, if the user is interested in a specific region, the matching unit can provide matches related to that region. Additionally, if the user is traveling, the matching unit can prioritize matches in their travel destination. This allows for matching based on the user's geographical location.

[0060] The matching unit can analyze a user's social media activity and suggest relevant matches when providing matching services. For example, the matching unit can provide relevant matches based on information shared by the user on social media. It can also analyze the content of a user's social media posts and suggest appropriate matches. Furthermore, the matching unit can consider the user's social media friendships when suggesting relevant matches. This allows for matching based on the user's social media activity.

[0061] The proposal department can suggest the most suitable relocation plan by referring to the user's past relocation history. For example, it can suggest similar plans based on relocation plans that the user has previously given high ratings to. Furthermore, the proposal department can analyze the user's past relocation history and suggest plans that might interest them. It can also suggest related plans based on themes the user has shown interest in in the past. This allows the department to provide plans tailored to the user's past relocation history.

[0062] The proposal department can customize the content of a relocation plan based on the user's living situation. For example, if the user is raising children, the proposal department will provide a plan related to childcare. It can also provide a plan related to work if the user is working. Furthermore, if the user is traveling, it can provide a plan related to travel. This allows the department to provide plans tailored to the user's living situation.

[0063] The proposal function can prioritize providing highly relevant plans when proposing relocation plans, taking into account the user's geographical location. For example, it can prioritize plans for locations close to the user's current location. Furthermore, if the user is interested in a specific region, the proposal function can provide plans related to that region. Additionally, if the user is traveling, the proposal function can prioritize plans for their travel destination. This allows for the provision of plans based on the user's geographical location.

[0064] The proposal department can analyze a user's social media activity and propose relevant plans when suggesting relocation plans. For example, the proposal department can provide relevant plans based on information shared by the user on social media. It can also analyze the content of a user's social media posts and propose appropriate plans. Furthermore, the proposal department can consider the user's social media friendships when providing relevant plans. This allows for the provision of plans based on the user's social media activity.

[0065] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0066] The local city experience system can also include a health management unit that monitors the user's health status and customizes the experience based on that information. For example, if the user is tired, it can provide a relaxing experience. If the user is active, it can provide an experience related to that activity. Furthermore, it can suggest appropriate rest times based on the user's health status. This allows for the provision of an experience tailored to the user's health condition.

[0067] The local city experience system can also include a history analysis unit that analyzes users' past experience history and proposes optimal experiences. For example, it can suggest similar experiences based on experiences that users have previously given high ratings to. It can also analyze users' past experience history and suggest experiences that they might be interested in. Furthermore, it can suggest related experiences based on themes that users have shown interest in in the past. This allows the system to provide experiences that are tailored to the user's past experience history.

[0068] The local city experience system can also include a geographic information unit that prioritizes providing relevant experiences based on the user's geographic location. For example, it can prioritize providing experiences in locations close to the user's current location. Furthermore, if the user is interested in a particular region, it can provide experiences related to that region. Additionally, if the user is traveling, it can prioritize providing experiences in their travel destination. This allows for the provision of experiences based on the user's geographic location.

[0069] The local city experience system can also include a social media analysis unit that analyzes users' social media activity and suggests relevant experiences. For example, it can suggest relevant experiences based on information shared by users on social media. It can also analyze users' social media posts and suggest appropriate experiences. Furthermore, it can suggest relevant experiences considering users' social media friendships. This allows the system to provide experiences based on users' social media activity.

[0070] The local city experience system can also include a lifestyle analysis unit that customizes the experience content based on the user's living situation. For example, if the user is raising children, it can provide experiences related to childcare. Similarly, if the user is working, it can provide experiences related to work. Furthermore, if the user is traveling, it can provide experiences related to travel. This allows for the provision of experiences tailored to the user's living situation.

[0071] The following briefly describes the processing flow for example form 1.

[0072] Step 1: The reception desk enters the information. For example, it enters information that allows the user to understand the living environment and facilities of the local city, as well as how to participate in the local community, in advance. Step 2: The generation unit generates the experience content based on the information received by the reception unit. The generation unit uses a generation AI to generate the experience content based on user feedback. For example, if a user inputs "I want to know about the local specialties of XX city," an introductory video of those specialties will be generated. Step 3: The chat section provides live chat based on the experience content generated by the generation section. For example, if a user enters "I want to see the scenery of XX city," a 360-degree video of that area will be generated. In addition, users can interact directly with local people through live chat. Step 4: The video unit provides a 360-degree video based on the experience content generated by the generation unit. For example, if a user enters "I want to see the scenery of XX city," a 360-degree video of that area will be generated. Step 5: The matching unit provides matching services based on the experience content generated by the generation unit. For example, if a user enters "I want to know about hobby activities in XX city," the unit will match them with local people who share that hobby. Step 6: The proposal unit proposes a relocation plan based on the experience content generated by the generation unit. The proposal unit uses generation AI to analyze the user's reactions and responses and propose the optimal relocation plan. For example, if a user inputs "I want to move to XX city," the optimal relocation plan for that user will be generated.

[0073] (Example of form 2) The local city experience system according to an embodiment of the present invention is a system that enables users to experience life in a local city online in advance by utilizing generative AI. This system begins with the user inputting information to understand the local city's living environment, facilities, and how to participate in the local community. Based on each user's feedback, the generative AI provides a personalized online local city experience. This experience includes introductions to local products, live chats with locals, and 360-degree videos of the surrounding scenery, allowing users to experience the charm of the region in detail. The system also provides a matching service with locals based on the individual user's aptitude, hobbies, and lifestyle, and the AI ​​further analyzes the user's reactions and responses to propose an optimal relocation plan. As a result, users can acquire concrete regional information in one place before actually considering relocation and also gain opportunities to interact with local residents. For example, a user inputs, "I want to know about the living environment of XX city." This information is input into the generative AI. Next, the generative AI analyzes the input information and provides a personalized online local city experience. Based on the user's feedback, the generative AI generates the most suitable experience for that user. For example, if a user enters "I want to know about the local products of XX city," a video introducing those products will be generated. The generated experience will include introductions to local products, live chats with locals, and 360-degree videos of the surrounding scenery. For example, if a user enters "I want to see the scenery of XX city," a 360-degree video of that area will be generated. In addition, users can directly interact with locals through live chats. Furthermore, a matching service with locals is provided based on the individual user's aptitude, hobbies, and lifestyle. For example, if a user enters "I want to know about hobby activities in XX city," the system will match them with locals who share that hobby. Finally, the AI ​​analyzes the user's reactions and responses to propose the optimal relocation plan. For example, if a user enters "I want to move to XX city," the system will generate a relocation plan that is best suited to that user. This allows users to obtain concrete regional information before actually considering relocation and also provides opportunities to interact with local residents.This system leverages the power of generative AI to provide "a personalized rural relocation experience" for each individual, bridging the information gap between urban and rural areas and contributing to the formation of a balanced society. Specifically, we aim to help users find rural relocation destinations that suit their lifestyles and envision new lifestyles, thereby contributing to the resolution of the major social challenge of revitalizing rural cities. This rural city experience system will allow users to experience rural life online in advance and obtain specific regional information before relocating.

[0074] The local city experience system according to this embodiment comprises a reception unit, a generation unit, a chat unit, a video unit, a matching unit, and a proposal unit. The reception unit receives information input. For example, the user inputs information to understand in advance the living environment and facilities of the local city, and how to participate in the local community. The generation unit generates experience content based on the information received by the reception unit. The generation unit uses a generation AI to generate experience content based on user feedback. For example, if the user inputs "I want to know about the local products of XX city," an introductory video of those local products is generated. The generated experience content includes introductions to local products, live chats with local people, and 360-degree videos of the surrounding scenery. The chat unit provides live chat based on the experience content generated by the generation unit. For example, if the user inputs "I want to see the scenery of XX city," a 360-degree video of that area is generated. In addition, the user can directly interact with local people through live chat. The video unit provides 360-degree videos based on the experience content generated by the generation unit. For example, if a user inputs "I want to see the scenery of XX city," a 360-degree video of that area is generated. The matching unit provides a matching service based on the experience content generated by the generation unit. For example, if a user inputs "I want to know about hobby activities in XX city," the system matches them with local people who share that hobby. The proposal unit proposes a relocation plan based on the experience content generated by the generation unit. The proposal unit uses generation AI to analyze the user's reactions and responses and proposes the optimal relocation plan. For example, if a user inputs "I want to move to XX city," the system generates a relocation plan that is optimal for that user. As a result, the local city experience system according to this embodiment allows users to experience life in a local city online in advance and obtain specific local information before relocating.

[0075] The reception department inputs information. For example, it inputs information that allows users to understand in advance the living environment and facilities of a local city, as well as how to participate in the local community. Specifically, users can input detailed information about areas of interest, activities of interest, and lifestyles through a dedicated interface. For example, if a user inputs "I want to know about the educational environment in XX city," the reception department receives that information and passes it on to the next processing step. Similarly, if a user inputs "I want to know about medical facilities in XX city," that information is also received. The reception department organizes the user's input and stores it in a database so that the generation department can process it efficiently. Furthermore, the reception department also has a function to automatically complete relevant information based on the user's input. For example, if a user inputs "I want to know about transportation options in XX city," the reception department automatically collects information about public transportation and major transportation routes in that area and prepares it to pass on to the generation department. This allows the reception department to quickly and accurately collect the information the user needs and smoothly pass it on to the next processing step.

[0076] The generation unit generates experience content based on information received by the reception unit. The generation unit uses a generation AI to generate experience content based on user feedback. Specifically, the generation AI analyzes the information entered by the user and generates the most suitable experience content for that information. For example, if a user enters "I want to know about local specialties of XX city," the generation AI will refer to a database of local specialties in that region and generate introductory videos and related information about the specialties. The generation AI uses natural language processing technology to understand the user's input and generate appropriate content. In addition, the generation AI can provide more personalized experience content by considering the user's past feedback and behavioral history. For example, if a user previously entered "I want to know about tourist spots in XX city," the generation AI will use that information to suggest new information and events related to tourist spots. Furthermore, the generation unit can update the generated experience content in real time and respond flexibly to user requests. As a result, the generation unit can always provide users with the latest and most optimal experience content and effectively convey the charm of local cities.

[0077] The chat section provides live chat based on the experience content generated by the generation section. Specifically, if a user enters "I want to see the scenery of XX city," a 360-degree video of that area will be generated. Furthermore, users can interact directly with locals through live chat. The chat section uses generation AI to produce appropriate responses to user questions and comments. For example, if a user asks "What are some recommended restaurants in XX city?", the generation AI will refer to restaurant information in the area and recommend a suitable restaurant. The chat section also includes translation and speech recognition functions to facilitate communication between users and locals. This allows users who speak different languages ​​to communicate smoothly. In addition, the chat section can continuously improve the quality of the experience content by collecting user feedback and providing it to the generation section. This allows the chat section to provide users with a real-time, interactive experience, enabling them to gain a deeper understanding of the charm of local cities.

[0078] The video unit provides 360-degree videos based on the experience content generated by the generation unit. Specifically, if a user inputs "I want to see the scenery of XX city," a 360-degree video of that area will be generated. The video unit uses high-resolution cameras and drones to film the scenery and landmarks of local cities, providing users with immersive footage. For example, if a user inputs "I want to see the cherry blossom spots in XX city," the video unit will provide a 360-degree video of the cherry blossom spots in that area. Furthermore, the video unit can use generation AI to customize the video content based on the user's interests. For example, if a user inputs "I want to learn about the historical buildings in XX city," the generation AI will collect information about the historical buildings in that area and generate a relevant video. In addition, the video unit can embed information points and links within the video so that users can interactively acquire information while watching the video. This allows users to delve deeper into information that interests them while watching the video. In this way, the video unit can provide users with an immersive video experience and effectively convey the charm of local cities.

[0079] The matching unit provides matching services based on the experience content generated by the generation unit. Specifically, if a user enters "I want to know about hobby activities in XX city," the unit will match them with local people who share that hobby. The matching unit uses generation AI to make optimal matches based on the user's interests. For example, if a user enters "I want to find hiking partners in XX city," the generation AI will search for hiking groups and individuals in that area and introduce them to the user. The matching unit can also provide more personalized matches by considering the user's profile information and past activity history. For example, if a user enters "I want to participate in a cooking class in XX city," the generation AI will search for cooking classes and related events in that area and suggest them to the user. Furthermore, the matching unit provides chat and event participation functions to support communication between users. This allows users to interact directly with local people and share hobbies and interests. In this way, the matching unit provides users with opportunities to build new relationships and communities in regional cities and experience the charm of these cities more deeply.

[0080] The proposal unit proposes relocation plans based on the experience content generated by the generation unit. The proposal unit uses generation AI to analyze user reactions and responses to propose the optimal relocation plan. Specifically, if a user inputs "I want to move to XX city," the optimal relocation plan for that user is generated. The generation AI considers the user's lifestyle and desired conditions to propose the best housing, jobs, educational institutions, etc. For example, if a user inputs "I want to find a job in XX city," the generation AI searches for job postings in that area and proposes jobs suitable for the user. Also, if a user inputs "I want to know about the childcare environment in XX city," the generation AI provides information on educational institutions and childcare support facilities in that area. Furthermore, the proposal unit can continuously improve the relocation plan based on user feedback. For example, if a user provides feedback on the proposed plan saying "I would prefer a place with a richer natural environment," the generation AI generates a new plan based on that information. In this way, the proposal unit can provide users with the optimal relocation plan and support their relocation to regional cities.

[0081] The suggestion function can analyze user responses and answers to propose the optimal relocation plan. For example, if a user inputs "I want to move to XX city," the suggestion function will generate the optimal relocation plan for that user. The suggestion function uses a generation AI to analyze user responses and answers and propose the optimal relocation plan. For example, if a user inputs "I want to move to XX city," the optimal relocation plan for that user will be generated. This allows the system to provide the optimal relocation plan based on user responses.

[0082] The generation unit can generate experience content based on user feedback. For example, if a user inputs "I want to know about the local specialties of XX city," the generation unit will generate an introductory video about those specialties. The generation unit uses a generation AI to generate experience content based on user feedback. For example, if a user inputs "I want to know about the local specialties of XX city," an introductory video about those specialties will be generated. This allows for the provision of personalized experience content based on user feedback.

[0083] The chat function can provide live chat with local people. For example, if a user enters "I want to see the scenery of XX city," the chat function will generate a 360-degree video of that area. Furthermore, through live chat with local people, users can interact directly with them. The chat function uses a generation AI to provide live chat with local people. For example, if a user enters "I want to see the scenery of XX city," a 360-degree video of that area will be generated. Furthermore, through live chat with local people, users can interact directly with them. This allows users to interact directly with local people.

[0084] The video function can provide 360-degree videos of the surrounding scenery. For example, if a user inputs "I want to see the scenery of XX city," the video function will generate a 360-degree video of that area. The video function uses generation AI to provide 360-degree videos of the surrounding scenery. For example, if a user inputs "I want to see the scenery of XX city," a 360-degree video of that area will be generated. This allows users to experience the local scenery in 360-degree video.

[0085] The matching function can provide a matching service with local people based on each user's aptitude, hobbies, and lifestyle. For example, if a user enters "I want to know about hobby activities in XX city," the matching function will match them with local people who share that hobby. The matching function uses a generative AI to provide a matching service with local people based on each user's aptitude, hobbies, and lifestyle. For example, if a user enters "I want to know about hobby activities in XX city," the matching function will match them with local people who share that hobby. This ensures that users are appropriately matched with local people.

[0086] The reception desk can estimate the user's emotions and customize the information input interface based on the estimated emotions. For example, if the user is tense, the reception desk can provide an interface with calming colors to reduce visual stress. If the user is enjoying themselves, the reception desk can provide an interface with bright colors to make the input process more enjoyable. Furthermore, if the user is tired, the reception desk can provide a simple and highly visible interface to facilitate the input process. This allows for the provision of an interface tailored to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0087] The reception desk can analyze the user's past input history and suggest the optimal input method. For example, it can automatically display information that the user has frequently entered in the past as a suggestion. It can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception desk can predict and suggest information that the user will use at specific times based on their past input history. This allows the system to suggest the optimal input method based on the user's past input history.

[0088] The input field can dynamically change input fields based on the user's current interests and lifestyle when they enter information. For example, the input field can automatically display relevant input fields based on the user's recent searches. It can also suggest appropriate input fields based on the user's lifestyle (e.g., raising children, working). Furthermore, the input field can customize input fields based on the user's interests (e.g., hobbies, travel). This allows the system to provide input fields tailored to the user's interests and lifestyle.

[0089] The reception desk can estimate the user's emotions and prioritize input information based on those emotions. For example, if the user is in a hurry, the reception desk may prioritize inputting important information. If the user is relaxed, the reception desk may also prioritize inputting detailed information. Furthermore, if the user is stressed, the reception desk may simplify the input process. This allows for the prioritization of input information according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0090] The reception system can prioritize inputting highly relevant information by considering the user's geographical location during data entry. For example, it can prioritize inputting information about locations close to the user's current location. Furthermore, if the user is interested in a particular region, it can prioritize inputting information related to that region. Additionally, if the user is traveling, it can prioritize inputting information about their travel destination. This allows the system to provide input information based on the user's geographical location.

[0091] The reception desk can analyze the user's social media activity during information entry and automatically input relevant information. For example, the reception desk can automatically display relevant input fields based on information the user has shared on social media. It can also analyze the user's social media posts and suggest appropriate input fields. Furthermore, the reception desk can consider the user's social media friendships and prompt them to input relevant information. This allows the system to provide input information based on the user's social media activity.

[0092] The generation unit can estimate the user's emotions and adjust the way the experience is presented based on those emotions. For example, if the user is relaxed, the generation unit will generate an experience that progresses at a leisurely pace. If the user is in a hurry, the generation unit can also generate a concise and to-the-point experience. Furthermore, if the user is excited, the generation unit can generate an experience with visually stimulating effects. This allows for the presentation of the experience in a way that suits the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0093] The generation unit can generate optimal experience content by referring to the user's past feedback during the experience content generation process. For example, the generation unit can generate similar experience content based on experiences that the user has previously given high ratings to. Furthermore, the generation unit can analyze the user's past feedback and generate experience content that reflects improvements. In addition, the generation unit can generate related experience content based on themes the user has shown interest in in the past. This allows the system to provide experience content that is based on the user's past feedback.

[0094] The generation unit can apply different generation algorithms depending on the user's lifestyle and hobbies when generating experience content. For example, if the user enjoys the outdoors, the generation unit can generate experience content related to nature and activities. If the user prefers indoor activities, the generation unit can also generate experience content related to culture and history. Furthermore, if the user is traveling with family, the generation unit can generate family-friendly experience content. This allows the system to provide experience content tailored to the user's lifestyle and hobbies.

[0095] The generation unit can estimate the user's emotions and adjust the length of the experience content based on those emotions. For example, if the user is in a hurry, the generation unit can generate a short, concise experience. If the user is relaxed, the generation unit can generate a longer experience with detailed explanations. Furthermore, if the user is excited, the generation unit can generate an experience with visually stimulating effects. This allows for the provision of experience content lengths that correspond to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0096] The generation unit can prioritize experience content based on the user's submission timing when generating the experience content. For example, if the user is in a hurry, the generation unit will prioritize providing the most important information. Conversely, if the user has ample time, the generation unit can also provide more detailed information. Furthermore, if the user has set a specific deadline, the generation unit can provide experience content tailored to that deadline. This allows for the provision of experience content based on the user's submission timing.

[0097] The generation unit can adjust the order of experience content based on user relevance when generating experience content. For example, if a user is interested in a particular theme, the generation unit will prioritize providing experience content related to that theme. The generation unit can also sequentially provide relevant experience content based on what the user has shown interest in in the past. Furthermore, the generation unit can customize the order of experience content based on the user's interests. This allows for the provision of experience content based on user relevance.

[0098] The chat function can estimate the user's emotions and adjust the chat topic based on those emotions. For example, if the user is relaxed, the chat function will offer light topics. If the user is stressed, the chat function can offer reassuring topics. Furthermore, if the user is excited, the chat function can offer interesting topics. This allows the chat to provide topics that are appropriate to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0099] The chat function can suggest the most suitable topics during live chats by referencing the user's past chat history. For example, it can suggest related topics based on topics the user has discussed in the past. It can also analyze the user's past chat history and suggest topics that might interest them. Furthermore, it can suggest similar topics based on topics the user has previously given high ratings to. This allows the chat function to provide topics tailored to the user's past chat history.

[0100] The chat function can customize chat content during live chats based on the user's life circumstances. For example, if the user is raising children, the chat function can offer topics related to childcare. It can also offer work-related topics if the user is at work, and travel-related topics if the user is traveling. This allows the chat content to be tailored to the user's situation.

[0101] The chat function can estimate the user's emotions and prioritize chats based on those emotions. For example, if the user is in a hurry, the chat function will prioritize important topics. If the user is relaxed, the chat function can also provide more detailed topics. Furthermore, if the user is stressed, the chat function can prioritize relaxing topics. This allows for chat prioritization tailored to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0102] The chat function can prioritize providing relevant topics during live chats, taking into account the user's geographical location. For example, it can prioritize topics related to the user's current location. It can also provide topics related to a specific region if the user is interested in that region. Furthermore, if the user is traveling, it can prioritize topics related to their travel destination. This allows for the provision of topics based on the user's geographical location.

[0103] The chat function can analyze a user's social media activity during live chats and suggest relevant topics. For example, it can provide relevant topics based on information shared by the user on social media. It can also analyze the content of a user's social media posts and suggest appropriate topics. Furthermore, it can consider the user's social media friendships when suggesting relevant topics. This allows the chat function to provide topics based on the user's social media activity.

[0104] The video component can estimate the user's emotions and adjust the video content based on those emotions. For example, if the user is relaxed, the video component can provide a video that progresses at a leisurely pace. If the user is in a hurry, the video component can provide a short, concise video. Furthermore, if the user is excited, the video component can provide a video with visually stimulating effects. This allows the video content to be tailored to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0105] The video department can suggest the most suitable 360-degree videos by referencing the user's past viewing history. For example, it can suggest similar videos based on videos the user has watched in the past. It can also analyze the user's past viewing history and suggest videos that might interest them. Furthermore, it can suggest related videos based on videos the user has previously rated highly. This allows the system to provide videos tailored to the user's past viewing history.

[0106] The video department can customize the content of 360-degree videos based on the user's lifestyle. For example, if the user is raising children, the video department can provide videos related to childcare. It can also provide videos related to work if the user is working, and videos related to travel if the user is traveling. This allows for the provision of video content tailored to the user's lifestyle.

[0107] The video section can estimate the user's emotions and prioritize videos based on those emotions. For example, if the user is in a hurry, the video section will prioritize important videos. If the user is relaxed, the video section can also provide more detailed videos. Furthermore, if the user is stressed, the video section can prioritize relaxing videos. This allows for video prioritization according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0108] The video section can prioritize providing highly relevant videos when delivering 360-degree videos, taking into account the user's geographical location. For example, it can prioritize videos of locations close to the user's current location. It can also provide videos related to a specific region if the user is interested in that region. Furthermore, if the user is traveling, it can prioritize videos of their travel destination. This allows for the provision of videos based on the user's geographical location.

[0109] The video department can analyze users' social media activity and suggest relevant videos when providing 360-degree videos. For example, the video department can provide relevant videos based on information shared by users on social media. It can also analyze the content of users' social media posts and suggest appropriate videos. Furthermore, the video department can consider users' social media friendships when suggesting relevant videos. This allows for the provision of videos based on users' social media activity.

[0110] 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 can provide matches based on hobbies and interests. If the user is tense, the matching unit can also provide matches that provide a sense of security. Furthermore, if the user is excited, the matching unit can provide matches that pique their interest. This allows the system to provide matching criteria that are appropriate 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 includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0111] The matching unit can suggest the most suitable match by referring to the user's past matching history when providing the matching service. For example, the matching unit can suggest similar matches based on matches that the user has previously given high ratings to. Furthermore, the matching unit can analyze the user's past matching history and suggest matches that might interest them. In addition, the matching unit can suggest related matches based on themes the user has shown interest in in the past. This allows the system to provide matches based on the user's past matching history.

[0112] The matching unit can customize the matching content based on the user's lifestyle when providing the matching service. For example, if the user is raising children, the matching unit can provide matching related to childcare. It can also provide matching related to work if the user is working. Furthermore, if the user is traveling, the matching unit can provide matching related to travel. This allows the system to provide matching content tailored to the user's lifestyle.

[0113] The matching unit can estimate the user's emotions and determine matching priorities based on those emotions. For example, if the user is in a hurry, the matching unit will prioritize important matches. If the user is relaxed, the matching unit can also provide more detailed matches. Furthermore, if the user is stressed, the matching unit can prioritize relaxing matches. This allows for matching priorities tailored to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0114] The matching unit can prioritize providing highly relevant matches by considering the user's geographical location when providing matching services. For example, the matching unit can prioritize matches in locations close to the user's current location. Furthermore, if the user is interested in a specific region, the matching unit can provide matches related to that region. Additionally, if the user is traveling, the matching unit can prioritize matches in their travel destination. This allows for matching based on the user's geographical location.

[0115] The matching unit can analyze a user's social media activity and suggest relevant matches when providing matching services. For example, the matching unit can provide relevant matches based on information shared by the user on social media. It can also analyze the content of a user's social media posts and suggest appropriate matches. Furthermore, the matching unit can consider the user's social media friendships when suggesting relevant matches. This allows for matching based on the user's social media activity.

[0116] The suggestion unit can estimate the user's emotions and adjust the content of the migration plan based on the estimated emotions. For example, if the user is relaxed, the suggestion unit can provide a migration plan that proceeds at a leisurely pace. If the user is in a hurry, the suggestion unit can also provide a concise migration plan that gets straight to the point. Furthermore, if the user is excited, the suggestion unit can provide a migration plan with visually stimulating effects. This allows for the provision of migration plans that are tailored 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 includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0117] The proposal department can suggest the most suitable relocation plan by referring to the user's past relocation history. For example, it can suggest similar plans based on relocation plans that the user has previously given high ratings to. Furthermore, the proposal department can analyze the user's past relocation history and suggest plans that might interest them. It can also suggest related plans based on themes the user has shown interest in in the past. This allows the department to provide plans tailored to the user's past relocation history.

[0118] The proposal department can customize the content of a relocation plan based on the user's living situation. For example, if the user is raising children, the proposal department will provide a plan related to childcare. It can also provide a plan related to work if the user is working. Furthermore, if the user is traveling, it can provide a plan related to travel. This allows the department to provide plans tailored to the user's living situation.

[0119] The suggestion function can estimate the user's emotions and prioritize relocation plans based on those emotions. For example, if the user is in a hurry, the suggestion function will prioritize important plans. If the user is relaxed, the suggestion function can also provide detailed plans. Furthermore, if the user is stressed, the suggestion function can prioritize relaxing plans. This allows for the prioritization of relocation plans according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0120] The proposal function can prioritize providing highly relevant plans when proposing relocation plans, taking into account the user's geographical location. For example, it can prioritize plans for locations close to the user's current location. Furthermore, if the user is interested in a specific region, the proposal function can provide plans related to that region. Additionally, if the user is traveling, the proposal function can prioritize plans for their travel destination. This allows for the provision of plans based on the user's geographical location.

[0121] The proposal department can analyze a user's social media activity and propose relevant plans when suggesting relocation plans. For example, the proposal department can provide relevant plans based on information shared by the user on social media. It can also analyze the content of a user's social media posts and propose appropriate plans. Furthermore, the proposal department can consider the user's social media friendships when providing relevant plans. This allows for the provision of plans based on the user's social media activity.

[0122] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0123] The local city experience system can also include a health management unit that monitors the user's health status and customizes the experience based on that information. For example, if the user is tired, it can provide a relaxing experience. If the user is active, it can provide an experience related to that activity. Furthermore, it can suggest appropriate rest times based on the user's health status. This allows for the provision of an experience tailored to the user's health condition.

[0124] The local city experience system can also estimate the user's emotions and adjust the difficulty level of the experience based on those emotions. For example, if the user is feeling stressed, it can provide an easy and relaxing experience. If the user is excited, it can provide a challenging experience. Furthermore, if the user is relaxed, it can provide an experience that includes detailed information. This allows the system to provide an experience difficulty level that matches the user's emotions.

[0125] The local city experience system can also include a history analysis unit that analyzes users' past experience history and proposes optimal experiences. For example, it can suggest similar experiences based on experiences that users have previously given high ratings to. It can also analyze users' past experience history and suggest experiences that they might be interested in. Furthermore, it can suggest related experiences based on themes that users have shown interest in in the past. This allows the system to provide experiences that are tailored to the user's past experience history.

[0126] The local city experience system can also estimate the user's emotions and adjust the visual representation of the experience based on those emotions. For example, if the user is relaxed, it can provide an experience with calming colors. If the user is excited, it can provide an experience with vibrant colors. Furthermore, if the user is stressed, it can provide an experience with a visually calming design. This allows for visual representations that respond to the user's emotions.

[0127] The local city experience system can also include a geographic information unit that prioritizes providing relevant experiences based on the user's geographic location. For example, it can prioritize providing experiences in locations close to the user's current location. Furthermore, if the user is interested in a particular region, it can provide experiences related to that region. Additionally, if the user is traveling, it can prioritize providing experiences in their travel destination. This allows for the provision of experiences based on the user's geographic location.

[0128] The local city experience system can also estimate the user's emotions and adjust the audio guide of the experience based on those emotions. For example, if the user is relaxed, it can provide a calm voice guide. If the user is excited, it can provide a lively voice guide. Furthermore, if the user is stressed, it can provide a calm voice guide. This allows for audio guides to be tailored to the user's emotions.

[0129] The local city experience system can also include a social media analysis unit that analyzes users' social media activity and suggests relevant experiences. For example, it can suggest relevant experiences based on information shared by users on social media. It can also analyze users' social media posts and suggest appropriate experiences. Furthermore, it can suggest relevant experiences considering users' social media friendships. This allows the system to provide experiences based on users' social media activity.

[0130] The local city experience system can also estimate the user's emotions and adjust the interaction of the experience based on those emotions. For example, if the user is relaxed, it can provide a gentle interaction. If the user is excited, it can provide a lively interaction. Furthermore, if the user is stressed, it can provide a simple and intuitive interaction. This allows for interaction that is tailored to the user's emotions.

[0131] The local city experience system can also include a lifestyle analysis unit that customizes the experience content based on the user's living situation. For example, if the user is raising children, it can provide experiences related to childcare. Similarly, if the user is working, it can provide experiences related to work. Furthermore, if the user is traveling, it can provide experiences related to travel. This allows for the provision of experiences tailored to the user's living situation.

[0132] The local city experience system can also estimate the user's emotions and adjust the feedback on the experience based on those emotions. For example, if the user is relaxed, it can provide calm feedback. If the user is excited, it can provide lively feedback. Furthermore, if the user is stressed, it can provide calming feedback. This allows for feedback tailored to the user's emotions.

[0133] The following briefly describes the processing flow for example form 2.

[0134] Step 1: The reception desk enters the information. For example, it enters information that allows the user to understand the living environment and facilities of the local city, as well as how to participate in the local community, in advance. Step 2: The generation unit generates the experience content based on the information received by the reception unit. The generation unit uses a generation AI to generate the experience content based on user feedback. For example, if a user inputs "I want to know about the local specialties of XX city," an introductory video of those specialties will be generated. Step 3: The chat section provides live chat based on the experience content generated by the generation section. For example, if a user enters "I want to see the scenery of XX city," a 360-degree video of that area will be generated. In addition, users can interact directly with local people through live chat. Step 4: The video unit provides a 360-degree video based on the experience content generated by the generation unit. For example, if a user enters "I want to see the scenery of XX city," a 360-degree video of that area will be generated. Step 5: The matching unit provides matching services based on the experience content generated by the generation unit. For example, if a user enters "I want to know about hobby activities in XX city," the unit will match them with local people who share that hobby. Step 6: The proposal unit proposes a relocation plan based on the experience content generated by the generation unit. The proposal unit uses generation AI to analyze the user's reactions and responses and propose the optimal relocation plan. For example, if a user inputs "I want to move to XX city," the optimal relocation plan for that user will be generated.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] Each of the multiple elements described above, including the reception unit, generation unit, chat unit, video unit, matching unit, and proposal unit, is implemented by at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the reception device 38 of the smart device 14, where the user inputs information about the living environment and facilities of a local city. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12, where experience content is generated using a generation AI. The chat unit is implemented by the control unit 46A of the smart device 14, providing live chat with local people. The video unit is implemented by the output device 40 of the smart device 14, providing 360-degree video. The matching unit is implemented by the specific processing unit 290 of the data processing unit 12, matching the user with local people who match their interests. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12, where the user's reactions and responses are analyzed to propose the optimal relocation plan. The correspondence between each unit and the devices and control units is not limited to the example described above, and various changes are possible.

[0139] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0140] 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.

[0141] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0142] The 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.

[0143] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0144] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (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).

[0145] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.).

[0151] 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.

[0152] 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.

[0153] 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.

[0154] Each of the multiple elements described above, including the reception unit, generation unit, chat unit, video unit, matching unit, and proposal unit, is implemented by at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the smart glasses 214, where the user inputs information about the living environment and facilities of a local city. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12, where experience content is generated using a generation AI. The chat unit is implemented by the control unit 46A of the smart glasses 214, providing live chat with local people. The video unit is implemented by the speaker 240 of the smart glasses 214, providing 360-degree video. The matching unit is implemented by the specific processing unit 290 of the data processing unit 12, matching the user with local people who match their interests. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12, where the user's reactions and responses are analyzed to propose the optimal relocation plan. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0155] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0156] 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.

[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 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.

[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 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.

[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 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.

[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 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.

[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 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.

[0170] Each of the multiple elements described above, including the reception unit, generation unit, chat unit, video unit, matching unit, and proposal unit, is implemented by at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the headset terminal 314, where the user inputs information about the living environment and facilities of the local city. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12, where experience content is generated using a generation AI. The chat unit is implemented by the control unit 46A of the headset terminal 314, providing live chat with local people. The video unit is implemented by the display 343 of the headset terminal 314, providing 360-degree video. The matching unit is implemented by the specific processing unit 290 of the data processing unit 12, matching the user with local people who match their interests. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12, where the user's reactions and responses are analyzed to propose the optimal relocation plan. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0171] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0172] 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.

[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 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.

[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 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).

[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] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.).

[0184] 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.

[0185] 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.

[0186] 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.

[0187] Each of the multiple elements described above, including the reception unit, generation unit, chat unit, video unit, matching unit, and proposal unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the robot 414, where the user inputs information about the living environment and facilities of the local city. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12, where experience content is generated using a generation AI. The chat unit is implemented by the control unit 46A of the robot 414, providing live chat with local people. The video unit is implemented by the speaker 240 of the robot 414, providing 360-degree video. The matching unit is implemented by the specific processing unit 290 of the data processing unit 12, matching the user with local people who match their interests. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12, where the user's reactions and responses are analyzed to propose the optimal relocation plan. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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."

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] 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.

[0205] 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.

[0206] (Note 1) The reception area where information is entered, A generation unit that generates experience content based on the information received by the reception unit, A chat unit provides live chat based on the experience content generated by the generation unit, A video unit provides a 360-degree video based on the experience content generated by the generation unit, A matching unit provides matching services based on the experience content generated by the generation unit, The system includes a proposal unit that proposes a relocation plan based on the experience content generated by the generation unit. A system characterized by the following features. (Note 2) The aforementioned proposal section is, We analyze user reactions and responses to propose the optimal relocation plan. The system described in Appendix 1, characterized by the features described herein. (Note 3) The generating unit is Experience content is generated based on user feedback. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned chat section is, Offering live chat with locals The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned video section is, We provide 360-degree videos of the surrounding scenery. The system described in Appendix 1, characterized by the features described herein. (Note 6) The matching unit is We provide a matching service that connects individual users with local people based on their aptitudes, hobbies, and lifestyles. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is It estimates the user's emotions and customizes the information input interface based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is It analyzes the user's past input history and suggests the optimal input method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is When users enter information, the input fields are dynamically changed based on their current interests and lifestyle. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is It estimates the user's emotions and prioritizes input information based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When users enter information, the system prioritizes inputting highly relevant information by considering their geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is When users enter information, the system analyzes their social media activity and automatically fills in relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 13) The generating unit is It estimates the user's emotions and adjusts how the experience is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The generating unit is When generating user experiences, the system references past user feedback to create the most optimal experience. The system described in Appendix 1, characterized by the features described herein. (Note 15) The generating unit is When generating the experience content, different generation algorithms are applied depending on the user's lifestyle and hobbies. The system described in Appendix 1, characterized by the features described herein. (Note 16) The generating unit is It estimates the user's emotions and adjusts the length of the experience based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The generating unit is When generating the experience content, the priority of the experience content is determined based on when the user submitted it. The system described in Appendix 1, characterized by the features described herein. (Note 18) The generating unit is When generating user experiences, the order of the experiences is adjusted based on user relevance. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned chat section is, It estimates the user's emotions and adjusts the chat topic based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned chat section is, During live chat, the system suggests the most suitable topics by referencing the user's past chat history. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned chat section is, During live chat, the chat content is customized based on the user's life circumstances. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned chat section is, It estimates the user's emotions and prioritizes chats based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned chat section is, During live chat, the system prioritizes providing relevant topics by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned chat section is, During live chat, we analyze the user's social media activity and suggest relevant topics. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned video section is, The system estimates the user's emotions and adjusts the video content based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned video section is, When providing 360-degree videos, we suggest the most suitable videos by referring to the user's past viewing history. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned video section is, When providing 360-degree videos, the video content is customized based on the user's lifestyle. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned video section is, It estimates the user's emotions and prioritizes videos based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned video section is, When providing 360-degree videos, we prioritize providing videos that are highly relevant to the user, taking into account their geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned video section is, When providing 360-degree videos, we analyze the user's social media activity and suggest relevant videos. The system described in Appendix 1, characterized by the features described herein. (Note 31) 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 32) The matching unit is When providing the matching service, we refer to the user's past matching history to suggest the most suitable match. The system described in Appendix 1, characterized by the features described herein. (Note 33) The matching unit is When providing a matching service, customize the matching content based on the user's living situation. The system described in Appendix 1, characterized by the features described herein. (Note 34) 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 35) The matching unit is When providing matching services, we prioritize providing highly relevant matches by taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 36) The matching unit is When providing matching services, we analyze users' social media activity and suggest relevant matches. The system described in Appendix 1, characterized by the features described herein. (Note 37) The aforementioned proposal section is, The system estimates the user's emotions and adjusts the content of the relocation plan based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 38) The aforementioned proposal section is, When proposing a relocation plan, we refer to the user's past relocation history to suggest the most suitable plan. The system described in Appendix 1, characterized by the features described herein. (Note 39) The aforementioned proposal section is, When proposing a relocation plan, customize the plan based on the user's living situation. The system described in Appendix 1, characterized by the features described herein. (Note 40) The aforementioned proposal section is, It estimates user emotions and prioritizes relocation plans based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 41) The aforementioned proposal section is, When proposing relocation plans, we prioritize providing highly relevant plans by taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 42) The aforementioned proposal section is, When proposing relocation plans, we analyze the user's social media activity and suggest relevant plans. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0207] 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. The reception area where information is entered, A generation unit that generates experience content based on the information received by the reception unit, A chat unit provides live chat based on the experience content generated by the generation unit, A video unit provides a 360-degree video based on the experience content generated by the generation unit, A matching unit provides matching services based on the experience content generated by the generation unit, The system includes a proposal unit that proposes a relocation plan based on the experience content generated by the generation unit. A system characterized by the following features.

2. The aforementioned proposal section is, We analyze user reactions and responses to propose the optimal relocation plan. The system according to feature 1.

3. The generating unit is Experience content is generated based on user feedback. The system according to feature 1.

4. The aforementioned chat section is, Offering live chat with locals The system according to feature 1.

5. The aforementioned video section is, We provide 360-degree videos of the surrounding scenery. The system according to feature 1.

6. The matching unit is We provide a matching service that connects individual users with local people based on their aptitudes, hobbies, and lifestyles. The system according to feature 1.

7. The aforementioned reception unit is It estimates the user's emotions and customizes the information input interface based on those estimated emotions. The system according to feature 1.

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

9. The aforementioned reception unit is When users enter information, the input fields are dynamically changed based on their current interests and lifestyle. The system according to feature 1.

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

Images