System

Abstract

An object of a system according to an embodiment is to propose a utilization value of an empty house and to support securing a house at a reasonable price.SOLUTION: A system includes a collection unit, a generation unit, a proposal unit, and a moving image generation unit. The collection unit collects basic information and images of a floor plan or building frame information, a water section or facility state, and the age of the building. The generation unit generates a reformed image on the basis of the information collected by the collection unit. The proposal unit proposes a plan based on the image generated by the generation unit. The moving image generation unit provides the plan proposed by the proposal unit as a moving image with audio guide.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, it was difficult to effectively propose the value of utilizing vacant houses, and there were also challenges in securing housing at affordable prices.

[0005] The system according to the embodiment aims to propose the value of utilizing vacant houses and to support the acquisition of housing at an affordable price. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, a generation unit, a proposal unit, and a video generation unit. The collection unit collects basic information and video, such as floor plan or structural information, plumbing or equipment status, and age of the building. The generation unit generates an image of the post-renovation state based on the information collected by the collection unit. The proposal unit proposes a plan based on the image generated by the generation unit. The video generation unit provides the plan proposed by the proposal unit as a video with audio guidance. [Effects of the Invention]

[0007] The system according to the embodiment can propose the value of utilizing vacant houses and help people secure housing at affordable prices. [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) A system according to an embodiment of the present invention simultaneously solves three challenges facing Japan today. This system uses generative AI to create renovation images for owners of unused vacant homes and propose their potential uses. It also proposes homes with modern floor plans and designs at affordable prices to families with children. Furthermore, it promotes the rental and resale of vacant homes, avoiding abandonment, demolition, and increased tax burdens following legal revisions. For example, when a vacant home owner registers property information, the system uses generative AI to analyze the information and create an image of what the property will look like after renovation. This image is provided as a video with audio guidance, proposing potential uses for the property to the owner. Furthermore, when parents use the system, general artificial intelligence proposes multiple home plans based on their budget and desired conditions. This allows them to obtain homes that are appropriate for the times at an affordable price. Furthermore, to promote the rental and resale of vacant homes, the system analyzes market rental prices and market trends and proposes optimal rental and resale plans. This prevents abandonment and demolition of vacant homes and avoids increased tax burdens following legal revisions. This system allows owners of unused vacant homes to discover the potential uses of their properties and allows parents to obtain homes at affordable prices. It will also contribute to revitalizing the local area by promoting the rental and resale of vacant houses.

[0029] A system according to an embodiment includes a collection unit, a generation unit, a proposal unit, and a video generation unit. The collection unit collects basic information and videos, such as floor plan or structural information, plumbing or equipment status, and age of the property. For example, the collection unit can collect floor plans, photos, and videos of the property. The collection unit can also collect information on the age of the property and the condition of the equipment. The generation unit generates an image of the property after renovation based on the information collected by the collection unit. For example, the generation unit uses a generation AI to generate an image of the property after renovation as a CG image or 3D model. The proposal unit proposes a plan based on the image generated by the generation unit. For example, the proposal unit can propose a blueprint and cost estimate for the renovation. The video generation unit provides the plan proposed by the proposal unit as a video with audio guidance. For example, the video generation unit generates a narrated video tour and provides it to the user. This enables the system according to an embodiment to efficiently summarize, cluster, investigate trends, and extract elements from users' ideas.

[0030] The collection unit can analyze market rents or market trends. For example, the collection unit can analyze past data and analyze market rents. The collection unit can also analyze current market trends and grasp market trends for properties. This enables more appropriate information collection by analyzing market rents and market trends. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input past rent data into AI, which then analyzes market rents.

[0031] The proposal unit can propose a rental plan or a resale plan. For example, the proposal unit can propose a rental plan based on rental conditions. The proposal unit can also propose a resale plan based on the resale price. This promotes the utilization of vacant houses by proposing rental plans or resale plans. Some or all of the above-mentioned processing in the proposal unit may be performed using AI, for example, or may be performed without using AI. For example, the proposal unit can input rental conditions and resale prices into AI, which can then propose the optimal plan.

[0032] The generation unit can generate an image of the post-renovation state using a generation AI. The generation unit can generate an image of the post-renovation state using, for example, deep learning. The generation unit can also generate an image of the post-renovation state using a GAN (generative artificial network). This improves the accuracy of the image of the post-renovation state by using the generation AI. Some or all of the above-mentioned processing in the generation unit is performed using the generation AI. For example, the generation unit inputs information about the property into the generation AI, and the generation AI generates an image of the post-renovation state.

[0033] The video generation unit can generate a video with audio guidance. The video generation unit can generate, for example, a video tour with narration. The video generation unit can also generate a video with audio guidance based on an image of the property after renovation. By generating a video with audio guidance, it is possible to provide users with easy-to-understand information. Some or all of the above-mentioned processing in the video generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the video generation unit can input an image of the property after renovation into AI, which then generates a video with audio guidance.

[0034] The proposal unit can propose a home with a floor plan or design to the child-rearing generation. The proposal unit can, for example, propose a floor plan that emphasizes safety. The proposal unit can also propose a home with a design that emphasizes convenience. This makes it possible to propose an appropriate home to the child-rearing generation. Some or all of the above-mentioned processing in the proposal unit may be performed using AI, for example, or may be performed without using AI. For example, the proposal unit can input the desired conditions of the child-rearing generation into AI, which can then propose a home with the optimal floor plan and design.

[0035] The collection unit can collect information based on the property's past repair history. For example, the collection unit references the property's past repair history and collects detailed information on areas where repairs have been performed. The collection unit can also identify areas that need repair from the repair history and collect the current state of those areas. Furthermore, the collection unit can collect information for evaluating the property's condition, taking into account the time and content of the repairs based on the repair history. This makes it possible to collect more accurate property information by taking into account the past repair history. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit inputs the property's repair history data into AI, which analyzes the repair history and collects the necessary information.

[0036] The collection unit can simultaneously collect information on the surrounding environment of the property. For example, the collection unit collects information on transportation access around the property and identifies the location of the nearest station or bus stop. The collection unit can also collect information on schools and hospitals around the property and evaluate the convenience for the child-rearing generation. Furthermore, the collection unit can collect information on commercial facilities and parks around the property and evaluate the quality of the living environment. In this way, by simultaneously collecting surrounding environment information, a comprehensive evaluation of the property becomes possible. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit inputs surrounding environment data of the property into AI, which analyzes the surrounding environment information and collects the necessary information.

[0037] The collection unit can collect information on the energy efficiency and environmental impact of a property. For example, the collection unit collects data on past electricity consumption and gas usage to evaluate the energy efficiency of the property. The collection unit can also collect information on the type of building materials and their recyclability to evaluate the environmental impact of the property. Furthermore, the collection unit can collect renovation history and proposals for improving the energy efficiency of the property. In this way, by collecting information on energy efficiency and environmental impact, the environmental performance of the property can be evaluated. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit inputs the energy consumption data of the property into AI, which analyzes the energy efficiency and collects the necessary information.

[0038] The collection unit can prioritize collecting highly relevant information based on the geographical location information of the property. For example, the collection unit prioritizes collecting information on the nearest public transportation based on the geographical location information of the property. The collection unit can also prioritize collecting information on surrounding commercial facilities and public facilities based on the geographical location information of the property. Furthermore, the collection unit can also prioritize collecting information on local public safety and the environment based on the geographical location information of the property. This allows for efficient collection of highly relevant information by taking geographical location information into consideration. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit inputs the geographical location data of the property into AI, which then prioritizes collecting highly relevant information.

[0039] The collection unit can analyze the social media activity of the property owner and collect related information. For example, the collection unit collects information about the characteristics and condition of the property based on information posted publicly by the property owner on social media. The collection unit can also evaluate the interior and exterior condition of the property based on photos and videos shared by the property owner on social media. Furthermore, the collection unit can collect information about repairs and renovations mentioned by the property owner on social media. In this way, detailed information about the property can be collected by analyzing social media activity. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit inputs the property owner's social media data into AI, which then collects related information.

[0040] The collection unit can customize the collection method based on past feedback about the property. For example, the collection unit adjusts the type and amount of information to be collected based on past feedback about the property. The collection unit can also change the priority of the information to be collected based on past feedback about the property. Furthermore, the collection unit can improve the collection method and collect information more efficiently based on past feedback about the property. In this way, the collection method can be optimized by reflecting past feedback. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit inputs past feedback data about the property into AI, which then customizes the collection method.

[0041] The generation unit can adjust the level of detail of the renovation image based on the importance of the property. For example, the generation unit generates a detailed renovation image for a property with a high level of importance. The generation unit can also generate a simplified renovation image for a property with a low level of importance. Furthermore, the generation unit can adjust whether to express the details of the renovation image or prioritize the overall image, depending on the importance of the property. This makes it possible to provide appropriate information by adjusting the level of detail of the renovation image depending on the importance of the property. Some or all of the above-mentioned processing in the generation unit is performed using a generation AI. For example, the generation unit inputs property importance data into the generation AI, which then adjusts the level of detail of the renovation image.

[0042] The generation unit can apply different generation algorithms depending on the property category. For example, for residential properties, the generation unit generates a renovation image that emphasizes the comfort of the living space. For commercial properties, the generation unit can also generate a renovation image that emphasizes business efficiency. Furthermore, for public facilities, the generation unit can also generate a renovation image that emphasizes user convenience. In this way, by applying a generation algorithm according to the property category, more appropriate renovation images can be provided. Some or all of the above-mentioned processing in the generation unit is performed using a generation AI. For example, the generation unit inputs property category data into the generation AI, and the generation AI applies an appropriate generation algorithm to generate a renovation image.

[0043] The generation unit can improve the accuracy of generation by referring to the user's past renovation images. For example, the generation unit uses the generation AI to learn the user's preferences and tendencies based on renovation images generated by the user in the past, and generates more accurate renovation images. The generation unit can also analyze the user's past renovation images and generate renovation images that incorporate common elements. Furthermore, the generation unit can refer to the user's past renovation images, and the generation AI can generate renovation images that meet the user's expectations. In this way, by referring to past renovation images, the accuracy of generation is improved. Some or all of the above-mentioned processes in the generation unit are performed using the generation AI. For example, the generation unit inputs the user's past renovation image data into the generation AI, and the generation AI uses it as a reference to generate a new renovation image.

[0044] The generation unit can determine the priority of renovation images based on the age of the property. For example, for older properties, the generation unit prioritizes areas that are most in need of renovation in the renovation images. In addition, for newer properties, the generation unit can also prioritize design changes and added functions in the renovation images. Furthermore, the generation unit can adjust the level of detail and priority of the renovation images according to the age of the property. This makes it possible to provide appropriate information by determining the priority of renovation images according to the age of the property. Some or all of the above-mentioned processing in the generation unit is performed using a generation AI. For example, the generation unit inputs data on the age of the property into the generation AI, and the generation AI determines the priority of the renovation images.

[0045] The generation unit can adjust the order of the renovation images based on the relevance of the property. For example, the generation unit prioritizes reflecting highly relevant parts of the property in the renovation images and adjusts the order. The generation unit can also place less relevant parts of the property later in the renovation images. Furthermore, the generation unit can optimize the order of the renovation images according to the relevance of the property. This makes it possible to provide more appropriate information by adjusting the order of the renovation images according to the relevance of the property. Some or all of the above-mentioned processing in the generation unit is performed using a generation AI. For example, the generation unit inputs property relevance data into the generation AI, which then adjusts the order of the renovation images.

[0046] The generation unit can adjust the use of technical terms in the renovation image according to the user's level of expertise. For example, if the user has technical expertise, the generation unit can generate a renovation image that uses a lot of technical terms. Alternatively, if the user does not have technical expertise, the generation unit can avoid technical terms and generate an easy-to-understand renovation image. Furthermore, the generation unit can adjust the way the renovation image is explained according to the user's level of expertise. This makes it possible to provide more understandable information by adjusting the use of technical terms according to the user's level of expertise. Some or all of the above-mentioned processing in the generation unit is performed using a generation AI. For example, the generation unit inputs the user's level of expertise data into the generation AI, which then adjusts the use of technical terms.

[0047] The suggestion unit can adjust the level of detail of the proposal based on the importance of the property. For example, the suggestion unit makes a detailed proposal for a property with a high level of importance. The suggestion unit can also make a simplified proposal for a property with a low level of importance. Furthermore, the suggestion unit can adjust whether to express the details of the proposal or to prioritize an overall proposal, depending on the importance of the property. This makes it possible to provide appropriate information by adjusting the level of detail of the proposal depending on the importance of the property. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, or may be performed without using AI. For example, the suggestion unit inputs property importance data into AI, and the AI ​​adjusts the level of detail of the proposal.

[0048] The suggestion unit can apply different suggestion algorithms depending on the property category. For example, for residential properties, the suggestion unit makes suggestions that emphasize the comfort of the living space. For commercial properties, the suggestion unit can also make suggestions that emphasize business efficiency. Furthermore, for public facilities, the suggestion unit can also make suggestions that emphasize user convenience. This enables more appropriate suggestions to be made by applying a suggestion algorithm depending on the property category. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, or may be performed without using AI. For example, the suggestion unit inputs property category data into AI, which then applies an appropriate suggestion algorithm to make suggestions.

[0049] The suggestion unit can improve the accuracy of suggestions by referring to the user's past suggestion results. For example, the suggestion unit learns the user's preferences and tendencies based on suggestions the user has received in the past, and makes more accurate suggestions. The suggestion unit can also analyze the user's past suggestion results and make suggestions that incorporate common elements. Furthermore, the suggestion unit can make suggestions that meet the user's expectations by referring to the user's past suggestion results. In this way, the accuracy of suggestions is improved by referring to the past suggestion results. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, or may be performed without using AI. For example, the suggestion unit inputs the user's past suggestion result data into AI, and the AI ​​uses the data to make new suggestions.

[0050] The proposal unit can determine the priority of proposals based on the time of submission of the property. For example, the proposal unit prioritizes proposals for properties submitted early. The proposal unit can also postpone proposals for properties submitted late. Furthermore, the proposal unit can adjust the priority of proposals according to the time of submission. This makes it possible to provide appropriate information by determining the priority of proposals according to the time of submission. Some or all of the above-mentioned processing in the proposal unit may be performed using AI, or may be performed without using AI. For example, the proposal unit inputs property submission time data into AI, and the AI ​​determines the priority of proposals.

[0051] The suggestion unit can adjust the order of suggestions based on the relevance of the property. For example, the suggestion unit prioritizes suggesting highly relevant parts of the property and adjusts the order. The suggestion unit can also place less relevant parts of the property later in the suggestions. Furthermore, the suggestion unit can optimize the order of suggestions based on the relevance of the property. This makes it possible to provide more appropriate information by adjusting the order of suggestions based on the relevance of the property. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, or may be performed without using AI. For example, the suggestion unit inputs property relevance data into AI, and the AI ​​adjusts the order of suggestions.

[0052] The suggestion unit can adjust the use of technical terms in the suggestions depending on the user's level of expertise. For example, if the user has technical expertise, the suggestion unit can make suggestions that use a lot of technical terms. Furthermore, if the user does not have technical expertise, the suggestion unit can avoid technical terms and make suggestions that are easier to understand. Furthermore, the suggestion unit can adjust the explanation method of the suggestions depending on the user's level of expertise. This makes it possible to provide more understandable information by adjusting the use of technical terms depending on the user's level of expertise. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, or may be performed without using AI. For example, the suggestion unit inputs the user's level of expertise data into AI, which then adjusts the use of technical terms.

[0053] The video generation unit can adjust the level of detail of the video based on the importance of the property. For example, the video generation unit generates a detailed video for a property with a high level of importance. The video generation unit can also generate a simplified video for a property with a low level of importance. Furthermore, the video generation unit can adjust whether to show the details of the video or prioritize the overall video, depending on the importance of the property. This makes it possible to provide appropriate information by adjusting the level of detail of the video depending on the importance of the property. Some or all of the above-mentioned processing in the video generation unit may be performed using AI, or may be performed without using AI. For example, the video generation unit inputs property importance data into AI, and the AI ​​adjusts the level of detail of the video.

[0054] The video generation unit can apply different video generation algorithms depending on the property category. For example, for residential properties, the video generation unit generates videos that emphasize the comfort of the living space. For commercial properties, the video generation unit can also generate videos that emphasize business efficiency. Furthermore, for public facilities, the video generation unit can also generate videos that emphasize user convenience. This makes it possible to provide more appropriate information by applying a video generation algorithm according to the property category. Some or all of the above-mentioned processing in the video generation unit may be performed using AI, or may be performed without using AI. For example, the video generation unit inputs property category data into AI, and the AI ​​applies an appropriate video generation algorithm to generate a video.

[0055] The video generation unit can improve the accuracy of the video by referring to the user's past video viewing history. For example, the video generation unit learns the user's preferences and tendencies based on videos the user has viewed in the past, and generates a more accurate video. The video generation unit can also analyze the user's past video viewing history and generate a video that incorporates common elements. Furthermore, the video generation unit can also generate a video that meets the user's expectations by referring to the user's past video viewing history. In this way, by referring to the past video viewing history, the accuracy of the video is improved. Some or all of the above-mentioned processing in the video generation unit may be performed using AI, or may be performed without using AI. For example, the video generation unit inputs the user's past video viewing history data into AI, which then uses the data to generate a new video.

[0056] The video generation unit can determine the priority of videos based on the time of submission of the property. For example, the video generation unit prioritizes the generation of videos for properties submitted early. The video generation unit can also postpone the generation of videos for properties submitted late. Furthermore, the video generation unit can adjust the priority of videos according to the time of submission. This makes it possible to provide appropriate information by determining the priority of videos according to the time of submission. Some or all of the above-mentioned processing in the video generation unit may be performed using AI, or may be performed without using AI. For example, the video generation unit inputs data on the time of submission of properties into AI, and the AI ​​determines the priority of videos.

[0057] The video generation unit can adjust the order of videos based on the relevance of the property. For example, the video generation unit prioritizes reflecting parts of the property that are highly relevant in the video and adjusts the order. The video generation unit can also place parts of the property that are less relevant in the latter half of the video. Furthermore, the video generation unit can optimize the order of videos based on the relevance of the property. This makes it possible to provide more appropriate information by adjusting the order of videos based on the relevance of the property. Some or all of the above-mentioned processing in the video generation unit may be performed using AI, or may be performed without using AI. For example, the video generation unit inputs property relevance data into AI, and the AI ​​adjusts the order of the videos.

[0058] The video generation unit can adjust the use of technical terms in the video according to the user's level of expertise. For example, if the user has technical expertise, the video generation unit generates a video that uses a lot of technical terms. Furthermore, if the user does not have technical expertise, the video generation unit can avoid technical terms and generate an easy-to-understand video. Furthermore, the video generation unit can adjust the explanation method of the video according to the user's level of expertise. This makes it possible to provide information that is easier to understand by adjusting the use of technical terms according to the user's level of expertise. Some or all of the above-mentioned processing in the video generation unit may be performed using AI, or may be performed without using AI. For example, the video generation unit inputs the user's level of expertise data into AI, which then adjusts the use of technical terms.

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

[0060] The collection unit can also collect crime rates and public safety information around the property. For example, the collection unit can collect public police data and local crime prevention information to evaluate the safety of the property. The collection unit can also analyze past crime histories and evaluate the risks of the property's surrounding environment. Furthermore, the collection unit can analyze local residents' word-of-mouth and social media posts to collect real-time information on public safety. By providing information on the safety of the property, users can select a property with peace of mind.

[0061] The suggestion unit can also propose rental plans and resale plans customized based on the user's lifestyle. For example, if the user likes outdoor activities, the suggestion unit can suggest properties with abundant parks and natural environments nearby. Also, if the user has a pet, the suggestion unit can suggest pet-friendly properties. Furthermore, if the user works remotely, the suggestion unit can suggest properties with good internet connections or quiet environments. This allows the user to make a more satisfying selection by suggesting properties that suit their lifestyle.

[0062] The generation unit can also generate renovation images to improve the energy efficiency of a property. For example, the generation unit can propose renovation plans to increase energy efficiency, such as installing solar panels or adding insulation. The generation unit can also generate renovation images that incorporate smart home technology. Furthermore, the generation unit can generate renovation images that take into account designs and equipment placement to minimize energy consumption. This allows for the realization of environmentally friendly homes by providing renovation plans that improve energy efficiency.

[0063] The video generation unit can also generate videos that introduce the history and cultural background of a property. For example, the video generation unit can explain when the property was built and the architectural style of that era. The video generation unit can also introduce how the property has been used in the past and historical events in the area. Furthermore, the video generation unit can also introduce historical landmarks and cultural facilities in the vicinity of the property. This allows users to understand the history and cultural background of the property, making it possible to more deeply convey the appeal of the property to the user.

[0064] The proposal unit can also propose a home plan based on the user's health condition. For example, if the user has allergies, the proposal unit can propose a home plan that uses materials and equipment that suppress allergens. In addition, if the user is elderly, the proposal unit can also propose a home plan that emphasizes barrier-free design and safety. Furthermore, if the user prioritizes fitness, the proposal unit can also propose a home gym space or a floor plan suitable for exercise. This makes it possible to realize a more comfortable living environment by providing a home plan that takes the user's health condition into consideration.

[0065] The processing flow of the first embodiment will be briefly explained below.

[0066] Step 1: The collection unit collects basic information and footage, such as floor plan or structural information, plumbing or equipment status, and age of the building. For example, the collection unit collects floor plans, photos, and videos of the property, as well as information on the age of the property and the condition of its equipment. Step 2: The generation unit generates an image of the property after renovation based on the information collected by the collection unit. For example, the generation unit uses a generative AI to generate an image of the property after renovation as a CG image or 3D model. Step 3: The proposal unit proposes a plan based on the image generated by the generation unit. For example, the proposal unit can propose a blueprint and cost estimate for a renovation. Step 4: The video generation unit provides the plan proposed by the proposal unit as a video with audio guidance. For example, the video generation unit generates a video tour with narration and provides it to the user.

[0067] (Example 2) A system according to an embodiment of the present invention simultaneously solves three challenges facing Japan today. This system uses generative AI to create renovation images for owners of unused vacant homes and propose their potential uses. It also proposes homes with modern floor plans and designs at affordable prices to families with children. Furthermore, it promotes the rental and resale of vacant homes, avoiding abandonment, demolition, and increased tax burdens following legal revisions. For example, when a vacant home owner registers property information, the system uses generative AI to analyze the information and create an image of what the property will look like after renovation. This image is provided as a video with audio guidance, proposing potential uses for the property to the owner. Furthermore, when parents use the system, general artificial intelligence proposes multiple home plans based on their budget and desired conditions. This allows them to obtain homes that are appropriate for the times at an affordable price. Furthermore, to promote the rental and resale of vacant homes, the system analyzes market rental prices and market trends and proposes optimal rental and resale plans. This prevents abandonment and demolition of vacant homes and avoids increased tax burdens following legal revisions. This system allows owners of unused vacant homes to discover the potential uses of their properties and allows parents to obtain homes at affordable prices. It will also contribute to revitalizing the local area by promoting the rental and resale of vacant houses.

[0068] A system according to an embodiment includes a collection unit, a generation unit, a proposal unit, and a video generation unit. The collection unit collects basic information and videos, such as floor plan or structural information, plumbing or equipment status, and age of the property. For example, the collection unit can collect floor plans, photos, and videos of the property. The collection unit can also collect information on the age of the property and the condition of the equipment. The generation unit generates an image of the property after renovation based on the information collected by the collection unit. For example, the generation unit uses a generation AI to generate an image of the property after renovation as a CG image or 3D model. The proposal unit proposes a plan based on the image generated by the generation unit. For example, the proposal unit can propose a blueprint and cost estimate for the renovation. The video generation unit provides the plan proposed by the proposal unit as a video with audio guidance. For example, the video generation unit generates a narrated video tour and provides it to the user. This enables the system according to an embodiment to efficiently summarize, cluster, investigate trends, and extract elements from users' ideas.

[0069] The collection unit can analyze market rents or market trends. For example, the collection unit can analyze past data and analyze market rents. The collection unit can also analyze current market trends and grasp market trends for properties. This enables more appropriate information collection by analyzing market rents and market trends. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input past rent data into AI, which then analyzes market rents.

[0070] The proposal unit can propose a rental plan or a resale plan. For example, the proposal unit can propose a rental plan based on rental conditions. The proposal unit can also propose a resale plan based on the resale price. This promotes the utilization of vacant houses by proposing rental plans or resale plans. Some or all of the above-mentioned processing in the proposal unit may be performed using AI, for example, or may be performed without using AI. For example, the proposal unit can input rental conditions and resale prices into AI, which can then propose the optimal plan.

[0071] The generation unit can generate an image of the post-renovation state using a generation AI. The generation unit can generate an image of the post-renovation state using, for example, deep learning. The generation unit can also generate an image of the post-renovation state using a GAN (generative artificial network). This improves the accuracy of the image of the post-renovation state by using the generation AI. Some or all of the above-mentioned processing in the generation unit is performed using the generation AI. For example, the generation unit inputs information about the property into the generation AI, and the generation AI generates an image of the post-renovation state.

[0072] The video generation unit can generate a video with audio guidance. The video generation unit can generate, for example, a video tour with narration. The video generation unit can also generate a video with audio guidance based on an image of the property after renovation. By generating a video with audio guidance, it is possible to provide users with easy-to-understand information. Some or all of the above-mentioned processing in the video generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the video generation unit can input an image of the property after renovation into AI, which then generates a video with audio guidance.

[0073] The proposal unit can propose a home with a floor plan or design to the child-rearing generation. The proposal unit can, for example, propose a floor plan that emphasizes safety. The proposal unit can also propose a home with a design that emphasizes convenience. This makes it possible to propose an appropriate home to the child-rearing generation. Some or all of the above-mentioned processing in the proposal unit may be performed using AI, for example, or may be performed without using AI. For example, the proposal unit can input the desired conditions of the child-rearing generation into AI, which can then propose a home with the optimal floor plan and design.

[0074] The collection unit can estimate the user's emotions and adjust the timing of information collection based on the estimated user emotions. For example, when the user is stressed, the collection unit reduces the frequency of information collection and collects information when the user is relaxed. Furthermore, when the user is relaxed, the collection unit can increase the frequency of information collection and collect detailed information. Furthermore, when the user is in a hurry, the collection unit can quickly collect information and prioritize collecting the minimum amount of information necessary. This enables more appropriate information collection by adjusting the timing of information collection according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit inputs the user's facial expression data into the generation AI, which then estimates the user's emotions and adjusts the timing of information collection based on the estimated emotions.

[0075] The collection unit can collect information based on the property's past repair history. For example, the collection unit references the property's past repair history and collects detailed information on areas where repairs have been performed. The collection unit can also identify areas that need repair from the repair history and collect the current state of those areas. Furthermore, the collection unit can collect information for evaluating the property's condition, taking into account the time and content of the repairs based on the repair history. This makes it possible to collect more accurate property information by taking into account the past repair history. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit inputs the property's repair history data into AI, which analyzes the repair history and collects the necessary information.

[0076] The collection unit can simultaneously collect information on the surrounding environment of the property. For example, the collection unit collects information on transportation access around the property and identifies the location of the nearest station or bus stop. The collection unit can also collect information on schools and hospitals around the property and evaluate the convenience for the child-rearing generation. Furthermore, the collection unit can collect information on commercial facilities and parks around the property and evaluate the quality of the living environment. In this way, by simultaneously collecting surrounding environment information, a comprehensive evaluation of the property becomes possible. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit inputs surrounding environment data of the property into AI, which analyzes the surrounding environment information and collects the necessary information.

[0077] The collection unit can collect information on the energy efficiency and environmental impact of a property. For example, the collection unit collects data on past electricity consumption and gas usage to evaluate the energy efficiency of the property. The collection unit can also collect information on the type of building materials and their recyclability to evaluate the environmental impact of the property. Furthermore, the collection unit can collect renovation history and proposals for improving the energy efficiency of the property. In this way, by collecting information on energy efficiency and environmental impact, the environmental performance of the property can be evaluated. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit inputs the energy consumption data of the property into AI, which analyzes the energy efficiency and collects the necessary information.

[0078] The collection unit can estimate the user's emotions and determine the priority of information to be collected based on the estimated user emotions. For example, when the user is feeling stressed, the collection unit prioritizes collecting important information and postpones detailed information. Furthermore, when the user is relaxed, the collection unit can prioritize collecting detailed information and provide it to the user. Furthermore, when the user is in a hurry, the collection unit can prioritize collecting information that can be collected quickly and provide the minimum necessary information. This enables more appropriate information to be provided by determining the priority of information according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit inputs the user's facial expression data into a generation AI, which then estimates the user's emotions and determines the priority of information based on the estimated emotions.

[0079] The collection unit can prioritize collecting highly relevant information based on the geographical location information of the property. For example, the collection unit prioritizes collecting information on the nearest public transportation based on the geographical location information of the property. The collection unit can also prioritize collecting information on surrounding commercial facilities and public facilities based on the geographical location information of the property. Furthermore, the collection unit can also prioritize collecting information on local public safety and the environment based on the geographical location information of the property. This allows for efficient collection of highly relevant information by taking geographical location information into consideration. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit inputs the geographical location data of the property into AI, which then prioritizes collecting highly relevant information.

[0080] The collection unit can analyze the social media activity of the property owner and collect related information. For example, the collection unit collects information about the characteristics and condition of the property based on information posted publicly by the property owner on social media. The collection unit can also evaluate the interior and exterior condition of the property based on photos and videos shared by the property owner on social media. Furthermore, the collection unit can collect information about repairs and renovations mentioned by the property owner on social media. In this way, detailed information about the property can be collected by analyzing social media activity. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit inputs the property owner's social media data into AI, which then collects related information.

[0081] The collection unit can customize the collection method based on past feedback about the property. For example, the collection unit adjusts the type and amount of information to be collected based on past feedback about the property. The collection unit can also change the priority of the information to be collected based on past feedback about the property. Furthermore, the collection unit can improve the collection method and collect information more efficiently based on past feedback about the property. In this way, the collection method can be optimized by reflecting past feedback. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit inputs past feedback data about the property into AI, which then customizes the collection method.

[0082] The generation unit can estimate the user's emotions and adjust the way the renovation image is presented based on the estimated user emotions. For example, if the user is relaxed, the generation unit can generate a renovation image with soft colors and a calm design. If the user is excited, the generation unit can also generate a renovation image with vivid colors and a bold design. If the user is stressed, the generation unit can also generate a renovation image with a simple and calm design. This allows the user to provide a more appropriate image by adjusting the way the renovation image is presented based on the user's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the generation unit is performed using the generation AI. For example, the generation unit inputs the user's facial expression data into the generation AI, which then estimates the user's emotions and adjusts the way the renovation image is presented based on the emotions.

[0083] The generation unit can adjust the level of detail of the renovation image based on the importance of the property. For example, the generation unit generates a detailed renovation image for a property with a high level of importance. The generation unit can also generate a simplified renovation image for a property with a low level of importance. Furthermore, the generation unit can adjust whether to express the details of the renovation image or prioritize the overall image, depending on the importance of the property. This makes it possible to provide appropriate information by adjusting the level of detail of the renovation image depending on the importance of the property. Some or all of the above-mentioned processing in the generation unit is performed using a generation AI. For example, the generation unit inputs property importance data into the generation AI, which then adjusts the level of detail of the renovation image.

[0084] The generation unit can apply different generation algorithms depending on the property category. For example, for residential properties, the generation unit generates a renovation image that emphasizes the comfort of the living space. For commercial properties, the generation unit can also generate a renovation image that emphasizes business efficiency. Furthermore, for public facilities, the generation unit can also generate a renovation image that emphasizes user convenience. In this way, by applying a generation algorithm according to the property category, more appropriate renovation images can be provided. Some or all of the above-mentioned processing in the generation unit is performed using a generation AI. For example, the generation unit inputs property category data into the generation AI, and the generation AI applies an appropriate generation algorithm to generate a renovation image.

[0085] The generation unit can improve the accuracy of generation by referring to the user's past renovation images. For example, the generation unit uses the generation AI to learn the user's preferences and tendencies based on renovation images generated by the user in the past, and generates more accurate renovation images. The generation unit can also analyze the user's past renovation images and generate renovation images that incorporate common elements. Furthermore, the generation unit can refer to the user's past renovation images, and the generation AI can generate renovation images that meet the user's expectations. In this way, by referring to past renovation images, the accuracy of generation is improved. Some or all of the above-mentioned processes in the generation unit are performed using the generation AI. For example, the generation unit inputs the user's past renovation image data into the generation AI, and the generation AI uses it as a reference to generate a new renovation image.

[0086] The generation unit can estimate the user's emotions and adjust the length of the renovation image based on the estimated user emotions. For example, if the user is in a hurry, the generation unit can generate a short, to-the-point renovation image. If the user is relaxed, the generation unit can also generate a longer renovation image with detailed explanations. Furthermore, if the user is excited, the generation unit can generate a renovation image with visually stimulating effects. This allows for more appropriate information provision by adjusting the length of the renovation image according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit is performed using the generation AI. For example, the generation unit inputs the user's facial expression data into the generation AI, which then estimates the user's emotions and adjusts the length of the renovation image based on the estimated emotions.

[0087] The generation unit can determine the priority of renovation images based on the age of the property. For example, for older properties, the generation unit prioritizes areas that are most in need of renovation in the renovation images. In addition, for newer properties, the generation unit can also prioritize design changes and added functions in the renovation images. Furthermore, the generation unit can adjust the level of detail and priority of the renovation images according to the age of the property. This makes it possible to provide appropriate information by determining the priority of renovation images according to the age of the property. Some or all of the above-mentioned processing in the generation unit is performed using a generation AI. For example, the generation unit inputs data on the age of the property into the generation AI, and the generation AI determines the priority of the renovation images.

[0088] The generation unit can adjust the order of the renovation images based on the relevance of the property. For example, the generation unit prioritizes reflecting highly relevant parts of the property in the renovation images and adjusts the order. The generation unit can also place less relevant parts of the property later in the renovation images. Furthermore, the generation unit can optimize the order of the renovation images according to the relevance of the property. This makes it possible to provide more appropriate information by adjusting the order of the renovation images according to the relevance of the property. Some or all of the above-mentioned processing in the generation unit is performed using a generation AI. For example, the generation unit inputs property relevance data into the generation AI, which then adjusts the order of the renovation images.

[0089] The generation unit can adjust the use of technical terms in the renovation image according to the user's level of expertise. For example, if the user has technical expertise, the generation unit can generate a renovation image that uses a lot of technical terms. Alternatively, if the user does not have technical expertise, the generation unit can avoid technical terms and generate an easy-to-understand renovation image. Furthermore, the generation unit can adjust the way the renovation image is explained according to the user's level of expertise. This makes it possible to provide more understandable information by adjusting the use of technical terms according to the user's level of expertise. Some or all of the above-mentioned processing in the generation unit is performed using a generation AI. For example, the generation unit inputs the user's level of expertise data into the generation AI, which then adjusts the use of technical terms.

[0090] The suggestion unit can estimate the user's emotions and adjust the way suggestions are expressed based on the estimated user emotions. For example, if the user is relaxed, the suggestion unit can make suggestions using soft language and a gentle tone. Furthermore, if the user is excited, the suggestion unit can make suggestions using energetic language and a bright tone. Furthermore, if the user is stressed, the suggestion unit can make suggestions using simple, calm language. This allows for more appropriate suggestions by adjusting the way suggestions are expressed based on the user's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the suggestion unit can be performed using AI or without AI. For example, the suggestion unit inputs the user's facial expression data into the generation AI, which then estimates the user's emotions and adjusts the way suggestions are expressed based on the estimated emotions.

[0091] The suggestion unit can adjust the level of detail of the proposal based on the importance of the property. For example, the suggestion unit makes a detailed proposal for a property with a high level of importance. The suggestion unit can also make a simplified proposal for a property with a low level of importance. Furthermore, the suggestion unit can adjust whether to express the details of the proposal or to prioritize an overall proposal, depending on the importance of the property. This makes it possible to provide appropriate information by adjusting the level of detail of the proposal depending on the importance of the property. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, or may be performed without using AI. For example, the suggestion unit inputs property importance data into AI, and the AI ​​adjusts the level of detail of the proposal.

[0092] The suggestion unit can apply different suggestion algorithms depending on the property category. For example, for residential properties, the suggestion unit makes suggestions that emphasize the comfort of the living space. For commercial properties, the suggestion unit can also make suggestions that emphasize business efficiency. Furthermore, for public facilities, the suggestion unit can also make suggestions that emphasize user convenience. This enables more appropriate suggestions to be made by applying a suggestion algorithm depending on the property category. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, or may be performed without using AI. For example, the suggestion unit inputs property category data into AI, which then applies an appropriate suggestion algorithm to make suggestions.

[0093] The suggestion unit can improve the accuracy of suggestions by referring to the user's past suggestion results. For example, the suggestion unit learns the user's preferences and tendencies based on suggestions the user has received in the past, and makes more accurate suggestions. The suggestion unit can also analyze the user's past suggestion results and make suggestions that incorporate common elements. Furthermore, the suggestion unit can make suggestions that meet the user's expectations by referring to the user's past suggestion results. In this way, the accuracy of suggestions is improved by referring to the past suggestion results. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, or may be performed without using AI. For example, the suggestion unit inputs the user's past suggestion result data into AI, and the AI ​​uses the data to make new suggestions.

[0094] The suggestion unit can estimate the user's emotions and adjust the length of the suggestions based on the estimated user emotions. For example, if the user is in a hurry, the suggestion unit can provide short, to-the-point suggestions. If the user is relaxed, the suggestion unit can provide longer suggestions with detailed explanations. If the user is excited, the suggestion unit can provide suggestions with visually stimulating effects. This allows for more appropriate information provision by adjusting the length of the suggestions according to the user's emotions. The emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the suggestion unit can be performed using AI, or without AI. For example, the suggestion unit inputs the user's facial expression data into the generation AI, which then estimates the user's emotions and adjusts the length of the suggestions based on the estimated emotions.

[0095] The proposal unit can determine the priority of proposals based on the time of submission of the property. For example, the proposal unit prioritizes proposals for properties submitted early. The proposal unit can also postpone proposals for properties submitted late. Furthermore, the proposal unit can adjust the priority of proposals according to the time of submission. This makes it possible to provide appropriate information by determining the priority of proposals according to the time of submission. Some or all of the above-mentioned processing in the proposal unit may be performed using AI, or may be performed without using AI. For example, the proposal unit inputs property submission time data into AI, and the AI ​​determines the priority of proposals.

[0096] The suggestion unit can adjust the order of suggestions based on the relevance of the property. For example, the suggestion unit prioritizes suggesting highly relevant parts of the property and adjusts the order. The suggestion unit can also place less relevant parts of the property later in the suggestions. Furthermore, the suggestion unit can optimize the order of suggestions based on the relevance of the property. This makes it possible to provide more appropriate information by adjusting the order of suggestions based on the relevance of the property. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, or may be performed without using AI. For example, the suggestion unit inputs property relevance data into AI, and the AI ​​adjusts the order of suggestions.

[0097] The suggestion unit can adjust the use of technical terms in the suggestions depending on the user's level of expertise. For example, if the user has technical expertise, the suggestion unit can make suggestions that use a lot of technical terms. Furthermore, if the user does not have technical expertise, the suggestion unit can avoid technical terms and make suggestions that are easier to understand. Furthermore, the suggestion unit can adjust the explanation method of the suggestions depending on the user's level of expertise. This makes it possible to provide more understandable information by adjusting the use of technical terms depending on the user's level of expertise. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, or may be performed without using AI. For example, the suggestion unit inputs the user's level of expertise data into AI, which then adjusts the use of technical terms.

[0098] The video generation unit can estimate the user's emotions and adjust the video presentation method based on the estimated user emotions. For example, if the user is relaxed, the video generation unit can generate a video with soft colors and a calm tone. Furthermore, if the user is excited, the video generation unit can generate a video with vivid colors and an energetic tone. Furthermore, if the user is stressed, the video generation unit can generate a video with a simple, calm tone. This enables more appropriate information provision by adjusting the video presentation method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the video generation unit can be performed using AI or without AI. For example, the video generation unit inputs the user's facial expression data into the generation AI, which then estimates the user's emotions and adjusts the video presentation method based on the estimated emotions.

[0099] The video generation unit can adjust the level of detail of the video based on the importance of the property. For example, the video generation unit generates a detailed video for a property with a high level of importance. The video generation unit can also generate a simplified video for a property with a low level of importance. Furthermore, the video generation unit can adjust whether to show the details of the video or prioritize the overall video, depending on the importance of the property. This makes it possible to provide appropriate information by adjusting the level of detail of the video depending on the importance of the property. Some or all of the above-mentioned processing in the video generation unit may be performed using AI, or may be performed without using AI. For example, the video generation unit inputs property importance data into AI, and the AI ​​adjusts the level of detail of the video.

[0100] The video generation unit can apply different video generation algorithms depending on the property category. For example, for residential properties, the video generation unit generates videos that emphasize the comfort of the living space. For commercial properties, the video generation unit can also generate videos that emphasize business efficiency. Furthermore, for public facilities, the video generation unit can also generate videos that emphasize user convenience. This makes it possible to provide more appropriate information by applying a video generation algorithm according to the property category. Some or all of the above-mentioned processing in the video generation unit may be performed using AI, or may be performed without using AI. For example, the video generation unit inputs property category data into AI, and the AI ​​applies an appropriate video generation algorithm to generate a video.

[0101] The video generation unit can improve the accuracy of the video by referring to the user's past video viewing history. For example, the video generation unit learns the user's preferences and tendencies based on videos the user has viewed in the past, and generates a more accurate video. The video generation unit can also analyze the user's past video viewing history and generate a video that incorporates common elements. Furthermore, the video generation unit can also generate a video that meets the user's expectations by referring to the user's past video viewing history. In this way, by referring to the past video viewing history, the accuracy of the video is improved. Some or all of the above-mentioned processing in the video generation unit may be performed using AI, or may be performed without using AI. For example, the video generation unit inputs the user's past video viewing history data into AI, which then uses the data to generate a new video.

[0102] The video generation unit can estimate the user's emotions and adjust the length of the video based on the estimated user emotions. For example, if the user is in a hurry, the video generation unit can generate a short, to-the-point video. Furthermore, if the user is relaxed, the video generation unit can generate a longer video with detailed explanations. Furthermore, if the user is excited, the video generation unit can generate a video with visually stimulating effects. This allows for more appropriate information provision by adjusting the length of the video according to the user's emotions. The emotion estimation is realized using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the video generation unit may be performed using AI, or may be performed without AI. For example, the video generation unit inputs the user's facial expression data into the generation AI, which then estimates the user's emotions and adjusts the length of the video based on the estimated emotions.

[0103] The video generation unit can determine the priority of videos based on the time of submission of the property. For example, the video generation unit prioritizes the generation of videos for properties submitted early. The video generation unit can also postpone the generation of videos for properties submitted late. Furthermore, the video generation unit can adjust the priority of videos according to the time of submission. This makes it possible to provide appropriate information by determining the priority of videos according to the time of submission. Some or all of the above-mentioned processing in the video generation unit may be performed using AI, or may be performed without using AI. For example, the video generation unit inputs data on the time of submission of properties into AI, and the AI ​​determines the priority of videos.

[0104] The video generation unit can adjust the order of videos based on the relevance of the property. For example, the video generation unit prioritizes reflecting parts of the property that are highly relevant in the video and adjusts the order. The video generation unit can also place parts of the property that are less relevant in the latter half of the video. Furthermore, the video generation unit can optimize the order of videos based on the relevance of the property. This makes it possible to provide more appropriate information by adjusting the order of videos based on the relevance of the property. Some or all of the above-mentioned processing in the video generation unit may be performed using AI, or may be performed without using AI. For example, the video generation unit inputs property relevance data into AI, and the AI ​​adjusts the order of the videos.

[0105] The video generation unit can adjust the use of technical terms in the video according to the user's level of expertise. For example, if the user has technical expertise, the video generation unit generates a video that uses a lot of technical terms. Furthermore, if the user does not have technical expertise, the video generation unit can avoid technical terms and generate an easy-to-understand video. Furthermore, the video generation unit can adjust the explanation method of the video according to the user's level of expertise. This makes it possible to provide information that is easier to understand by adjusting the use of technical terms according to the user's level of expertise. Some or all of the above-mentioned processing in the video generation unit may be performed using AI, or may be performed without using AI. For example, the video generation unit inputs the user's level of expertise data into AI, which then adjusts the use of technical terms. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, generation unit, proposal unit, and video generation unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit can collect floor plans, photos, and videos of the property using the camera 42 and microphone 38B of the smart device 14. The collection unit can also collect information regarding the age of the property and the condition of the facilities using the specific processing unit 290 of the data processing device 12. The generation unit generates an image of the remodeled property based on the collected information using the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. The proposal unit can propose a plan based on the image generated by the generation unit, and can propose a blueprint and cost estimate for the remodeling using the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. The video generation unit provides the plan proposed by the proposal unit as an audio-guided video, and generates a narrated video tour using the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12 to provide to the user. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, generation unit, suggestion unit, and video generation unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit can collect floor plans, photos, and videos of the property using the camera 42 and microphone 238 of the smart glasses 214. The collection unit can also collect information regarding the age of the property and the condition of the facilities via the specific processing unit 290 of the data processing device 12. The generation unit generates an image of the remodeled property based on the collected information via the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. The suggestion unit can propose a plan based on the image generated by the generation unit, and propose a blueprint and cost estimate for the remodeling via the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. The video generation unit provides the plan proposed by the suggestion unit as an audio-guided video, and generates a narrated video tour via the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12 to provide to the user. === Hard Collateral 1-3 === Each of the multiple elements, including the collection unit, generation unit, proposal unit, and video generation unit, described above, is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the collection unit can collect floor plans, photos, and videos of the property using the camera 42 and microphone 238 of the headset terminal 314. The collection unit can also collect information regarding the age of the property and the condition of the facilities using the specific processing unit 290 of the data processing device 12. The generation unit generates an image of the remodeled property based on the collected information using the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing device 12. The proposal unit can propose a plan based on the image generated by the generation unit, and propose a blueprint and cost estimate for the remodeling using the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing device 12. The video generation unit provides the plan proposed by the proposal unit as a video with audio guidance, and generates a video tour with narration by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12 and provides it to the user. === Hard Collateral 1-4 === Each of the multiple elements, including the collection unit, generation unit, proposal unit, and video generation unit, described above, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit can collect floor plans, photos, and videos of the property using the camera 42 and microphone 238 of the robot 414. The collection unit can also collect information regarding the age of the property and the condition of the facilities via the specific processing unit 290 of the data processing device 12. The generation unit generates an image of the remodeled property based on the collected information via the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. The proposal unit can propose a plan based on the image generated by the generation unit, and can propose a blueprint and cost estimate for the remodeling via the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. The video generation unit provides the plan proposed by the proposal unit as an audio-guided video, and generates a narrated video tour via the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12 to provide to the user.

[0106] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0107] The collection unit can also collect crime rates and public safety information around the property. For example, the collection unit can collect public police data and local crime prevention information to evaluate the safety of the property. The collection unit can also analyze past crime histories and evaluate the risks of the property's surrounding environment. Furthermore, the collection unit can analyze local residents' word-of-mouth and social media posts to collect real-time information on public safety. By providing information on the safety of the property, users can select a property with peace of mind.

[0108] The suggestion unit can also propose rental plans and resale plans customized based on the user's lifestyle. For example, if the user likes outdoor activities, the suggestion unit can suggest properties with abundant parks and natural environments nearby. Also, if the user has a pet, the suggestion unit can suggest pet-friendly properties. Furthermore, if the user works remotely, the suggestion unit can suggest properties with good internet connections or quiet environments. This allows the user to make a more satisfying selection by suggesting properties that suit their lifestyle.

[0109] The generation unit can also generate renovation images to improve the energy efficiency of a property. For example, the generation unit can propose renovation plans to increase energy efficiency, such as installing solar panels or adding insulation. The generation unit can also generate renovation images that incorporate smart home technology. Furthermore, the generation unit can generate renovation images that take into account designs and equipment placement to minimize energy consumption. This allows for the realization of environmentally friendly homes by providing renovation plans that improve energy efficiency.

[0110] The video generation unit can also generate videos that introduce the history and cultural background of a property. For example, the video generation unit can explain when the property was built and the architectural style of that era. The video generation unit can also introduce how the property has been used in the past and historical events in the area. Furthermore, the video generation unit can also introduce historical landmarks and cultural facilities in the vicinity of the property. This allows users to understand the history and cultural background of the property, making it possible to more deeply convey the appeal of the property to the user.

[0111] The proposal unit can also propose a home plan based on the user's health condition. For example, if the user has allergies, the proposal unit can propose a home plan that uses materials and equipment that suppress allergens. In addition, if the user is elderly, the proposal unit can also propose a home plan that emphasizes barrier-free design and safety. Furthermore, if the user prioritizes fitness, the proposal unit can also propose a home gym space or a floor plan suitable for exercise. This makes it possible to realize a more comfortable living environment by providing a home plan that takes the user's health condition into consideration.

[0112] The collection unit can also estimate the user's emotions and adjust the information presentation method based on the estimated user's emotions. For example, if the user is feeling stressed, the collection unit can present information in a concise summary. If the user is relaxed, the collection unit can also provide detailed information. Furthermore, if the user is excited, the collection unit can present information using visually appealing graphics or animations. This makes it possible to provide information more effectively by adopting an information presentation method that corresponds to the user's emotions.

[0113] The suggestion unit can also estimate the user's emotions and adjust the timing of suggestions based on the estimated user's emotions. For example, if the user is feeling stressed, the suggestion unit can reduce the frequency of suggestions and make suggestions when the user is relaxed. Also, if the user is relaxed, the suggestion unit can increase the frequency of suggestions and make detailed suggestions. Furthermore, if the user is in a hurry, the suggestion unit can make suggestions quickly and prioritize providing the minimum necessary information. This makes it possible to make more appropriate suggestions by adjusting the timing of suggestions according to the user's emotions.

[0114] The generation unit can also estimate the user's emotions and adjust the color and design of the renovation image based on the estimated user's emotions. For example, if the user is relaxed, the generation unit can generate a renovation image with soft colors and a calm design. If the user is excited, the generation unit can generate a renovation image with vivid colors and a bold design. Furthermore, if the user is stressed, the generation unit can generate a renovation image with a simple and calm design. In this way, by adjusting the color and design of the renovation image according to the user's emotions, a more appropriate image can be provided.

[0115] The video generation unit can also estimate the user's emotions and adjust the tone and speed of the video narration based on the estimated user's emotions. For example, if the user is relaxed, the video generation unit can generate a slow narration with a calm tone. If the user is excited, the video generation unit can generate a fast narration with an energetic tone. Furthermore, if the user is stressed, the video generation unit can generate a simple narration with a calm tone. This makes it possible to provide information more effectively by adjusting the video narration according to the user's emotions.

[0116] The suggestion unit can also estimate the user's emotions and adjust the content of the suggestions based on the estimated user's emotions. For example, if the user is relaxed, the suggestion unit can provide detailed suggestions and present multiple options. If the user is excited, the suggestion unit can provide visually appealing suggestions to attract the user's attention. Furthermore, if the user is stressed, the suggestion unit can provide simple and to-the-point suggestions. This allows for more appropriate suggestions to be made by adjusting the content of the suggestions according to the user's emotions.

[0117] The processing flow of the second embodiment will be briefly explained below.

[0118] Step 1: The collection unit collects basic information and footage, such as floor plan or structural information, plumbing or equipment status, and age of the building. For example, the collection unit collects floor plans, photos, and videos of the property, as well as information on the age of the property and the condition of its equipment. Step 2: The generation unit generates an image of the property after renovation based on the information collected by the collection unit. For example, the generation unit uses a generative AI to generate an image of the property after renovation as a CG image or 3D model. Step 3: The proposal unit proposes a plan based on the image generated by the generation unit. For example, the proposal unit can propose a blueprint and cost estimate for a renovation. Step 4: The video generation unit provides the plan proposed by the proposal unit as a video with audio guidance. For example, the video generation unit generates a video tour with narration and provides it to the user.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0123] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0124] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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).

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0139] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0140] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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).

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification 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 identification processing unit 290 using these models.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0155] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0156] 7, a 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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).

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also 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 perform the same process as the identification processing unit 290 using these models.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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).

[0176] 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.

[0177] 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."

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] [Explanation of symbols]

[0191] 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. A collection unit that collects basic information and images such as floor plan or structural information, plumbing or equipment status, and age of the building; A generation unit that generates an image of the remodeled home based on the information collected by the collection unit; a proposal unit that proposes a plan based on the image generated by the generation unit; a video generation unit that provides the plan proposed by the proposal unit as a video with audio guidance; A system characterized by:

2. The collecting unit Analyze rental rates or market trends The system of claim 1 .

3. The proposal unit Offer a rental or resale plan The system of claim 1 .

4. The generation unit Generate an image of the remodeled home using generative AI The system of claim 1 .

5. The video generation unit Generate audio-described videos The system of claim 1 .

6. The proposal unit Proposing house layouts or designs for the child-rearing generation The system of claim 1 .

7. The collecting unit Estimate the user's emotions and adjust the timing of information collection based on the estimated user emotions. The system of claim 1 .

8. The collecting unit Gather information based on the property's past repair history The system of claim 1 .

Images