system

Abstract

An object of the system according to the embodiment is to make a preliminary view of a property by utilizing a high-quality 3D model or a VR technique without going to the site.SOLUTION: A system according to an embodiment includes an image generation unit, a VR providing unit, and a data analysis unit. The image generation unit generates a 3D model on the basis of photographs or drawings of the property. The VR providing unit provides a VR tour based on the 3D model generated by the image generating unit. The data analysis unit analyzes detailed data of the property.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] With conventional technology, remote viewings are popular in some areas, but there are issues such as the screen being small and difficult to see, or the need to go to the property in person.

[0005] The system of the embodiment aims to enable viewing of properties using high-quality 3D models and VR technology without visiting the site. [Means for solving the problem]

[0006] The system according to the embodiment includes an image generation unit, a VR provision unit, and a data analysis unit. The image generation unit generates a 3D model based on a photograph or drawing of the property. The VR provision unit provides a virtual viewing tour based on the 3D model generated by the image generation unit. The data analysis unit analyzes detailed data about the property. [Effects of the Invention]

[0007] The system of the embodiment allows users to view properties using high-quality 3D models and VR technology without visiting the site. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

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

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

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

[0017] 1, a 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also 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 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0024] 2, in the data processing device 12, a specific process 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" according to the technology of the present 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 process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0027] Note that a device other than the data processing device 12 may 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 a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The VirtuEstate system according to an embodiment of the present invention is a system that allows viewing of real estate properties using VR generated by a generation AI, without the need to actually visit the property. As a result, the VirtuEstate system can provide a realistic viewing experience through high-quality 3D models and reproductions of the surrounding environment, eliminating the need for both real estate agents and customers to visit the property.

[0029] The VirtuEstate system according to the embodiment includes an image generation unit, a VR provision unit, and a data analysis unit. The image generation unit generates a 3D model based on photographs or drawings of a property. For example, the image generation unit reproduces the interior spaces of the property, such as the living room, kitchen, and bathroom, in detail. The image generation unit can also reproduce the property's exterior and surrounding environment. The VR provision unit provides a virtual viewing tour based on the generated 3D model. For example, the VR provision unit allows customers to use a VR headset to experience the property as if they were actually visiting it. The VR provision unit also reproduces the surrounding environment, allowing customers to see the property's exterior and the surrounding area. The data analysis unit analyzes detailed property data. For example, the data analysis unit analyzes the property's floor plan, furniture arrangement, wall color, floor texture, and other information, and provides the results to customers. The data analysis unit also analyzes the property's surrounding environment and transportation access. As a result, the VirtuEstate system according to the embodiment allows users to experience a high-quality virtual viewing tour without visiting the property. For example, customers can view properties from home, while sales representatives can show them remotely from their offices.

[0030] The image generation unit can simulate not only the interior of a property, but also changes in light depending on the time of day or season. For example, when simulating the interior of a property, the image generation unit reproduces changes in light depending on the time of day. For example, it simulates the way light enters and the position of shadows during each time of day - morning, noon, evening, and night - and provides this to the user. The image generation unit also simulates changes in light depending on the season. For example, it reproduces the difference in the position of the sun and the intensity of light between summer and winter, allowing the user to experience the atmosphere of the property depending on the season. The image generation unit also simulates changes in weather. For example, it reproduces how the property looks under different weather conditions, such as sunny, cloudy, rainy, and snowy, and provides this to the user. This allows the user to experience the atmosphere of the property depending on the time of day or season.

[0031] The image generation unit can predict the past state of a property or its future state after renovation. The image generation unit, for example, recreates the past state of a property. For example, based on past photographs and drawings of a property that has been built for many years, it simulates the state of the property when it was first built and provides this to the user. The image generation unit also predicts the state of the property after future renovation. For example, based on drawings and design proposals for the renovation plan, it simulates the interior of the property after renovation and provides this to the user. The image generation unit also simulates the changes that a property undergoes over time. For example, it predicts the fading of walls and the degree of damage to floors in several years and provides this to the user. This allows the user to check the past and future states of the property.

[0032] The image generation unit can simulate not only the inside of a property but also the surrounding traffic conditions or noise levels in real time. The image generation unit, for example, simulates the traffic conditions around the property in real time. For example, it recreates the traffic volume and congestion during rush hour and provides this to the user. The image generation unit also simulates the noise level around the property in real time. For example, it recreates the noise level of a property located near a major road or railway and provides this to the user. The image generation unit also simulates the air quality around the property in real time. For example, it recreates the air quality of a property located near an industrial area or a busy downtown area and provides this to the user. This allows the user to check the surrounding environment of the property in detail.

[0033] The image generation unit can simulate different interior designs or furniture arrangements, allowing the user to customize the property to suit their preferences. For example, the image generation unit simulates different interior designs. For example, it recreates design styles such as modern, classic, and minimalist and provides them to the user. The image generation unit also simulates furniture arrangements. For example, it changes the arrangement of sofas and tables in a living room, allowing the user to find the optimal layout. The image generation unit also provides customizable interior options. For example, it changes the wall color or the type of flooring, allowing the user to customize the property to suit their preferences. This allows the user to customize the property to suit their preferences.

[0034] When a user focuses on a specific location during the VR viewing tour, the VR providing unit can automatically display detailed information or historical background about that location. For example, when a user focuses on a specific piece of furniture in the living room during the VR viewing tour, the VR providing unit automatically displays detailed information about that furniture, such as its brand, material, and where it was purchased. Also, when a user focuses on a specific piece of equipment in the kitchen during the VR viewing tour, the VR providing unit automatically displays information about how to use and maintain that equipment. Also, when a user focuses on the exterior of the property during the VR viewing tour, the VR providing unit automatically displays information about the building's history and architectural style. This allows the user to check detailed information and historical background about the property.

[0035] When the user touches specific furniture or equipment within the property during the VR viewing tour, the VR providing unit can simulate the feel of using it or how to operate it. For example, when the user touches a kitchen sink during the VR viewing tour, the VR providing unit simulates the feel of water flow and the feel of operating the faucet. For example, it reproduces the feel of water force and the feel of operating the temperature control. When the user touches a sofa in the living room during the VR viewing tour, the VR providing unit simulates the comfort of sitting and the softness of the cushions. For example, it reproduces the sofa's material and the rebound force of the cushions. When the user touches a shower in the bathroom during the VR viewing tour, the VR providing unit simulates the feel of water temperature and how to operate the shower head. For example, it reproduces the feel of adjusting the water temperature and the angle of the shower head. This allows the user to experience the feel of using and how to operate the furniture and equipment.

[0036] The VR providing unit can provide an open-world experience during the VR viewing tour, allowing the user to freely explore the exterior of the property or the surrounding environment. For example, the VR providing unit provides an open-world experience during the VR viewing tour, allowing the user to freely explore the exterior of the property. For example, the user can walk around the property and check out the surrounding area. The VR providing unit also provides an open-world experience during the VR viewing tour, allowing the user to freely explore the surrounding environment of the property. For example, the user can visit nearby parks or commercial facilities. The VR providing unit also provides an open-world experience during the VR viewing tour, allowing the user to freely explore the exterior of the property or the surrounding environment. For example, the user can check the access status of public transportation near the property. This allows the user to freely explore the exterior of the property or the surrounding environment.

[0037] The VR providing unit can add a function that allows multiple users to participate simultaneously during a VR viewing tour and view the property while communicating in real time. The VR providing unit provides, for example, a function that allows multiple users to participate simultaneously during a VR viewing tour and view the property while communicating in real time. For example, a property can be viewed with family or friends. The VR providing unit also provides a function that allows a real estate agent's sales representative to participate remotely during the VR viewing tour and answer customer questions in real time. For example, the customer can check any points of concern on the spot. The VR providing unit also provides a function that allows multiple users to participate simultaneously during a VR viewing tour and exchange opinions in real time. For example, users can share their impressions of the property with each other. This allows multiple users to view the property simultaneously and communicate in real time.

[0038] When analyzing detailed data about a property, the data analysis unit can also analyze technical information such as the durability or energy efficiency of building materials. For example, when analyzing detailed data about a property, the data analysis unit includes information about the durability of building materials in its analysis. For example, it evaluates the durability of wall materials and floor materials and provides the results to the user. When analyzing detailed data about a property, the data analysis unit also includes information about energy efficiency in its analysis. For example, it evaluates the performance of insulation materials and the energy efficiency of windows and provides the results to the user. When analyzing detailed data about a property, the data analysis unit also includes technical information in its analysis. For example, it evaluates the condition of electrical wiring and piping and provides the results to the user. This allows the user to understand the technical information about the property in detail.

[0039] When analyzing detailed data about a property, the data analysis unit can analyze the data including past repair history or future maintenance predictions. For example, when analyzing detailed data about a property, the data analysis unit analyzes the data including past repair history. For example, it evaluates the history of repairs and renovations that have been carried out in the past and provides this to the user. Furthermore, when analyzing detailed data about a property, the data analysis unit analyzes the data including future maintenance predictions. For example, it predicts maintenance that will be required in the future and provides this to the user. Furthermore, when analyzing detailed data about a property, the data analysis unit analyzes the data including repair history and maintenance predictions. For example, it integrates the past repair history and future maintenance predictions and provides this to the user. This allows the user to understand the repair history and maintenance predictions of the property.

[0040] When analyzing detailed property data, the data analysis unit can also include social information such as the crime rate in the surrounding area or the evaluation of schools. For example, when analyzing detailed property data, the data analysis unit may include information on the crime rate in the surrounding area. For example, the crime rate in the area surrounding the property may be evaluated and provided to the user. When analyzing detailed property data, the data analysis unit may also include information on the evaluation of the surrounding schools. For example, the evaluation and ranking of schools near the property may be evaluated and provided to the user. When analyzing detailed property data, the data analysis unit may also include social information in the analysis. For example, the safety and educational environment in the area surrounding the property may be evaluated and provided to the user. This allows the user to understand social information around the property.

[0041] The data analysis unit can simulate the performance of a property under different climatic conditions when analyzing detailed property data. For example, when analyzing detailed property data, the data analysis unit simulates the performance of the property under different climatic conditions. For example, it evaluates the insulation performance of the property in a hot and humid environment in summer and a cold environment in winter, and provides the results to the user. Furthermore, when analyzing detailed property data, the data analysis unit simulates the energy efficiency of the property under different climatic conditions. For example, it evaluates the efficiency of heating and cooling and energy consumption, and provides the results to the user. Furthermore, when analyzing detailed property data, the data analysis unit simulates the durability of the property under different climatic conditions. For example, it evaluates the deterioration of building materials due to humidity and ultraviolet rays, and provides the results to the user. This allows the user to understand the performance of the property under different climatic conditions.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] The VirtuEstate system can also be equipped with a voice recognition unit. The voice recognition unit can recognize questions and comments made by users during a viewing in real time and provide information accordingly. For example, if a user asks, "How big is this room?", the voice recognition unit will analyze the question and display information about the room's size. Also, if a user comments, "Are the kitchen facilities up to date?", the voice recognition unit will analyze the comment and provide detailed information about the kitchen facilities. This allows users to obtain the information they need in real time during a viewing.

[0044] The VirtuEstate system can further include a behavioral analysis unit that analyzes the user's behavioral history. The behavioral analysis unit analyzes the user's preferences and tendencies based on data on properties the user has previously viewed and areas in which the user has shown interest. For example, the behavioral analysis unit can analyze data on properties the user has previously viewed and identify the characteristics of properties that the user prefers. The behavioral analysis unit can also recommend properties to the user based on data on areas in which the user has shown interest. This allows the user to efficiently find properties that match their preferences.

[0045] The VirtuEstate system can further include a customization suggestion unit that customizes properties based on the user's preferences. The customization suggestion unit makes customization suggestions for the property's interior and facilities based on the user's preferences and lifestyle. For example, if the user has a pet, the system will suggest interior and facilities suitable for the pet. Also, if the user needs a home office, the system will suggest the optimal office space layout. This allows the user to find a property that suits their lifestyle.

[0046] The VirtuEstate system can further include a behavioral analysis unit that analyzes the user's behavioral history. The behavioral analysis unit analyzes the user's preferences and tendencies based on data on properties the user has previously viewed and areas in which the user has shown interest. For example, the behavioral analysis unit can analyze data on properties the user has previously viewed and identify the characteristics of properties that the user prefers. The behavioral analysis unit can also recommend properties to the user based on data on areas in which the user has shown interest. This allows the user to efficiently find properties that match their preferences.

[0047] The processing flow of the first embodiment will be briefly explained below.

[0048] Step 1: The image generation unit generates a 3D model based on photos or drawings of the property. For example, it can reproduce the interior spaces, such as the living room, kitchen, and bathroom, in detail, as well as the exterior and surrounding environment of the property. Step 2: The VR department provides a virtual tour based on the generated 3D model. For example, customers can use a VR headset to experience the property as if they were actually visiting it, and can see the exterior of the property and the surrounding area. Step 3: The data analysis department analyzes detailed property data, such as the layout, furniture placement, wall color, flooring texture, surrounding environment, and transportation access, and provides this information to the customer.

[0049] (Example 2) The VirtuEstate system according to an embodiment of the present invention is a system that allows viewing of real estate properties using VR generated by a generation AI, without the need to actually visit the property. As a result, the VirtuEstate system can provide a realistic viewing experience through high-quality 3D models and reproductions of the surrounding environment, eliminating the need for both real estate agents and customers to visit the property.

[0050] The VirtuEstate system according to the embodiment includes an image generation unit, a VR provision unit, and a data analysis unit. The image generation unit generates a 3D model based on photographs or drawings of a property. For example, the image generation unit reproduces the interior spaces of the property, such as the living room, kitchen, and bathroom, in detail. The image generation unit can also reproduce the property's exterior and surrounding environment. The VR provision unit provides a virtual viewing tour based on the generated 3D model. For example, the VR provision unit allows customers to use a VR headset to experience the property as if they were actually visiting it. The VR provision unit also reproduces the surrounding environment, allowing customers to see the property's exterior and the surrounding area. The data analysis unit analyzes detailed property data. For example, the data analysis unit analyzes the property's floor plan, furniture arrangement, wall color, floor texture, and other information, and provides the results to customers. The data analysis unit also analyzes the property's surrounding environment and transportation access. As a result, the VirtuEstate system according to the embodiment allows users to experience a high-quality virtual viewing tour without visiting the property. For example, customers can view properties from home, while sales representatives can show them remotely from their offices.

[0051] The image generation unit can simulate not only the interior of a property, but also changes in light depending on the time of day or season. For example, when simulating the interior of a property, the image generation unit reproduces changes in light depending on the time of day. For example, it simulates the way light enters and the position of shadows during each time of day - morning, noon, evening, and night - and provides this to the user. The image generation unit also simulates changes in light depending on the season. For example, it reproduces the difference in the position of the sun and the intensity of light between summer and winter, allowing the user to experience the atmosphere of the property depending on the season. The image generation unit also simulates changes in weather. For example, it reproduces how the property looks under different weather conditions, such as sunny, cloudy, rainy, and snowy, and provides this to the user. This allows the user to experience the atmosphere of the property depending on the time of day or season.

[0052] The image generation unit can predict the past state of a property or its future state after renovation. The image generation unit, for example, recreates the past state of a property. For example, based on past photographs and drawings of a property that has been built for many years, it simulates the state of the property when it was first built and provides this to the user. The image generation unit also predicts the state of the property after future renovation. For example, based on drawings and design proposals for the renovation plan, it simulates the interior of the property after renovation and provides this to the user. The image generation unit also simulates the changes that a property undergoes over time. For example, it predicts the fading of walls and the degree of damage to floors in several years and provides this to the user. This allows the user to check the past and future states of the property.

[0053] The image generation unit can use the emotion estimation function to analyze the emotions a user has toward a particular room or area and add visual effects that emphasize the property's appeal based on the emotions. For example, the image generation unit can use the emotion estimation function to analyze the emotions a user has toward a living room and add visual effects to elicit positive emotions. For example, warm-toned lighting and comfortable furniture arrangement can be simulated. The image generation unit can also use the emotion estimation function to analyze the emotions a user has toward a kitchen and add visual effects to make cooking more enjoyable. For example, bright lighting and spacious counter space can be simulated. The image generation unit can also use the emotion estimation function to analyze the emotions a user has toward a bathroom and add visual effects to provide a relaxing environment. For example, soft lighting and luxurious interior design can be simulated. This allows the appeal of the property to be emphasized based on the user's emotions.

[0054] The image generation unit can simulate not only the inside of a property but also the surrounding traffic conditions or noise levels in real time. The image generation unit, for example, simulates the traffic conditions around the property in real time. For example, it recreates the traffic volume and congestion during rush hour and provides this to the user. The image generation unit also simulates the noise level around the property in real time. For example, it recreates the noise level of a property located near a major road or railway and provides this to the user. The image generation unit also simulates the air quality around the property in real time. For example, it recreates the air quality of a property located near an industrial area or a busy downtown area and provides this to the user. This allows the user to check the surrounding environment of the property in detail.

[0055] The image generation unit can simulate different interior designs or furniture arrangements, allowing the user to customize the property to suit their preferences. For example, the image generation unit simulates different interior designs. For example, it recreates design styles such as modern, classic, and minimalist and provides them to the user. The image generation unit also simulates furniture arrangements. For example, it changes the arrangement of sofas and tables in a living room, allowing the user to find the optimal layout. The image generation unit also provides customizable interior options. For example, it changes the wall color or the type of flooring, allowing the user to customize the property to suit their preferences. This allows the user to customize the property to suit their preferences.

[0056] The image generation unit can use the emotion estimation function to monitor the emotions felt by the user in real time when viewing a property and provide a guide to elicit positive emotions. For example, the image generation unit uses the emotion estimation function to monitor the emotions felt by the user during the viewing in real time. For example, when the user feels excitement or joy, the image generation unit provides a guide to reinforce those emotions. The image generation unit also uses the emotion estimation function to detect in real time any anxieties or questions felt by the user during the viewing and provide information to resolve those anxieties or questions. For example, the image generation unit displays a detailed description of the property or FAQs. The image generation unit also uses the emotion estimation function to provide an interactive guide to elicit positive emotions felt by the user during the viewing. For example, the image generation unit displays additional information and recommended points about an area that the user finds interesting. This makes it possible to provide a guide to elicit positive emotions from the user.

[0057] When a user focuses on a specific location during the VR viewing tour, the VR providing unit can automatically display detailed information or historical background about that location. For example, when a user focuses on a specific piece of furniture in the living room during the VR viewing tour, the VR providing unit automatically displays detailed information about that furniture, such as its brand, material, and where it was purchased. Also, when a user focuses on a specific piece of equipment in the kitchen during the VR viewing tour, the VR providing unit automatically displays information about how to use and maintain that equipment. Also, when a user focuses on the exterior of the property during the VR viewing tour, the VR providing unit automatically displays information about the building's history and architectural style. This allows the user to check detailed information and historical background about the property.

[0058] When the user touches specific furniture or equipment within the property during the VR viewing tour, the VR providing unit can simulate the feel of using it or how to operate it. For example, when the user touches a kitchen sink during the VR viewing tour, the VR providing unit simulates the feel of water flow and the feel of operating the faucet. For example, it reproduces the feel of water force and the feel of operating the temperature control. When the user touches a sofa in the living room during the VR viewing tour, the VR providing unit simulates the comfort of sitting and the softness of the cushions. For example, it reproduces the sofa's material and the rebound force of the cushions. When the user touches a shower in the bathroom during the VR viewing tour, the VR providing unit simulates the feel of water temperature and how to operate the shower head. For example, it reproduces the feel of adjusting the water temperature and the angle of the shower head. This allows the user to experience the feel of using and how to operate the furniture and equipment.

[0059] The VR provision unit can use the emotion estimation function to provide music or images with a relaxing effect to reduce stress or anxiety felt by the user while viewing a property. For example, the VR provision unit can use the emotion estimation function to detect stress felt by the user while viewing a property and automatically play music with a relaxing effect. For example, it can play sounds of nature or classical music. The VR provision unit can also use the emotion estimation function to detect anxiety felt by the user while viewing a property and automatically display images with a relaxing effect. For example, it can play images of tranquil landscapes or the ocean. The VR provision unit can also use the emotion estimation function to provide guidance to reduce stress or anxiety felt by the user while viewing a property. For example, it can display advice on deep breathing or relaxation. This can reduce the user's stress and anxiety.

[0060] The VR providing unit can provide an open-world experience during the VR viewing tour, allowing the user to freely explore the exterior of the property or the surrounding environment. For example, the VR providing unit provides an open-world experience during the VR viewing tour, allowing the user to freely explore the exterior of the property. For example, the user can walk around the property and check out the surrounding area. The VR providing unit also provides an open-world experience during the VR viewing tour, allowing the user to freely explore the surrounding environment of the property. For example, the user can visit nearby parks or commercial facilities. The VR providing unit also provides an open-world experience during the VR viewing tour, allowing the user to freely explore the exterior of the property or the surrounding environment. For example, the user can check the access status of public transportation near the property. This allows the user to freely explore the exterior of the property or the surrounding environment.

[0061] The VR providing unit can add a function that allows multiple users to participate simultaneously during a VR viewing tour and view the property while communicating in real time. The VR providing unit provides, for example, a function that allows multiple users to participate simultaneously during a VR viewing tour and view the property while communicating in real time. For example, a property can be viewed with family or friends. The VR providing unit also provides a function that allows a real estate agent's sales representative to participate remotely during the VR viewing tour and answer customer questions in real time. For example, the customer can check any points of concern on the spot. The VR providing unit also provides a function that allows multiple users to participate simultaneously during a VR viewing tour and exchange opinions in real time. For example, users can share their impressions of the property with each other. This allows multiple users to view the property simultaneously and communicate in real time.

[0062] The VR provision unit can use the emotion estimation function to provide an interactive guide to enhance the positive emotions felt by the user while viewing a property. The VR provision unit, for example, uses the emotion estimation function to provide an interactive guide to enhance the positive emotions felt by the user while viewing a property. For example, the VR provision unit displays additional information and recommended points about an area that the user is interested in. The VR provision unit also uses the emotion estimation function to provide an interactive guide to enhance the positive emotions felt by the user while viewing a property. For example, the VR provision unit analyzes the emotions the user feels toward a particular room and adds visual effects that emphasize the appeal of the room. The VR provision unit also uses the emotion estimation function to provide an interactive guide to enhance the positive emotions felt by the user while viewing a property. For example, the VR provision unit analyzes the emotions the user feels toward the exterior of the property and the surrounding environment and adds visual effects that emphasize the appeal of the property based on the emotions. This makes it possible to provide an interactive guide that enhances the user's positive emotions.

[0063] When analyzing detailed data about a property, the data analysis unit can also analyze technical information such as the durability or energy efficiency of building materials. For example, when analyzing detailed data about a property, the data analysis unit includes information about the durability of building materials in its analysis. For example, it evaluates the durability of wall materials and floor materials and provides the results to the user. When analyzing detailed data about a property, the data analysis unit also includes information about energy efficiency in its analysis. For example, it evaluates the performance of insulation materials and the energy efficiency of windows and provides the results to the user. When analyzing detailed data about a property, the data analysis unit also includes technical information in its analysis. For example, it evaluates the condition of electrical wiring and piping and provides the results to the user. This allows the user to understand the technical information about the property in detail.

[0064] When analyzing detailed data about a property, the data analysis unit can analyze the data including past repair history or future maintenance predictions. For example, when analyzing detailed data about a property, the data analysis unit analyzes the data including past repair history. For example, it evaluates the history of repairs and renovations that have been carried out in the past and provides this to the user. Furthermore, when analyzing detailed data about a property, the data analysis unit analyzes the data including future maintenance predictions. For example, it predicts maintenance that will be required in the future and provides this to the user. Furthermore, when analyzing detailed data about a property, the data analysis unit analyzes the data including repair history and maintenance predictions. For example, it integrates the past repair history and future maintenance predictions and provides this to the user. This allows the user to understand the repair history and maintenance predictions of the property.

[0065] The data analysis unit can use the emotion estimation function to analyze the emotions a user has toward a specific design or color and propose an optimal interior design based on the emotions. For example, the data analysis unit can use the emotion estimation function to analyze the emotions a user has toward a specific design and propose an optimal interior design based on the emotions. For example, the data analysis unit customizes the interior based on a design style preferred by the user. The data analysis unit can also use the emotion estimation function to analyze the emotions a user has toward a specific color and propose optimal colors based on the emotions. For example, the data analysis unit can customize the interior based on colors that make the user feel relaxed. The data analysis unit can also use the emotion estimation function to analyze the emotions a user has toward a specific design or color and propose an optimal interior design based on the emotions. For example, the data analysis unit can customize the interior based on designs and colors that evoke positive emotions in the user. This makes it possible to propose an optimal interior design based on the user's emotions.

[0066] When analyzing detailed property data, the data analysis unit can also include social information such as the crime rate in the surrounding area or the evaluation of schools. For example, when analyzing detailed property data, the data analysis unit may include information on the crime rate in the surrounding area. For example, the crime rate in the area surrounding the property may be evaluated and provided to the user. When analyzing detailed property data, the data analysis unit may also include information on the evaluation of the surrounding schools. For example, the evaluation and ranking of schools near the property may be evaluated and provided to the user. When analyzing detailed property data, the data analysis unit may also include social information in the analysis. For example, the safety and educational environment in the area surrounding the property may be evaluated and provided to the user. This allows the user to understand social information around the property.

[0067] The data analysis unit can simulate the performance of a property under different climatic conditions when analyzing detailed property data. For example, when analyzing detailed property data, the data analysis unit simulates the performance of the property under different climatic conditions. For example, it evaluates the insulation performance of the property in a hot and humid environment in summer and a cold environment in winter, and provides the results to the user. Furthermore, when analyzing detailed property data, the data analysis unit simulates the energy efficiency of the property under different climatic conditions. For example, it evaluates the efficiency of heating and cooling and energy consumption, and provides the results to the user. Furthermore, when analyzing detailed property data, the data analysis unit simulates the durability of the property under different climatic conditions. For example, it evaluates the deterioration of building materials due to humidity and ultraviolet rays, and provides the results to the user. This allows the user to understand the performance of the property under different climatic conditions.

[0068] The data analysis unit can use the emotion estimation function to monitor the emotions of a user when viewing detailed property data in real time and provide information to elicit positive emotions. For example, the data analysis unit uses the emotion estimation function to monitor the emotions of a user when viewing detailed property data in real time. For example, when a user feels excitement or joy, it provides information to reinforce that emotion. The data analysis unit can also use the emotion estimation function to detect anxieties or questions that a user may have when viewing detailed property data in real time and provide information to resolve those anxieties or questions. For example, it can display detailed property descriptions and FAQs. The data analysis unit can also use the emotion estimation function to provide information to elicit positive emotions when a user views detailed property data. For example, it can display additional information or recommended points about an area that the user is interested in. This makes it possible to provide information that elicits positive emotions from the user.

[0069] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0070] The VirtuEstate system can also be equipped with a voice recognition unit. The voice recognition unit can recognize questions and comments made by users during a viewing in real time and provide information accordingly. For example, if a user asks, "How big is this room?", the voice recognition unit will analyze the question and display information about the room's size. Also, if a user comments, "Are the kitchen facilities up to date?", the voice recognition unit will analyze the comment and provide detailed information about the kitchen facilities. This allows users to obtain the information they need in real time during a viewing.

[0071] The VirtuEstate system can further include a behavioral analysis unit that analyzes the user's behavioral history. The behavioral analysis unit analyzes the user's preferences and tendencies based on data on properties the user has previously viewed and areas in which the user has shown interest. For example, the behavioral analysis unit can analyze data on properties the user has previously viewed and identify the characteristics of properties that the user prefers. The behavioral analysis unit can also recommend properties to the user based on data on areas in which the user has shown interest. This allows the user to efficiently find properties that match their preferences.

[0072] The VirtuEstate system can also include a health monitoring unit that monitors the user's health. The health monitoring unit monitors the user's heart rate and stress level in real time to manage the user's health during the viewing. For example, if the user's heart rate rises, the health monitoring unit will provide advice on how to relax. If the user's stress level is high, the health monitoring unit will provide music or images that have a relaxing effect. This allows the user to manage their health during the viewing and enjoy a comfortable experience.

[0073] The VirtuEstate system can further include a customization suggestion unit that customizes properties based on the user's preferences. The customization suggestion unit makes customization suggestions for the property's interior and facilities based on the user's preferences and lifestyle. For example, if the user has a pet, the system will suggest interior and facilities suitable for the pet. Also, if the user needs a home office, the system will suggest the optimal office space layout. This allows the user to find a property that suits their lifestyle.

[0074] The VirtuEstate system can also estimate the user's emotions and provide visual effects that emphasize the property's appeal based on the estimated emotions. For example, if the user has a positive emotion toward the living room, the system can simulate warm lighting and comfortable furniture arrangement to reinforce that emotion. If the user has a positive emotion toward the kitchen, the system can add visual effects for enjoying cooking. This allows the system to emphasize the property's appeal based on the user's emotions.

[0075] The VirtuEstate system can also estimate the user's emotions and provide guidance during the viewing based on the estimated emotions. For example, it can detect any anxieties or questions the user may have during the viewing in real time and provide information to resolve them. For example, it can display detailed property descriptions and FAQs. It can also provide interactive guides to elicit positive emotions felt by the user during the viewing. For example, it can display additional information and recommended points about areas that interest the user. This allows the viewing experience to be optimized based on the user's emotions.

[0076] The VirtuEstate system can also estimate the user's emotions and provide music or images with a relaxing effect based on the estimated emotions. For example, it can detect the stress the user feels while viewing a property and automatically play relaxing music, such as sounds of nature or classical music. It can also detect the anxiety the user feels while viewing a property and automatically display images with a relaxing effect, such as images of tranquil landscapes or the ocean. This can help reduce the user's stress and anxiety.

[0077] The VirtuEstate system can also estimate the user's emotions and suggest optimal interior designs based on the estimated emotions. For example, if a user has positive emotions toward a particular design, the system can customize the interior based on that design. Also, if a user has positive emotions toward a particular color, the system can customize the interior based on that color. This allows the system to suggest optimal interior designs based on the user's emotions.

[0078] The VirtuEstate system can also estimate a user's emotions and provide detailed property data based on the estimated emotions. For example, it can detect in real time any anxieties or questions a user may have while browsing detailed property data and provide information to resolve those concerns. For example, it can display detailed property descriptions and FAQs. It can also provide information to elicit positive emotions felt by users while browsing detailed property data. For example, it can display additional information and recommended points about areas that interest the user. This makes it possible to provide detailed property data based on the user's emotions.

[0079] The VirtuEstate system can further include a behavioral analysis unit that analyzes the user's behavioral history. The behavioral analysis unit analyzes the user's preferences and tendencies based on data on properties the user has previously viewed and areas in which the user has shown interest. For example, the behavioral analysis unit can analyze data on properties the user has previously viewed and identify the characteristics of properties that the user prefers. The behavioral analysis unit can also recommend properties to the user based on data on areas in which the user has shown interest. This allows the user to efficiently find properties that match their preferences.

[0080] The processing flow of the second embodiment will be briefly explained below.

[0081] Step 1: The image generation unit generates a 3D model based on photos or drawings of the property. For example, it can reproduce the interior spaces, such as the living room, kitchen, and bathroom, in detail, as well as the exterior and surrounding environment of the property. Step 2: The VR department provides a virtual tour based on the generated 3D model. For example, customers can use a VR headset to experience the property as if they were actually visiting it, and can see the exterior of the property and the surrounding area. Step 3: The data analysis department analyzes detailed property data, such as the layout, furniture placement, wall color, flooring texture, surrounding environment, and transportation access, and provides this information to the customer.

[0082] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the 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.

[0083] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0084] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, 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.

[0085] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0086] 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.

[0087] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0088] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0089] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0090] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0091] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0092] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0093] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0094] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0095] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0096] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0097] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0098] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0099] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0100] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0101] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0102] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0103] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0104] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0105] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0106] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0107] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0108] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0109] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0110] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0111] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0112] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0113] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0114] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0115] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0116] 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.

[0117] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0118] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0119] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0120] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0121] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0122] The control 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0123] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0124] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0125] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0126] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0127] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0128] 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 control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0129] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0130] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0131] The emotion identification model 59 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 an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0132] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0133] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0134] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0135] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0136] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0137] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0138] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0139] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0140] 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.

[0141] It is not necessary to store all 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 all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0142] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0143] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with 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). Also, the hardware resource that executes the specific process may be a single processor.

[0144] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0145] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0146] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0147] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0148] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0149] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. an image generation unit that generates a 3D model based on a photo or drawing of the property; a VR providing unit that provides a virtual preview tour based on the 3D model generated by the image generating unit; a data analysis unit that analyzes detailed data of the property; A system characterized by:

2. The image generation unit Simulate changes in light not only inside the property but also over time or seasons 2. The system of claim 1.

3. The image generation unit Predict the past condition of the property or its future condition after renovation 2. The system of claim 1.

4. The image generation unit Analyze how users feel about a particular room or area and add visual effects to highlight the property's appeal based on that emotion 2. The system of claim 1.

5. The image generation unit Simulate traffic conditions or noise levels in real time not only inside the property but also in the surrounding area 2. The system of claim 1.

6. The image generation unit Simulating different interior designs or furniture arrangements, allowing users to customize the property to their own tastes 2. The system of claim 1.

7. The image generation unit Monitor users' emotions in real time when viewing properties and provide guidance to elicit positive emotions.

2. The system of claim 1.

8. The VR providing unit: During a VR tour, when a user focuses on a specific location, detailed information or historical context for that location is automatically displayed.

2. The system of claim 1.

Images