system

Abstract

The system according to this embodiment aims to allow users to experience working styles and lifestyles in rural areas in advance. [Solution] The system according to the embodiment comprises a reception unit, an analysis unit, a generation unit, and a provision unit. The reception unit receives information from the user. The analysis unit analyzes the information received by the reception unit. The generation unit generates a simulation based on the information analyzed by the analysis unit. The provision unit provides the simulation generated by the generation unit as a visual tour.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there was a problem that there were few opportunities to experience in advance the working styles and lifestyles in local areas.

[0005] The system according to the embodiment aims to enable users to experience in advance the working styles and lifestyles in local areas.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, an analysis unit, a generation unit, and a provision unit. The reception unit receives information from the user. The analysis unit analyzes the information received by the reception unit. The generation unit generates a simulation based on the information analyzed by the analysis unit. The provision unit provides the simulation generated by the generation unit as a visual tour. [Effects of the Invention]

[0007] The system according to this embodiment can allow users to experience working styles and lifestyles in rural areas 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, and the like. The communication I / F controls communication between a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The visual tour system according to an embodiment of the present invention is a system that utilizes a large-scale language model to simulate a realistic experience of rural life and provides a visual tour service that allows urban residents considering remote work to experience rural work styles and lifestyles. The visual tour system receives input from the user regarding rural life and work styles. Next, the large-scale language model analyzes this information and simulates rural life and work styles. The simulation results are provided to the user as a visual tour. This visual tour is designed to allow the user to realistically experience rural life. Furthermore, it can also send users to workation programs offered by local governments. This mechanism is expected to increase the number of people involved with rural communities and revitalize rural areas. For example, a user might input, "I want to know about the remote work environment in rural areas." This information is input into the large-scale language model. Next, the large-scale language model analyzes the input information and simulates rural life and work styles. Based on data regarding rural living environments and work styles, the large-scale language model generates a simulation that the user can experience. For example, it visualizes rural office environments and daily life. The generated simulation results are provided to the user as a visual tour. Through visual tours, users can realistically experience life and work in rural areas. For example, they can visually experience the work environment in a rural office and interactions with local people. Furthermore, the system can also direct users to workation programs offered by local governments. If a user becomes interested in rural life through a visual tour, they can actually experience rural life by utilizing the workation programs offered by local governments. This system is expected to increase the number of people connected to rural communities and revitalize rural areas. By experiencing rural life and work in advance, users will be more likely to consider relocating to rural areas or participating in workation programs. For example, knowing about remote work environments and lifestyles in rural areas beforehand can reduce anxieties about relocation or workation. In this way, the visual tour system can increase the number of people connected to rural communities and promote the revitalization of rural areas.

[0029] The visual tour system according to this embodiment comprises a reception unit, an analysis unit, a generation unit, and a provision unit. The reception unit receives information from the user. User information includes, but is not limited to, text information, image information, and audio information. For example, the reception unit receives text information entered by the user. The reception unit can also receive image information uploaded by the user. Furthermore, the reception unit can also receive audio information recorded by the user. For example, the reception unit transmits the text information entered by the user to the analysis unit. The analysis unit analyzes the information received by the reception unit. For example, the analysis unit analyzes the information using data mining techniques. Furthermore, the analysis unit can also analyze the information using statistical analysis techniques. Furthermore, the analysis unit can also analyze the information using machine learning techniques. For example, the analysis unit extracts important keywords from the information entered by the user using data mining techniques. Statistical analysis techniques are used to analyze trends and patterns in information. Machine learning techniques are used to learn from large amounts of data and improve the accuracy of information analysis. The generation unit generates a simulation based on the information analyzed by the analysis unit. The generation unit generates simulations using, for example, 3D modeling technology. The generation unit can also generate simulations using virtual reality technology. Furthermore, the generation unit can generate scenario-based simulations. For example, the generation unit simulates a local office environment using 3D modeling technology. Virtual reality technology is used to allow users to experience the simulation. Scenario-based simulations are used to generate simulations based on specific scenarios. The delivery unit provides the simulations generated by the generation unit as visual tours. The delivery unit provides visual tours in, for example, video format. The delivery unit can also provide visual tours in interactive map format. Furthermore, the delivery unit can provide visual tours in virtual reality format. For example, the delivery unit presents a local office environment in video format.An interactive map format is used to allow users to select a specific location on a map and view information about that location. A virtual reality format is used to allow users to experience rural life in a virtual space. As a result, the visual tour system according to this embodiment can provide a visual tour in which users can realistically experience rural life and work.

[0030] The reception unit receives information from users. This information includes, but is not limited to, text information, image information, and audio information. For example, the reception unit receives text information entered by the user. It can also receive image information uploaded by the user. Furthermore, it can receive audio information recorded by the user. For example, the reception unit sends text information entered by the user to the analysis unit. The reception unit has multiple interfaces to efficiently receive the diverse information provided by the user. For example, text information can be entered through web forms or mobile applications. For image information, it provides drag-and-drop functionality and file selection dialogs so that users can easily upload photos taken with their smartphones or digital cameras. For audio information, it has functions for users to record directly using a microphone or to upload existing audio files. This allows the reception unit to quickly and accurately receive the information provided by the user and smoothly move to the next processing step. Furthermore, the reception unit also has functions to perform initial filtering and preprocessing of the received information. For example, it performs spell checks and grammar checks on text information, checks the resolution and format of image information, and performs noise reduction and volume adjustment on audio information. This allows the reception unit to improve the quality of the information it transmits to the analysis unit, thereby increasing the overall accuracy and efficiency of the system.

[0031] The analysis unit analyzes the information received by the reception unit. The analysis unit analyzes the information using, for example, data mining techniques. It can also analyze the information using statistical analysis techniques. Furthermore, it can analyze the information using machine learning techniques. For example, the analysis unit uses data mining techniques to extract important keywords from the information entered by the user. Statistical analysis techniques are used to analyze trends and patterns in information. Machine learning techniques are used to learn from large amounts of data and improve the accuracy of information analysis. The analysis unit combines these techniques to accurately grasp the user's intentions and needs. For example, it uses natural language processing techniques to understand the context from text information, image recognition techniques to analyze the content from image information, and speech recognition techniques to convert audio information into text. This allows the analysis unit to analyze the user's information from multiple angles and generate more accurate results. Furthermore, the analysis unit utilizes high-performance computing resources to perform real-time analysis. For example, it uses a cloud-based distributed processing system to quickly analyze large amounts of data and send the results to the generation unit. Furthermore, the analysis unit can utilize past data and user history information to provide more personalized analysis results. This allows the analysis unit to provide a foundation for generating optimal visual tours tailored to user needs.

[0032] The generation unit generates simulations based on information analyzed by the analysis unit. The generation unit can generate simulations using, for example, 3D modeling technology. It can also generate simulations using virtual reality technology. Furthermore, the generation unit can generate scenario-based simulations. For example, the generation unit can use 3D modeling technology to simulate a local office environment. Virtual reality technology is used to allow users to experience the simulation. Scenario-based simulations are used to generate simulations based on specific scenarios. The generation unit utilizes these technologies to create a visual tour that users can experience realistically. For example, 3D modeling technology is used to reproduce the interior structure of a building and the arrangement of furniture in detail, providing users with an experience as if they were actually there. Using virtual reality technology, users can wear a VR headset and experience the simulation with a 360-degree view. In scenario-based simulations, users can choose actions based on specific scenarios and experience the results. For example, a simulation of a day in a local office environment allows users to experience various scenarios by trying different choices. This allows the generation unit to provide users with a visual tour that realistically allows them to experience life and work in rural areas. Furthermore, the generation unit can improve the simulation based on user feedback, providing a higher quality visual tour.

[0033] The provider delivers the simulations generated by the generator as visual tours. For example, the provider can provide visual tours in video format. It can also provide visual tours in interactive map format. Furthermore, it can provide visual tours in virtual reality format. For example, the provider can showcase a rural office environment in video format. The interactive map format is used to allow users to select specific locations on a map and view information about those locations. The virtual reality format is used to allow users to experience rural life in a virtual space. The provider combines these formats to provide users with diverse experiences. For example, in video format, narration and text overlays can be added to provide not only visual information but also auditory information. In interactive map format, users can click on specific points on the map to display detailed information and related visual content. In virtual reality format, users can wear a VR headset and freely move around in a virtual space, experiencing rural life and work styles. This allows the provider to deliver visual tours that allow users to realistically experience rural life and work styles. Furthermore, the service provider can collect user feedback and continuously improve the content and format of the visual tours. For example, if a user expresses interest in a particular location or scenario, new content can be added based on that information. This allows the service provider to always provide users with the latest and most engaging visual tours.

[0034] The service includes a section dedicated to introducing workation programs offered by various local governments. This section, for example, provides detailed information about the workation programs offered by each local government. It can also collect information on workation programs from local government websites and provide it to users. Furthermore, it can provide users with brochures and materials related to each local government's workation program. Additionally, it can provide users with information on events and seminars related to each local government's workation program. For example, it can automatically collect information on workation programs from local government websites and provide it to users. Brochures and materials are provided in a format that users can download. Information on events and seminars is provided so that users can register to participate. This allows users to learn about the workation programs offered by each local government.

[0035] The reception section includes a collection section for gathering user feedback. The collection section can collect user feedback, for example, through a questionnaire. For instance, it might display a questionnaire after a user experiences a visual tour, requesting feedback. The collection section can also collect user feedback in comment format. For example, it could provide comment fields in each section of the visual tour, allowing users to freely enter comments. Furthermore, the collection section can collect user feedback in rating format. For example, it could provide rating buttons in each section of the visual tour, allowing users to enter ratings. This allows for the collection of user feedback, which can then be used to improve the service.

[0036] The analysis department performs analyses based on data related to local living environments and work styles. For example, the analysis department analyzes local housing information. For example, it collects local housing information and analyzes the types and price ranges of housing. The analysis department can also analyze data related to local transportation. For example, it collects data on local transportation and analyzes the types and usage of transportation. Furthermore, the analysis department can also analyze data related to local work environments. For example, it collects data on local work environments and analyzes the types of workplaces and work styles. By performing analyses based on data related to local living environments and work styles, it is possible to provide more accurate simulations.

[0037] The generation unit visualizes the office environment and daily life in rural areas. For example, it visualizes the office environment in rural areas using 3D modeling technology. For example, it generates the layout and facilities of a rural office as a 3D model. The generation unit can also visualize daily life in rural areas using virtual reality technology. For example, it can recreate the streetscapes and living scenes of rural areas in a virtual reality space. Furthermore, the generation unit can visualize the office environment and daily life in rural areas on a scenario basis. For example, it can visualize the flow of a day in a rural office and interactions with local people based on a scenario. By visualizing the office environment and daily life in rural areas, users can experience them realistically.

[0038] The service provider offers a visual tour that users can experience. For example, the service provider can offer a visual tour in virtual reality format. For example, the service provider can allow users to wear a virtual reality headset and experience rural life in a virtual space. The service provider can also offer a visual tour in the form of an interactive map. For example, the service provider can allow users to select a specific location on a map and view information about that location. Furthermore, the service provider can offer a visual tour in video format. For example, the service provider can provide videos of rural office environments and daily life. This allows users to realistically experience life and work in rural areas.

[0039] The reception desk analyzes the user's past input history and selects the optimal information reception method. For example, the reception desk prioritizes suggesting input methods (voice, text, etc.) that the user has frequently used in the past. For example, the reception desk predicts and suggests input methods to be used during specific time periods based on the user's past input history. The reception desk can also automatically supplement relevant information based on information the user has previously entered. This allows the reception desk to select the optimal information reception method by analyzing the user's past input history. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI.

[0040] The reception unit filters information upon receipt based on the user's current areas of interest. For example, the reception unit only accepts information related to topics the user is currently interested in. For example, the reception unit filters out unnecessary information based on the user's areas of interest. The reception unit can also prioritize the acceptance of new information related to the user's areas of interest. This allows for the elimination of unnecessary information by filtering information based on the user's current areas of interest. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI.

[0041] The reception unit, upon receiving information, prioritizes receiving highly relevant information by considering the user's geographical location. For example, the reception unit prioritizes receiving information related to the area where the user is currently located. For example, the reception unit filters out unnecessary information based on the user's geographical location. Furthermore, if the user is on the move, the reception unit can also receive information related to their current location in real time. This allows for the priority reception of highly relevant information based on the user's geographical location. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI.

[0042] The reception unit analyzes the user's social media activity upon receiving information and receives relevant information. For example, the reception unit prioritizes receiving information related to topics the user has shown interest in on social media. For example, the reception unit filters out unnecessary information from the user's social media activity. The reception unit can also prioritize receiving information related to accounts the user follows on social media. This allows the reception unit to receive relevant information based on the user's social media activity. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI.

[0043] The analysis unit adjusts the level of detail of the analysis based on the importance of data related to local living environments and work styles. For example, if data related to local living environments is important, the analysis unit performs a detailed analysis. For example, if data related to work styles in local areas is important, the analysis unit performs a detailed analysis. The analysis unit can also perform a balanced analysis if data related to both local living environments and work styles is important. By adjusting the level of detail of the analysis based on the importance of the data, more accurate analysis results can be provided. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI.

[0044] The analysis unit applies different analysis algorithms depending on the regional category during analysis. For example, the analysis unit applies a dedicated analysis algorithm for office environments to data related to the office environment in a region. For example, the analysis unit applies a dedicated analysis algorithm for daily life to data related to daily life in a region. The analysis unit can also apply a dedicated analysis algorithm for tourism information to data related to tourism information in a region. By applying analysis algorithms according to the category, more appropriate analysis results can be provided. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI.

[0045] The analysis unit determines the priority of analysis based on the submission date of local data during the analysis. For example, the analysis unit prioritizes the analysis of the most recent data. For example, the analysis unit postpones the analysis of older data. The analysis unit can also prioritize the analysis of data that has been submitted recently. In this way, by determining the priority of analysis based on the submission date of the data, the latest information can be analyzed preferentially. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI.

[0046] The analysis unit adjusts the order of analysis based on regional relevance during the analysis. For example, the analysis unit prioritizes the analysis of data related to the living environment in a region. For example, the analysis unit prioritizes the analysis of data related to working styles in a region. The analysis unit can also prioritize the analysis of data related to tourism information in a region. By adjusting the order of analysis based on the relevance of the data, more relevant information can be analyzed preferentially. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI.

[0047] The generation unit adjusts the level of detail in the simulation based on the importance of the local office environment and daily life. For example, if the local office environment is important, the generation unit generates a detailed simulation. For example, if the daily life in the local area is important, the generation unit generates a detailed simulation. The generation unit can also generate a balanced simulation if both the local office environment and daily life are important. This allows for more accurate simulations by adjusting the level of detail based on the importance of the data. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI.

[0048] The generation unit applies different generation algorithms depending on the region category when generating simulations. For example, the generation unit applies a generation algorithm specifically for office environments to simulations related to regional office environments. For example, the generation unit applies a generation algorithm specifically for daily life to simulations related to daily life in regional areas. The generation unit can also apply a generation algorithm specifically for tourism information to simulations related to tourism information in regional areas. By applying a generation algorithm according to the category, a more appropriate simulation can be provided. Some or all of the above processing in the generation unit may be performed using AI, for example, or without using AI.

[0049] The generation unit determines the generation priority based on the submission timing of local data when generating simulations. For example, the generation unit prioritizes reflecting the latest data in the simulation. For example, it postpones older data submissions. The generation unit can also prioritize reflecting data with recent submission dates in the simulation. In this way, by determining the generation priority based on the data submission timing, the latest information can be reflected in the simulation preferentially. Some or all of the above processing in the generation unit may be performed using AI, for example, or without using AI.

[0050] The generation unit adjusts the generation order based on the relevance of the region when generating the simulation. For example, the generation unit prioritizes reflecting data related to the living environment of the region in the simulation. For example, the generation unit prioritizes reflecting data related to working styles in the region in the simulation. The generation unit can also prioritize reflecting data related to tourism information of the region in the simulation. By adjusting the generation order based on the relevance of the data, more relevant information can be prioritized and reflected in the simulation. Some or all of the above processing in the generation unit may be performed using AI, for example, or without using AI.

[0051] The service provider selects the optimal delivery method when providing a visual tour by referring to the user's past experience history. For example, the service provider selects the optimal delivery method based on the user's past experience history of visual tours. For example, the service provider prioritizes providing content that is likely to be of interest to the user based on their past experience history. The service provider can also analyze the user's past experience history and select the most effective delivery method. In this way, the optimal delivery method for the visual tour can be selected by referring to the user's past experience history. Some or all of the above processing in the service provider may be performed using AI, for example, or without using AI.

[0052] The service provider customizes the content of the visual tour based on the user's current areas of interest. For example, the service provider provides a visual tour related to a topic the user is currently interested in. For example, the service provider filters out unnecessary information based on the user's areas of interest. The service provider can also prioritize providing new information related to the user's areas of interest. This allows for the provision of more relevant visual tours by customizing the content based on the user's current areas of interest. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI.

[0053] The service provider selects the optimal delivery method when providing a visual tour, taking into account the user's geographical location. For example, the service provider prioritizes providing visual tours relevant to the user's current location. For example, the service provider filters out unnecessary information based on the user's geographical location. Furthermore, if the user is on the move, the service provider can provide visual tours relevant to their current location in real time. This allows the service provider to select the optimal method for delivering a visual tour based on the user's geographical location. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI.

[0054] The service provider analyzes the user's social media activity when providing visual tours and customizes the content accordingly. For example, the service provider prioritizes providing visual tours related to topics the user has shown interest in on social media. For example, the service provider filters out unnecessary information from the user's social media activity. The service provider can also prioritize providing visual tours related to accounts the user follows on social media. This allows for the provision of more relevant visual tours by customizing the content based on the user's social media activity. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI.

[0055] The introduction department selects the most suitable introduction method when introducing the workation program by referring to the user's past experience history. For example, the introduction department selects the most suitable introduction method based on the user's past workation experiences. For example, the introduction department prioritizes introducing content that is likely to be of interest to the user based on their past experience history. The introduction department can also analyze the user's past experience history and select the most effective introduction method. In this way, the optimal method for introducing the workation program can be selected by referring to the user's past experience history. Some or all of the above processing in the introduction department may be performed using AI, for example, or without using AI.

[0056] The referral unit selects the optimal referral method when introducing workation programs, taking into account the user's geographical location. For example, the referral unit prioritizes introducing workation programs related to the user's current location. For example, the referral unit filters out unnecessary information based on the user's geographical location. Furthermore, if the user is on the move, the referral unit can also introduce workation programs related to their current location in real time. This allows the referral unit to select the optimal method for introducing workation programs based on the user's geographical location. Some or all of the above processing in the referral unit may be performed using AI, for example, or without AI.

[0057] The data collection unit selects the optimal data collection method by referring to the user's past experience history when collecting feedback. For example, the data collection unit selects the optimal data collection method based on the user's past feedback history. For example, the data collection unit requests feedback on topics that the user might be interested in based on the user's past experience history. The data collection unit can also analyze the user's past experience history and select the most effective data collection method. This allows the optimal data collection method to be selected by referring to the user's past experience history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without using AI.

[0058] The data collection unit selects the optimal data collection method when collecting feedback, taking into account the user's geographical location. For example, the data collection unit prioritizes collecting feedback related to the user's current location. For example, the data collection unit filters out unnecessary feedback based on the user's geographical location. Furthermore, if the user is on the move, the data collection unit can collect feedback related to their current location in real time. This allows for the selection of the optimal data collection method based on the user's geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI.

[0059] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0060] The reception desk analyzes the user's past input history and selects the optimal information reception method. For example, the reception desk prioritizes suggesting input methods (voice, text, etc.) that the user has frequently used in the past. For example, the reception desk predicts and suggests input methods to be used during specific time periods based on the user's past input history. The reception desk can also automatically supplement relevant information based on information the user has previously entered. This allows the reception desk to select the optimal information reception method by analyzing the user's past input history. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI.

[0061] The information provisioning unit prioritizes receiving highly relevant information, taking into account the user's geographical location. The information receiving unit prioritizes receiving information related to the user's current location, for example. The information receiving unit filters out unnecessary information based on the user's geographical location. The information receiving unit can also receive information related to the user's current location in real time if the user is on the move. This allows for the priority reception of highly relevant information based on the user's geographical location. Some or all of the processing described above in the information receiving unit may be performed using AI, for example, or without AI.

[0062] The generation unit adjusts the level of detail in the simulation based on the importance of the local office environment and daily life. For example, if the local office environment is important, the generation unit generates a detailed simulation. For example, if the daily life in the local area is important, the generation unit generates a detailed simulation. The generation unit can also generate a balanced simulation if both the local office environment and daily life are important. This allows for more accurate simulations by adjusting the level of detail based on the importance of the data. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI.

[0063] The service provider selects the optimal delivery method when providing a visual tour by referring to the user's past experience history. For example, the service provider selects the optimal delivery method based on the user's past experience history of visual tours. For example, the service provider prioritizes providing content that is likely to be of interest to the user based on their past experience history. The service provider can also analyze the user's past experience history and select the most effective delivery method. In this way, the optimal delivery method for the visual tour can be selected by referring to the user's past experience history. Some or all of the above processing in the service provider may be performed using AI, for example, or without using AI.

[0064] The data collection unit selects the optimal data collection method by referring to the user's past experience history when collecting feedback. For example, the data collection unit selects the optimal data collection method based on the user's past feedback history. For example, the data collection unit requests feedback on topics that the user might be interested in based on the user's past experience history. The data collection unit can also analyze the user's past experience history and select the most effective data collection method. This allows the optimal data collection method to be selected by referring to the user's past experience history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without using AI.

[0065] The following briefly describes the processing flow for example form 1.

[0066] Step 1: The reception desk receives information from the user. This information includes text, images, and audio. For example, it receives text information entered by the user, uploaded images, and recorded audio. Step 2: The analysis unit analyzes the information received by the reception unit. Data mining techniques, statistical analysis techniques, and machine learning techniques are used for the analysis. For example, important keywords are extracted using data mining techniques, trends and patterns in the information are analyzed using statistical analysis techniques, and the accuracy of the analysis is improved using machine learning techniques. Step 3: The generation unit generates a simulation based on the information analyzed by the analysis unit. 3D modeling technology, virtual reality technology, and scenario-based simulation are used for generation. For example, 3D modeling technology is used to simulate a local office environment, virtual reality technology is used to allow users to experience the simulation, and scenario-based simulation is used to generate a simulation based on a specific scenario. Step 4: The delivery unit provides the simulation generated by the generation unit as a visual tour. The delivery can be in video format, interactive map format, or virtual reality format. For example, a video format could introduce a local office environment, an interactive map format could allow users to select a specific location on a map and view information about that location, and a virtual reality format could allow users to experience local life in a virtual space.

[0067] (Example of form 2) The visual tour system according to an embodiment of the present invention is a system that utilizes a large-scale language model to simulate a realistic experience of rural life and provides a visual tour service that allows urban residents considering remote work to experience rural work styles and lifestyles. The visual tour system receives input from the user regarding rural life and work styles. Next, the large-scale language model analyzes this information and simulates rural life and work styles. The simulation results are provided to the user as a visual tour. This visual tour is designed to allow the user to realistically experience rural life. Furthermore, it can also send users to workation programs offered by local governments. This mechanism is expected to increase the number of people involved with rural communities and revitalize rural areas. For example, a user might input, "I want to know about the remote work environment in rural areas." This information is input into the large-scale language model. Next, the large-scale language model analyzes the input information and simulates rural life and work styles. Based on data regarding rural living environments and work styles, the large-scale language model generates a simulation that the user can experience. For example, it visualizes rural office environments and daily life. The generated simulation results are provided to the user as a visual tour. Through visual tours, users can realistically experience life and work in rural areas. For example, they can visually experience the work environment in a rural office and interactions with local people. Furthermore, the system can also direct users to workation programs offered by local governments. If a user becomes interested in rural life through a visual tour, they can actually experience rural life by utilizing the workation programs offered by local governments. This system is expected to increase the number of people connected to rural communities and revitalize rural areas. By experiencing rural life and work in advance, users will be more likely to consider relocating to rural areas or participating in workation programs. For example, knowing about remote work environments and lifestyles in rural areas beforehand can reduce anxieties about relocation or workation. In this way, the visual tour system can increase the number of people connected to rural communities and promote the revitalization of rural areas.

[0068] The visual tour system according to this embodiment comprises a reception unit, an analysis unit, a generation unit, and a provision unit. The reception unit receives information from the user. User information includes, but is not limited to, text information, image information, and audio information. For example, the reception unit receives text information entered by the user. The reception unit can also receive image information uploaded by the user. Furthermore, the reception unit can also receive audio information recorded by the user. For example, the reception unit transmits the text information entered by the user to the analysis unit. The analysis unit analyzes the information received by the reception unit. For example, the analysis unit analyzes the information using data mining techniques. Furthermore, the analysis unit can also analyze the information using statistical analysis techniques. Furthermore, the analysis unit can also analyze the information using machine learning techniques. For example, the analysis unit extracts important keywords from the information entered by the user using data mining techniques. Statistical analysis techniques are used to analyze trends and patterns in information. Machine learning techniques are used to learn from large amounts of data and improve the accuracy of information analysis. The generation unit generates a simulation based on the information analyzed by the analysis unit. The generation unit generates simulations using, for example, 3D modeling technology. The generation unit can also generate simulations using virtual reality technology. Furthermore, the generation unit can generate scenario-based simulations. For example, the generation unit simulates a local office environment using 3D modeling technology. Virtual reality technology is used to allow users to experience the simulation. Scenario-based simulations are used to generate simulations based on specific scenarios. The delivery unit provides the simulations generated by the generation unit as visual tours. The delivery unit provides visual tours in, for example, video format. The delivery unit can also provide visual tours in interactive map format. Furthermore, the delivery unit can provide visual tours in virtual reality format. For example, the delivery unit presents a local office environment in video format.An interactive map format is used to allow users to select a specific location on a map and view information about that location. A virtual reality format is used to allow users to experience rural life in a virtual space. As a result, the visual tour system according to this embodiment can provide a visual tour in which users can realistically experience rural life and work.

[0069] The reception unit receives information from users. This information includes, but is not limited to, text information, image information, and audio information. For example, the reception unit receives text information entered by the user. It can also receive image information uploaded by the user. Furthermore, it can receive audio information recorded by the user. For example, the reception unit sends text information entered by the user to the analysis unit. The reception unit has multiple interfaces to efficiently receive the diverse information provided by the user. For example, text information can be entered through web forms or mobile applications. For image information, it provides drag-and-drop functionality and file selection dialogs so that users can easily upload photos taken with their smartphones or digital cameras. For audio information, it has functions for users to record directly using a microphone or to upload existing audio files. This allows the reception unit to quickly and accurately receive the information provided by the user and smoothly move to the next processing step. Furthermore, the reception unit also has functions to perform initial filtering and preprocessing of the received information. For example, it performs spell checks and grammar checks on text information, checks the resolution and format of image information, and performs noise reduction and volume adjustment on audio information. This allows the reception unit to improve the quality of the information it transmits to the analysis unit, thereby increasing the overall accuracy and efficiency of the system.

[0070] The analysis unit analyzes the information received by the reception unit. The analysis unit analyzes the information using, for example, data mining techniques. It can also analyze the information using statistical analysis techniques. Furthermore, it can analyze the information using machine learning techniques. For example, the analysis unit uses data mining techniques to extract important keywords from the information entered by the user. Statistical analysis techniques are used to analyze trends and patterns in information. Machine learning techniques are used to learn from large amounts of data and improve the accuracy of information analysis. The analysis unit combines these techniques to accurately grasp the user's intentions and needs. For example, it uses natural language processing techniques to understand the context from text information, image recognition techniques to analyze the content from image information, and speech recognition techniques to convert audio information into text. This allows the analysis unit to analyze the user's information from multiple angles and generate more accurate results. Furthermore, the analysis unit utilizes high-performance computing resources to perform real-time analysis. For example, it uses a cloud-based distributed processing system to quickly analyze large amounts of data and send the results to the generation unit. Furthermore, the analysis unit can utilize past data and user history information to provide more personalized analysis results. This allows the analysis unit to provide a foundation for generating optimal visual tours tailored to user needs.

[0071] The generation unit generates simulations based on information analyzed by the analysis unit. The generation unit can generate simulations using, for example, 3D modeling technology. It can also generate simulations using virtual reality technology. Furthermore, the generation unit can generate scenario-based simulations. For example, the generation unit can use 3D modeling technology to simulate a local office environment. Virtual reality technology is used to allow users to experience the simulation. Scenario-based simulations are used to generate simulations based on specific scenarios. The generation unit utilizes these technologies to create a visual tour that users can experience realistically. For example, 3D modeling technology is used to reproduce the interior structure of a building and the arrangement of furniture in detail, providing users with an experience as if they were actually there. Using virtual reality technology, users can wear a VR headset and experience the simulation with a 360-degree view. In scenario-based simulations, users can choose actions based on specific scenarios and experience the results. For example, a simulation of a day in a local office environment allows users to experience various scenarios by trying different choices. This allows the generation unit to provide users with a visual tour that realistically allows them to experience life and work in rural areas. Furthermore, the generation unit can improve the simulation based on user feedback, providing a higher quality visual tour.

[0072] The provider delivers the simulations generated by the generator as visual tours. For example, the provider can provide visual tours in video format. It can also provide visual tours in interactive map format. Furthermore, it can provide visual tours in virtual reality format. For example, the provider can showcase a rural office environment in video format. The interactive map format is used to allow users to select specific locations on a map and view information about those locations. The virtual reality format is used to allow users to experience rural life in a virtual space. The provider combines these formats to provide users with diverse experiences. For example, in video format, narration and text overlays can be added to provide not only visual information but also auditory information. In interactive map format, users can click on specific points on the map to display detailed information and related visual content. In virtual reality format, users can wear a VR headset and freely move around in a virtual space, experiencing rural life and work styles. This allows the provider to deliver visual tours that allow users to realistically experience rural life and work styles. Furthermore, the service provider can collect user feedback and continuously improve the content and format of the visual tours. For example, if a user expresses interest in a particular location or scenario, new content can be added based on that information. This allows the service provider to always provide users with the latest and most engaging visual tours.

[0073] The service includes a section dedicated to introducing workation programs offered by various local governments. This section, for example, provides detailed information about the workation programs offered by each local government. It can also collect information on workation programs from local government websites and provide it to users. Furthermore, it can provide users with brochures and materials related to each local government's workation program. Additionally, it can provide users with information on events and seminars related to each local government's workation program. For example, it can automatically collect information on workation programs from local government websites and provide it to users. Brochures and materials are provided in a format that users can download. Information on events and seminars is provided so that users can register to participate. This allows users to learn about the workation programs offered by each local government.

[0074] The reception section includes a collection section for gathering user feedback. The collection section can collect user feedback, for example, through a questionnaire. For instance, it might display a questionnaire after a user experiences a visual tour, requesting feedback. The collection section can also collect user feedback in comment format. For example, it could provide comment fields in each section of the visual tour, allowing users to freely enter comments. Furthermore, the collection section can collect user feedback in rating format. For example, it could provide rating buttons in each section of the visual tour, allowing users to enter ratings. This allows for the collection of user feedback, which can then be used to improve the service.

[0075] The analysis department performs analyses based on data related to local living environments and work styles. For example, the analysis department analyzes local housing information. For example, it collects local housing information and analyzes the types and price ranges of housing. The analysis department can also analyze data related to local transportation. For example, it collects data on local transportation and analyzes the types and usage of transportation. Furthermore, the analysis department can also analyze data related to local work environments. For example, it collects data on local work environments and analyzes the types of workplaces and work styles. By performing analyses based on data related to local living environments and work styles, it is possible to provide more accurate simulations.

[0076] The generation unit visualizes the office environment and daily life in rural areas. For example, it visualizes the office environment in rural areas using 3D modeling technology. For example, it generates the layout and facilities of a rural office as a 3D model. The generation unit can also visualize daily life in rural areas using virtual reality technology. For example, it can recreate the streetscapes and living scenes of rural areas in a virtual reality space. Furthermore, the generation unit can visualize the office environment and daily life in rural areas on a scenario basis. For example, it can visualize the flow of a day in a rural office and interactions with local people based on a scenario. By visualizing the office environment and daily life in rural areas, users can experience them realistically.

[0077] The service provider offers a visual tour that users can experience. For example, the service provider can offer a visual tour in virtual reality format. For example, the service provider can allow users to wear a virtual reality headset and experience rural life in a virtual space. The service provider can also offer a visual tour in the form of an interactive map. For example, the service provider can allow users to select a specific location on a map and view information about that location. Furthermore, the service provider can offer a visual tour in video format. For example, the service provider can provide videos of rural office environments and daily life. This allows users to realistically experience life and work in rural areas.

[0078] The reception desk estimates the user's emotions and adjusts the timing of information reception based on the estimated emotions. For example, if the user is stressed, the reception desk will receive information during a time when the user can relax. If the user is relaxed, the reception desk will receive information immediately and respond quickly. Furthermore, if the user is tired, the reception desk can also receive information after the user has rested. In this way, by adjusting the timing of information reception according to the user's emotions, information can be received at a more appropriate time. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is not limited to, but may include, text generation AI (e.g., LLM) or multimodal generation AI.

[0079] The reception desk analyzes the user's past input history and selects the optimal information reception method. For example, the reception desk prioritizes suggesting input methods (voice, text, etc.) that the user has frequently used in the past. For example, the reception desk predicts and suggests input methods to be used during specific time periods based on the user's past input history. The reception desk can also automatically supplement relevant information based on information the user has previously entered. This allows the reception desk to select the optimal information reception method by analyzing the user's past input history. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI.

[0080] The reception unit filters information upon receipt based on the user's current areas of interest. For example, the reception unit only accepts information related to topics the user is currently interested in. For example, the reception unit filters out unnecessary information based on the user's areas of interest. The reception unit can also prioritize the acceptance of new information related to the user's areas of interest. This allows for the elimination of unnecessary information by filtering information based on the user's current areas of interest. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI.

[0081] The reception desk estimates the user's emotions and determines the priority of the information to be received based on the estimated emotions. For example, if the user is stressed, the reception desk will postpone receiving less important information. For example, if the user is relaxed, the reception desk will prioritize receiving detailed information. The reception desk can also prioritize receiving high-priority information if the user is in a hurry. In this way, by prioritizing information according to the user's emotions, important information can be received preferentially. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0082] The reception unit, upon receiving information, prioritizes receiving highly relevant information by considering the user's geographical location. For example, the reception unit prioritizes receiving information related to the area where the user is currently located. For example, the reception unit filters out unnecessary information based on the user's geographical location. Furthermore, if the user is on the move, the reception unit can also receive information related to their current location in real time. This allows for the priority reception of highly relevant information based on the user's geographical location. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI.

[0083] The reception unit analyzes the user's social media activity upon receiving information and receives relevant information. For example, the reception unit prioritizes receiving information related to topics the user has shown interest in on social media. For example, the reception unit filters out unnecessary information from the user's social media activity. The reception unit can also prioritize receiving information related to accounts the user follows on social media. This allows the reception unit to receive relevant information based on the user's social media activity. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI.

[0084] The analysis unit estimates the user's emotions and adjusts the presentation of the analysis based on the estimated emotions. For example, if the user is relaxed, the analysis unit provides detailed analysis results. If the user is in a hurry, the analysis unit provides concise analysis results that get straight to the point. Furthermore, if the user is excited, the analysis unit can also provide analysis results with visually stimulating effects. In this way, by adjusting the presentation of the analysis according to the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0085] The analysis unit adjusts the level of detail of the analysis based on the importance of data related to local living environments and work styles. For example, if data related to local living environments is important, the analysis unit performs a detailed analysis. For example, if data related to work styles in local areas is important, the analysis unit performs a detailed analysis. The analysis unit can also perform a balanced analysis if data related to both local living environments and work styles is important. By adjusting the level of detail of the analysis based on the importance of the data, more accurate analysis results can be provided. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI.

[0086] The analysis unit applies different analysis algorithms depending on the regional category during analysis. For example, the analysis unit applies a dedicated analysis algorithm for office environments to data related to the office environment in a region. For example, the analysis unit applies a dedicated analysis algorithm for daily life to data related to daily life in a region. The analysis unit can also apply a dedicated analysis algorithm for tourism information to data related to tourism information in a region. By applying analysis algorithms according to the category, more appropriate analysis results can be provided. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI.

[0087] The analysis unit estimates the user's emotions and adjusts the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, the analysis unit provides a short, concise analysis. If the user is relaxed, the analysis unit provides a detailed analysis. The analysis unit can also provide an analysis with visually stimulating effects if the user is excited. By adjusting the length of the analysis according to the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0088] The analysis unit determines the priority of analysis based on the submission date of local data during the analysis. For example, the analysis unit prioritizes the analysis of the most recent data. For example, the analysis unit postpones the analysis of older data. The analysis unit can also prioritize the analysis of data that has been submitted recently. In this way, by determining the priority of analysis based on the submission date of the data, the latest information can be analyzed preferentially. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI.

[0089] The analysis unit adjusts the order of analysis based on regional relevance during the analysis. For example, the analysis unit prioritizes the analysis of data related to the living environment in a region. For example, the analysis unit prioritizes the analysis of data related to working styles in a region. The analysis unit can also prioritize the analysis of data related to tourism information in a region. By adjusting the order of analysis based on the relevance of the data, more relevant information can be analyzed preferentially. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI.

[0090] The generation unit estimates the user's emotions and adjusts the simulation generation method based on the estimated emotions. For example, if the user is relaxed, the generation unit generates a simulation that proceeds at a leisurely pace. If the user is in a hurry, the generation unit generates a simulation that emphasizes the shortest route. The generation unit can also generate a simulation with visually stimulating effects if the user is excited. In this way, by adjusting the simulation generation method according to the user's emotions, a more appropriate simulation can be provided. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0091] The generation unit adjusts the level of detail in the simulation based on the importance of the local office environment and daily life. For example, if the local office environment is important, the generation unit generates a detailed simulation. For example, if the daily life in the local area is important, the generation unit generates a detailed simulation. The generation unit can also generate a balanced simulation if both the local office environment and daily life are important. This allows for more accurate simulations by adjusting the level of detail based on the importance of the data. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI.

[0092] The generation unit applies different generation algorithms depending on the region category when generating simulations. For example, the generation unit applies a generation algorithm specifically for office environments to simulations related to regional office environments. For example, the generation unit applies a generation algorithm specifically for daily life to simulations related to daily life in regional areas. The generation unit can also apply a generation algorithm specifically for tourism information to simulations related to tourism information in regional areas. By applying a generation algorithm according to the category, a more appropriate simulation can be provided. Some or all of the above processing in the generation unit may be performed using AI, for example, or without using AI.

[0093] The generation unit estimates the user's emotions and adjusts the simulation length based on the estimated emotions. For example, if the user is in a hurry, the generation unit generates a short, concise simulation. If the user is relaxed, for example, the generation unit generates a longer simulation with detailed explanations. The generation unit can also generate a simulation with visually stimulating effects if the user is excited. By adjusting the simulation length according to the user's emotions, a more appropriate simulation can be provided. 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.

[0094] The generation unit determines the generation priority based on the submission timing of local data when generating simulations. For example, the generation unit prioritizes reflecting the latest data in the simulation. For example, it postpones older data submissions. The generation unit can also prioritize reflecting data with recent submission dates in the simulation. In this way, by determining the generation priority based on the data submission timing, the latest information can be reflected in the simulation preferentially. Some or all of the above processing in the generation unit may be performed using AI, for example, or without using AI.

[0095] The generation unit adjusts the generation order based on the relevance of the region when generating the simulation. For example, the generation unit prioritizes reflecting data related to the living environment of the region in the simulation. For example, the generation unit prioritizes reflecting data related to working styles in the region in the simulation. The generation unit can also prioritize reflecting data related to tourism information of the region in the simulation. By adjusting the generation order based on the relevance of the data, more relevant information can be prioritized and reflected in the simulation. Some or all of the above processing in the generation unit may be performed using AI, for example, or without using AI.

[0096] The service provider estimates the user's emotions and adjusts the way the visual tour is delivered based on those emotions. For example, if the user is relaxed, the service provider will provide a visual tour that proceeds at a leisurely pace. If the user is in a hurry, the service provider will provide a visual tour that gets straight to the point. The service provider can also provide a visual tour with visually stimulating effects if the user is excited. By adjusting the way the visual tour is delivered according to the user's emotions, a more appropriate visual tour can be provided. 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.

[0097] The service provider selects the optimal delivery method when providing a visual tour by referring to the user's past experience history. For example, the service provider selects the optimal delivery method based on the user's past experience history of visual tours. For example, the service provider prioritizes providing content that is likely to be of interest to the user based on their past experience history. The service provider can also analyze the user's past experience history and select the most effective delivery method. In this way, the optimal delivery method for the visual tour can be selected by referring to the user's past experience history. Some or all of the above processing in the service provider may be performed using AI, for example, or without using AI.

[0098] The service provider customizes the content of the visual tour based on the user's current areas of interest. For example, the service provider provides a visual tour related to a topic the user is currently interested in. For example, the service provider filters out unnecessary information based on the user's areas of interest. The service provider can also prioritize providing new information related to the user's areas of interest. This allows for the provision of more relevant visual tours by customizing the content based on the user's current areas of interest. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI.

[0099] The service provider estimates the user's emotions and prioritizes visual tours based on those emotions. For example, if the user is stressed, the service provider will prioritize providing a relaxing visual tour. If the user is relaxed, the service provider will prioritize providing a detailed visual tour. Furthermore, if the user is in a hurry, the service provider can prioritize providing a concise visual tour. This allows for the provision of more appropriate visual tours by prioritizing them 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.

[0100] The service provider selects the optimal delivery method when providing a visual tour, taking into account the user's geographical location. For example, the service provider prioritizes providing visual tours relevant to the user's current location. For example, the service provider filters out unnecessary information based on the user's geographical location. Furthermore, if the user is on the move, the service provider can provide visual tours relevant to their current location in real time. This allows the service provider to select the optimal method for delivering a visual tour based on the user's geographical location. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI.

[0101] The service provider analyzes the user's social media activity when providing visual tours and customizes the content accordingly. For example, the service provider prioritizes providing visual tours related to topics the user has shown interest in on social media. For example, the service provider filters out unnecessary information from the user's social media activity. The service provider can also prioritize providing visual tours related to accounts the user follows on social media. This allows for the provision of more relevant visual tours by customizing the content based on the user's social media activity. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI.

[0102] The introduction section estimates the user's emotions and adjusts the way it introduces the workation program based on those emotions. For example, if the user is relaxed, the introduction section will provide a detailed introduction to the workation program. If the user is in a hurry, the introduction section will provide a concise introduction that gets straight to the point. If the user is excited, the introduction section can also add visually stimulating effects to the introduction. This allows for a more appropriate introduction by adjusting the way the workation program is introduced 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.

[0103] The introduction department selects the most suitable introduction method when introducing the workation program by referring to the user's past experience history. For example, the introduction department selects the most suitable introduction method based on the user's past workation experiences. For example, the introduction department prioritizes introducing content that is likely to be of interest to the user based on their past experience history. The introduction department can also analyze the user's past experience history and select the most effective introduction method. In this way, the optimal method for introducing the workation program can be selected by referring to the user's past experience history. Some or all of the above processing in the introduction department may be performed using AI, for example, or without using AI.

[0104] The recommendation system estimates the user's emotions and prioritizes workation options based on those emotions. For example, if the user is stressed, the recommendation system will prioritize workation options that promote relaxation. If the user is relaxed, the recommendation system will prioritize workation options with detailed content. Furthermore, if the user is in a hurry, the recommendation system can prioritize workation options that get straight to the point. This allows for the recommendation of more appropriate workation options by prioritizing them 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.

[0105] The referral unit selects the optimal referral method when introducing workation programs, taking into account the user's geographical location. For example, the referral unit prioritizes introducing workation programs related to the user's current location. For example, the referral unit filters out unnecessary information based on the user's geographical location. Furthermore, if the user is on the move, the referral unit can also introduce workation programs related to their current location in real time. This allows the referral unit to select the optimal method for introducing workation programs based on the user's geographical location. Some or all of the above processing in the referral unit may be performed using AI, for example, or without AI.

[0106] The data collection unit estimates the user's emotions and adjusts the feedback collection method based on the estimated emotions. For example, if the user is relaxed, the data collection unit requests detailed feedback. If the user is in a hurry, for example, the data collection unit requests concise feedback. The data collection unit can also provide a feedback form with visually stimulating effects if the user is excited. This allows for the collection of more appropriate feedback by adjusting the feedback collection method 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.

[0107] The data collection unit selects the optimal data collection method by referring to the user's past experience history when collecting feedback. For example, the data collection unit selects the optimal data collection method based on the user's past feedback history. For example, the data collection unit requests feedback on topics that the user might be interested in based on the user's past experience history. The data collection unit can also analyze the user's past experience history and select the most effective data collection method. This allows the optimal data collection method to be selected by referring to the user's past experience history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without using AI.

[0108] The data collection unit estimates the user's emotions and prioritizes feedback based on the estimated emotions. For example, if the user is stressed, the data collection unit will postpone less important feedback. For example, if the user is relaxed, the data collection unit will prioritize collecting detailed feedback. The data collection unit can also prioritize collecting high-importance feedback if the user is in a hurry. This allows for the collection of more appropriate feedback by prioritizing feedback 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.

[0109] The data collection unit selects the optimal data collection method when collecting feedback, taking into account the user's geographical location. For example, the data collection unit prioritizes collecting feedback related to the user's current location. For example, the data collection unit filters out unnecessary feedback based on the user's geographical location. Furthermore, if the user is on the move, the data collection unit can collect feedback related to their current location in real time. This allows for the selection of the optimal data collection method based on the user's geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI.

[0110] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0111] The reception desk analyzes the user's past input history and selects the optimal information reception method. For example, the reception desk prioritizes suggesting input methods (voice, text, etc.) that the user has frequently used in the past. For example, the reception desk predicts and suggests input methods to be used during specific time periods based on the user's past input history. The reception desk can also automatically supplement relevant information based on information the user has previously entered. This allows the reception desk to select the optimal information reception method by analyzing the user's past input history. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI.

[0112] The information provisioning unit prioritizes receiving highly relevant information, taking into account the user's geographical location. The information receiving unit prioritizes receiving information related to the user's current location, for example. The information receiving unit filters out unnecessary information based on the user's geographical location. The information receiving unit can also receive information related to the user's current location in real time if the user is on the move. This allows for the priority reception of highly relevant information based on the user's geographical location. Some or all of the processing described above in the information receiving unit may be performed using AI, for example, or without AI.

[0113] The analysis unit estimates the user's emotions and adjusts the presentation of the analysis based on the estimated emotions. For example, if the user is relaxed, the analysis unit provides detailed analysis results. If the user is in a hurry, the analysis unit provides concise analysis results that get straight to the point. Furthermore, if the user is excited, the analysis unit can also provide analysis results with visually stimulating effects. In this way, by adjusting the presentation of the analysis according to the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0114] The generation unit adjusts the level of detail in the simulation based on the importance of the local office environment and daily life. For example, if the local office environment is important, the generation unit generates a detailed simulation. For example, if the daily life in the local area is important, the generation unit generates a detailed simulation. The generation unit can also generate a balanced simulation if both the local office environment and daily life are important. This allows for more accurate simulations by adjusting the level of detail based on the importance of the data. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI.

[0115] The service provider estimates the user's emotions and adjusts the way the visual tour is delivered based on those emotions. For example, if the user is relaxed, the service provider will provide a visual tour that proceeds at a leisurely pace. If the user is in a hurry, the service provider will provide a visual tour that gets straight to the point. The service provider can also provide a visual tour with visually stimulating effects if the user is excited. By adjusting the way the visual tour is delivered according to the user's emotions, a more appropriate visual tour can be provided. 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.

[0116] The data collection unit estimates the user's emotions and adjusts the feedback collection method based on the estimated emotions. For example, if the user is relaxed, the data collection unit requests detailed feedback. If the user is in a hurry, for example, the data collection unit requests concise feedback. The data collection unit can also provide a feedback form with visually stimulating effects if the user is excited. This allows for the collection of more appropriate feedback by adjusting the feedback collection method 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.

[0117] The service provider selects the optimal delivery method when providing a visual tour by referring to the user's past experience history. For example, the service provider selects the optimal delivery method based on the user's past experience history of visual tours. For example, the service provider prioritizes providing content that is likely to be of interest to the user based on their past experience history. The service provider can also analyze the user's past experience history and select the most effective delivery method. In this way, the optimal delivery method for the visual tour can be selected by referring to the user's past experience history. Some or all of the above processing in the service provider may be performed using AI, for example, or without using AI.

[0118] The introduction section estimates the user's emotions and adjusts the way it introduces the workation program based on those emotions. For example, if the user is relaxed, the introduction section will provide a detailed introduction to the workation program. If the user is in a hurry, the introduction section will provide a concise introduction that gets straight to the point. If the user is excited, the introduction section can also add visually stimulating effects to the introduction. This allows for a more appropriate introduction by adjusting the way the workation program is introduced 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.

[0119] The data collection unit selects the optimal data collection method by referring to the user's past experience history when collecting feedback. For example, the data collection unit selects the optimal data collection method based on the user's past feedback history. For example, the data collection unit requests feedback on topics that the user might be interested in based on the user's past experience history. The data collection unit can also analyze the user's past experience history and select the most effective data collection method. This allows the optimal data collection method to be selected by referring to the user's past experience history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without using AI.

[0120] The generation unit estimates the user's emotions and adjusts the simulation generation method based on the estimated emotions. For example, if the user is relaxed, the generation unit generates a simulation that proceeds at a leisurely pace. If the user is in a hurry, the generation unit generates a simulation that emphasizes the shortest route. The generation unit can also generate a simulation with visually stimulating effects if the user is excited. In this way, by adjusting the simulation generation method according to the user's emotions, a more appropriate simulation can be provided. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0121] The following briefly describes the processing flow for example form 2.

[0122] Step 1: The reception desk receives information from the user. This information includes text, images, and audio. For example, it receives text information entered by the user, uploaded images, and recorded audio. Step 2: The analysis unit analyzes the information received by the reception unit. Data mining techniques, statistical analysis techniques, and machine learning techniques are used for the analysis. For example, important keywords are extracted using data mining techniques, trends and patterns in the information are analyzed using statistical analysis techniques, and the accuracy of the analysis is improved using machine learning techniques. Step 3: The generation unit generates a simulation based on the information analyzed by the analysis unit. 3D modeling technology, virtual reality technology, and scenario-based simulation are used for generation. For example, 3D modeling technology is used to simulate a local office environment, virtual reality technology is used to allow users to experience the simulation, and scenario-based simulation is used to generate a simulation based on a specific scenario. Step 4: The delivery unit provides the simulation generated by the generation unit as a visual tour. The delivery can be in video format, interactive map format, or virtual reality format. For example, a video format could introduce a local office environment, an interactive map format could allow users to select a specific location on a map and view information about that location, and a virtual reality format could allow users to experience local life in a virtual space.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] Each of the multiple elements described above, including the reception unit, analysis unit, generation unit, and provision unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14 and receives information from the user. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the received information. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates a simulation based on the analysis results. The provision unit is implemented by the control unit 46A of the smart device 14 and provides the generated simulation as a visual tour. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0127] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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).

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.).

[0139] 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.

[0140] 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.

[0141] 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.

[0142] Each of the multiple elements described above, including the reception unit, analysis unit, generation unit, and provision unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart glasses 214 and receives information from the user. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the received information. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates a simulation based on the analysis results. The provision unit is implemented by the control unit 46A of the smart glasses 214 and provides the generated simulation as a visual tour. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0143] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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).

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.).

[0155] 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.

[0156] 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.

[0157] 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.

[0158] Each of the multiple elements described above, including the reception unit, analysis unit, generation unit, and provision unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the headset terminal 314 and receives information from the user. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the received information. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates a simulation based on the analysis results. The provision unit is implemented by the control unit 46A of the headset terminal 314 and provides the generated simulation as a visual tour. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0159] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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).

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.).

[0172] 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.

[0173] 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.

[0174] 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.

[0175] Each of the multiple elements described above, including the reception unit, analysis unit, generation unit, and provision unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the robot 414 and receives information from the user. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the received information. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates a simulation based on the analysis results. The provision unit is implemented by the control unit 46A of the robot 414 and provides the generated simulation as a visual tour. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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."

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] (Note 1) A reception desk that receives information from users, An analysis unit that analyzes the information received by the reception unit, A generation unit that generates a simulation based on the information analyzed by the analysis unit, A providing unit that provides the simulation generated by the generation unit as a visual tour, Equipped with A system characterized by the following features. (Note 2) The aforementioned supply unit is, It features a section introducing workation programs offered by various local governments. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned reception unit is It includes a data collection unit for gathering user feedback. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned analysis unit, The analysis will be based on data regarding living conditions and work styles in rural areas. The system described in Appendix 1, characterized by the features described herein. (Note 5) The generating unit is Visualizing the office environment and daily life in regional areas. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned supply unit is, Provides a visual tour that users can experience. 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 adjusts the timing of information reception based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is Analyze the user's past input history and select the optimal method for receiving information. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is When receiving information, filtering is performed based on the user's current areas of interest. 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 determines the priority of the information to be received based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When receiving information, the system prioritizes receiving highly relevant information by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is When receiving information, the system analyzes the user's social media activity and collects relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During the analysis, the level of detail is adjusted based on the importance of data related to local living environments and work styles. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the regional category. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, the priority of the analysis will be determined based on the timing of data submission from local areas. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During the analysis, the order of analysis is adjusted based on the relevance of the region. The system described in Appendix 1, characterized by the features described herein. (Note 19) The generating unit is It estimates the user's emotions and adjusts the simulation generation method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The generating unit is When generating simulations, adjust the level of detail based on the importance of local office environments and aspects of daily life. The system described in Appendix 1, characterized by the features described herein. (Note 21) The generating unit is When generating simulations, different generation algorithms are applied depending on the regional category. The system described in Appendix 1, characterized by the features described herein. (Note 22) The generating unit is It estimates the user's emotions and adjusts the simulation length based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The generating unit is When generating simulations, the generation priority is determined based on the timing of data submission from local areas. The system described in Appendix 1, characterized by the features described herein. (Note 24) The generating unit is During simulation generation, the generation order is adjusted based on the relevance of the regions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned supply unit is, It estimates the user's emotions and adjusts how the visual tour is delivered based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned supply unit is, When providing a visual tour, the system selects the optimal delivery method by referring to the user's past experience history. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned supply unit is, When providing a visual tour, customize the content based on the user's current areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned supply unit is, It estimates the user's emotions and prioritizes the visual tour based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned supply unit is, When providing a visual tour, the optimal delivery method is selected considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned supply unit is, When providing visual tours, we analyze users' social media activity to customize the content offered. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned introductory section is, We estimate the user's emotions and adjust how the workation program is presented based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 32) The aforementioned introductory section is, When introducing the workation program, the system selects the most suitable presentation method by referring to the user's past experience history. The system described in Appendix 2, characterized by the features described herein. (Note 33) The aforementioned introductory section is, The system estimates user emotions and prioritizes workation programs based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 34) The aforementioned introductory section is, When introducing the workation program, the most suitable method of introduction will be selected, taking into account the user's geographical location. The system described in Appendix 2, characterized by the features described herein. (Note 35) The aforementioned collection unit is We estimate the user's emotions and adjust the feedback collection method based on the estimated user emotions. The system described in Appendix 3, characterized by the features described herein. (Note 36) The aforementioned collection unit is When collecting feedback, the system selects the optimal collection method by referring to the user's past experience history. The system described in Appendix 3, characterized by the features described herein. (Note 37) The aforementioned collection unit is It estimates the user's emotions and prioritizes feedback based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 38) The aforementioned collection unit is When collecting feedback, the optimal collection method is selected considering the user's geographical location. The system described in Appendix 3, characterized by the features described herein. [Explanation of Symbols]

[0195] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception desk that receives information from users, An analysis unit that analyzes the information received by the reception unit, A generation unit that generates a simulation based on the information analyzed by the analysis unit, A providing unit that provides the simulation generated by the generation unit as a visual tour, Equipped with A system characterized by the following features.

2. The aforementioned supply unit is, It features a section introducing workation programs offered by various local governments. The system according to feature 1.

3. The aforementioned reception unit is It includes a data collection unit for gathering user feedback. The system according to feature 1.

4. The aforementioned analysis unit, The analysis will be based on data regarding living conditions and work styles in rural areas. The system according to feature 1.

5. The generating unit is Visualizing the office environment and daily life in regional areas. The system according to feature 1.

6. The aforementioned supply unit is, Provides a visual tour that users can experience. The system according to feature 1.

7. The aforementioned reception unit is It estimates the user's emotions and adjusts the timing of information reception based on the estimated user emotions. The system according to feature 1.

8. The aforementioned reception unit is Analyze the user's past input history and select the optimal method for receiving information. The system according to feature 1.

9. The aforementioned reception unit is When receiving information, filtering is performed based on the user's current areas of interest. The system according to feature 1.

10. The aforementioned reception unit is It estimates the user's emotions and determines the priority of the information to be received based on the estimated user emotions. The system according to feature 1.

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