System

Abstract

An object of the system according to the embodiment is to embody a vision of renovation and to improve efficiency of communication with a business operator, acquisition of an estimate, and management of a renovation process.SOLUTION: A system includes a reception unit, a generation unit, a sharing unit, an estimation unit, and a management unit. The reception unit receives a photograph from a user. The generation section analyzes the photograph received by the reception section, and generates a reformed vision. The sharing unit shares the vision generated by the generation unit with the agent. The estimation unit automatically generates an estimate based on the vision shared by the sharing unit. The management unit manages the reforming process based on the quotation generated by the quotation unit.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] Conventional technology had the challenge of making the renovation vision concrete, communicating with contractors, obtaining estimates, and managing the renovation process complicated.

[0005] The system of the embodiment aims to make the vision for renovation concrete and to streamline communication with contractors, obtaining estimates, and managing the renovation process. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a generation unit, a sharing unit, an estimate unit, and a management unit. The reception unit receives photos from a user. The generation unit analyzes the photos received by the reception unit and generates a vision of the post-renovation look. The sharing unit shares the vision generated by the generation unit with a contractor. The estimate unit automatically generates an estimate based on the vision shared by the sharing unit. The management unit manages the renovation process based on the estimate generated by the estimate unit. [Effects of the Invention]

[0007] The system according to the embodiment can materialize the vision for the renovation and streamline communication with contractors, obtaining estimates, and managing the renovation process. [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 renovation experience system according to an embodiment of the present invention generates a vision of the renovated room based on photos taken by a user, facilitating smooth communication with contractors, obtaining estimates, and streamlining the renovation process. In the renovation experience system, a user takes photos of a room they want to renovate and uploads them to the system. A generation AI analyzes the photos and generates a realistic vision of the renovated room. This vision is customized based on the user's desired design and style. The generated vision supports smooth communication between the user and the renovation contractor, enabling them to obtain estimates and streamline the renovation process. For example, in a renovation experience system, a user takes photos of a room they want to renovate from multiple angles and uploads them to the system. The generation AI analyzes the uploaded photos and generates a realistic vision of the renovated room. The generation AI creates a 3D model of the room based on the photos and customizes it based on the user's desired design and style. The generated vision supports smooth communication between the user and the renovation contractor. The user can share the generated vision with the contractor and discuss specific renovation plans. This allows the user and the contractor to share the image of the renovation, preventing misunderstandings and mistakes. Furthermore, the renovation experience system automatically generates a list of required materials and work based on the generated vision and prepares an estimate, allowing users to obtain a quick and accurate estimate. The system also manages the progress of the renovation process, allowing the renovation to proceed efficiently. This allows users to easily imagine and realize their future living space. This allows the renovation experience system to streamline the user's renovation process and realize their ideal living space. For example, a user can upload photos of the room they want to renovate, consult with a contractor based on the vision created by the generative AI, obtain an estimate, and proceed with the renovation process efficiently.This significantly reduces the time and effort required for renovations and allows you to create your ideal living space.

[0029] A renovation experience system according to an embodiment includes a reception unit, a generation unit, a sharing unit, an estimate unit, and a management unit. The reception unit receives photos from a user. The photos from the user may be in, for example, JPEG format, PNG format, or resolution, but are not limited to these examples. The reception unit receives photos of a room the user wants to renovate from multiple angles. For example, photos from the front, side, top, and other angles may be received. The generation unit uses a generation AI to analyze the photos received by the reception unit and generate a vision of the renovated room. The generation AI analyzes the photos using, for example, deep learning or computer vision technology and creates a 3D model of the room. The generation unit also customizes the vision based on the user's desired design and style. For example, the vision may be generated based on a modern design, a classic design, a minimalist style, or the like. The sharing unit shares the vision generated by the generation unit with a contractor. Sharing is performed, for example, via a cloud service, email, or a specific file format. The sharing unit shares the generated vision with a contractor, allowing the contractor to discuss a specific renovation plan. The estimating unit automatically generates a list of required materials and work based on the generated vision and creates an estimate. The estimate is automatically generated based on, for example, material costs, work costs, and a specific algorithm. The management unit manages the renovation process based on the estimate generated by the estimating unit. The management unit performs, for example, progress management, task management, and schedule management. This allows the renovation experience system according to the embodiment to streamline the user's renovation process and realize their ideal living space. For example, the user can upload photos of the room they want to renovate, consult with a contractor based on the vision created by the generation AI, obtain an estimate, and efficiently proceed with the renovation process. This significantly reduces the effort and time required for renovation and enables them to realize their ideal living space.

[0030] The reception unit can receive photos of a room that the user wants to renovate from multiple angles. For example, the reception unit can receive photos of a room that the user wants to renovate from multiple angles. For example, it can receive photos from angles such as the front, side, and top. By receiving photos from multiple angles, it becomes easier to grasp the overall picture of the room. Some or all of the above-described processing in the reception unit can be performed using, for example, AI, or can be performed without using AI. For example, the reception unit can input photos from multiple angles uploaded by the user into the generation AI and cause the generation AI to grasp the overall picture of the room.

[0031] The generation unit can analyze photos using a generation AI and create a 3D model of the room. The generation unit, for example, uses a generation AI to analyze photos received by the reception unit and create a 3D model of the room. The generation AI analyzes photos using, for example, deep learning or computer vision technology and creates a 3D model of the room. This allows for the generation of a more realistic vision by analyzing photos and creating a 3D model. Some or all of the above-mentioned processing in the generation unit is performed using the generation AI. For example, the generation unit can input photos uploaded by a user into the generation AI and have the generation AI create a 3D model of the room.

[0032] The generation unit can customize the vision based on the design and style desired by the user. For example, the generation unit customizes the vision based on the design and style desired by the user. For example, the generation unit generates a vision based on a modern design, a classic design, a minimalist style, etc. By customizing the vision based on the user's wishes, a more satisfying renovation plan 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 can input information about the design and style desired by the user into the generation AI and have the generation AI customize the vision.

[0033] The sharing unit can share the generated vision with a contractor and discuss a specific renovation plan. The sharing unit, for example, shares the vision generated by the generation unit with a contractor. Sharing is performed, for example, using a cloud service, email, or a specific file format. The sharing unit can share the generated vision with a contractor and discuss a specific renovation plan. By sharing the generated vision with a contractor, it is possible to smoothly discuss a specific renovation plan. Some or all of the above-mentioned processing in the sharing unit may be performed using AI, or may be performed without using AI. For example, the sharing unit can share the vision by uploading the generated vision to a cloud service and granting access rights to the contractor.

[0034] The estimation unit can automatically generate a list of required materials and work based on the generated vision and create an estimate. The estimation unit can, for example, automatically generate a list of required materials and work based on the vision generated by the generation unit and create an estimate. The estimate is automatically generated based on, for example, material costs, work costs, and a specific algorithm. This makes it possible to provide a quick and accurate estimate by automatically creating an estimate based on the generated vision. Some or all of the above-mentioned processing in the estimation unit may be performed using AI, or may be performed without using AI. For example, the estimation unit can create an estimate using an AI model that takes the generated vision as input and outputs a list of required materials and work.

[0035] The management unit manages the progress of the renovation process, allowing the renovation to proceed efficiently. The management unit manages the renovation process based on, for example, an estimate generated by the estimation unit. The management unit performs, for example, progress management, task management, and schedule management. By managing the progress of the renovation process, the renovation can proceed efficiently. Some or all of the above-mentioned processing in the management unit may be performed using AI, or may be performed without using AI. For example, the management unit can build a system that monitors the progress of the renovation process in real time and visualizes the progress.

[0036] The reception unit can analyze the user's past renovation history and select the optimal photo reception method. For example, the reception unit analyzes the user's past renovation history and selects the optimal photo reception method. For example, the reception unit analyzes photos of rooms the user has previously renovated and prioritizes receiving photos of similar rooms. The reception unit can also customize the photo reception method based on specific styles and designs from the user's past renovation history. The reception unit can also recommend taking photos at optimal angles and lighting conditions by referring to photos of rooms the user has previously renovated. In this way, the reception unit can provide the optimal photo reception method by analyzing the user's past renovation history. Some or all of the above-mentioned processing in the reception unit can be performed using AI. For example, the reception unit can input the user's past renovation history data into the generation AI and have the generation AI select the optimal photo reception method.

[0037] The reception unit can filter photos based on the user's current living environment and the purpose of the renovation when receiving the photos. For example, the reception unit can analyze the user's current living environment and accept only photos that match the purpose of the renovation. Photos can also be filtered based on specific elements (walls, floors, ceilings, etc.) of the room the user wants to renovate. Only related photos can be accepted based on the user's purpose of the renovation (design change, functional improvement, etc.). In this way, by filtering photos based on the user's living environment and the purpose of the renovation, highly relevant photos can be accepted. Some or all of the above-mentioned processing in the reception unit can be performed using AI. For example, the reception unit can input the user's living environment data and renovation purpose data into the generation AI and have the generation AI perform photo filtering.

[0038] The reception unit can select the optimal reception means according to the user's input method when receiving a photo. For example, when receiving a photo, the reception unit selects the optimal reception means according to the user's input method (voice, text, image, etc.). For example, if the user gives instructions by voice, the reception unit can prioritize voice input and accept photos. Also, if the user gives instructions by text, the reception unit can prioritize text input and accept photos. Also, if the user uploads an image, the reception unit can prioritize image input and accept photos. This allows for selecting the optimal reception means according to the user's input method, thereby improving user convenience. Some or all of the above-mentioned processing in the reception unit can be performed using AI. For example, the reception unit can input the user's input method data to the generation AI and cause the generation AI to select the optimal reception means.

[0039] When accepting photos, the reception unit can prioritize accepting photos that are highly relevant based on the user's geographical location information. For example, when accepting photos, the reception unit prioritizes accepting photos that are highly relevant based on the user's geographical location information. For example, if the user lives in a specific area, photos related to that area can be prioritized. Furthermore, based on the user's geographical location information, photos related to nearby renovation examples can also be prioritized. Furthermore, taking the user's geographical location information into consideration, photos related to designs and styles unique to the area can be prioritized. In this way, by preferentially accepting photos that are highly relevant based on the user's geographical location information, it is possible to accommodate designs and styles unique to the area. Some or all of the above-mentioned processing in the reception unit is performed using AI. For example, the reception unit can input the user's geographical location information data into the generation AI and cause the generation AI to select highly relevant photos.

[0040] The reception unit can analyze the user's social media activity and accept related photos when accepting a photo. For example, the reception unit analyzes the user's social media activity and accepts related photos when accepting a photo. For example, the reception unit may preferentially accept related photos based on photos shared by the user on social media. The reception unit can also analyze the user's social media activity and accept related photos. The reception unit can also accept related photos with reference to the activity of the user's friends on social media. In this way, by analyzing the user's social media activity, it is possible to accept highly relevant photos. Some or all of the above-mentioned processing in the reception unit can be performed using AI. For example, the reception unit can input the user's social media activity data into the generation AI and cause the generation AI to select related photos.

[0041] The reception unit can customize the reception method by reflecting the user's past feedback when receiving a photo. For example, the reception unit customizes the reception method by reflecting the user's past feedback when receiving a photo. For example, the reception unit selects the optimal photo reception method based on feedback provided by the user in the past. It can also preferentially apply a specific reception method based on the user's past feedback. It can also customize the reception method by reflecting the user's past feedback to improve user satisfaction. In this way, it is possible to improve user satisfaction by reflecting the user's past feedback. Some or all of the above-mentioned processing in the reception unit is performed using AI. For example, the reception unit can input the user's past feedback data into the generation AI and cause the generation AI to customize the reception method.

[0042] The generation unit can adjust the level of detail of the vision based on the importance of the renovation when generating the vision. For example, the generation unit can adjust the level of detail of the vision based on the importance of the renovation when generating the vision. For example, if the importance of the renovation is high, a detailed vision can be generated. On the other hand, if the importance of the renovation is low, a simplified vision can be generated. The level of detail of the vision can also be dynamically adjusted according to the importance of the renovation. This makes it possible to provide a vision that meets the user's needs by adjusting the level of detail of the vision based on the importance of the renovation. Some or all of the above-mentioned processing in the generation unit is performed using a generation AI. For example, the generation unit can input renovation importance data into the generation AI and have the generation AI adjust the level of detail of the vision.

[0043] The generation unit can apply different generation algorithms depending on the category of renovation when generating a vision. For example, the generation unit applies different generation algorithms depending on the category of renovation when generating a vision. For example, in the case of kitchen renovation, a generation algorithm dedicated to kitchens can be applied to generate a vision specialized for kitchens. In addition, in the case of bathroom renovation, a generation algorithm dedicated to bathrooms can be applied to generate a vision specialized for bathrooms. In addition, in the case of living room renovation, a generation algorithm dedicated to living rooms can be applied to generate a vision specialized for living rooms. In this way, by applying different generation algorithms depending on the category of renovation, it is possible to generate a vision optimal for each category. Some or all of the above-mentioned processing in the generation unit is performed using a generation AI. For example, the generation unit can input renovation category data into the generation AI and cause the generation AI to apply different generation algorithms.

[0044] The generation unit can improve the accuracy of generation by referring to the user's past vision results when generating a vision. For example, the generation unit can improve the accuracy of generation by referring to the user's past vision results when generating a vision. For example, the generation unit can improve the accuracy of the current vision by referring to visions generated by the user in the past. Also, it can preferentially apply specific designs or styles from the user's past vision results. It can also analyze the user's past vision results and optimize the generation algorithm. In this way, it is possible to improve the accuracy of generation by referring to the user's past vision results. Some or all of the above-mentioned processing in the generation unit is performed using a generation AI. For example, the generation unit can input the user's past vision result data into the generation AI and cause the generation AI to improve the accuracy of generation.

[0045] The generation unit can determine the priority of visions based on the time of submission of renovations when generating visions. For example, the generation unit determines the priority of visions based on the time of submission of renovations when generating visions. For example, if the time of submission of renovations is approaching, the generation of visions is prioritized. Also, if the time of submission of renovations is far away, the generation of visions can be postponed. Also, the order in which visions are generated can be dynamically adjusted depending on the time of submission of renovations. This enables efficient vision generation by determining the priority of visions based on the time of submission of renovations. Some or all of the above-mentioned processing in the generation unit is performed using a generation AI. For example, the generation unit can input data on the time of submission of renovations into the generation AI and have the generation AI determine the priority of visions.

[0046] The generation unit can adjust the order of visions based on the relevance of the renovations when generating visions. For example, the generation unit adjusts the order of visions based on the relevance of the renovations when generating visions. For example, if the relevance of the renovations is high, the visions can be generated preferentially. Also, if the relevance of the renovations is low, the visions can be generated later. The generation order of visions can also be dynamically adjusted according to the relevance of the renovations. In this way, by adjusting the order of visions based on the relevance of the renovations, highly relevant visions can be generated preferentially. Some or all of the above-mentioned processing in the generation unit can be performed using a generation AI. For example, the generation unit can input relevance data of the renovations into the generation AI and have the generation AI adjust the order of the visions.

[0047] The generation unit can adjust the use of technical terms in the vision according to the user's level of expertise when generating the vision. For example, the generation unit can adjust the use of technical terms in the vision according to the user's level of expertise when generating the vision. For example, if the user has technical expertise, the generation unit can generate a vision that uses a lot of technical terms. On the other hand, if the user does not have technical expertise, the generation unit can generate a vision that explains things in simple terms. The generation unit can also dynamically adjust the use of technical terms in the vision according to the user's level of expertise. This makes it possible to provide a vision that is easy for the user to understand by adjusting the use of technical terms in the vision 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 can input the user's level of expertise data into the generation AI and cause the generation AI to adjust the use of technical terms in the vision.

[0048] The sharing unit can adjust the level of detail of the shared information based on the importance of the renovation when sharing the vision. For example, when sharing the vision, the sharing unit adjusts the level of detail of the shared information based on the importance of the renovation. For example, if the importance of the renovation is high, a detailed vision can be shared. On the other hand, if the importance of the renovation is low, a simplified vision can be shared. The level of detail of the shared information can also be dynamically adjusted according to the importance of the renovation. This makes it possible to share information according to the user's needs by adjusting the level of detail of the shared information based on the importance of the renovation. Some or all of the above-mentioned processing in the sharing unit can be performed using AI. For example, the sharing unit can input the importance data of the renovation into the generating AI and have the generating AI adjust the level of detail of the shared information.

[0049] When sharing a vision, the sharing unit can apply different sharing algorithms depending on the category of the renovation. For example, when sharing a vision, the sharing unit applies different sharing algorithms depending on the category of the renovation. For example, in the case of a kitchen renovation, a sharing algorithm dedicated to kitchens can be applied to provide information specialized for the kitchen. In addition, in the case of a bathroom renovation, a sharing algorithm dedicated to bathrooms can be applied to provide information specialized for the bathroom. In addition, in the case of a living room renovation, a sharing algorithm dedicated to living rooms can be applied to provide information specialized for the living room. In this way, by applying different sharing algorithms depending on the category of the renovation, optimal information sharing for each category becomes possible. Some or all of the above-mentioned processing in the sharing unit is performed using AI. For example, the sharing unit can input renovation category data into a generation AI and have the generation AI apply different sharing algorithms.

[0050] The sharing unit can improve the accuracy of sharing by referring to the user's past sharing results when sharing a vision. For example, the sharing unit can improve the accuracy of sharing by referring to the user's past sharing results when sharing a vision. For example, the sharing unit can improve the accuracy of sharing the current vision by referring to visions shared by the user in the past. Furthermore, it can prioritize sharing specific designs or styles based on the user's past sharing results. It can also analyze the user's past sharing results and optimize the sharing algorithm. This can improve the accuracy of sharing by referring to the user's past sharing results. Some or all of the above-mentioned processing in the sharing unit can be performed using AI. For example, the sharing unit can input the user's past sharing result data into the generation AI and cause the generation AI to improve the accuracy of sharing.

[0051] The sharing unit can determine the priority of sharing based on the time of submission of the renovations when sharing the vision. For example, when sharing the vision, the sharing unit determines the priority of sharing based on the time of submission of the renovations. For example, if the time of submission of the renovations is approaching, the vision is shared as a priority. Also, if the time of submission of the renovations is far away, the vision can be shared at a later date. Also, the order in which the visions are shared can be dynamically adjusted depending on the time of submission of the renovations. This enables efficient information sharing by determining the priority of sharing based on the time of submission of the renovations. Some or all of the above-mentioned processing in the sharing unit is performed using AI. For example, the sharing unit can input data on the time of submission of the renovations into the generation AI and have the generation AI determine the priority of sharing.

[0052] The sharing unit can adjust the order of sharing based on the relevance of the renovations when sharing a vision. For example, the sharing unit adjusts the order of sharing based on the relevance of the renovations when sharing a vision. For example, if the relevance of the renovations is high, the vision is shared as a priority. Also, if the relevance of the renovations is low, the vision can be shared at a later date. Also, the order of sharing visions can be dynamically adjusted according to the relevance of the renovations. In this way, by adjusting the order of sharing based on the relevance of the renovations, highly relevant information can be shared preferentially. Some or all of the above-mentioned processing in the sharing unit is performed using AI. For example, the sharing unit can input the relevance data of the renovations into a generating AI and have the generating AI adjust the order of sharing.

[0053] The sharing unit can adjust the use of technical terms in the vision according to the user's level of expertise when sharing the vision. For example, when sharing the vision, the sharing unit adjusts the use of technical terms in the vision according to the user's level of expertise. For example, if the user has technical expertise, the sharing unit can share a vision that uses a lot of technical terms. On the other hand, if the user does not have technical expertise, the sharing unit can share a vision that is explained in simple terms. The use of technical terms in the vision can also be dynamically adjusted according to the user's level of expertise. This makes it possible to provide information that is easy for the user to understand by adjusting the use of technical terms in the vision according to the user's level of expertise. Some or all of the above-mentioned processing in the sharing unit can be performed using AI. For example, the sharing unit can input the user's level of expertise data into the generation AI and cause the generation AI to adjust the use of technical terms in the vision.

[0054] The estimating unit can adjust the level of detail of the estimate based on the importance of the renovation when generating an estimate. For example, when generating an estimate, the estimating unit adjusts the level of detail of the estimate based on the importance of the renovation. For example, if the importance of the renovation is high, a detailed estimate can be generated. On the other hand, if the importance of the renovation is low, a simplified estimate can be generated. The level of detail of the estimate can also be dynamically adjusted according to the importance of the renovation. This makes it possible to provide an estimate that meets the user's needs by adjusting the level of detail of the estimate based on the importance of the renovation. Some or all of the above-mentioned processing in the estimating unit can be performed using AI. For example, the estimating unit can input the importance data of the renovation into the generating AI and have the generating AI adjust the level of detail of the estimate.

[0055] The estimating unit can apply different estimation algorithms depending on the category of renovation when generating an estimate. For example, when generating an estimate, the estimating unit applies different estimation algorithms depending on the category of renovation. For example, in the case of a kitchen renovation, an estimation algorithm dedicated to kitchens can be applied to generate an estimate specialized for the kitchen. In addition, in the case of a bathroom renovation, an estimation algorithm dedicated to bathrooms can be applied to generate an estimate specialized for the bathroom. In addition, in the case of a living room renovation, an estimation algorithm dedicated to living rooms can be applied to generate an estimate specialized for the living room. In this way, by applying different estimation algorithms depending on the category of renovation, it is possible to provide an optimal estimate for each category. Some or all of the above-mentioned processing in the estimating unit is performed using AI. For example, the estimating unit can input renovation category data into the generating AI and cause the generating AI to apply different estimation algorithms.

[0056] The estimating unit can improve the accuracy of an estimate by referring to the user's past estimate results when generating an estimate. For example, the estimating unit can improve the accuracy of a current estimate by referring to estimates obtained by the user in the past. Specific materials or tasks can be prioritized based on the user's past estimate results. The estimating unit can also analyze the user's past estimate results and optimize the estimation algorithm. By referring to the user's past estimate results, the accuracy of the estimate can be improved. Some or all of the above-described processing in the estimating unit can be performed using AI. For example, the estimating unit can input the user's past estimate result data into the generating AI and have the generating AI improve the accuracy of the estimate.

[0057] The estimating unit can determine the priority of estimates based on the submission date of the renovations when generating estimates. The estimating unit, for example, determines the priority of estimates based on the submission date of the renovations when generating estimates. For example, if the submission date of the renovations is approaching, the estimates are generated as a priority. Also, if the submission date of the renovations is far away, the estimates can be generated at a later date. The order in which estimates are generated can also be dynamically adjusted depending on the submission date of the renovations. This enables efficient estimate generation by determining the priority of estimates based on the submission date of the renovations. Some or all of the above-mentioned processing in the estimating unit is performed using AI. For example, the estimating unit can input data on the submission date of the renovations into the generation AI and have the generation AI determine the priority of estimates.

[0058] The estimating unit can adjust the order of estimates based on the relevance of the renovations when generating estimates. For example, the estimating unit adjusts the order of estimates based on the relevance of the renovations when generating estimates. For example, if the relevance of the renovations is high, the estimates can be generated as a priority. Also, if the relevance of the renovations is low, the estimates can be generated at a later date. The order of estimate generation can also be dynamically adjusted according to the relevance of the renovations. In this way, by adjusting the order of estimates based on the relevance of the renovations, highly relevant estimates can be generated as a priority. Some or all of the above-mentioned processing in the estimating unit can be performed using AI. For example, the estimating unit can input relevance data of the renovations into the generation AI and have the generation AI adjust the order of the estimates.

[0059] The estimating unit can adjust the use of technical terminology in the estimate according to the user's level of expertise when generating an estimate. For example, when generating an estimate, the estimating unit adjusts the use of technical terminology in the estimate according to the user's level of expertise. For example, if the user has technical expertise, the estimating unit can generate an estimate that uses a lot of technical terminology. On the other hand, if the user does not have technical expertise, the estimating unit can generate an estimate that explains things in simple terms. The use of technical terminology in the estimate can also be dynamically adjusted according to the user's level of expertise. This makes it possible to provide an estimate that is easy for the user to understand by adjusting the use of technical terminology in the estimate according to the user's level of expertise. Some or all of the above-mentioned processing in the estimating unit can be performed using AI. For example, the estimating unit can input the user's level of expertise data into the generating AI and cause the generating AI to adjust the use of technical terminology in the estimate.

[0060] The management unit can adjust the level of detail of management based on the importance of the renovation when managing the renovation process. For example, when managing the renovation process, the management unit adjusts the level of detail of management based on the importance of the renovation. For example, when the importance of the renovation is high, detailed management can be performed. On the other hand, when the importance of the renovation is low, simplified management can be performed. Furthermore, the level of detail of management can be dynamically adjusted according to the importance of the renovation. This allows management according to the user's needs by adjusting the level of detail of management based on the importance of the renovation. Some or all of the above-mentioned processing in the management unit can be performed using AI. For example, the management unit can input the importance data of the renovation into the generation AI and have the generation AI adjust the level of detail of management.

[0061] The management unit can apply different management algorithms depending on the category of the renovation when managing the renovation process. For example, when managing the renovation process, the management unit applies different management algorithms depending on the category of the renovation. For example, in the case of a kitchen renovation, a management algorithm dedicated to kitchens can be applied to perform management specialized for the kitchen. In addition, in the case of a bathroom renovation, a management algorithm dedicated to bathrooms can be applied to perform management specialized for the bathroom. In addition, in the case of a living room renovation, a management algorithm dedicated to living rooms can be applied to perform management specialized for the living room. In this way, by applying different management algorithms depending on the category of the renovation, optimal management for each category is possible. Some or all of the above-mentioned processing in the management unit is performed using AI. For example, the management unit can input renovation category data into a generation AI and have the generation AI apply different management algorithms.

[0062] The management unit can improve the accuracy of management by referring to the user's past management results when managing a renovation process. For example, the management unit can improve the accuracy of management by referring to the user's past management results when managing a renovation process. For example, the management unit can improve the accuracy of current management by referring to renovation processes that the user has managed in the past. In addition, it can preferentially apply specific management methods based on the user's past management results. It can also analyze the user's past management results and optimize the management algorithm. This makes it possible to improve the accuracy of management by referring to the user's past management results. Some or all of the above-mentioned processing in the management unit is performed using AI. For example, the management unit can input the user's past management result data into a generation AI and have the generation AI improve the accuracy of management.

[0063] The management unit can determine management priorities based on the timing of renovation submissions when managing renovation processes. For example, when managing renovation processes, the management unit determines management priorities based on the timing of renovation submissions. For example, if the renovation submission deadline is approaching, the management of the process can be prioritized. Also, if the renovation submission deadline is far away, the management of the process can be postponed. The process management order can also be dynamically adjusted depending on the timing of renovation submission. This enables efficient management by determining management priorities based on the timing of renovation submissions. Some or all of the above-mentioned processing in the management unit can be performed using AI. For example, the management unit can input renovation submission deadline data into a generation AI and have the generation AI determine management priorities.

[0064] The management unit can adjust the management order based on the relevance of renovations when managing renovation processes. For example, when managing renovation processes, the management unit adjusts the management order based on the relevance of renovations. For example, if the relevance of renovations is high, the process can be managed as a priority. Also, if the relevance of renovations is low, the process can be managed at a later date. Furthermore, the management order of processes can be dynamically adjusted according to the relevance of renovations. In this way, by adjusting the management order based on the relevance of renovations, highly relevant processes can be managed as a priority. Some or all of the above-mentioned processing in the management unit is performed using AI. For example, the management unit can input relevance data of renovations into a generation AI and have the generation AI adjust the management order.

[0065] The management unit can adjust the use of management terminology according to the user's level of expertise when managing a renovation process. For example, when managing a renovation process, the management unit adjusts the use of management terminology according to the user's level of expertise. For example, if the user has expertise, the management unit can perform management that makes extensive use of technical terms. On the other hand, if the user does not have expertise, the management unit can perform management that is explained in simple terms. The use of management terminology can also be dynamically adjusted according to the user's level of expertise. This allows for management that is easy for the user to understand by adjusting the use of management terminology according to the user's level of expertise. Some or all of the above-mentioned processing in the management unit can be performed using AI. For example, the management unit can input the user's level of expertise data into a generation AI and have the generation AI adjust the use of management terminology.

[0066] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0067] The renovation experience system can further include a history analysis unit that analyzes the user's past renovation history. The history analysis unit collects and analyzes data on renovations the user has undertaken in the past. For example, it can identify the success and failure factors of past renovation projects and reflect them in the current renovation plan. It can also understand trends in designs and styles chosen by the user in the past and provide this information to the generation unit to generate a vision that better suits the user's preferences. Furthermore, the history analysis unit can provide the estimating unit with information on the costs and duration of past renovation projects as a reference, allowing it to create more accurate estimates.

[0068] The renovation experience system may further include a lifestyle analysis unit that analyzes the user's lifestyle. The lifestyle analysis unit collects and analyzes the user's daily life patterns and preferences. For example, it can determine which rooms the user frequently uses at what time of day and optimize the renovation plan based on that information. It can also analyze the user's preferred colors and materials and provide the analysis results to the generation unit to generate a vision that better suits the user's preferences. Furthermore, the lifestyle analysis unit can customize the renovation plan by taking into account information such as the user's health condition and family composition.

[0069] The renovation experience system can further include a geographic information support unit that optimizes renovation plans based on the user's geographic location information. The geographic information support unit customizes renovation plans taking into account the climate, culture, architectural style, etc. of the user's region. For example, a renovation plan using materials with high thermal insulation performance can be proposed to a user living in a cold region. A renovation plan incorporating the traditional designs and styles of a particular region can also be proposed. Furthermore, the geographic information support unit can provide legal renovation plans taking into account local building regulations and laws.

[0070] The renovation experience system may further include a social media analysis unit that analyzes users' social media activities. The social media analysis unit collects and analyzes photos and posts shared by users on social media. For example, the social media analysis unit can identify designs and styles that have received many likes and comments on social media and optimize renovation plans based on that information. The system may also suggest renovation plans that incorporate trends by referring to renovation examples of the user's friends and followers. Furthermore, the social media analysis unit can identify users' interests and expectations regarding renovations through their social media activities and reflect these in renovation plans.

[0071] The renovation experience system can further include a health support unit that monitors the user's health condition and reflects it in the renovation plan. The health support unit collects and analyzes the user's health data. For example, if the user has allergies, it can propose a renovation plan that uses allergen-free materials. Also, if the user is elderly, it can propose a renovation plan that incorporates barrier-free design. Furthermore, the health support unit can adjust the progress speed and work time of the renovation plan according to the user's health condition. This can improve user satisfaction by providing a renovation plan that takes the user's health condition into consideration.

[0072] The processing flow of the first embodiment will be briefly explained below.

[0073] Step 1: The reception unit receives photos from the user. The photos from the user may be in, for example, JPEG format, PNG format, resolution, etc., but are not limited to these examples. The reception unit receives, for example, photos of a room the user wants to renovate from multiple angles. For example, photos from angles such as the front, side, and top can be received. Step 2: The generation unit uses a generation AI to analyze the photos received by the reception unit and generate a vision of the renovated room. The generation AI analyzes the photos using, for example, deep learning or computer vision technology and creates a 3D model of the room. The generation unit also customizes the vision based on the user's desired design and style. For example, the generation unit generates a vision based on a modern design, a classic design, a minimalist style, etc. Step 3: The sharing unit shares the vision generated by the generation unit with the contractor. Sharing can be done, for example, using a cloud service, email, or a specific file format. The sharing unit shares the generated vision with the contractor, allowing them to discuss specific renovation plans. Step 4: The estimation unit automatically generates a list of required materials and work based on the generated vision and creates an estimate. The estimate is automatically generated based on, for example, material costs and work costs, and a specific algorithm. Step 5: The management unit manages the renovation process based on the estimate generated by the estimation unit. The management unit performs, for example, progress management, task management, and schedule management. As a result, the renovation experience system according to the embodiment can streamline the renovation process for the user and realize their ideal living space.

[0074] (Example 2) A renovation experience system according to an embodiment of the present invention generates a vision of the renovated room based on photos taken by a user, facilitating smooth communication with contractors, obtaining estimates, and streamlining the renovation process. In the renovation experience system, a user takes photos of a room they want to renovate and uploads them to the system. A generation AI analyzes the photos and generates a realistic vision of the renovated room. This vision is customized based on the user's desired design and style. The generated vision supports smooth communication between the user and the renovation contractor, enabling them to obtain estimates and streamline the renovation process. For example, in a renovation experience system, a user takes photos of a room they want to renovate from multiple angles and uploads them to the system. The generation AI analyzes the uploaded photos and generates a realistic vision of the renovated room. The generation AI creates a 3D model of the room based on the photos and customizes it based on the user's desired design and style. The generated vision supports smooth communication between the user and the renovation contractor. The user can share the generated vision with the contractor and discuss specific renovation plans. This allows the user and the contractor to share the image of the renovation, preventing misunderstandings and mistakes. Furthermore, the renovation experience system automatically generates a list of required materials and work based on the generated vision and prepares an estimate, allowing users to obtain a quick and accurate estimate. The system also manages the progress of the renovation process, allowing the renovation to proceed efficiently. This allows users to easily imagine and realize their future living space. This allows the renovation experience system to streamline the user's renovation process and realize their ideal living space. For example, a user can upload photos of the room they want to renovate, consult with a contractor based on the vision created by the generative AI, obtain an estimate, and proceed with the renovation process efficiently.This significantly reduces the time and effort required for renovations and allows you to create your ideal living space.

[0075] A renovation experience system according to an embodiment includes a reception unit, a generation unit, a sharing unit, an estimate unit, and a management unit. The reception unit receives photos from a user. The photos from the user may be in, for example, JPEG format, PNG format, or resolution, but are not limited to these examples. The reception unit receives photos of a room the user wants to renovate from multiple angles. For example, photos from the front, side, top, and other angles may be received. The generation unit uses a generation AI to analyze the photos received by the reception unit and generate a vision of the renovated room. The generation AI analyzes the photos using, for example, deep learning or computer vision technology and creates a 3D model of the room. The generation unit also customizes the vision based on the user's desired design and style. For example, the vision may be generated based on a modern design, a classic design, a minimalist style, or the like. The sharing unit shares the vision generated by the generation unit with a contractor. Sharing is performed, for example, via a cloud service, email, or a specific file format. The sharing unit shares the generated vision with a contractor, allowing the contractor to discuss a specific renovation plan. The estimating unit automatically generates a list of required materials and work based on the generated vision and creates an estimate. The estimate is automatically generated based on, for example, material costs, work costs, and a specific algorithm. The management unit manages the renovation process based on the estimate generated by the estimating unit. The management unit performs, for example, progress management, task management, and schedule management. This allows the renovation experience system according to the embodiment to streamline the user's renovation process and realize their ideal living space. For example, the user can upload photos of the room they want to renovate, consult with a contractor based on the vision created by the generation AI, obtain an estimate, and efficiently proceed with the renovation process. This significantly reduces the effort and time required for renovation and enables them to realize their ideal living space.

[0076] The reception unit can receive photos of a room that the user wants to renovate from multiple angles. For example, the reception unit can receive photos of a room that the user wants to renovate from multiple angles. For example, it can receive photos from angles such as the front, side, and top. By receiving photos from multiple angles, it becomes easier to grasp the overall picture of the room. Some or all of the above-described processing in the reception unit can be performed using, for example, AI, or can be performed without using AI. For example, the reception unit can input photos from multiple angles uploaded by the user into the generation AI and cause the generation AI to grasp the overall picture of the room.

[0077] The generation unit can analyze photos using a generation AI and create a 3D model of the room. The generation unit, for example, uses a generation AI to analyze photos received by the reception unit and create a 3D model of the room. The generation AI analyzes photos using, for example, deep learning or computer vision technology and creates a 3D model of the room. This allows for the generation of a more realistic vision by analyzing photos and creating a 3D model. Some or all of the above-mentioned processing in the generation unit is performed using the generation AI. For example, the generation unit can input photos uploaded by a user into the generation AI and have the generation AI create a 3D model of the room.

[0078] The generation unit can customize the vision based on the design and style desired by the user. For example, the generation unit customizes the vision based on the design and style desired by the user. For example, the generation unit generates a vision based on a modern design, a classic design, a minimalist style, etc. By customizing the vision based on the user's wishes, a more satisfying renovation plan 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 can input information about the design and style desired by the user into the generation AI and have the generation AI customize the vision.

[0079] The sharing unit can share the generated vision with a contractor and discuss a specific renovation plan. The sharing unit, for example, shares the vision generated by the generation unit with a contractor. Sharing is performed, for example, using a cloud service, email, or a specific file format. The sharing unit can share the generated vision with a contractor and discuss a specific renovation plan. By sharing the generated vision with a contractor, it is possible to smoothly discuss a specific renovation plan. Some or all of the above-mentioned processing in the sharing unit may be performed using AI, or may be performed without using AI. For example, the sharing unit can share the vision by uploading the generated vision to a cloud service and granting access rights to the contractor.

[0080] The estimation unit can automatically generate a list of required materials and work based on the generated vision and create an estimate. The estimation unit can, for example, automatically generate a list of required materials and work based on the vision generated by the generation unit and create an estimate. The estimate is automatically generated based on, for example, material costs, work costs, and a specific algorithm. This makes it possible to provide a quick and accurate estimate by automatically creating an estimate based on the generated vision. Some or all of the above-mentioned processing in the estimation unit may be performed using AI, or may be performed without using AI. For example, the estimation unit can create an estimate using an AI model that takes the generated vision as input and outputs a list of required materials and work.

[0081] The management unit manages the progress of the renovation process, allowing the renovation to proceed efficiently. The management unit manages the renovation process based on, for example, an estimate generated by the estimation unit. The management unit performs, for example, progress management, task management, and schedule management. By managing the progress of the renovation process, the renovation can proceed efficiently. Some or all of the above-mentioned processing in the management unit may be performed using AI, or may be performed without using AI. For example, the management unit can build a system that monitors the progress of the renovation process in real time and visualizes the progress.

[0082] The reception unit can estimate the user's emotions and adjust the timing of accepting photos based on the estimated user emotions. The reception unit, for example, estimates the user's emotions and adjusts the timing of accepting photos based on the estimated user emotions. For example, when the user is relaxed, the reception unit flexibly sets the timing of accepting photos so that the user can upload photos at the timing most comfortable for the user. Furthermore, when the user is stressed, the burden on the user can be reduced by quickly accepting photos. Furthermore, when the user is excited, the reception unit adjusts the timing of accepting photos so that the user can upload photos smoothly while maintaining the user's excitement. In this way, the burden on the user can be reduced by adjusting the timing of accepting photos based on 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 can be, for example, 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-mentioned processing in the reception unit can be performed using AI. For example, the reception unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0083] The reception unit can analyze the user's past renovation history and select the optimal photo reception method. For example, the reception unit analyzes the user's past renovation history and selects the optimal photo reception method. For example, the reception unit analyzes photos of rooms the user has previously renovated and prioritizes receiving photos of similar rooms. The reception unit can also customize the photo reception method based on specific styles and designs from the user's past renovation history. The reception unit can also recommend taking photos at optimal angles and lighting conditions by referring to photos of rooms the user has previously renovated. In this way, the reception unit can provide the optimal photo reception method by analyzing the user's past renovation history. Some or all of the above-mentioned processing in the reception unit can be performed using AI. For example, the reception unit can input the user's past renovation history data into the generation AI and have the generation AI select the optimal photo reception method.

[0084] The reception unit can filter photos based on the user's current living environment and the purpose of the renovation when receiving the photos. For example, the reception unit can analyze the user's current living environment and accept only photos that match the purpose of the renovation. Photos can also be filtered based on specific elements (walls, floors, ceilings, etc.) of the room the user wants to renovate. Only related photos can be accepted based on the user's purpose of the renovation (design change, functional improvement, etc.). In this way, by filtering photos based on the user's living environment and the purpose of the renovation, highly relevant photos can be accepted. Some or all of the above-mentioned processing in the reception unit can be performed using AI. For example, the reception unit can input the user's living environment data and renovation purpose data into the generation AI and have the generation AI perform photo filtering.

[0085] The reception unit can select the optimal reception means according to the user's input method when receiving a photo. For example, when receiving a photo, the reception unit selects the optimal reception means according to the user's input method (voice, text, image, etc.). For example, if the user gives instructions by voice, the reception unit can prioritize voice input and accept photos. Also, if the user gives instructions by text, the reception unit can prioritize text input and accept photos. Also, if the user uploads an image, the reception unit can prioritize image input and accept photos. This allows for selecting the optimal reception means according to the user's input method, thereby improving user convenience. Some or all of the above-mentioned processing in the reception unit can be performed using AI. For example, the reception unit can input the user's input method data to the generation AI and cause the generation AI to select the optimal reception means.

[0086] The reception unit can estimate the user's emotions and determine the priority of photos to be accepted based on the estimated user emotions. For example, the reception unit can estimate the user's emotions and determine the priority of photos to be accepted based on the estimated user emotions. For example, if the user is relaxed, photos with high importance can be preferentially accepted. Also, if the user is stressed, photos that can be processed quickly can be preferentially accepted. Also, if the user is excited, interesting photos can be preferentially accepted to maintain the user's excitement. In this way, by determining the priority of photos based on the user's emotions, photos that match the user's interests and concerns can be preferentially accepted. Emotion estimation is realized 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-mentioned processing in the reception unit is performed using AI. For example, the reception unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate emotions.

[0087] When accepting photos, the reception unit can prioritize accepting photos that are highly relevant based on the user's geographical location information. For example, when accepting photos, the reception unit prioritizes accepting photos that are highly relevant based on the user's geographical location information. For example, if the user lives in a specific area, photos related to that area can be prioritized. Furthermore, based on the user's geographical location information, photos related to nearby renovation examples can also be prioritized. Furthermore, taking the user's geographical location information into consideration, photos related to designs and styles unique to the area can be prioritized. In this way, by preferentially accepting photos that are highly relevant based on the user's geographical location information, it is possible to accommodate designs and styles unique to the area. Some or all of the above-mentioned processing in the reception unit is performed using AI. For example, the reception unit can input the user's geographical location information data into the generation AI and cause the generation AI to select highly relevant photos.

[0088] The reception unit can analyze the user's social media activity and accept related photos when accepting a photo. For example, the reception unit analyzes the user's social media activity and accepts related photos when accepting a photo. For example, the reception unit may preferentially accept related photos based on photos shared by the user on social media. The reception unit can also analyze the user's social media activity and accept related photos. The reception unit can also accept related photos with reference to the activity of the user's friends on social media. In this way, by analyzing the user's social media activity, it is possible to accept highly relevant photos. Some or all of the above-mentioned processing in the reception unit can be performed using AI. For example, the reception unit can input the user's social media activity data into the generation AI and cause the generation AI to select related photos.

[0089] The reception unit can customize the reception method by reflecting the user's past feedback when receiving a photo. For example, the reception unit customizes the reception method by reflecting the user's past feedback when receiving a photo. For example, the reception unit selects the optimal photo reception method based on feedback provided by the user in the past. It can also preferentially apply a specific reception method based on the user's past feedback. It can also customize the reception method by reflecting the user's past feedback to improve user satisfaction. In this way, it is possible to improve user satisfaction by reflecting the user's past feedback. Some or all of the above-mentioned processing in the reception unit is performed using AI. For example, the reception unit can input the user's past feedback data into the generation AI and cause the generation AI to customize the reception method.

[0090] The generation unit can estimate the user's emotions and adjust the way the vision is expressed based on the estimated user emotions. For example, the generation unit can estimate the user's emotions and adjust the way the vision is expressed based on the estimated user emotions. For example, if the user is relaxed, the generation unit can generate a vision with calm colors and a design. If the user is excited, the generation unit can generate a vision with vivid colors and a dynamic design. If the user is stressed, the generation unit can generate a simple, visually relaxing vision. This allows the user's satisfaction to be improved by adjusting the way the vision is expressed based on 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 generation unit can be performed using the generation AI. For example, the generation unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the way the vision is expressed.

[0091] The generation unit can adjust the level of detail of the vision based on the importance of the renovation when generating the vision. For example, the generation unit can adjust the level of detail of the vision based on the importance of the renovation when generating the vision. For example, if the importance of the renovation is high, a detailed vision can be generated. On the other hand, if the importance of the renovation is low, a simplified vision can be generated. The level of detail of the vision can also be dynamically adjusted according to the importance of the renovation. This makes it possible to provide a vision that meets the user's needs by adjusting the level of detail of the vision based on the importance of the renovation. Some or all of the above-mentioned processing in the generation unit is performed using a generation AI. For example, the generation unit can input renovation importance data into the generation AI and have the generation AI adjust the level of detail of the vision.

[0092] The generation unit can apply different generation algorithms depending on the category of renovation when generating a vision. For example, the generation unit applies different generation algorithms depending on the category of renovation when generating a vision. For example, in the case of kitchen renovation, a generation algorithm dedicated to kitchens can be applied to generate a vision specialized for kitchens. In addition, in the case of bathroom renovation, a generation algorithm dedicated to bathrooms can be applied to generate a vision specialized for bathrooms. In addition, in the case of living room renovation, a generation algorithm dedicated to living rooms can be applied to generate a vision specialized for living rooms. In this way, by applying different generation algorithms depending on the category of renovation, it is possible to generate a vision optimal for each category. Some or all of the above-mentioned processing in the generation unit is performed using a generation AI. For example, the generation unit can input renovation category data into the generation AI and cause the generation AI to apply different generation algorithms.

[0093] The generation unit can improve the accuracy of generation by referring to the user's past vision results when generating a vision. For example, the generation unit can improve the accuracy of generation by referring to the user's past vision results when generating a vision. For example, the generation unit can improve the accuracy of the current vision by referring to visions generated by the user in the past. Also, it can preferentially apply specific designs or styles from the user's past vision results. It can also analyze the user's past vision results and optimize the generation algorithm. In this way, it is possible to improve the accuracy of generation by referring to the user's past vision results. Some or all of the above-mentioned processing in the generation unit is performed using a generation AI. For example, the generation unit can input the user's past vision result data into the generation AI and cause the generation AI to improve the accuracy of generation.

[0094] The generation unit can estimate the user's emotions and adjust the length of the vision based on the estimated user emotions. For example, the generation unit can estimate the user's emotions and adjust the length of the vision based on the estimated user emotions. For example, if the user is relaxed, a detailed vision can be generated and a longer vision can be provided. If the user is in a hurry, a concise vision can be generated and a shorter vision can be provided. If the user is excited, a visually stimulating vision can be generated and a vision of appropriate length can be provided. This allows for adjusting the length of the vision based on the user's emotions to provide a vision that meets the user's needs. 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 can be performed using the generation AI. For example, the generation unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the length of the vision.

[0095] The generation unit can determine the priority of visions based on the time of submission of renovations when generating visions. For example, the generation unit determines the priority of visions based on the time of submission of renovations when generating visions. For example, if the time of submission of renovations is approaching, the generation of visions is prioritized. Also, if the time of submission of renovations is far away, the generation of visions can be postponed. Also, the order in which visions are generated can be dynamically adjusted depending on the time of submission of renovations. This enables efficient vision generation by determining the priority of visions based on the time of submission of renovations. Some or all of the above-mentioned processing in the generation unit is performed using a generation AI. For example, the generation unit can input data on the time of submission of renovations into the generation AI and have the generation AI determine the priority of visions.

[0096] The generation unit can adjust the order of visions based on the relevance of the renovations when generating visions. For example, the generation unit adjusts the order of visions based on the relevance of the renovations when generating visions. For example, if the relevance of the renovations is high, the visions can be generated preferentially. Also, if the relevance of the renovations is low, the visions can be generated later. The generation order of visions can also be dynamically adjusted according to the relevance of the renovations. In this way, by adjusting the order of visions based on the relevance of the renovations, highly relevant visions can be generated preferentially. Some or all of the above-mentioned processing in the generation unit can be performed using a generation AI. For example, the generation unit can input relevance data of the renovations into the generation AI and have the generation AI adjust the order of the visions.

[0097] The generation unit can adjust the use of technical terms in the vision according to the user's level of expertise when generating the vision. For example, the generation unit can adjust the use of technical terms in the vision according to the user's level of expertise when generating the vision. For example, if the user has technical expertise, the generation unit can generate a vision that uses a lot of technical terms. On the other hand, if the user does not have technical expertise, the generation unit can generate a vision that explains things in simple terms. The generation unit can also dynamically adjust the use of technical terms in the vision according to the user's level of expertise. This makes it possible to provide a vision that is easy for the user to understand by adjusting the use of technical terms in the vision 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 can input the user's level of expertise data into the generation AI and cause the generation AI to adjust the use of technical terms in the vision.

[0098] The sharing unit can estimate the user's emotions and adjust the vision sharing method based on the estimated user emotions. For example, the sharing unit estimates the user's emotions and adjusts the vision sharing method based on the estimated user emotions. For example, if the user is relaxed, a detailed vision can be shared and explained until the user is satisfied. If the user is in a hurry, a concise vision can be shared to quickly convey information. If the user is excited, a visually stimulating vision can be shared to maintain the user's excitement. This allows the user's satisfaction to be improved by adjusting the vision sharing method based on 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 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-mentioned processing in the sharing unit can be performed using AI. For example, the sharing unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the vision sharing method.

[0099] The sharing unit can adjust the level of detail of the shared information based on the importance of the renovation when sharing the vision. For example, when sharing the vision, the sharing unit adjusts the level of detail of the shared information based on the importance of the renovation. For example, if the importance of the renovation is high, a detailed vision can be shared. On the other hand, if the importance of the renovation is low, a simplified vision can be shared. The level of detail of the shared information can also be dynamically adjusted according to the importance of the renovation. This makes it possible to share information according to the user's needs by adjusting the level of detail of the shared information based on the importance of the renovation. Some or all of the above-mentioned processing in the sharing unit can be performed using AI. For example, the sharing unit can input the importance data of the renovation into the generating AI and have the generating AI adjust the level of detail of the shared information.

[0100] When sharing a vision, the sharing unit can apply different sharing algorithms depending on the category of the renovation. For example, when sharing a vision, the sharing unit applies different sharing algorithms depending on the category of the renovation. For example, in the case of a kitchen renovation, a sharing algorithm dedicated to kitchens can be applied to provide information specialized for the kitchen. In addition, in the case of a bathroom renovation, a sharing algorithm dedicated to bathrooms can be applied to provide information specialized for the bathroom. In addition, in the case of a living room renovation, a sharing algorithm dedicated to living rooms can be applied to provide information specialized for the living room. In this way, by applying different sharing algorithms depending on the category of the renovation, optimal information sharing for each category becomes possible. Some or all of the above-mentioned processing in the sharing unit is performed using AI. For example, the sharing unit can input renovation category data into a generation AI and have the generation AI apply different sharing algorithms.

[0101] The sharing unit can improve the accuracy of sharing by referring to the user's past sharing results when sharing a vision. For example, the sharing unit can improve the accuracy of sharing by referring to the user's past sharing results when sharing a vision. For example, the sharing unit can improve the accuracy of sharing the current vision by referring to visions shared by the user in the past. Furthermore, it can prioritize sharing specific designs or styles based on the user's past sharing results. It can also analyze the user's past sharing results and optimize the sharing algorithm. This can improve the accuracy of sharing by referring to the user's past sharing results. Some or all of the above-mentioned processing in the sharing unit can be performed using AI. For example, the sharing unit can input the user's past sharing result data into the generation AI and cause the generation AI to improve the accuracy of sharing.

[0102] The sharing unit can estimate the user's emotions and adjust the order in which visions are shared based on the estimated user emotions. For example, the sharing unit estimates the user's emotions and adjusts the order in which visions are shared based on the estimated user emotions. For example, if the user is relaxed, it can prioritize sharing of important visions. Also, if the user is in a hurry, it can prioritize sharing of visions that can be processed quickly. Also, if the user is excited, it can prioritize sharing of interesting visions to maintain the user's excitement. This enables information sharing according to the user's interests and concerns by adjusting the order in which visions are shared based on the user's emotions. The emotion estimation is realized 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 sharing unit is performed using AI. For example, the sharing unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the order in which visions are shared.

[0103] The sharing unit can determine the priority of sharing based on the time of submission of the renovations when sharing the vision. For example, when sharing the vision, the sharing unit determines the priority of sharing based on the time of submission of the renovations. For example, if the time of submission of the renovations is approaching, the vision is shared as a priority. Also, if the time of submission of the renovations is far away, the vision can be shared at a later date. Also, the order in which the visions are shared can be dynamically adjusted depending on the time of submission of the renovations. This enables efficient information sharing by determining the priority of sharing based on the time of submission of the renovations. Some or all of the above-mentioned processing in the sharing unit is performed using AI. For example, the sharing unit can input data on the time of submission of the renovations into the generation AI and have the generation AI determine the priority of sharing.

[0104] The sharing unit can adjust the order of sharing based on the relevance of the renovations when sharing a vision. For example, the sharing unit adjusts the order of sharing based on the relevance of the renovations when sharing a vision. For example, if the relevance of the renovations is high, the vision is shared as a priority. Also, if the relevance of the renovations is low, the vision can be shared at a later date. Also, the order of sharing visions can be dynamically adjusted according to the relevance of the renovations. In this way, by adjusting the order of sharing based on the relevance of the renovations, highly relevant information can be shared preferentially. Some or all of the above-mentioned processing in the sharing unit is performed using AI. For example, the sharing unit can input the relevance data of the renovations into a generating AI and have the generating AI adjust the order of sharing.

[0105] The sharing unit can adjust the use of technical terms in the vision according to the user's level of expertise when sharing the vision. For example, when sharing the vision, the sharing unit adjusts the use of technical terms in the vision according to the user's level of expertise. For example, if the user has technical expertise, the sharing unit can share a vision that uses a lot of technical terms. On the other hand, if the user does not have technical expertise, the sharing unit can share a vision that is explained in simple terms. The use of technical terms in the vision can also be dynamically adjusted according to the user's level of expertise. This makes it possible to provide information that is easy for the user to understand by adjusting the use of technical terms in the vision according to the user's level of expertise. Some or all of the above-mentioned processing in the sharing unit can be performed using AI. For example, the sharing unit can input the user's level of expertise data into the generation AI and cause the generation AI to adjust the use of technical terms in the vision.

[0106] The estimation unit can estimate the user's emotions and adjust the expression of the estimate based on the estimated user's emotions. For example, the estimation unit estimates the user's emotions and adjusts the expression of the estimate based on the estimated user's emotions. For example, if the user is relaxed, a detailed estimate can be provided and explained until the user is satisfied. If the user is in a hurry, a concise estimate can be provided to quickly convey information. If the user is excited, a visually stimulating estimate can be provided to maintain the user's excitement. This allows the user's satisfaction to be improved by adjusting the expression of the estimate based on 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 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-mentioned processing in the estimation unit can be performed using AI. For example, the estimation unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the expression of the estimate.

[0107] The estimating unit can adjust the level of detail of the estimate based on the importance of the renovation when generating an estimate. For example, when generating an estimate, the estimating unit adjusts the level of detail of the estimate based on the importance of the renovation. For example, if the importance of the renovation is high, a detailed estimate can be generated. On the other hand, if the importance of the renovation is low, a simplified estimate can be generated. The level of detail of the estimate can also be dynamically adjusted according to the importance of the renovation. This makes it possible to provide an estimate that meets the user's needs by adjusting the level of detail of the estimate based on the importance of the renovation. Some or all of the above-mentioned processing in the estimating unit can be performed using AI. For example, the estimating unit can input the importance data of the renovation into the generating AI and have the generating AI adjust the level of detail of the estimate.

[0108] The estimating unit can apply different estimation algorithms depending on the category of renovation when generating an estimate. For example, when generating an estimate, the estimating unit applies different estimation algorithms depending on the category of renovation. For example, in the case of a kitchen renovation, an estimation algorithm dedicated to kitchens can be applied to generate an estimate specialized for the kitchen. In addition, in the case of a bathroom renovation, an estimation algorithm dedicated to bathrooms can be applied to generate an estimate specialized for the bathroom. In addition, in the case of a living room renovation, an estimation algorithm dedicated to living rooms can be applied to generate an estimate specialized for the living room. In this way, by applying different estimation algorithms depending on the category of renovation, it is possible to provide an optimal estimate for each category. Some or all of the above-mentioned processing in the estimating unit is performed using AI. For example, the estimating unit can input renovation category data into the generating AI and cause the generating AI to apply different estimation algorithms.

[0109] The estimating unit can improve the accuracy of an estimate by referring to the user's past estimate results when generating an estimate. For example, the estimating unit can improve the accuracy of a current estimate by referring to estimates obtained by the user in the past. Specific materials or tasks can be prioritized based on the user's past estimate results. The estimating unit can also analyze the user's past estimate results and optimize the estimation algorithm. By referring to the user's past estimate results, the accuracy of the estimate can be improved. Some or all of the above-described processing in the estimating unit can be performed using AI. For example, the estimating unit can input the user's past estimate result data into the generating AI and have the generating AI improve the accuracy of the estimate.

[0110] The estimation unit can estimate the user's emotion and adjust the length of the estimate based on the estimated user's emotion. For example, the estimation unit estimates the user's emotion and adjusts the length of the estimate based on the estimated user's emotion. For example, if the user is relaxed, the estimation unit can provide a detailed estimate and generate a longer estimate. If the user is in a hurry, the estimation unit can provide a concise estimate and generate a shorter estimate. If the user is excited, the estimation unit can provide a visually stimulating estimate and generate an estimate of appropriate length. This allows the estimation unit to provide an estimate that meets the user's needs by adjusting the length of the estimate based on the user's emotion. Emotion estimation is realized 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-mentioned processing in the estimation unit can be performed using AI. For example, the estimation unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the length of the estimate.

[0111] The estimating unit can determine the priority of estimates based on the submission date of the renovations when generating estimates. The estimating unit, for example, determines the priority of estimates based on the submission date of the renovations when generating estimates. For example, if the submission date of the renovations is approaching, the estimates are generated as a priority. Also, if the submission date of the renovations is far away, the estimates can be generated at a later date. The order in which estimates are generated can also be dynamically adjusted depending on the submission date of the renovations. This enables efficient estimate generation by determining the priority of estimates based on the submission date of the renovations. Some or all of the above-mentioned processing in the estimating unit is performed using AI. For example, the estimating unit can input data on the submission date of the renovations into the generation AI and have the generation AI determine the priority of estimates.

[0112] The estimating unit can adjust the order of estimates based on the relevance of the renovations when generating estimates. For example, the estimating unit adjusts the order of estimates based on the relevance of the renovations when generating estimates. For example, if the relevance of the renovations is high, the estimates can be generated as a priority. Also, if the relevance of the renovations is low, the estimates can be generated at a later date. The order of estimate generation can also be dynamically adjusted according to the relevance of the renovations. In this way, by adjusting the order of estimates based on the relevance of the renovations, highly relevant estimates can be generated as a priority. Some or all of the above-mentioned processing in the estimating unit can be performed using AI. For example, the estimating unit can input relevance data of the renovations into the generation AI and have the generation AI adjust the order of the estimates.

[0113] The estimating unit can adjust the use of technical terminology in the estimate according to the user's level of expertise when generating an estimate. For example, when generating an estimate, the estimating unit adjusts the use of technical terminology in the estimate according to the user's level of expertise. For example, if the user has technical expertise, the estimating unit can generate an estimate that uses a lot of technical terminology. On the other hand, if the user does not have technical expertise, the estimating unit can generate an estimate that explains things in simple terms. The use of technical terminology in the estimate can also be dynamically adjusted according to the user's level of expertise. This makes it possible to provide an estimate that is easy for the user to understand by adjusting the use of technical terminology in the estimate according to the user's level of expertise. Some or all of the above-mentioned processing in the estimating unit can be performed using AI. For example, the estimating unit can input the user's level of expertise data into the generating AI and cause the generating AI to adjust the use of technical terminology in the estimate.

[0114] The management unit can estimate the user's emotions and adjust the management method of the renovation process based on the estimated user emotions. For example, the management unit estimates the user's emotions and adjusts the management method of the renovation process based on the estimated user emotions. For example, if the user is relaxed, the management unit can provide detailed progress updates and explain the progress until the user is satisfied. If the user is in a hurry, the management unit can provide concise progress updates and quickly convey information. If the user is excited, the management unit can provide visually stimulating progress updates to maintain the user's excitement. This improves user satisfaction by adjusting the management method of the renovation process based on 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 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-mentioned processing in the management unit can be performed using AI. For example, the management unit can input the user's emotion data into the generation AI and have the generation AI adjust the management method of the renovation process.

[0115] The management unit can adjust the level of detail of management based on the importance of the renovation when managing the renovation process. For example, when managing the renovation process, the management unit adjusts the level of detail of management based on the importance of the renovation. For example, when the importance of the renovation is high, detailed management can be performed. On the other hand, when the importance of the renovation is low, simplified management can be performed. Furthermore, the level of detail of management can be dynamically adjusted according to the importance of the renovation. This allows management according to the user's needs by adjusting the level of detail of management based on the importance of the renovation. Some or all of the above-mentioned processing in the management unit can be performed using AI. For example, the management unit can input the importance data of the renovation into the generation AI and have the generation AI adjust the level of detail of management.

[0116] The management unit can apply different management algorithms depending on the category of the renovation when managing the renovation process. For example, when managing the renovation process, the management unit applies different management algorithms depending on the category of the renovation. For example, in the case of a kitchen renovation, a management algorithm dedicated to kitchens can be applied to perform management specialized for the kitchen. In addition, in the case of a bathroom renovation, a management algorithm dedicated to bathrooms can be applied to perform management specialized for the bathroom. In addition, in the case of a living room renovation, a management algorithm dedicated to living rooms can be applied to perform management specialized for the living room. In this way, by applying different management algorithms depending on the category of the renovation, optimal management for each category is possible. Some or all of the above-mentioned processing in the management unit is performed using AI. For example, the management unit can input renovation category data into a generation AI and have the generation AI apply different management algorithms.

[0117] The management unit can improve the accuracy of management by referring to the user's past management results when managing a renovation process. For example, the management unit can improve the accuracy of management by referring to the user's past management results when managing a renovation process. For example, the management unit can improve the accuracy of current management by referring to renovation processes that the user has managed in the past. In addition, it can preferentially apply specific management methods based on the user's past management results. It can also analyze the user's past management results and optimize the management algorithm. This makes it possible to improve the accuracy of management by referring to the user's past management results. Some or all of the above-mentioned processing in the management unit is performed using AI. For example, the management unit can input the user's past management result data into a generation AI and have the generation AI improve the accuracy of management.

[0118] The management unit can estimate the user's emotions and adjust the management order of the renovation process based on the estimated user emotions. For example, the management unit estimates the user's emotions and adjusts the management order of the renovation process based on the estimated user emotions. For example, if the user is relaxed, it can prioritize management of processes with high importance. Also, if the user is in a hurry, it can prioritize management of processes that can be completed quickly. Also, if the user is excited, it can prioritize management of interesting processes to maintain the user's excitement. This allows management according to the user's interests and concerns by adjusting the management order of the renovation process based on 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 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 management unit is performed using AI. For example, the management unit can input the user's emotion data into the generation AI and have the generation AI adjust the management order of the renovation process.

[0119] The management unit can determine management priorities based on the timing of renovation submissions when managing renovation processes. For example, when managing renovation processes, the management unit determines management priorities based on the timing of renovation submissions. For example, if the renovation submission deadline is approaching, the management of the process can be prioritized. Also, if the renovation submission deadline is far away, the management of the process can be postponed. The process management order can also be dynamically adjusted depending on the timing of renovation submission. This enables efficient management by determining management priorities based on the timing of renovation submissions. Some or all of the above-mentioned processing in the management unit can be performed using AI. For example, the management unit can input renovation submission deadline data into a generation AI and have the generation AI determine management priorities.

[0120] The management unit can adjust the management order based on the relevance of renovations when managing renovation processes. For example, when managing renovation processes, the management unit adjusts the management order based on the relevance of renovations. For example, if the relevance of renovations is high, the process can be managed as a priority. Also, if the relevance of renovations is low, the process can be managed at a later date. Furthermore, the management order of processes can be dynamically adjusted according to the relevance of renovations. In this way, by adjusting the management order based on the relevance of renovations, highly relevant processes can be managed as a priority. Some or all of the above-mentioned processing in the management unit is performed using AI. For example, the management unit can input relevance data of renovations into a generation AI and have the generation AI adjust the management order.

[0121] The management unit can adjust the use of management terminology according to the user's level of expertise when managing a renovation process. For example, when managing a renovation process, the management unit adjusts the use of management terminology according to the user's level of expertise. For example, if the user has expertise, the management unit can perform management that makes extensive use of technical terms. On the other hand, if the user does not have expertise, the management unit can perform management that is explained in simple terms. The use of management terminology can also be dynamically adjusted according to the user's level of expertise. This allows for management that is easy for the user to understand by adjusting the use of management terminology according to the user's level of expertise. Some or all of the above-mentioned processing in the management unit can be performed using AI. For example, the management unit can input the user's level of expertise data into a generation AI and have the generation AI adjust the use of management terminology. === Hard Collateral 1-1 === Each of the multiple elements, including the reception unit, generation unit, sharing unit, estimate unit, and management 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 reception unit can receive photos from a user using the camera 42 or microphone 38B of the smart device 14. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the photos using a generation AI to generate a vision of the remodeled home. The sharing unit is realized by the control unit 46A of the smart device 14 as a processing unit that shares the generated vision with contractors via a cloud service or email. The estimate unit is realized by the specific processing unit 290 of the data processing device 12 and creates an estimate based on the generated vision. The management unit is realized by the specific processing unit 290 of the data processing device 12 and manages the remodeling process. === Hard Collateral 1-2 === Each of the multiple elements, including the above-mentioned reception unit, generation unit, sharing unit, estimate unit, and management unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit can receive photos from a user using the camera 42 or microphone 238 of the smart glasses 214. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the photos using a generation AI to generate a vision of the remodeled home. The sharing unit is realized by the control unit 46A of the smart glasses 214 as a processing unit that shares the generated vision with contractors via a cloud service or email. The estimate unit is realized by the specific processing unit 290 of the data processing device 12 and creates an estimate based on the generated vision. The management unit is realized by the specific processing unit 290 of the data processing device 12 and manages the remodeling process. === Hard Collateral 1-3 === Each of the multiple elements, including the above-mentioned reception unit, generation unit, sharing unit, estimate unit, and management unit, is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit can receive photos from a user using the camera 42 or microphone 238 of the headset-type terminal 314. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the photos using a generation AI to generate a vision of the remodeled home. The sharing unit is realized by the control unit 46A of the headset-type terminal 314 as a processing unit that shares the generated vision with contractors via a cloud service or email. The estimate unit is realized by the specific processing unit 290 of the data processing device 12 and creates an estimate based on the generated vision. The management unit is realized by the specific processing unit 290 of the data processing device 12 and manages the remodeling process. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, generation unit, sharing unit, estimate unit, and management unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit can receive photos from a user using the camera 42 or microphone 238 of the robot 414. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the photos using a generation AI to generate a vision of the post-renovation area. The sharing unit is realized by the control unit 46A of the robot 414 as a processing unit that shares the generated vision with contractors via a cloud service or email. The estimate unit is realized by the specific processing unit 290 of the data processing device 12 and creates an estimate based on the generated vision. The management unit is realized by the specific processing unit 290 of the data processing device 12 and manages the renovation process.

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

[0123] The renovation experience system can further include a history analysis unit that analyzes the user's past renovation history. The history analysis unit collects and analyzes data on renovations the user has undertaken in the past. For example, it can identify the success and failure factors of past renovation projects and reflect them in the current renovation plan. It can also understand trends in designs and styles chosen by the user in the past and provide this information to the generation unit to generate a vision that better suits the user's preferences. Furthermore, the history analysis unit can provide the estimating unit with information on the costs and duration of past renovation projects as a reference, allowing it to create more accurate estimates.

[0124] The renovation experience system may further include an emotion response suggestion unit that estimates the user's emotions and proposes a renovation plan based on the estimated emotions. The emotion response suggestion unit monitors the user's emotions in real time, and if the user is relaxed, it can propose a renovation plan with a calming design. If the user is feeling stressed, it can propose a simple, hassle-free renovation plan. If the user is excited, it can propose a renovation plan with a novel and stimulating design. This makes it possible to improve user satisfaction by providing a renovation plan that matches the user's emotions.

[0125] The renovation experience system may further include a lifestyle analysis unit that analyzes the user's lifestyle. The lifestyle analysis unit collects and analyzes the user's daily life patterns and preferences. For example, it can determine which rooms the user frequently uses at what time of day and optimize the renovation plan based on that information. It can also analyze the user's preferred colors and materials and provide the analysis results to the generation unit to generate a vision that better suits the user's preferences. Furthermore, the lifestyle analysis unit can customize the renovation plan by taking into account information such as the user's health condition and family composition.

[0126] The home renovation experience system may further include an emotion-responsive progress unit that estimates the user's emotions and adjusts the progress of the home renovation process based on the estimated emotions. The emotion-responsive progress unit monitors the user's emotions in real time, and if the user is relaxed, it can provide detailed progress information and explain the process until the user is satisfied. If the user is in a hurry, it can provide concise progress information and quickly convey information. Furthermore, if the user is excited, it can provide visually stimulating progress information to maintain the user's excitement. In this way, adjusting the progress of the home renovation process based on the user's emotions can improve user satisfaction.

[0127] The renovation experience system can further include a geographic information support unit that optimizes renovation plans based on the user's geographic location information. The geographic information support unit customizes renovation plans taking into account the climate, culture, architectural style, etc. of the user's region. For example, a renovation plan using materials with high thermal insulation performance can be proposed to a user living in a cold region. A renovation plan incorporating the traditional designs and styles of a particular region can also be proposed. Furthermore, the geographic information support unit can provide legal renovation plans taking into account local building regulations and laws.

[0128] The home improvement experience system may further include an emotion-responsive estimate unit that estimates the user's emotions and adjusts the way the estimate is presented based on the estimated emotions. The emotion-responsive estimate unit monitors the user's emotions in real time, and if the user is relaxed, it can provide a detailed estimate and explain until the user is satisfied. If the user is in a hurry, it can provide a concise estimate and quickly convey information. Furthermore, if the user is excited, it can provide a visually stimulating estimate to maintain the user's excitement. In this way, by adjusting the way the estimate is presented based on the user's emotions, it is possible to improve user satisfaction.

[0129] The renovation experience system may further include a social media analysis unit that analyzes users' social media activities. The social media analysis unit collects and analyzes photos and posts shared by users on social media. For example, the social media analysis unit can identify designs and styles that have received many likes and comments on social media and optimize renovation plans based on that information. The system may also suggest renovation plans that incorporate trends by referring to renovation examples of the user's friends and followers. Furthermore, the social media analysis unit can identify users' interests and expectations regarding renovations through their social media activities and reflect these in renovation plans.

[0130] The renovation experience system may further include an emotion response priority unit that estimates the user's emotions and prioritizes renovation plans based on the estimated emotions. The emotion response priority unit monitors the user's emotions in real time, and if the user is relaxed, it can prioritize suggesting renovation plans with high importance. If the user is in a hurry, it can prioritize suggesting renovation plans that can be implemented quickly. If the user is excited, it can prioritize suggesting renovation plans that are visually stimulating. Thus, by prioritizing renovation plans based on the user's emotions, it is possible to improve user satisfaction.

[0131] The renovation experience system can further include a health support unit that monitors the user's health condition and reflects it in the renovation plan. The health support unit collects and analyzes the user's health data. For example, if the user has allergies, it can propose a renovation plan that uses allergen-free materials. Also, if the user is elderly, it can propose a renovation plan that incorporates barrier-free design. Furthermore, the health support unit can adjust the progress speed and work time of the renovation plan according to the user's health condition. This can improve user satisfaction by providing a renovation plan that takes the user's health condition into consideration.

[0132] The home improvement experience system may further include an emotion-responsive feedback unit that estimates the user's emotions and adjusts the feedback of the home improvement plan based on the estimated emotions. The emotion-responsive feedback unit monitors the user's emotions in real time, and if the user is relaxed, it can provide detailed feedback and explain until the user is satisfied. If the user is in a hurry, it can provide concise feedback and quickly convey information. Furthermore, if the user is excited, it can provide visually stimulating feedback to maintain the user's excitement. Thus, by adjusting the feedback of the home improvement plan based on the user's emotions, it is possible to improve user satisfaction.

[0133] The processing flow of the second embodiment will be briefly explained below.

[0134] Step 1: The reception unit receives photos from the user. The photos from the user may be in, for example, JPEG format, PNG format, resolution, etc., but are not limited to these examples. The reception unit receives, for example, photos of a room the user wants to renovate from multiple angles. For example, photos from angles such as the front, side, and top can be received. Step 2: The generation unit uses a generation AI to analyze the photos received by the reception unit and generate a vision of the renovated room. The generation AI analyzes the photos using, for example, deep learning or computer vision technology and creates a 3D model of the room. The generation unit also customizes the vision based on the user's desired design and style. For example, the generation unit generates a vision based on a modern design, a classic design, a minimalist style, etc. Step 3: The sharing unit shares the vision generated by the generation unit with the contractor. Sharing can be done, for example, using a cloud service, email, or a specific file format. The sharing unit shares the generated vision with the contractor, allowing them to discuss specific renovation plans. Step 4: The estimation unit automatically generates a list of required materials and work based on the generated vision and creates an estimate. The estimate is automatically generated based on, for example, material costs and work costs, and a specific algorithm. Step 5: The management unit manages the renovation process based on the estimate generated by the estimation unit. The management unit performs, for example, progress management, task management, and schedule management. As a result, the renovation experience system according to the embodiment can streamline the renovation process for the user and realize their ideal living space.

[0135] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating 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.

[0136] 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.

[0137] 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.

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

[0140] 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.

[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 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.

[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. 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.

[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 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.

[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 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.

[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 AI 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 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.

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

[0156] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[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 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.

[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 (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).

[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] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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 AI 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.

[0169] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is 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.

[0170] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

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

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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).

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the 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.

[0185] 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 AI 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.

[0186] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is 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.

[0187] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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).

[0192] 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 "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0193] 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."

[0194] 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.

[0195] 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.

[0196] 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.

[0197] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] 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.

[0205] 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.

[0206] [Explanation of symbols]

[0207] 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 reception unit that receives a photo from a user; A generation unit that analyzes the photos received by the reception unit and generates a vision of the area after renovation; a sharing unit that shares the vision generated by the generation unit with a contractor; an estimate unit that automatically generates an estimate based on the vision shared by the sharing unit; a management unit that manages the renovation process based on the estimate generated by the estimate unit. A system characterized by:

2. The reception unit Users can submit photos of the room they want to renovate from multiple angles.

2. The system of claim 1.

3. The generation unit Generative AI analyzes photos and creates a 3D model of the room 2. The system of claim 1.

4. The generation unit Customize your vision based on your desired design and style 2. The system of claim 1.

5. The common part is Share the generated vision with the contractor and discuss the specific renovation plan.

2. The system of claim 1.

6. The estimation unit Automatically generate a list of materials and work required based on the generated vision, and create a quote 2. The system of claim 1.

7. The management unit Manage the progress of the renovation process and proceed efficiently 2. The system of claim 1.

8. The reception unit Estimates the user's emotions and adjusts the timing of accepting photos based on the estimated user emotions.

2. The system of claim 1.

Images