system

Abstract

The system according to this embodiment aims to reduce mismatches in relocation by simulating life in rural areas in advance. [Solution] The system according to the embodiment comprises a generation unit, a search unit, and a simulation unit. The generation unit analyzes geographic data and lifestyle information to generate a 3D virtual space. The search unit explores the 3D virtual space generated by the generation unit. The simulation unit performs a simulation based on the user's occupation and family structure.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, when considering moving to a local area, it is difficult to sufficiently simulate the actual living environment and conditions in advance, and there is a risk of mismatch after moving.

[0005] The system according to the embodiment aims to reduce the mismatch in moving by simulating life in the local area in advance.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a generation unit, a search unit, and a simulation unit. The generation unit analyzes geographic data and lifestyle information to generate a 3D virtual space. The search unit explores the 3D virtual space generated by the generation unit. The simulation unit performs a simulation based on the user's occupation and family structure. [Effects of the Invention]

[0007] The system according to this embodiment can reduce mismatches in relocation by simulating life 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, etc. 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 system according to an embodiment of the present invention is a system that allows users to virtually experience life in a rural area using a generative AI. In this system, the user inputs a departure point and a destination, and the generative AI analyzes geographical data and lifestyle information to generate a 3D virtual space. The user can explore the virtual space through their avatar and communicate with local people. Furthermore, it is possible to experience life under various conditions, such as changes in season and time of day, and the reproduction of local events. In addition, it is possible to simulate work and childcare based on the user's occupation and family structure. For example, it is possible to virtually experience a new job in a rural area or simulate the use of local schools and childcare facilities. This mechanism allows users to make a decision about relocation after obtaining sufficient information and experience. For example, the user inputs a departure point and a destination. In this case, the user only needs to input the departure point and destination. For example, the user inputs, "I want to go from my home to the station." This information is input into the generative AI. Next, the generative AI analyzes the input information and creates a video showing how to get from the current location to the destination. The generative AI calculates the optimal route based on map data and generates a video along that route. For example, if a user enters a route from their home to the train station, a video is generated that follows that route. The generated video starts navigating according to the orientation of the user's smartphone. For example, if the user is pointing their smartphone north, the video will also start navigating in the direction of north. This allows the user to receive navigation that is aligned with the direction they are facing. Furthermore, the video screen moves in accordance with the user's walking speed. For example, if the user is walking slowly, the video will also progress slowly. This allows the user to receive navigation at their own pace. This mechanism results in a simple structure that is easy for children and the elderly to use, making it enjoyable for everyone. Users can receive navigation intuitively without having to perform complex operations. Also, since the viewpoint of the smartphone is the axis of all directions, there is no chance of getting lost, and walking safety is ensured because the smartphone is held horizontally. For example, if the user is walking with their smartphone held horizontally, the video will also be displayed horizontally, allowing the user to walk safely.This allows the system to allow users to virtually experience life in rural areas.

[0029] The system according to this embodiment comprises a generation unit, a search unit, and a simulation unit. The generation unit analyzes geographic data and lifestyle information to generate a 3D virtual space. For example, the generation unit analyzes map data, terrain data, climate data, etc., as geographic data. The generation unit can also analyze demographic data, traffic information, local facility information, etc., as lifestyle information. For example, the generation unit generates building models based on map data and renders terrain based on terrain data. Furthermore, the generation unit can reproduce seasonal and weather changes based on climate data. The generation unit uses a generation AI to analyze geographic data and lifestyle information to generate a 3D virtual space. The generation AI uses technologies such as deep learning and generation models to generate a realistic 3D virtual space. For example, the generation AI receives geographic data as input and outputs a 3D virtual space. The generation AI can also receive lifestyle information as input and reflect it in the 3D virtual space. The search unit explores the 3D virtual space generated by the generation unit. The search unit can explore the virtual space through the user's avatar. An avatar can be a model that mimics the user's appearance or a customizable character. For example, the exploration unit allows the user to freely move around the virtual space by controlling their avatar. The exploration unit can also communicate with local people within the virtual space. For example, the exploration unit has the ability to interact with characters in the virtual space. The simulation unit performs simulations based on the user's occupation and family structure. For example, the simulation unit can simulate work based on the user's occupation. The simulation unit can also simulate childcare based on the user's family structure. For example, the simulation unit allows the user to virtually experience a new job in a rural area. The simulation unit can also simulate the use of local schools and childcare facilities. As a result, the system according to the embodiment allows the user to virtually experience life in a rural area.

[0030] The generation unit analyzes geographic data and lifestyle information to generate a 3D virtual space. Specifically, it analyzes geographic data such as map data, topographic data, and climate data, and constructs a detailed 3D model based on this data. Map data is used to accurately reproduce the placement of buildings and the layout of roads, while topographic data is used to realistically represent natural terrain such as mountains, rivers, and hills. Climate data is used to simulate seasonal changes and weather fluctuations, thereby reproducing realistic environmental changes within the virtual space. Furthermore, the generation unit analyzes lifestyle information such as demographics, traffic information, and local facility information, and reflects this information in the 3D virtual space. Demographic data shows the population density and age distribution of the region, and traffic information is used to reproduce road congestion and the operation status of public transportation. Local facility information shows the location and size of schools, hospitals, commercial facilities, etc., allowing users to experience the actual local living environment within the virtual space. The generation unit uses generation AI to analyze this geographic data and lifestyle information and generate a realistic 3D virtual space. Generative AI utilizes technologies such as deep learning and generative models to generate highly accurate 3D models based on input data. For example, it can take geographical data as input and output detailed 3D models of buildings and terrain. It can also take lifestyle information as input and generate realistic virtual spaces that reflect population density and traffic conditions. This allows the generation unit to enable users to realistically experience the living environment of their actual region within the virtual space.

[0031] The exploration unit provides the functionality to explore the 3D virtual space generated by the generation unit. Specifically, users can freely move around the virtual space through their avatar and explore various locations. The avatar is a model that mimics the user's appearance or a customizable character, and users can set their avatar to their liking. The exploration unit enables users to freely move around within the virtual space by controlling their avatar, for example, walking through a city, entering buildings, or strolling through nature. The exploration unit also has the functionality to communicate with local people within the virtual space. For example, the exploration unit provides the functionality to interact with characters in the virtual space, allowing users to enjoy conversations and exchange information with local people. This allows users to experience life in the virtual space more realistically. Furthermore, the exploration unit can also provide events and activities within the virtual space. For example, users can participate in local festivals and events, or enjoy sports and recreational activities. This allows users to enjoy a variety of experiences within the virtual space. The exploration unit can also collect user behavior data and, based on this, provide content tailored to the user's interests and preferences. For example, if a user frequently visits a particular location, the system can notify them of information and events related to that location. This allows the exploration unit to provide users with a personalized experience and enhance their exploration within the virtual space.

[0032] The simulation unit provides a function to perform simulations based on the user's occupation and family structure. Specifically, it simulates work based on the user's occupation, allowing the user to virtually experience a new work environment and job duties. For example, the simulation unit provides a scenario in which the user experiences a new job in a rural area, allowing the user to simulate actual work in a virtual space. This allows the user to prepare to adapt to a new work environment. The simulation unit can also simulate childcare based on the user's family structure. For example, the simulation unit can simulate the use of local schools and childcare facilities, allowing the user to obtain information about children's education and care. This allows the user to concretely imagine what childcare is like in rural life. Furthermore, the simulation unit can also provide simulations based on the user's lifestyle and hobbies. For example, it can simulate the user's favorite sports and recreational activities in a virtual space, providing the user with information to enjoy life in a new area. Through these simulations, the simulation unit can help users concretely imagine life in a rural area and support their decisions regarding relocation or changing jobs. The simulation unit can continuously improve the simulation content based on user feedback, providing a more realistic and useful experience. This allows the simulation unit to allow users to virtually experience life in rural areas and effectively prepare for relocation or a career change.

[0033] The generation unit can generate a 3D virtual space by analyzing geographic data and lifestyle information using a generative AI. For example, the generation unit uses a generative AI to analyze geographic data and generate a 3D virtual space. The generative AI uses technologies such as deep learning and generative models to generate a realistic 3D virtual space. For example, the generative AI takes geographic data as input and outputs a 3D virtual space. The generation unit can also use the generative AI to analyze lifestyle information and reflect it in the 3D virtual space. The generative AI takes lifestyle information as input and reflects it in the 3D virtual space. In this way, a realistic 3D virtual space can be generated by using the generative AI.

[0034] The exploration unit can explore the virtual space through the user's avatar. For example, the exploration unit allows the user to freely move around the virtual space by controlling their avatar. The avatar can be a model that mimics the user's appearance or a customizable character. The exploration unit can also communicate with local people within the virtual space. For example, the exploration unit has the functionality to interact with characters in the virtual space. This allows the user to freely explore the virtual space.

[0035] The simulation unit can simulate work and childcare based on the user's occupation and family structure. For example, the simulation unit can simulate work based on the user's occupation. It can also simulate childcare based on the user's family structure. For example, the simulation unit can allow the user to virtually experience a new job in a rural area. It can also simulate the use of local schools and childcare facilities. This makes it possible to perform simulations tailored to the user's occupation and family structure.

[0036] The environmental change unit can simulate seasonal and time-of-day changes, as well as local events. For example, it can simulate seasonal weather changes. It can also simulate changes in light throughout the day; for instance, it can simulate light changes in the morning, noon, evening, and night. Furthermore, it can simulate local events, such as festivals, sporting events, and community events. This allows for diverse life experiences under various conditions.

[0037] The communications department can communicate with local people. For example, the communications department has the functionality to interact with characters in a virtual space. For instance, the communications department enables users to communicate with local people within a virtual space. This allows users to communicate with local people.

[0038] The generation unit can generate an optimal virtual space based on the user's past relocation preference history when analyzing geographical data and lifestyle information. For example, the generation unit can generate a detailed virtual space of a region based on data of a user's past desired relocation destinations. The generation unit can also generate virtual spaces of similar regions based on the user's past relocation preference history. Furthermore, the generation unit can generate a virtual space of a region based on data of a region the user has visited in the past. This allows for the generation of a virtual space that takes into account the user's past relocation preference history.

[0039] The generation unit can customize the elements of the virtual space based on the user's current lifestyle and areas of interest during the generation process. For example, if the user is raising children, the generation AI can create a virtual space that includes local schools and childcare facilities. If the user is interested in outdoor activities, the generation AI can also create a virtual space that includes nature parks and hiking trails. Furthermore, if the user is working remotely, the generation AI can create a virtual space that includes cafes and co-working spaces. This allows for the creation of virtual spaces tailored to the user's current lifestyle and areas of interest.

[0040] The generation unit can prioritize the creation of highly relevant virtual spaces based on the user's geographical location information during the generation process. For example, the generation unit can create a virtual space of a local city close to the user's current location. It can also create virtual spaces of popular relocation destinations based on the user's geographical location information. Furthermore, the generation unit can create virtual spaces of easily accessible areas, taking the user's geographical location information into consideration. This allows for the creation of virtual spaces that take the user's geographical location into account.

[0041] The generation unit can analyze the user's social media activity during generation and generate relevant virtual spaces. For example, the generation unit can generate virtual spaces of regions the user has shown interest in on social media. It can also generate virtual spaces of regions of interest based on the content of the user's social media posts. Furthermore, the generation unit can generate virtual spaces of relevant regions based on information about accounts the user follows. This allows for the generation of virtual spaces that take the user's social media activity into consideration.

[0042] The exploration unit can suggest the optimal route during exploration based on the user's past exploration history. For example, the exploration unit's generating AI suggests the optimal route based on places the user has visited in the past. Furthermore, the exploration unit can suggest routes that pass through places of interest based on the user's past exploration history. In addition, the exploration unit can analyze the user's past exploration history and suggest efficient routes. This allows for the suggestion of the optimal route that takes the user's past exploration history into account.

[0043] The exploration unit can customize the exploration route based on the user's current lifestyle and areas of interest. For example, if the user is raising children, the generating AI can suggest a route that passes through local schools and childcare facilities. If the user is interested in outdoor activities, the generating AI can suggest a route that passes through nature parks and hiking trails. Furthermore, if the user is working remotely, the generating AI can suggest a route that passes through cafes and co-working spaces. This allows the system to suggest exploration routes tailored to the user's current lifestyle and areas of interest.

[0044] The search unit can prioritize searching for highly relevant routes based on the user's geographical location information during a search. For example, the search unit can prioritize searching for routes to local cities close to the user's current location. Furthermore, the search unit can also prioritize searching for routes to popular relocation destinations based on the user's geographical location information. In addition, the search unit can prioritize searching for routes to easily accessible areas, taking the user's geographical location information into consideration. This allows for the prioritization of route searches that take the user's geographical location into account.

[0045] The exploration unit can analyze the user's social media activity during exploration and search for relevant routes. For example, the exploration unit can search for routes in areas the user has shown interest in on social media. It can also search for routes in areas of interest based on the content of the user's social media posts. Furthermore, the exploration unit can search for routes in relevant areas based on information about accounts the user follows. This allows for route searching that takes the user's social media activity into consideration.

[0046] The simulation unit can perform optimal simulations based on the user's past occupations and family structure history. For example, the simulation unit can use the user's past occupations to generate simulations related to those occupations using its AI. Furthermore, the simulation unit can use the user's family structure history to generate simulations involving all family members using its AI. In addition, the simulation unit can analyze the user's past occupations and family structure history to perform the most appropriate simulation. This allows for optimal simulations that take into account the user's past occupations and family structure history.

[0047] The simulation unit can customize the simulation content based on the user's current lifestyle and areas of interest. For example, if the user is raising children, the AI ​​can generate simulations of local schools and childcare facilities. If the user is interested in outdoor activities, the AI ​​can generate simulations of nature parks and hiking trails. Furthermore, if the user is working remotely, the AI ​​can generate simulations of cafes and co-working spaces. This allows for simulations tailored to the user's current lifestyle and areas of interest.

[0048] The simulation unit can prioritize highly relevant simulations based on the user's geographical location information. For example, it can prioritize simulations of local cities close to the user's current location. It can also prioritize simulations of popular relocation destinations based on the user's geographical location. Furthermore, it can prioritize simulations of easily accessible areas, taking the user's geographical location into consideration. This allows for simulations that prioritize the user's geographical location.

[0049] The simulation unit can analyze the user's social media activity during the simulation process and perform relevant simulations. For example, the simulation unit can perform simulations related to occupations and activities that the user has shown interest in on social media. It can also perform simulations of occupations and activities of interest based on the user's social media posts. Furthermore, the simulation unit can perform simulations of related occupations and activities based on information about accounts the user follows. This allows for simulations that take the user's social media activity into consideration.

[0050] The environmental change unit can reproduce optimal environmental changes based on the user's past history of seasonal and time-of-day changes when the environment changes. For example, the environmental change unit can use a generating AI to reproduce environmental changes based on the seasons and time-of-days the user has experienced in the past. The environmental change unit can also reproduce similar environmental changes from the user's past history of seasonal and time-of-day changes. Furthermore, the environmental change unit can use a generating AI to reproduce environmental changes based on the seasonal and time-of-day changes of regions the user has visited in the past. This allows for the reproduction of optimal environmental changes that take into account the user's past history of seasonal and time-of-day changes.

[0051] The environmental change unit can customize environmental changes based on the user's current lifestyle and areas of interest when the environment changes. For example, if the user is raising children, the generating AI can recreate seasonal and time-of-day changes in local schools and childcare facilities. If the user is interested in outdoor activities, the generating AI can recreate seasonal and time-of-day changes in nature parks and hiking trails. Furthermore, if the user is working remotely, the generating AI can recreate seasonal and time-of-day changes in cafes and co-working spaces. This allows for the recreation of environmental changes tailored to the user's current lifestyle and areas of interest.

[0052] The environmental change unit can prioritize reproducing highly relevant environmental changes based on the user's geographical location information when the environment changes. For example, the environmental change unit can prioritize reproducing environmental changes in local cities close to the user's current location. Furthermore, based on the user's geographical location information, the environmental change unit can also prioritize reproducing environmental changes in areas popular as relocation destinations. In addition, considering the user's geographical location information, the environmental change unit can prioritize reproducing environmental changes in easily accessible areas. This allows for the priority reproduction of environmental changes that take the user's geographical location information into account.

[0053] The communication department can suggest the optimal communication method based on the user's past communication history. For example, the communication department's AI can suggest the best method based on the communication style the user has preferred in the past. Furthermore, the communication department can suggest communication that includes topics of interest based on the user's past communication history. In addition, the communication department can analyze the user's past communication history and suggest efficient communication methods. This allows the communication department to suggest the optimal communication method that takes the user's past communication history into consideration.

[0054] The communication department can customize communication content based on the user's current lifestyle and areas of interest. For example, if the user is raising children, the AI ​​can provide information about local schools and childcare facilities. If the user is interested in outdoor activities, the AI ​​can provide information about nature parks and hiking trails. Furthermore, if the user is working remotely, the AI ​​can provide information about cafes and co-working spaces. This allows the communication content to be tailored to the user's current lifestyle and areas of interest.

[0055] The communication department can prioritize highly relevant communications based on the user's geographical location information during communication. For example, the communication department's AI generates communications based on information about the user's current location. It can also provide communications about popular relocation destinations based on the user's geographical location. Furthermore, the communication department can provide communications about easily accessible areas, taking the user's geographical location into consideration. This allows for the prioritization of communications that take the user's geographical location into account.

[0056] The communications department can analyze users' social media activity and provide relevant communications during the communication process. For example, the communications department can provide communications related to topics that users have shown interest in on social media. It can also provide communications on topics of interest based on the content of users' social media posts. Furthermore, the communications department can provide communications on relevant topics based on information about accounts that users follow. This allows for communications that take into account users' social media activity.

[0057] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0058] The generation unit can generate an optimal virtual space based on the user's past relocation preference history. For example, the generation unit can use data on the user's past desired relocation destinations to generate a detailed virtual space for that region. The generation unit can also generate virtual spaces for similar regions based on the user's past relocation preference history. Furthermore, the generation unit can use data on regions the user has visited in the past to generate a virtual space for those regions. This allows for the generation of virtual spaces that take into account the user's past relocation preference history.

[0059] The exploration unit can propose the optimal route based on the user's past exploration history. For example, the exploration unit's generating AI can propose the optimal route based on places the user has visited in the past. Furthermore, the exploration unit can propose routes that pass through places of interest based on the user's past exploration history. In addition, the exploration unit can analyze the user's past exploration history and propose efficient routes. This allows the system to propose the optimal route that takes the user's past exploration history into account.

[0060] The simulation unit can perform optimal simulations based on the user's past occupations and family structure history. For example, the simulation unit can use the user's past occupations to generate simulations related to those occupations using its AI. Furthermore, the simulation unit can use the user's family structure history to generate simulations involving all family members using its AI. In addition, the simulation unit can analyze the user's past occupations and family structure history to perform the most appropriate simulation. This allows for optimal simulations that take into account the user's past occupations and family structure history.

[0061] The environmental change unit can reproduce optimal environmental changes based on the user's past history of seasonal and time-of-day changes. For example, the environmental change unit can use a generating AI to reproduce environmental changes based on the seasons and times of day the user has experienced in the past. Furthermore, the environmental change unit can also reproduce similar environmental changes based on the user's past history of seasonal and time-of-day changes. In addition, the environmental change unit can use a generating AI to reproduce environmental changes based on the seasonal and time-of-day changes of regions the user has visited in the past. This allows for the reproduction of optimal environmental changes that take into account the user's past history of seasonal and time-of-day changes.

[0062] The communications department can suggest the optimal communication method based on the user's past communication history. For example, the communications department's AI can suggest the best method based on the communication style the user has preferred in the past. Furthermore, the communications department can suggest communication that includes topics of interest based on the user's past communication history. In addition, the communications department can analyze the user's past communication history and suggest efficient communication methods. This allows the communications department to suggest the optimal communication method that takes the user's past communication history into consideration.

[0063] The following briefly describes the processing flow for example form 1.

[0064] Step 1: The generation unit analyzes geographic data and lifestyle information to generate a 3D virtual space. The generation unit analyzes geographic data such as map data, terrain data, and climate data to render building models and terrain. It also analyzes lifestyle information such as demographics, traffic information, and local facility information and reflects it in the 3D virtual space. Using generation AI, it utilizes technologies such as deep learning and generative models to generate a realistic 3D virtual space. Step 2: The exploration unit explores the 3D virtual space generated by the generation unit. The user can move freely through the virtual space via their avatar and communicate with the local people. The avatar is a model that mimics the user's appearance or a customizable character, and it also has the function to interact with characters in the virtual space. Step 3: The simulation unit performs simulations based on the user's occupation and family structure. It simulates work based on the user's occupation and childcare based on their family structure. For example, it simulates new jobs in rural areas and the use of local schools and childcare facilities, allowing the user to have a simulated experience of life in a rural area.

[0065] (Example of form 2) The system according to an embodiment of the present invention is a system that allows users to virtually experience life in a rural area using a generative AI. In this system, the user inputs a departure point and a destination, and the generative AI analyzes geographical data and lifestyle information to generate a 3D virtual space. The user can explore the virtual space through their avatar and communicate with local people. Furthermore, it is possible to experience life under various conditions, such as changes in season and time of day, and the reproduction of local events. In addition, it is possible to simulate work and childcare based on the user's occupation and family structure. For example, it is possible to virtually experience a new job in a rural area or simulate the use of local schools and childcare facilities. This mechanism allows users to make a decision about relocation after obtaining sufficient information and experience. For example, the user inputs a departure point and a destination. In this case, the user only needs to input the departure point and destination. For example, the user inputs, "I want to go from my home to the station." This information is input into the generative AI. Next, the generative AI analyzes the input information and creates a video showing how to get from the current location to the destination. The generative AI calculates the optimal route based on map data and generates a video along that route. For example, if a user enters a route from their home to the train station, a video is generated that follows that route. The generated video starts navigating according to the orientation of the user's smartphone. For example, if the user is pointing their smartphone north, the video will also start navigating in the direction of north. This allows the user to receive navigation that is aligned with the direction they are facing. Furthermore, the video screen moves in accordance with the user's walking speed. For example, if the user is walking slowly, the video will also progress slowly. This allows the user to receive navigation at their own pace. This mechanism results in a simple structure that is easy for children and the elderly to use, making it enjoyable for everyone. Users can receive navigation intuitively without having to perform complex operations. Also, since the viewpoint of the smartphone is the axis of all directions, there is no chance of getting lost, and walking safety is ensured because the smartphone is held horizontally. For example, if the user is walking with their smartphone held horizontally, the video will also be displayed horizontally, allowing the user to walk safely.This allows the system to allow users to virtually experience life in rural areas.

[0066] The system according to this embodiment comprises a generation unit, a search unit, and a simulation unit. The generation unit analyzes geographic data and lifestyle information to generate a 3D virtual space. For example, the generation unit analyzes map data, terrain data, climate data, etc., as geographic data. The generation unit can also analyze demographic data, traffic information, local facility information, etc., as lifestyle information. For example, the generation unit generates building models based on map data and renders terrain based on terrain data. Furthermore, the generation unit can reproduce seasonal and weather changes based on climate data. The generation unit uses a generation AI to analyze geographic data and lifestyle information to generate a 3D virtual space. The generation AI uses technologies such as deep learning and generation models to generate a realistic 3D virtual space. For example, the generation AI receives geographic data as input and outputs a 3D virtual space. The generation AI can also receive lifestyle information as input and reflect it in the 3D virtual space. The search unit explores the 3D virtual space generated by the generation unit. The search unit can explore the virtual space through the user's avatar. An avatar can be a model that mimics the user's appearance or a customizable character. For example, the exploration unit allows the user to freely move around the virtual space by controlling their avatar. The exploration unit can also communicate with local people within the virtual space. For example, the exploration unit has the ability to interact with characters in the virtual space. The simulation unit performs simulations based on the user's occupation and family structure. For example, the simulation unit can simulate work based on the user's occupation. The simulation unit can also simulate childcare based on the user's family structure. For example, the simulation unit allows the user to virtually experience a new job in a rural area. The simulation unit can also simulate the use of local schools and childcare facilities. As a result, the system according to the embodiment allows the user to virtually experience life in a rural area.

[0067] The generation unit analyzes geographic data and lifestyle information to generate a 3D virtual space. Specifically, it analyzes geographic data such as map data, topographic data, and climate data, and constructs a detailed 3D model based on this data. Map data is used to accurately reproduce the placement of buildings and the layout of roads, while topographic data is used to realistically represent natural terrain such as mountains, rivers, and hills. Climate data is used to simulate seasonal changes and weather fluctuations, thereby reproducing realistic environmental changes within the virtual space. Furthermore, the generation unit analyzes lifestyle information such as demographics, traffic information, and local facility information, and reflects this information in the 3D virtual space. Demographic data shows the population density and age distribution of the region, and traffic information is used to reproduce road congestion and the operation status of public transportation. Local facility information shows the location and size of schools, hospitals, commercial facilities, etc., allowing users to experience the actual local living environment within the virtual space. The generation unit uses generation AI to analyze this geographic data and lifestyle information and generate a realistic 3D virtual space. Generative AI utilizes technologies such as deep learning and generative models to generate highly accurate 3D models based on input data. For example, it can take geographical data as input and output detailed 3D models of buildings and terrain. It can also take lifestyle information as input and generate realistic virtual spaces that reflect population density and traffic conditions. This allows the generation unit to enable users to realistically experience the living environment of their actual region within the virtual space.

[0068] The exploration unit provides the functionality to explore the 3D virtual space generated by the generation unit. Specifically, users can freely move around the virtual space through their avatar and explore various locations. The avatar is a model that mimics the user's appearance or a customizable character, and users can set their avatar to their liking. The exploration unit enables users to freely move around within the virtual space by controlling their avatar, for example, walking through a city, entering buildings, or strolling through nature. The exploration unit also has the functionality to communicate with local people within the virtual space. For example, the exploration unit provides the functionality to interact with characters in the virtual space, allowing users to enjoy conversations and exchange information with local people. This allows users to experience life in the virtual space more realistically. Furthermore, the exploration unit can also provide events and activities within the virtual space. For example, users can participate in local festivals and events, or enjoy sports and recreational activities. This allows users to enjoy a variety of experiences within the virtual space. The exploration unit can also collect user behavior data and, based on this, provide content tailored to the user's interests and preferences. For example, if a user frequently visits a particular location, the system can notify them of information and events related to that location. This allows the exploration unit to provide users with a personalized experience and enhance their exploration within the virtual space.

[0069] The simulation unit provides a function to perform simulations based on the user's occupation and family structure. Specifically, it simulates work based on the user's occupation, allowing the user to virtually experience a new work environment and job duties. For example, the simulation unit provides a scenario in which the user experiences a new job in a rural area, allowing the user to simulate actual work in a virtual space. This allows the user to prepare to adapt to a new work environment. The simulation unit can also simulate childcare based on the user's family structure. For example, the simulation unit can simulate the use of local schools and childcare facilities, allowing the user to obtain information about children's education and care. This allows the user to concretely imagine what childcare is like in rural life. Furthermore, the simulation unit can also provide simulations based on the user's lifestyle and hobbies. For example, it can simulate the user's favorite sports and recreational activities in a virtual space, providing the user with information to enjoy life in a new area. Through these simulations, the simulation unit can help users concretely imagine life in a rural area and support their decisions regarding relocation or changing jobs. The simulation unit can continuously improve the simulation content based on user feedback, providing a more realistic and useful experience. This allows the simulation unit to allow users to virtually experience life in rural areas and effectively prepare for relocation or a career change.

[0070] The generation unit can generate a 3D virtual space by analyzing geographic data and lifestyle information using a generative AI. For example, the generation unit uses a generative AI to analyze geographic data and generate a 3D virtual space. The generative AI uses technologies such as deep learning and generative models to generate a realistic 3D virtual space. For example, the generative AI takes geographic data as input and outputs a 3D virtual space. The generation unit can also use the generative AI to analyze lifestyle information and reflect it in the 3D virtual space. The generative AI takes lifestyle information as input and reflects it in the 3D virtual space. In this way, a realistic 3D virtual space can be generated by using the generative AI.

[0071] The exploration unit can explore the virtual space through the user's avatar. For example, the exploration unit allows the user to freely move around the virtual space by controlling their avatar. The avatar can be a model that mimics the user's appearance or a customizable character. The exploration unit can also communicate with local people within the virtual space. For example, the exploration unit has the functionality to interact with characters in the virtual space. This allows the user to freely explore the virtual space.

[0072] The simulation unit can simulate work and childcare based on the user's occupation and family structure. For example, the simulation unit can simulate work based on the user's occupation. It can also simulate childcare based on the user's family structure. For example, the simulation unit can allow the user to virtually experience a new job in a rural area. It can also simulate the use of local schools and childcare facilities. This makes it possible to perform simulations tailored to the user's occupation and family structure.

[0073] The environmental change unit can simulate seasonal and time-of-day changes, as well as local events. For example, it can simulate seasonal weather changes. It can also simulate changes in light throughout the day; for instance, it can simulate light changes in the morning, noon, evening, and night. Furthermore, it can simulate local events, such as festivals, sporting events, and community events. This allows for diverse life experiences under various conditions.

[0074] The communications department can communicate with local people. For example, the communications department has the functionality to interact with characters in a virtual space. For instance, the communications department enables users to communicate with local people within a virtual space. This allows users to communicate with local people.

[0075] The generation unit can estimate the user's emotions and adjust the level of detail in the generated virtual space based on the estimated emotions. For example, if the user is relaxed, the generation AI can generate a highly detailed virtual space including detailed landscapes and building designs. If the user is stressed, the generation AI can also generate a simple and visually calming virtual space. Furthermore, if the user is excited, the generation AI can generate a virtual space with visually stimulating effects. This allows the level of detail in the virtual space to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0076] The generation unit can generate an optimal virtual space based on the user's past relocation preference history when analyzing geographical data and lifestyle information. For example, the generation unit can generate a detailed virtual space of a region based on data of a user's past desired relocation destinations. The generation unit can also generate virtual spaces of similar regions based on the user's past relocation preference history. Furthermore, the generation unit can generate a virtual space of a region based on data of a region the user has visited in the past. This allows for the generation of a virtual space that takes into account the user's past relocation preference history.

[0077] The generation unit can customize the elements of the virtual space based on the user's current lifestyle and areas of interest during the generation process. For example, if the user is raising children, the generation AI can create a virtual space that includes local schools and childcare facilities. If the user is interested in outdoor activities, the generation AI can also create a virtual space that includes nature parks and hiking trails. Furthermore, if the user is working remotely, the generation AI can create a virtual space that includes cafes and co-working spaces. This allows for the creation of virtual spaces tailored to the user's current lifestyle and areas of interest.

[0078] The generation unit can estimate the user's emotions and change the theme of the generated virtual space based on the estimated emotions. For example, if the user is relaxed, the generation AI can generate a virtual space themed around nature or a resort. If the user is stressed, the generation AI can generate a virtual space themed around a quiet countryside or a hot spring resort. Furthermore, if the user is excited, the generation AI can generate a virtual space themed around an urban area or an event venue. This allows the theme of the virtual space to change according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0079] The generation unit can prioritize the creation of highly relevant virtual spaces based on the user's geographical location information during the generation process. For example, the generation unit can create a virtual space of a local city close to the user's current location. It can also create virtual spaces of popular relocation destinations based on the user's geographical location information. Furthermore, the generation unit can create virtual spaces of easily accessible areas, taking the user's geographical location information into consideration. This allows for the creation of virtual spaces that take the user's geographical location into account.

[0080] The generation unit can analyze the user's social media activity during generation and generate relevant virtual spaces. For example, the generation unit can generate virtual spaces of regions the user has shown interest in on social media. It can also generate virtual spaces of regions of interest based on the content of the user's social media posts. Furthermore, the generation unit can generate virtual spaces of relevant regions based on information about accounts the user follows. This allows for the generation of virtual spaces that take the user's social media activity into consideration.

[0081] The exploration unit can estimate the user's emotions and adjust the route through the virtual space based on the estimated emotions. For example, if the user is relaxed, the generative AI can suggest a route with pleasant scenery. If the user is stressed, the generative AI can suggest a route through quiet places. Furthermore, if the user is excited, the generative AI can suggest a route with more activity. This allows the exploration route to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0082] The exploration unit can suggest the optimal route during exploration based on the user's past exploration history. For example, the exploration unit's generating AI suggests the optimal route based on places the user has visited in the past. Furthermore, the exploration unit can suggest routes that pass through places of interest based on the user's past exploration history. In addition, the exploration unit can analyze the user's past exploration history and suggest efficient routes. This allows for the suggestion of the optimal route that takes the user's past exploration history into account.

[0083] The exploration unit can customize the exploration route based on the user's current lifestyle and areas of interest. For example, if the user is raising children, the generating AI can suggest a route that passes through local schools and childcare facilities. If the user is interested in outdoor activities, the generating AI can suggest a route that passes through nature parks and hiking trails. Furthermore, if the user is working remotely, the generating AI can suggest a route that passes through cafes and co-working spaces. This allows the system to suggest exploration routes tailored to the user's current lifestyle and areas of interest.

[0084] The exploration unit can estimate the user's emotions and determine the priority of virtual spaces to explore based on the estimated emotions. For example, if the user is relaxed, the generative AI may prioritize exploring nature or resort areas. If the user is stressed, the generative AI may prioritize exploring quiet countryside or hot spring areas. Furthermore, if the user is excited, the generative AI may prioritize exploring urban areas or event venues. This allows for the determination of exploration priorities according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. The generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0085] The search unit can prioritize searching for highly relevant routes based on the user's geographical location information during a search. For example, the search unit can prioritize searching for routes to local cities close to the user's current location. Furthermore, the search unit can also prioritize searching for routes to popular relocation destinations based on the user's geographical location information. In addition, the search unit can prioritize searching for routes to easily accessible areas, taking the user's geographical location information into consideration. This allows for the prioritization of route searches that take the user's geographical location into account.

[0086] The exploration unit can analyze the user's social media activity during exploration and search for relevant routes. For example, the exploration unit can search for routes in areas the user has shown interest in on social media. It can also search for routes in areas of interest based on the content of the user's social media posts. Furthermore, the exploration unit can search for routes in relevant areas based on information about accounts the user follows. This allows for route searching that takes the user's social media activity into consideration.

[0087] The simulation unit can estimate the user's emotions and adjust the level of detail of the simulation based on the estimated emotions. For example, if the user is relaxed, the generation AI can perform a detailed simulation. If the user is stressed, the generation AI can perform a simpler simulation. Furthermore, if the user is excited, the generation AI can perform a visually stimulating simulation. This allows the level of detail of the simulation to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0088] The simulation unit can perform optimal simulations based on the user's past occupations and family structure history. For example, the simulation unit can use the user's past occupations to generate simulations related to those occupations using its AI. Furthermore, the simulation unit can use the user's family structure history to generate simulations involving all family members using its AI. In addition, the simulation unit can analyze the user's past occupations and family structure history to perform the most appropriate simulation. This allows for optimal simulations that take into account the user's past occupations and family structure history.

[0089] The simulation unit can customize the simulation content based on the user's current lifestyle and areas of interest. For example, if the user is raising children, the AI ​​can generate simulations of local schools and childcare facilities. If the user is interested in outdoor activities, the AI ​​can generate simulations of nature parks and hiking trails. Furthermore, if the user is working remotely, the AI ​​can generate simulations of cafes and co-working spaces. This allows for simulations tailored to the user's current lifestyle and areas of interest.

[0090] The simulation unit can estimate the user's emotions and determine the priority of simulations based on those emotions. For example, if the user is relaxed, the generation AI will prioritize simulations of nature or resort areas. If the user is stressed, the generation AI can prioritize simulations of quiet countryside or hot spring resorts. Furthermore, if the user is excited, the generation AI can prioritize simulations of urban areas or event venues. This allows for the determination of simulation priorities according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0091] The simulation unit can prioritize highly relevant simulations based on the user's geographical location information. For example, it can prioritize simulations of local cities close to the user's current location. It can also prioritize simulations of popular relocation destinations based on the user's geographical location. Furthermore, it can prioritize simulations of easily accessible areas, taking the user's geographical location into consideration. This allows for simulations that prioritize the user's geographical location.

[0092] The simulation unit can analyze the user's social media activity during the simulation process and perform relevant simulations. For example, the simulation unit can perform simulations related to occupations and activities that the user has shown interest in on social media. It can also perform simulations of occupations and activities of interest based on the user's social media posts. Furthermore, the simulation unit can perform simulations of related occupations and activities based on information about accounts the user follows. This allows for simulations that take the user's social media activity into consideration.

[0093] The environmental change unit can estimate the user's emotions and adjust the level of detail in the environmental changes based on the estimated emotions. For example, if the user is relaxed, the generating AI can reproduce detailed seasonal and time-of-day changes. If the user is stressed, the generating AI can reproduce simple and visually calming environmental changes. Furthermore, if the user is excited, the generating AI can reproduce environmental changes with visually stimulating effects. This allows the level of detail in the environmental changes to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or a generating AI. The generating AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0094] The environmental change unit can reproduce optimal environmental changes based on the user's past history of seasonal and time-of-day changes when the environment changes. For example, the environmental change unit can use a generating AI to reproduce environmental changes based on the seasons and time-of-days the user has experienced in the past. The environmental change unit can also reproduce similar environmental changes from the user's past history of seasonal and time-of-day changes. Furthermore, the environmental change unit can use a generating AI to reproduce environmental changes based on the seasonal and time-of-day changes of regions the user has visited in the past. This allows for the reproduction of optimal environmental changes that take into account the user's past history of seasonal and time-of-day changes.

[0095] The environmental change unit can customize environmental changes based on the user's current lifestyle and areas of interest when the environment changes. For example, if the user is raising children, the generating AI can recreate seasonal and time-of-day changes in local schools and childcare facilities. If the user is interested in outdoor activities, the generating AI can recreate seasonal and time-of-day changes in nature parks and hiking trails. Furthermore, if the user is working remotely, the generating AI can recreate seasonal and time-of-day changes in cafes and co-working spaces. This allows for the recreation of environmental changes tailored to the user's current lifestyle and areas of interest.

[0096] The environmental change unit can estimate the user's emotions and determine the priority of environmental changes based on the estimated emotions. For example, if the user is relaxed, the generating AI will prioritize reproducing environmental changes in nature or resort areas. If the user is stressed, the generating AI can also prioritize reproducing environmental changes in quiet rural areas or hot spring resorts. Furthermore, if the user is excited, the generating AI can also prioritize reproducing environmental changes in urban areas or event venues. This allows for the determination of environmental change priorities according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generating AI. The generating AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0097] The environmental change unit can prioritize reproducing highly relevant environmental changes based on the user's geographical location information when the environment changes. For example, the environmental change unit can prioritize reproducing environmental changes in local cities close to the user's current location. Furthermore, based on the user's geographical location information, the environmental change unit can also prioritize reproducing environmental changes in areas popular as relocation destinations. In addition, considering the user's geographical location information, the environmental change unit can prioritize reproducing environmental changes in easily accessible areas. This allows for the priority reproduction of environmental changes that take the user's geographical location information into account.

[0098] The communication unit can estimate the user's emotions and adjust the communication method based on the estimated emotions. For example, if the user is relaxed, the generation AI can provide friendly and casual communication. If the user is stressed, the generation AI can provide communication in a calm tone. Furthermore, if the user is excited, the generation AI can provide energetic and lively communication. This allows the communication method to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0099] The communication department can suggest the optimal communication method based on the user's past communication history. For example, the communication department's AI can suggest the best method based on the communication style the user has preferred in the past. Furthermore, the communication department can suggest communication that includes topics of interest based on the user's past communication history. In addition, the communication department can analyze the user's past communication history and suggest efficient communication methods. This allows the communication department to suggest the optimal communication method that takes the user's past communication history into consideration.

[0100] The communication department can customize communication content based on the user's current lifestyle and areas of interest. For example, if the user is raising children, the AI ​​can provide information about local schools and childcare facilities. If the user is interested in outdoor activities, the AI ​​can provide information about nature parks and hiking trails. Furthermore, if the user is working remotely, the AI ​​can provide information about cafes and co-working spaces. This allows the communication content to be tailored to the user's current lifestyle and areas of interest.

[0101] The communication unit can estimate the user's emotions and determine communication priorities based on those estimated emotions. For example, if the user is relaxed, the generation AI will prioritize friendly and casual communication. If the user is stressed, the generation AI can prioritize communication in a calm tone. Furthermore, if the user is excited, the generation AI can prioritize energetic and lively communication. This allows for the determination of communication priorities according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generation AI. The generation AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0102] The communication department can prioritize highly relevant communications based on the user's geographical location information during communication. For example, the communication department's AI generates communications based on information about the user's current location. It can also provide communications about popular relocation destinations based on the user's geographical location. Furthermore, the communication department can provide communications about easily accessible areas, taking the user's geographical location into consideration. This allows for the prioritization of communications that take the user's geographical location into account.

[0103] The communications department can analyze users' social media activity and provide relevant communications during the communication process. For example, the communications department can provide communications related to topics that users have shown interest in on social media. It can also provide communications on topics of interest based on the content of users' social media posts. Furthermore, the communications department can provide communications on relevant topics based on information about accounts that users follow. This allows for communications that take into account users' social media activity.

[0104] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0105] The generation unit can estimate the user's emotions and adjust the level of detail in the generated virtual space based on those emotions. For example, if the user is relaxed, the generation AI can generate a highly detailed virtual space including detailed landscapes and building designs. If the user is stressed, the generation AI can also generate a simple and visually calming virtual space. Furthermore, if the user is excited, the generation AI can generate a virtual space with visually stimulating effects. This allows the level of detail in the virtual space to be adjusted according to the user's emotions.

[0106] The generation unit can generate an optimal virtual space based on the user's past relocation preference history. For example, the generation unit can use data on the user's past desired relocation destinations to generate a detailed virtual space for that region. The generation unit can also generate virtual spaces for similar regions based on the user's past relocation preference history. Furthermore, the generation unit can use data on regions the user has visited in the past to generate a virtual space for those regions. This allows for the generation of virtual spaces that take into account the user's past relocation preference history.

[0107] The exploration unit can estimate the user's emotions and adjust the route through the virtual space based on those emotions. For example, if the user is relaxed, the generating AI can suggest a route with pleasant scenery. If the user is stressed, the generating AI can suggest a route through quiet places. Furthermore, if the user is excited, the generating AI can suggest a route with more activity. This allows the exploration route to be adjusted according to the user's emotions.

[0108] The exploration unit can propose the optimal route based on the user's past exploration history. For example, the exploration unit's generating AI can propose the optimal route based on places the user has visited in the past. Furthermore, the exploration unit can propose routes that pass through places of interest based on the user's past exploration history. In addition, the exploration unit can analyze the user's past exploration history and propose efficient routes. This allows the system to propose the optimal route that takes the user's past exploration history into account.

[0109] The simulation unit can estimate the user's emotions and adjust the level of detail of the simulation based on those emotions. For example, if the user is relaxed, the generation AI will perform a detailed simulation. If the user is stressed, the simulation unit can also perform a simpler simulation. Furthermore, if the user is excited, the simulation unit can also perform a visually stimulating simulation. This allows the level of detail of the simulation to be adjusted according to the user's emotions.

[0110] The simulation unit can perform optimal simulations based on the user's past occupations and family structure history. For example, the simulation unit can use the user's past occupations to generate simulations related to those occupations using its AI. Furthermore, the simulation unit can use the user's family structure history to generate simulations involving all family members using its AI. In addition, the simulation unit can analyze the user's past occupations and family structure history to perform the most appropriate simulation. This allows for optimal simulations that take into account the user's past occupations and family structure history.

[0111] The environmental change unit can estimate the user's emotions and adjust the level of detail in the environmental changes based on those emotions. For example, if the user is relaxed, the generating AI can reproduce detailed seasonal and time-of-day changes. If the user is stressed, the generating AI can reproduce simple and visually calming environmental changes. Furthermore, if the user is excited, the generating AI can reproduce environmental changes with visually stimulating effects. This allows the level of detail in the environmental changes to be adjusted according to the user's emotions.

[0112] The environmental change unit can reproduce optimal environmental changes based on the user's past history of seasonal and time-of-day changes. For example, the environmental change unit can use a generating AI to reproduce environmental changes based on the seasons and times of day the user has experienced in the past. Furthermore, the environmental change unit can also reproduce similar environmental changes based on the user's past history of seasonal and time-of-day changes. In addition, the environmental change unit can use a generating AI to reproduce environmental changes based on the seasonal and time-of-day changes of regions the user has visited in the past. This allows for the reproduction of optimal environmental changes that take into account the user's past history of seasonal and time-of-day changes.

[0113] The communication unit can estimate the user's emotions and adjust the communication method based on those emotions. For example, if the user is relaxed, the generating AI can provide friendly and casual communication. If the user is stressed, the generating AI can provide communication in a calm tone. Furthermore, if the user is excited, the generating AI can provide energetic and lively communication. This allows the communication method to be adjusted according to the user's emotions.

[0114] The communications department can suggest the optimal communication method based on the user's past communication history. For example, the communications department's AI can suggest the best method based on the communication style the user has preferred in the past. Furthermore, the communications department can suggest communication that includes topics of interest based on the user's past communication history. In addition, the communications department can analyze the user's past communication history and suggest efficient communication methods. This allows the communications department to suggest the optimal communication method that takes the user's past communication history into consideration.

[0115] The following briefly describes the processing flow for example form 2.

[0116] Step 1: The generation unit analyzes geographic data and lifestyle information to generate a 3D virtual space. The generation unit analyzes geographic data such as map data, terrain data, and climate data to render building models and terrain. It also analyzes lifestyle information such as demographics, traffic information, and local facility information and reflects it in the 3D virtual space. Using generation AI, it utilizes technologies such as deep learning and generative models to generate a realistic 3D virtual space. Step 2: The exploration unit explores the 3D virtual space generated by the generation unit. The user can move freely through the virtual space via their avatar and communicate with the local people. The avatar is a model that mimics the user's appearance or a customizable character, and it also has the function to interact with characters in the virtual space. Step 3: The simulation unit performs simulations based on the user's occupation and family structure. It simulates work based on the user's occupation and childcare based on their family structure. For example, it simulates new jobs in rural areas and the use of local schools and childcare facilities, allowing the user to have a simulated experience of life in a rural area.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] Each of the multiple elements described above, including the generation unit, search unit, simulation unit, environment change unit, and communication unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the generation unit is implemented by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing unit 12. The search unit is implemented by the control unit 46A of the smart device 14. The simulation unit is implemented by the specific processing unit 290 of the data processing unit 12. The environment change unit is implemented by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing unit 12. The communication unit is implemented by the control unit 46A of the smart device 14. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0121] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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).

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.).

[0133] 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.

[0134] 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.

[0135] 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.

[0136] Each of the multiple elements described above, including the generation unit, search unit, simulation unit, environment change unit, and communication unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the generation unit is implemented by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing unit 12. The search unit is implemented by the control unit 46A of the smart glasses 214. The simulation unit is implemented by the specific processing unit 290 of the data processing unit 12. The environment change unit is implemented by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing unit 12. The communication unit is implemented by the control unit 46A of the smart glasses 214. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0137] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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).

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.).

[0149] 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.

[0150] 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.

[0151] 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.

[0152] Each of the multiple elements described above, including the generation unit, search unit, simulation unit, environment change unit, and communication unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the generation unit is implemented by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing unit 12. The search unit is implemented by the control unit 46A of the headset terminal 314. The simulation unit is implemented by the specific processing unit 290 of the data processing unit 12. The environment change unit is implemented by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing unit 12. The communication unit is implemented by the control unit 46A of the headset terminal 314. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0153] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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).

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.).

[0166] 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.

[0167] 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.

[0168] 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.

[0169] Each of the multiple elements described above, including the generation unit, search unit, simulation unit, environment change unit, and communication unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the generation unit is implemented by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing unit 12. The search unit is implemented by the control unit 46A of the robot 414. The simulation unit is implemented by the specific processing unit 290 of the data processing unit 12. The environment change unit is implemented by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing unit 12. The communication unit is implemented by the control unit 46A of the robot 414. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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."

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] (Note 1) A system characterized by comprising: a generation unit that analyzes geographic data and lifestyle information to generate a 3D virtual space; a search unit that explores the 3D virtual space generated by the generation unit; and a simulation unit that performs simulations based on the user's occupation and family structure. (Note 2) The generating unit is Generative AI analyzes geographical data and lifestyle information to generate a 3D virtual space. The system described in Appendix 1, characterized by the features described herein. (Note 3) The search unit, Explore the virtual world through your avatar. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned simulation unit, Based on the user's occupation and family structure, it simulates work and childcare scenarios. The system described in Appendix 1, characterized by the features described herein. (Note 5) The system described in Appendix 1, characterized by having an environment change unit that reproduces seasonal and time-of-day changes and local events. (Note 6) It has a communications department that can communicate with local people. The system described in Appendix 1, characterized by the features described herein. (Note 7) The generating unit is It estimates the user's emotions and adjusts the level of detail in the virtual space generated based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The generating unit is When analyzing geographical data and lifestyle information, the system generates an optimal virtual space based on the user's past relocation preferences. The system described in Appendix 1, characterized by the features described herein. (Note 9) The generating unit is During creation, the elements of the virtual space are customized based on the user's current lifestyle and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The generating unit is It estimates the user's emotions and changes the theme of the virtual space generated based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The generating unit is During generation, the system prioritizes generating virtual spaces that are highly relevant to the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The generating unit is During creation, the system analyzes the user's social media activity and generates a relevant virtual space. The system described in Appendix 1, characterized by the features described herein. (Note 13) The search unit, It estimates the user's emotions and adjusts the virtual route to explore based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The search unit, During exploration, the system suggests the optimal route based on the user's past exploration history. The system described in Appendix 1, characterized by the features described herein. (Note 15) The search unit, During exploration, the exploration route is customized based on the user's current lifestyle and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 16) The search unit, It estimates the user's emotions and determines the priority of virtual spaces to explore based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The search unit, During exploration, the system prioritizes finding the most relevant routes based on the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 18) The search unit, During exploration, the system analyzes the user's social media activity and explores relevant routes. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned simulation unit, It estimates the user's emotions and adjusts the level of detail in the simulation based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned simulation unit, During the simulation, the system performs an optimal simulation based on the user's past occupation and family structure history. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned simulation unit, During the simulation, the simulation content is customized based on the user's current lifestyle and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned simulation unit, It estimates the user's emotions and determines the priority of simulations based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned simulation unit, During simulations, the system prioritizes highly relevant simulations based on the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned simulation unit, During the simulation, we analyze the user's social media activity and perform related simulations. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned environmental change unit is It estimates the user's emotions and adjusts the level of detail of environmental changes based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned environmental change unit is When the environment changes, the system reproduces the optimal environmental changes based on the user's past history of seasonal and time-of-day changes. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned environmental change unit is When the environment changes, the system customizes the environmental changes based on the user's current lifestyle and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned environmental change unit is It estimates user emotions and determines the priority of environmental changes based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned environmental change unit is When the environment changes, the system prioritizes reproducing the most relevant environmental changes based on the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned communications department, It estimates the user's emotions and adjusts the communication method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned communications department, During communication, the system suggests the optimal communication method based on the user's past communication history. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned communications department, During communication, the content of the communication is customized based on the user's current life situation and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned communications department, It estimates the user's emotions and determines communication priorities based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned communications department, During communication, the system prioritizes highly relevant communications based on the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned communications department, During communication, we analyze the user's social media activity and conduct relevant communications. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0189] 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 system characterized by comprising: a generation unit that analyzes geographic data and lifestyle information to generate a 3D virtual space; a search unit that explores the 3D virtual space generated by the generation unit; and a simulation unit that performs simulations based on the user's occupation and family structure.

2. The generating unit is Generative AI analyzes geographical data and lifestyle information to generate a 3D virtual space. The system according to feature 1.

3. The search unit, Explore the virtual world through your avatar. The system according to feature 1.

4. The aforementioned simulation unit, Based on the user's occupation and family structure, it simulates work and childcare scenarios. The system according to feature 1.

5. The system according to claim 1, characterized by comprising an environmental change unit that reproduces seasonal and time-of-day changes and local events.

6. It has a communications department that can communicate with local people. The system according to feature 1.

7. The generating unit is It estimates the user's emotions and adjusts the level of detail in the virtual space generated based on those estimated emotions. The system according to feature 1.

8. The generating unit is When analyzing geographical data and lifestyle information, the system generates an optimal virtual space based on the user's past relocation preferences. The system according to feature 1.

9. The generating unit is During creation, the elements of the virtual space are customized based on the user's current lifestyle and areas of interest. The system according to feature 1.

10. The generating unit is It estimates the user's emotions and changes the theme of the virtual space generated based on the estimated user emotions. The system according to feature 1.

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