System

Abstract

An object of a system according to an embodiment is to enable residents to efficiently collect information and exchange opinions on renovation.SOLUTION: A system according to an embodiment includes a question reception unit, a design proposal unit, a business operator selection unit, a feedback collection unit, and a communication promotion unit. The question reception unit receives a question or a request regarding a renovation from a resident. The design proposing unit proposes a design plan of the renovation on the basis of the question or the request received by the question receiving unit. The contractor selection unit selects an optimum contractor on the basis of the design plan proposed by the design proposal unit. The feedback collection unit collects feedback from the residents after the renovation is completed. The communication promotion unit promotes communication between residents and supports planning of an event in a community.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 problem of making it difficult for residents to efficiently gather information and exchange opinions about renovations.

[0005] The system according to the embodiment aims to enable residents to efficiently gather information and exchange opinions about renovations. [Means for solving the problem]

[0006] The system according to the embodiment includes a question receiving unit, a design proposal unit, a contractor selection unit, a feedback collection unit, and a communication promotion unit. The question receiving unit receives questions or requests regarding renovation from residents. The design proposal unit proposes a renovation design plan based on the questions or requests received by the question receiving unit. The contractor selection unit selects the most suitable construction company based on the design plan proposed by the design proposal unit. The feedback collection unit collects feedback from residents after the renovation is completed. The communication promotion unit promotes communication between residents and supports event planning within the community. [Effects of the Invention]

[0007] The system according to the embodiment enables residents to efficiently collect information and exchange opinions regarding renovations. [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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[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 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[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 platform according to an embodiment of the present invention allows residents to easily gather information and exchange opinions about renovations. This platform integrates generative AI to support everything from sharing ideas for renovation projects to planning, selecting contractors, and sharing feedback after implementation. This allows the renovation platform to improve the quality of residents' lifestyles, quickly responding to questions about renovations, proposing design ideas, and providing information about local contractors.

[0029] A renovation platform according to an embodiment includes a question receiving unit, a design proposal unit, a contractor selection unit, a feedback collection unit, and a communication promotion unit. The question receiving unit receives questions and requests from residents regarding renovations. For example, if a resident inputs a question such as, "What kind of design would be good for renovating my living room?", the question receiving unit accepts the question. The question receiving unit also analyzes the resident's past renovation history and provides customized renovation information optimal for each resident. For example, the question receiving unit analyzes the types of renovations previously undertaken, the materials used, and design trends, and provides information tailored to the resident's preferences. The question receiving unit also learns the resident's lifestyle and hobbies and preferences and makes renovation proposals based on those information. For example, if a resident enjoys the outdoors, the design proposal unit proposes renovation ideas for the garden or balcony. The design proposal unit proposes renovation design ideas based on the questions and requests received by the question receiving unit. For example, if a resident inputs a request such as, "I want to renovate my kitchen in a modern style," the design proposal unit generates specific design proposals and presents them to the resident. The design proposal unit also considers the resident's family structure and future life events to propose design proposals from a long-term perspective. For example, it proposes a design proposal that allows for future room division in anticipation of children growing up. Furthermore, the design proposal unit uses an emotion estimation function to analyze the emotions residents have toward the design proposals and prioritizes presenting design proposals that elicit the most positive reactions. For example, it analyzes the residents' facial expressions and voice when viewing the design proposals and prioritizes presenting design proposals that elicit a positive reaction. The contractor selection unit selects the optimal construction company based on the design proposals proposed by the design proposal unit. For example, if a resident inputs a request to "be introduced to a reliable construction company," the contractor selection unit will propose multiple companies based on their past performance and evaluations. The contractor selection unit also considers the resident's budget and schedule to select the optimal construction company. For example, it will propose a company that can provide high-quality construction within the budget. Furthermore, the contractor selection unit uses an emotion estimation function to analyze the emotions residents have toward construction companies and prioritizes recommending reliable companies.For example, if a resident expresses positive feelings toward a contractor's proposal, the system will prioritize recommending that contractor. The feedback collection unit collects feedback from residents after the renovation is completed. For example, when a resident asks, "Are you satisfied with the results of the renovation?", the feedback collection unit prompts the resident to enter their opinions and thoughts. The feedback collection unit also analyzes the resident feedback, extracts common problems and areas for improvement, and shares them. For example, it extracts problems pointed out by multiple residents and proposes improvement plans for those problems. The feedback collection unit also uses an emotion estimation function to analyze the resident's feelings toward the feedback and prioritizes sharing positive feedback. For example, if a resident expresses positive feelings toward the renovation, it prioritizes sharing that feedback. The communication promotion unit promotes communication between residents and supports event planning within the community. For example, if a resident inputs a request to "hold a workshop on renovation," the communication promotion unit will suggest the optimal date, time, and location, and support event planning. The communication promotion unit also analyzes the resident's interests and suggests optimal events based on those interests. For example, if a resident is interested in DIY, a DIY workshop will be suggested. Furthermore, the Communication Promotion Department uses emotion estimation to analyze the emotions residents have toward events and prioritizes planning events that elicit positive emotions. For example, if a resident expresses positive emotions toward an event, the department will plan an event that incorporates elements that elicit those emotions. This allows the renovation platform to easily allow residents to gather information about renovations and exchange opinions. For example, residents can choose a design that best suits their needs, select the most suitable contractor, and share feedback on the results of the renovation. Residents can also share their knowledge and experiences about renovations, deepening community ties.

[0030] The question reception unit can analyze residents' past renovation history and provide customized renovation information that is optimal for each individual resident. In the question reception unit, for example, the generation AI retrieves and analyzes residents' past renovation history from a database. For example, it analyzes the types of renovations that have been carried out in the past, the materials used, and design trends, and provides information that matches the resident's preferences. The question reception unit also uses the generation AI to propose customized renovation plans based on the resident's renovation history. For example, it generates new renovation proposals by taking into account designs and color patterns chosen in the past. The question reception unit also uses the generation AI to analyze residents' renovation history and provide optimal renovation information by referring to past successes and failures. For example, it makes suggestions for improving areas where problems have occurred in the past. This makes it possible to provide residents with the optimal renovation information.

[0031] The question reception unit can learn the residents' lifestyles and hobbies and preferences and make renovation suggestions based on them. For example, the question reception unit collects and analyzes the residents' life log data so that the generation AI can learn the residents' lifestyles and hobbies and preferences. For example, it can understand what activities the residents engage in at what times and make renovation suggestions based on that. In addition, to learn the residents' hobbies and preferences, the generation AI can analyze the residents' social media and online shopping history to identify their preferred designs and interior styles. For example, it can propose renovation suggestions based on data on the furniture and decorative items that the residents frequently purchase. In addition, the question reception unit can learn the residents' lifestyles and hobbies and preferences so that the generation AI can make renovation suggestions based on that information. For example, it can propose garden and balcony renovation suggestions to a resident who enjoys the outdoors, and kitchen renovation suggestions to a resident who enjoys cooking. This makes it possible to make renovation suggestions based on the residents' lifestyles and hobbies and preferences.

[0032] The question reception unit can analyze residents' voice input and extract information about renovations. In the question reception unit, for example, the generation AI analyzes the residents' voice input and extracts information about renovations. For example, if a resident says, "I want to make the living room brighter," the generation AI will propose appropriate renovation plans based on that request. The question reception unit also analyzes the residents' voice input and extracts specific requests and questions about renovations. For example, in response to the voice input, "I want to increase the storage space in the kitchen," the generation AI will provide renovation plans to increase the storage space. In the question reception unit, the generation AI analyzes the residents' voice input and extracts information about renovations. For example, if a resident says, "I want to renovate the bathroom," the generation AI will provide information about bathroom renovations. This makes it possible to extract renovation information from the residents' voice input.

[0033] The question reception unit can work in conjunction with other smart home devices to make renovation suggestions based on the residents' lifestyle data. In the question reception unit, for example, the generation AI works in conjunction with smart home devices to collect the residents' lifestyle data. For example, based on data from smart lighting and smart thermostats, the system understands the residents' lifestyle patterns and makes renovation suggestions based on that. The question reception unit also works in conjunction with other smart home devices to make renovation suggestions based on the residents' lifestyle data. For example, based on data from a smart refrigerator, the system proposes kitchen renovation ideas. In addition, the question reception unit works in conjunction with smart home devices to make renovation suggestions based on the residents' lifestyle data. For example, based on data from a smart security system, the system proposes renovation ideas that will improve the safety of residents. This makes it possible to make renovation suggestions in conjunction with smart home devices.

[0034] The design proposal unit can propose design proposals from a long-term perspective, taking into account the resident's family composition or future life events. In the design proposal unit, for example, the generation AI considers the resident's family composition and proposes design proposals from a long-term perspective. For example, in anticipation of children growing up, it proposes a design proposal that allows rooms to be divided in the future. The design proposal unit also considers the resident's future life events and proposes design proposals. For example, if there is a possibility that the resident will live with their parents in the future, it proposes a barrier-free design proposal. The design proposal unit also considers the resident's family composition and future life events and proposes design proposals from a long-term perspective. For example, in anticipation of the need for a home office in the future, it proposes a design proposal that includes office space. This makes it possible to provide design proposals that take into account the resident's family composition and future life events.

[0035] The design proposal unit can learn residents' past design preferences and generate more personalized design proposals based on them. For example, the design proposal unit uses a generation AI to learn residents' past design preferences and generate personalized design proposals based on them. For example, it can propose new design proposals based on colors and materials selected in the past. In addition, to learn residents' past design preferences, the design proposal unit uses a generation AI to analyze residents' renovation history and interior photos and generate design proposals based on them. For example, it can propose design proposals that reflect residents' preferred styles and themes. In addition, the design proposal unit uses a generation AI to learn residents' past design preferences and generate more personalized design proposals based on them. For example, it can propose new design proposals based on data on furniture and decorative items selected by residents in the past. This makes it possible to provide personalized design proposals based on residents' past design preferences.

[0036] The design proposal unit can incorporate design styles from different cultures and regions to propose design proposals from a global perspective. For example, the generative AI learns the design styles of different cultures and regions and proposes design proposals that incorporate them. For example, it may present a proposal that combines a simple Scandinavian design with a traditional Asian design. In addition, to incorporate design styles from different cultures and regions, the generative AI collects design examples from around the world and proposes design proposals based on them. For example, it may present a proposal that incorporates a Mediterranean resort style or an African-style design. In addition, the design proposal unit incorporates design styles from different cultures and regions to propose design proposals from a global perspective. For example, it may present a proposal that combines classic European design with modern American design. This makes it possible to provide design proposals from a global perspective that incorporate design styles from different cultures and regions.

[0037] The design proposal unit can take into account the presence of residents' pets or plants and generate design proposals that take these into consideration. For example, the generation AI in the design proposal unit takes into account the presence of residents' pets and generates design proposals that take this into consideration. For example, it proposes renovation proposals that incorporate space for pets and safety measures. The design proposal unit also takes into account the presence of residents' plants and the generation AI generates design proposals that take this into consideration. For example, it proposes renovation proposals that take into consideration lighting and placement to create an environment where plants can grow easily. The design proposal unit also takes into account the presence of residents' pets and plants and generates design proposals that take this into consideration. For example, it proposes renovation proposals that take into consideration spaces where pets can spend time comfortably and placements that make it easy for plants to grow easily. This makes it possible to provide design proposals that take residents' pets and plants into consideration.

[0038] The contractor selection unit can analyze the past project data of construction companies and recommend the contractor that best suits the residents' requests. For example, the generation AI collects and analyzes the past project data of construction companies. For example, it recommends the contractor that best suits the residents' requests based on the types and evaluations of past renovations. The contractor selection unit also analyzes the past project data of construction companies based on the residents' requests and recommends the most suitable contractor. For example, it proposes a contractor that matches the design and budget desired by the residents. The contractor selection unit also analyzes the past project data of construction companies and recommends the contractor that best suits the residents' requests. For example, it prioritizes the proposal of contractors that have received high ratings in past projects. This makes it possible to recommend the construction company that best suits the residents' requests.

[0039] The contractor selection unit can select the most suitable contractor by taking into account the resident's budget or schedule. For example, the generation AI receives the resident's budget and schedule as input data and selects the most suitable contractor based on that. For example, it proposes contractors that can provide high-quality construction within the budget. The contractor selection unit also considers the resident's budget and schedule and the generation AI selects the most suitable contractor. For example, it prioritizes proposing contractors that can meet the resident's desired construction period. The contractor selection unit also considers the resident's budget and schedule and the generation AI selects the most suitable contractor. For example, it proposes the contractor with the best cost performance within the budget. This makes it possible to select the most suitable contractor based on the resident's budget and schedule.

[0040] The contractor selection unit is able to recommend the best contractor for a specific renovation, taking into account the contractor's area of ​​expertise and specialization. For example, the generation AI retrieves the contractor's area of ​​expertise and specialization from a database, and based on that, recommends the best contractor for a specific renovation. For example, it may suggest a contractor who is good at kitchen renovations. The contractor selection unit also considers the contractor's area of ​​expertise and specialization, and the generation AI recommends the best contractor for a specific renovation. For example, it may preferentially suggest contractors who are good at bathroom renovations. The contractor selection unit also considers the contractor's area of ​​expertise and specialization, and recommends the best contractor for a specific renovation. For example, it may suggest a contractor who specializes in eco-renovations. This makes it possible to recommend the best contractor for a specific renovation.

[0041] The feedback collection unit can analyze resident feedback, extract common problems and areas for improvement, and share them. In the feedback collection unit, for example, the generation AI collects resident feedback and analyzes common problems and areas for improvement. For example, it extracts problems pointed out by multiple residents and proposes improvement plans for them. In addition, the feedback collection unit uses the generation AI to extract common problems and areas for improvement based on resident feedback and share them with other residents. For example, it summarizes and presents common problems that arose during the renovation process. In addition, the feedback collection unit uses the generation AI to analyze resident feedback, extract common problems and areas for improvement, and share them. For example, it summarizes the dissatisfaction residents felt after the renovation and proposes improvement plans for those problems. This allows common problems and areas for improvement to be extracted and shared.

[0042] The feedback collection unit can make proposals for the next renovation project based on resident feedback. In the feedback collection unit, for example, the generation AI collects resident feedback and makes proposals for the next renovation project based on that feedback. For example, it makes proposals to improve areas of dissatisfaction that residents felt in the previous renovation. In addition, the feedback collection unit can make proposals for the next renovation project based on resident feedback. For example, it makes new proposals that incorporate design elements that residents liked in the previous renovation. In addition, the feedback collection unit can make proposals for the next renovation project based on resident feedback. For example, it makes new proposals to solve problems that residents pointed out in the previous renovation. In this way, proposals for the next renovation project can be made.

[0043] The feedback collection unit can share feedback anonymously with other residents, encouraging empathetic feedback. In the feedback collection unit, for example, the generation AI collects residents' feedback anonymously and shares it with other residents. For example, impressions and opinions on renovations can be anonymously published, encouraging empathetic feedback. The feedback collection unit also encourages empathy between residents by sharing feedback anonymously. For example, examples of successful and unsuccessful renovations can be shared anonymously so that other residents can use them as reference. In the feedback collection unit, the generation AI also shares feedback anonymously, encouraging empathetic feedback. For example, residents can anonymously publish their feelings and opinions on renovations so that other residents can empathize. This encourages empathetic feedback.

[0044] The feedback collection unit can compile and share examples of successful and unsuccessful renovations based on the feedback. For example, the generation AI collects resident feedback and compiles and shares examples of successful and unsuccessful renovations based on that feedback. For example, it introduces specific examples of renovations that satisfied residents to other residents. The feedback collection unit also compiles and shares examples of successful and unsuccessful renovations based on the feedback. For example, it provides other residents with information about problems that residents faced in renovations and how they were solved. The feedback collection unit also compiles and shares examples of successful and unsuccessful renovations based on the resident feedback. For example, residents communicate lessons learned and advice they learned from renovations to other residents. This allows examples of successful and unsuccessful renovations to be shared together.

[0045] The communication promotion unit can analyze residents' interests and concerns and suggest the most suitable event based on that. For example, the generation AI in the communication promotion unit analyzes residents' interests and concerns and suggests the most suitable event based on that. For example, if a resident is interested in renovation workshops, it will suggest an event based on that theme. In addition, to analyze residents' interests and concerns, the generation AI analyzes the residents' past event participation history and survey results and suggests events based on that. For example, it will suggest a new event based on data on events that residents have previously participated in. In addition, the communication promotion unit analyzes residents' interests and concerns and suggests the most suitable event based on that. For example, if a resident is interested in DIY, it will suggest a DIY workshop. This makes it possible to suggest the most suitable event based on residents' interests and concerns.

[0046] The communication promotion unit analyzes past event data, extracts the characteristics of successful events, and can use them in planning the next event. In the communication promotion unit, for example, the generation AI collects past event data and analyzes the characteristics of successful events. For example, it extracts common points between events with high participant numbers and satisfaction, and uses these in planning the next event. The communication promotion unit also extracts the characteristics of successful events based on past event data using the generation AI, and uses these in planning the next event. For example, if a particular theme or format is a factor in success, it can incorporate this into the next event. The communication promotion unit also analyzes past event data using the generation AI, extracts the characteristics of successful events, and uses these in planning the next event. For example, based on participant feedback, it can reflect the elements of successful events in planning the next event. This makes it possible to use the characteristics of successful events in planning the next event.

[0047] The communication promotion unit can incorporate event examples from different communities and propose new event ideas. For example, the generation AI collects event examples from different communities and proposes new event ideas based on them. For example, it can plan a new event by taking successful events in other communities into consideration. In addition, to incorporate event examples from different communities, the generation AI collects event data from around the world and proposes new event ideas based on that data. For example, it can plan a new event that incorporates elements of international events. In addition, the communication promotion unit can incorporate event examples from different communities and propose new event ideas. For example, it can plan a new event based on events that are popular in other communities. This makes it possible to propose new event ideas based on event examples from different communities.

[0048] The communication promotion unit can take residents' schedules into consideration and suggest the optimal date, time, and location. For example, the generation AI in the communication promotion unit collects residents' schedules and suggests the optimal date, time, and location based on that. For example, it selects a date and time that most residents can attend and plans an event. The communication promotion unit also takes residents' schedules into consideration and suggests the optimal date, time, and location. For example, it proposes the most convenient date and time based on residents' calendar information. The communication promotion unit also takes residents' schedules into consideration and suggests the optimal date, time, and location. For example, it selects a location where most residents can easily gather and plans an event. This makes it possible to suggest the optimal date, time, and location based on residents' schedules.

[0049] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0050] The renovation platform can further include an energy efficiency improvement department. The energy efficiency improvement department makes suggestions to optimize energy efficiency in residents' renovation plans. For example, by optimizing the selection of insulation materials and window placement, the efficiency of heating and cooling can be improved. The energy efficiency improvement department also analyzes residents' electricity consumption data and proposes optimal energy management systems. For example, it can support the introduction of solar power generation systems and the selection of energy-efficient home appliances. Furthermore, the energy efficiency improvement department makes energy efficiency suggestions tailored to residents' lifestyles. For example, for residents who spend a lot of time at home during the day, it can make suggestions to reduce daytime electricity consumption.

[0051] The question reception unit can further include a health management proposal unit. The health management proposal unit makes renovation proposals that take into account the health condition of residents. For example, for residents with allergies, it can propose the use of materials that suppress allergens or the introduction of an air purification system. The health management proposal unit also analyzes residents' fitness data and provides renovation proposals to create an environment that is easy to exercise in. For example, it can propose the installation of a home gym or the provision of an exercise space. Furthermore, the health management proposal unit makes proposals to create a comfortable sleeping environment based on residents' sleep data. For example, it can propose wall materials with high sound insulation and appropriate lighting placement.

[0052] The question receiving unit can further include a disaster prevention measures proposal unit. The disaster prevention measures proposal unit makes renovation proposals to improve the safety of residents' homes. For example, it provides renovation proposals that incorporate earthquake reinforcement and fire prevention measures. The disaster prevention measures proposal unit also analyzes disaster risks in the residents' areas and proposes disaster prevention measures based on the results. For example, it proposes renovation proposals that incorporate waterproofing measures in areas with a high risk of flooding. Furthermore, the disaster prevention measures proposal unit provides renovation proposals that take residents' evacuation routes into consideration. For example, it can propose floor plans that make evacuation easier and the provision of emergency supply storage space.

[0053] The question receiving unit may further include an entertainment suggestion unit. The entertainment suggestion unit makes renovation suggestions based on the resident's hobbies and preferences. For example, it may provide suggestions for installing a home theater or renovating a game room. The entertainment suggestion unit may also analyze the resident's preferences for music and movies and make suggestions for audio and video equipment based on that analysis. For example, it may suggest speaker placement and projector installation to maximize acoustic effects. Furthermore, the entertainment suggestion unit may take into account the resident's family composition and make suggestions for entertainment spaces that the whole family can enjoy. For example, it may suggest the installation of a playroom for children or a board game space that the whole family can enjoy.

[0054] The design proposal department can further include an eco-friendly proposal department. The eco-friendly proposal department proposes renovation proposals that are environmentally friendly. For example, it provides renovation proposals that use renewable materials and highly energy-efficient equipment. The eco-friendly proposal department also makes proposals to raise residents' environmental awareness. For example, it proposes the introduction of recyclable materials and energy-efficient home appliances. Furthermore, the eco-friendly proposal department provides eco-friendly renovation proposals that are tailored to the residents' lifestyles. For example, it can propose the installation of a home vegetable garden space or the introduction of a rainwater utilization system.

[0055] The processing flow of the first embodiment will be briefly explained below.

[0056] Step 1: The question reception unit accepts questions and requests from residents regarding renovations. For example, if a resident inputs a question such as, "What kind of design would be good for renovating the living room?", the question reception unit will accept the question. The question reception unit will also analyze the resident's past renovation history and provide customized renovation information that is best suited to each individual resident. For example, it will analyze the types of renovations that have been carried out in the past, the materials used, and design trends, and provide information that matches the resident's preferences. Furthermore, the question reception unit will learn about the resident's lifestyle and hobbies and preferences, and make renovation suggestions based on that. For example, if a resident enjoys the outdoors, it will suggest renovation ideas for the garden or balcony. Step 2: The design proposal unit proposes renovation design proposals based on the questions and requests received by the question reception unit. For example, if a resident inputs a request such as "I want to renovate my kitchen in a modern style," the design proposal unit generates specific design proposals and presents them to the resident. The design proposal unit also considers the resident's family composition and future life events to propose design proposals from a long-term perspective. For example, it proposes a design proposal that allows rooms to be divided in the future, anticipating the growth of children. Furthermore, the design proposal unit uses an emotion estimation function to analyze the emotions that residents have toward the design proposals and prioritizes presenting design proposals that elicit the most positive reactions. For example, it analyzes the facial expressions and voice of residents when they view the design proposals and prioritizes presenting design proposals that elicit a positive reaction. Step 3: The contractor selection unit selects the most suitable contractor based on the design proposal proposed by the design proposal unit. For example, if a resident inputs a request to "be introduced to a reliable contractor," the contractor selection unit will suggest multiple contractors based on their past performance and evaluations. The contractor selection unit also considers the resident's budget and schedule to select the most suitable contractor. For example, it will suggest a contractor that can provide high-quality construction within the budget. Furthermore, the contractor selection unit uses an emotion estimation function to analyze the residents' emotions toward the contractor and prioritize recommending reliable contractors. For example, if the resident expresses positive emotions toward a contractor's proposal, it will prioritize recommending that contractor. Step 4: The feedback collection unit collects feedback from residents after the renovation is completed. For example, when a resident asks, "Are you satisfied with the results of the renovation?", the feedback collection unit prompts the resident to enter their opinions and thoughts. The feedback collection unit also analyzes the resident feedback, extracts common problems and areas for improvement, and shares them. For example, it extracts problems pointed out by multiple residents and proposes solutions to address them. Furthermore, the feedback collection unit uses an emotion estimation function to analyze the resident's feelings toward the feedback and prioritizes sharing positive feedback. For example, if a resident expresses positive feelings toward the renovation, it prioritizes sharing that feedback. Step 5: The Communication Promotion Department promotes communication between residents and supports event planning within the community. For example, if a resident inputs a request such as "I want to hold a workshop on renovation," the Communication Promotion Department will suggest the optimal date, time, and location and support event planning. The Communication Promotion Department also analyzes residents' interests and suggests the most suitable event based on that. For example, if a resident is interested in DIY, it will suggest a DIY workshop. Furthermore, the Communication Promotion Department uses an emotion estimation function to analyze residents' emotions toward events and prioritizes planning events that elicit positive emotions. For example, if residents express positive emotions toward an event, it will plan an event that incorporates elements that elicit those emotions.

[0057] (Example 2) A renovation platform according to an embodiment of the present invention allows residents to easily gather information and exchange opinions about renovations. This platform integrates generative AI to support everything from sharing ideas for renovation projects to planning, selecting contractors, and sharing feedback after implementation. This allows the renovation platform to improve the quality of residents' lifestyles, quickly responding to questions about renovations, proposing design ideas, and providing information about local contractors.

[0058] A renovation platform according to an embodiment includes a question receiving unit, a design proposal unit, a contractor selection unit, a feedback collection unit, and a communication promotion unit. The question receiving unit receives questions and requests from residents regarding renovations. For example, if a resident inputs a question such as, "What kind of design would be good for renovating my living room?", the question receiving unit accepts the question. The question receiving unit also analyzes the resident's past renovation history and provides customized renovation information optimal for each resident. For example, the question receiving unit analyzes the types of renovations previously undertaken, the materials used, and design trends, and provides information tailored to the resident's preferences. The question receiving unit also learns the resident's lifestyle and hobbies and preferences and makes renovation proposals based on those information. For example, if a resident enjoys the outdoors, the design proposal unit proposes renovation ideas for the garden or balcony. The design proposal unit proposes renovation design ideas based on the questions and requests received by the question receiving unit. For example, if a resident inputs a request such as, "I want to renovate my kitchen in a modern style," the design proposal unit generates specific design proposals and presents them to the resident. The design proposal unit also considers the resident's family structure and future life events to propose design proposals from a long-term perspective. For example, it proposes a design proposal that allows for future room division in anticipation of children growing up. Furthermore, the design proposal unit uses an emotion estimation function to analyze the emotions residents have toward the design proposals and prioritizes presenting design proposals that elicit the most positive reactions. For example, it analyzes the residents' facial expressions and voice when viewing the design proposals and prioritizes presenting design proposals that elicit a positive reaction. The contractor selection unit selects the optimal construction company based on the design proposals proposed by the design proposal unit. For example, if a resident inputs a request to "be introduced to a reliable construction company," the contractor selection unit will propose multiple companies based on their past performance and evaluations. The contractor selection unit also considers the resident's budget and schedule to select the optimal construction company. For example, it will propose a company that can provide high-quality construction within the budget. Furthermore, the contractor selection unit uses an emotion estimation function to analyze the emotions residents have toward construction companies and prioritizes recommending reliable companies.For example, if a resident expresses positive feelings toward a contractor's proposal, the system will prioritize recommending that contractor. The feedback collection unit collects feedback from residents after the renovation is completed. For example, when a resident asks, "Are you satisfied with the results of the renovation?", the feedback collection unit prompts the resident to enter their opinions and thoughts. The feedback collection unit also analyzes the resident feedback, extracts common problems and areas for improvement, and shares them. For example, it extracts problems pointed out by multiple residents and proposes improvement plans for those problems. The feedback collection unit also uses an emotion estimation function to analyze the resident's feelings toward the feedback and prioritizes sharing positive feedback. For example, if a resident expresses positive feelings toward the renovation, it prioritizes sharing that feedback. The communication promotion unit promotes communication between residents and supports event planning within the community. For example, if a resident inputs a request to "hold a workshop on renovation," the communication promotion unit will suggest the optimal date, time, and location, and support event planning. The communication promotion unit also analyzes the resident's interests and suggests optimal events based on those interests. For example, if a resident is interested in DIY, a DIY workshop will be suggested. Furthermore, the Communication Promotion Department uses emotion estimation to analyze the emotions residents have toward events and prioritizes planning events that elicit positive emotions. For example, if a resident expresses positive emotions toward an event, the department will plan an event that incorporates elements that elicit those emotions. This allows the renovation platform to easily allow residents to gather information about renovations and exchange opinions. For example, residents can choose a design that best suits their needs, select the most suitable contractor, and share feedback on the results of the renovation. Residents can also share their knowledge and experiences about renovations, deepening community ties.

[0059] The question reception unit can analyze residents' past renovation history and provide customized renovation information that is optimal for each individual resident. In the question reception unit, for example, the generation AI retrieves and analyzes residents' past renovation history from a database. For example, it analyzes the types of renovations that have been carried out in the past, the materials used, and design trends, and provides information that matches the resident's preferences. The question reception unit also uses the generation AI to propose customized renovation plans based on the resident's renovation history. For example, it generates new renovation proposals by taking into account designs and color patterns chosen in the past. The question reception unit also uses the generation AI to analyze residents' renovation history and provide optimal renovation information by referring to past successes and failures. For example, it makes suggestions for improving areas where problems have occurred in the past. This makes it possible to provide residents with the optimal renovation information.

[0060] The question reception unit can learn the residents' lifestyles and hobbies and preferences and make renovation suggestions based on them. For example, the question reception unit collects and analyzes the residents' life log data so that the generation AI can learn the residents' lifestyles and hobbies and preferences. For example, it can understand what activities the residents engage in at what times and make renovation suggestions based on that. In addition, to learn the residents' hobbies and preferences, the generation AI can analyze the residents' social media and online shopping history to identify their preferred designs and interior styles. For example, it can propose renovation suggestions based on data on the furniture and decorative items that the residents frequently purchase. In addition, the question reception unit can learn the residents' lifestyles and hobbies and preferences so that the generation AI can make renovation suggestions based on that information. For example, it can propose garden and balcony renovation suggestions to a resident who enjoys the outdoors, and kitchen renovation suggestions to a resident who enjoys cooking. This makes it possible to make renovation suggestions based on the residents' lifestyles and hobbies and preferences.

[0061] The question reception unit can use the emotion estimation function to analyze the emotions residents have toward the renovation and provide information that elicits positive emotions preferentially. The question reception unit, for example, uses the emotion estimation function to analyze the emotions residents have toward the renovation in real time. For example, it analyzes the facial expressions and voices of residents when they view renovation proposals and provides information that elicits positive emotions. The question reception unit also analyzes the emotions residents have toward the renovation and provides information that elicits positive emotions preferentially. For example, it proposes new renovation proposals based on designs and materials that residents have responded positively to in the past. The question reception unit also uses the emotion estimation function to analyze the emotions residents have toward the renovation and provides information that elicits positive emotions. For example, it preferentially displays information that elicits the most positive reactions based on the emotion scores of residents when they view renovation proposals. This makes it possible to provide information that elicits positive emotions from residents.

[0062] The question reception unit can analyze residents' voice input and extract information about renovations. In the question reception unit, for example, the generation AI analyzes the residents' voice input and extracts information about renovations. For example, if a resident says, "I want to make the living room brighter," the generation AI will propose appropriate renovation plans based on that request. The question reception unit also analyzes the residents' voice input and extracts specific requests and questions about renovations. For example, in response to the voice input, "I want to increase the storage space in the kitchen," the generation AI will provide renovation plans to increase the storage space. In the question reception unit, the generation AI analyzes the residents' voice input and extracts information about renovations. For example, if a resident says, "I want to renovate the bathroom," the generation AI will provide information about bathroom renovations. This makes it possible to extract renovation information from the residents' voice input.

[0063] The question reception unit can work in conjunction with other smart home devices to make renovation suggestions based on the residents' lifestyle data. In the question reception unit, for example, the generation AI works in conjunction with smart home devices to collect the residents' lifestyle data. For example, based on data from smart lighting and smart thermostats, the system understands the residents' lifestyle patterns and makes renovation suggestions based on that. The question reception unit also works in conjunction with other smart home devices to make renovation suggestions based on the residents' lifestyle data. For example, based on data from a smart refrigerator, the system proposes kitchen renovation ideas. In addition, the question reception unit works in conjunction with smart home devices to make renovation suggestions based on the residents' lifestyle data. For example, based on data from a smart security system, the system proposes renovation ideas that will improve the safety of residents. This makes it possible to make renovation suggestions in conjunction with smart home devices.

[0064] The design proposal unit can propose design proposals from a long-term perspective, taking into account the resident's family composition or future life events. In the design proposal unit, for example, the generation AI considers the resident's family composition and proposes design proposals from a long-term perspective. For example, in anticipation of children growing up, it proposes a design proposal that allows rooms to be divided in the future. The design proposal unit also considers the resident's future life events and proposes design proposals. For example, if there is a possibility that the resident will live with their parents in the future, it proposes a barrier-free design proposal. The design proposal unit also considers the resident's family composition and future life events and proposes design proposals from a long-term perspective. For example, in anticipation of the need for a home office in the future, it proposes a design proposal that includes office space. This makes it possible to provide design proposals that take into account the resident's family composition and future life events.

[0065] The design proposal unit can learn residents' past design preferences and generate more personalized design proposals based on them. For example, the design proposal unit uses a generation AI to learn residents' past design preferences and generate personalized design proposals based on them. For example, it can propose new design proposals based on colors and materials selected in the past. In addition, to learn residents' past design preferences, the design proposal unit uses a generation AI to analyze residents' renovation history and interior photos and generate design proposals based on them. For example, it can propose design proposals that reflect residents' preferred styles and themes. In addition, the design proposal unit uses a generation AI to learn residents' past design preferences and generate more personalized design proposals based on them. For example, it can propose new design proposals based on data on furniture and decorative items selected by residents in the past. This makes it possible to provide personalized design proposals based on residents' past design preferences.

[0066] The design proposal unit can use the emotion estimation function to analyze the emotions residents have toward the design proposal and preferentially present the design proposal that elicits the most positive response. The design proposal unit, for example, uses the emotion estimation function to analyze the emotions residents have toward the design proposal in real time. For example, it analyzes the facial expressions and voices of residents when they view the design proposal and preferentially presents design proposals that elicit a positive response. The design proposal unit also analyzes the emotions residents have toward the design proposal and preferentially presents design proposals that elicit the most positive response. For example, it proposes new design proposals based on design elements that residents have previously responded positively to. The design proposal unit also uses the emotion estimation function to analyze the emotions residents have toward the design proposal and preferentially presents design proposals that elicit the most positive response. For example, it preferentially displays design proposals that elicit the most positive response based on the emotion scores of residents when they view the design proposals. This makes it possible to provide design proposals that elicit a positive response from residents.

[0067] The design proposal unit can incorporate design styles from different cultures and regions to propose design proposals from a global perspective. For example, the generative AI learns the design styles of different cultures and regions and proposes design proposals that incorporate them. For example, it may present a proposal that combines a simple Scandinavian design with a traditional Asian design. In addition, to incorporate design styles from different cultures and regions, the generative AI collects design examples from around the world and proposes design proposals based on them. For example, it may present a proposal that incorporates a Mediterranean resort style or an African-style design. In addition, the design proposal unit incorporates design styles from different cultures and regions to propose design proposals from a global perspective. For example, it may present a proposal that combines classic European design with modern American design. This makes it possible to provide design proposals from a global perspective that incorporate design styles from different cultures and regions.

[0068] The design proposal unit can take into account the presence of residents' pets or plants and generate design proposals that take these into consideration. For example, the generation AI in the design proposal unit takes into account the presence of residents' pets and generates design proposals that take this into consideration. For example, it proposes renovation proposals that incorporate space for pets and safety measures. The design proposal unit also takes into account the presence of residents' plants and the generation AI generates design proposals that take this into consideration. For example, it proposes renovation proposals that take into consideration lighting and placement to create an environment where plants can grow easily. The design proposal unit also takes into account the presence of residents' pets and plants and generates design proposals that take this into consideration. For example, it proposes renovation proposals that take into consideration spaces where pets can spend time comfortably and placements that make it easy for plants to grow easily. This makes it possible to provide design proposals that take residents' pets and plants into consideration.

[0069] The design proposal unit uses the emotion estimation function to monitor the emotions of residents when they are choosing design proposals in real time and support their selection. The design proposal unit, for example, uses the emotion estimation function to monitor the emotions of residents when they are choosing design proposals in real time. For example, it analyzes the facial expressions and voices of residents when they view design proposals and prioritizes presenting the design proposal that elicits the most positive response. The design proposal unit also monitors the emotions of residents when they are choosing design proposals in real time and supports their selection. For example, if a resident feels anxious about a design proposal, the generation AI provides information that gives them a sense of security. The design proposal unit also uses the emotion estimation function to monitor the emotions of residents when they are choosing design proposals in real time and support their selection. For example, it prioritizes displaying the design proposal that elicits the most positive response based on the emotion score when the resident views the design proposals. This makes it possible to monitor residents' emotions in real time and support their selection of design proposals.

[0070] The contractor selection unit can analyze the past project data of construction companies and recommend the contractor that best suits the residents' requests. For example, the generation AI collects and analyzes the past project data of construction companies. For example, it recommends the contractor that best suits the residents' requests based on the types and evaluations of past renovations. The contractor selection unit also analyzes the past project data of construction companies based on the residents' requests and recommends the most suitable contractor. For example, it proposes a contractor that matches the design and budget desired by the residents. The contractor selection unit also analyzes the past project data of construction companies and recommends the contractor that best suits the residents' requests. For example, it prioritizes the proposal of contractors that have received high ratings in past projects. This makes it possible to recommend the construction company that best suits the residents' requests.

[0071] The contractor selection unit can select the most suitable contractor by taking into account the resident's budget or schedule. For example, the generation AI receives the resident's budget and schedule as input data and selects the most suitable contractor based on that. For example, it proposes contractors that can provide high-quality construction within the budget. The contractor selection unit also considers the resident's budget and schedule and the generation AI selects the most suitable contractor. For example, it prioritizes proposing contractors that can meet the resident's desired construction period. The contractor selection unit also considers the resident's budget and schedule and the generation AI selects the most suitable contractor. For example, it proposes the contractor with the best cost performance within the budget. This makes it possible to select the most suitable contractor based on the resident's budget and schedule.

[0072] The contractor selection unit uses the emotion estimation function to analyze the emotions that residents have toward construction companies and can preferentially recommend reliable contractors. The contractor selection unit, for example, uses the emotion estimation function to analyze the emotions that residents have toward construction companies in real time. For example, if residents express positive emotions toward a contractor's proposal, the contractor is preferentially recommended. The contractor selection unit also analyzes the emotions that residents have toward construction companies and preferentially recommends reliable contractors. For example, it proposes contractors that have received high ratings in past projects. The contractor selection unit also uses the emotion estimation function to analyze the emotions that residents have toward construction companies and preferentially recommends reliable contractors. For example, if residents express positive emotions toward a contractor's proposal, the contractor is preferentially displayed. This makes it possible to recommend reliable construction companies based on the emotions of residents.

[0073] The contractor selection unit is able to recommend the best contractor for a specific renovation, taking into account the contractor's area of ​​expertise and specialization. For example, the generation AI retrieves the contractor's area of ​​expertise and specialization from a database, and based on that, recommends the best contractor for a specific renovation. For example, it may suggest a contractor who is good at kitchen renovations. The contractor selection unit also considers the contractor's area of ​​expertise and specialization, and the generation AI recommends the best contractor for a specific renovation. For example, it may preferentially suggest contractors who are good at bathroom renovations. The contractor selection unit also considers the contractor's area of ​​expertise and specialization, and recommends the best contractor for a specific renovation. For example, it may suggest a contractor who specializes in eco-renovations. This makes it possible to recommend the best contractor for a specific renovation.

[0074] The contractor selection unit uses the emotion estimation function to monitor residents' emotions in real time when selecting a construction company and support their selection. For example, the contractor selection unit uses the emotion estimation function to monitor residents' emotions in real time when selecting a construction company. For example, if residents express positive emotions toward a contractor's proposal, that contractor will be recommended with priority. The contractor selection unit also monitors residents' emotions in real time when selecting a construction company and supports their selection. For example, if residents feel uneasy about a contractor's proposal, the generation AI provides information that gives them peace of mind. The contractor selection unit also uses the emotion estimation function to monitor residents' emotions in real time when selecting a construction company and support their selection. For example, if residents express positive emotions toward a contractor's proposal, that contractor will be displayed with priority. This makes it possible to monitor residents' emotions in real time and support their selection of a construction company.

[0075] The feedback collection unit can analyze resident feedback, extract common problems and areas for improvement, and share them. In the feedback collection unit, for example, the generation AI collects resident feedback and analyzes common problems and areas for improvement. For example, it extracts problems pointed out by multiple residents and proposes improvement plans for them. In addition, the feedback collection unit uses the generation AI to extract common problems and areas for improvement based on resident feedback and share them with other residents. For example, it summarizes and presents common problems that arose during the renovation process. In addition, the feedback collection unit uses the generation AI to analyze resident feedback, extract common problems and areas for improvement, and share them. For example, it summarizes the dissatisfaction residents felt after the renovation and proposes improvement plans for those problems. This allows common problems and areas for improvement to be extracted and shared.

[0076] The feedback collection unit can make proposals for the next renovation project based on resident feedback. In the feedback collection unit, for example, the generation AI collects resident feedback and makes proposals for the next renovation project based on that feedback. For example, it makes proposals to improve areas of dissatisfaction that residents felt in the previous renovation. In addition, the feedback collection unit can make proposals for the next renovation project based on resident feedback. For example, it makes new proposals that incorporate design elements that residents liked in the previous renovation. In addition, the feedback collection unit can make proposals for the next renovation project based on resident feedback. For example, it makes new proposals to solve problems that residents pointed out in the previous renovation. In this way, proposals for the next renovation project can be made.

[0077] The feedback collection unit can use the emotion estimation function to analyze residents' emotions toward the feedback and prioritize sharing positive feedback. The feedback collection unit, for example, uses the emotion estimation function to analyze residents' emotions toward the feedback in real time. For example, if a resident expresses positive emotions toward the renovation, the feedback is shared preferentially. The feedback collection unit also analyzes residents' emotions toward the feedback and prioritizes sharing positive feedback. For example, if a resident is satisfied with the renovation, the feedback is shared with other residents. The feedback collection unit also uses the emotion estimation function to analyze residents' emotions toward the feedback and prioritize sharing positive feedback. For example, if a resident expresses positive emotions toward the renovation, the feedback is displayed preferentially. This allows positive feedback to be shared preferentially.

[0078] The feedback collection unit can share feedback anonymously with other residents, encouraging empathetic feedback. In the feedback collection unit, for example, the generation AI collects residents' feedback anonymously and shares it with other residents. For example, impressions and opinions on renovations can be anonymously published, encouraging empathetic feedback. The feedback collection unit also encourages empathy between residents by sharing feedback anonymously. For example, examples of successful and unsuccessful renovations can be shared anonymously so that other residents can use them as reference. In the feedback collection unit, the generation AI also shares feedback anonymously, encouraging empathetic feedback. For example, residents can anonymously publish their feelings and opinions on renovations so that other residents can empathize. This encourages empathetic feedback.

[0079] The feedback collection unit can compile and share examples of successful and unsuccessful renovations based on the feedback. For example, the generation AI collects resident feedback and compiles and shares examples of successful and unsuccessful renovations based on that feedback. For example, it introduces specific examples of renovations that satisfied residents to other residents. The feedback collection unit also compiles and shares examples of successful and unsuccessful renovations based on the feedback. For example, it provides other residents with information about problems that residents faced in renovations and how they were solved. The feedback collection unit also compiles and shares examples of successful and unsuccessful renovations based on the resident feedback. For example, residents communicate lessons learned and advice they learned from renovations to other residents. This allows examples of successful and unsuccessful renovations to be shared together.

[0080] The feedback collection unit can use the emotion estimation function to monitor the emotions of residents when they enter feedback in real time and support their input. The feedback collection unit, for example, uses the emotion estimation function to monitor the emotions of residents when they enter feedback in real time. For example, if a resident feels stressed when entering feedback, the generation AI makes suggestions to help them relax. The feedback collection unit also monitors the emotions of residents when they enter feedback in real time and supports their input. For example, if a resident feels anxious when entering feedback, the generation AI provides information that gives them a sense of security. The feedback collection unit also uses the emotion estimation function to monitor the emotions of residents when they enter feedback in real time and support their input. For example, an interface is provided to elicit positive emotions when residents enter feedback. This makes it possible to monitor the emotions of residents when they enter feedback and support their input.

[0081] The communication promotion unit can analyze residents' interests and concerns and suggest the most suitable event based on that. For example, the generation AI in the communication promotion unit analyzes residents' interests and concerns and suggests the most suitable event based on that. For example, if a resident is interested in renovation workshops, it will suggest an event based on that theme. In addition, to analyze residents' interests and concerns, the generation AI analyzes the residents' past event participation history and survey results and suggests events based on that. For example, it will suggest a new event based on data on events that residents have previously participated in. In addition, the communication promotion unit analyzes residents' interests and concerns and suggests the most suitable event based on that. For example, if a resident is interested in DIY, it will suggest a DIY workshop. This makes it possible to suggest the most suitable event based on residents' interests and concerns.

[0082] The communication promotion unit analyzes past event data, extracts the characteristics of successful events, and can use them in planning the next event. In the communication promotion unit, for example, the generation AI collects past event data and analyzes the characteristics of successful events. For example, it extracts common points between events with high participant numbers and satisfaction, and uses these in planning the next event. The communication promotion unit also extracts the characteristics of successful events based on past event data using the generation AI, and uses these in planning the next event. For example, if a particular theme or format is a factor in success, it can incorporate this into the next event. The communication promotion unit also analyzes past event data using the generation AI, extracts the characteristics of successful events, and uses these in planning the next event. For example, based on participant feedback, it can reflect the elements of successful events in planning the next event. This makes it possible to use the characteristics of successful events in planning the next event.

[0083] The communication promotion unit can incorporate event examples from different communities and propose new event ideas. For example, the generation AI collects event examples from different communities and proposes new event ideas based on them. For example, it can plan a new event by taking successful events in other communities into consideration. In addition, to incorporate event examples from different communities, the generation AI collects event data from around the world and proposes new event ideas based on that data. For example, it can plan a new event that incorporates elements of international events. In addition, the communication promotion unit can incorporate event examples from different communities and propose new event ideas. For example, it can plan a new event based on events that are popular in other communities. This makes it possible to propose new event ideas based on event examples from different communities.

[0084] The communication promotion unit can take residents' schedules into consideration and suggest the optimal date, time, and location. For example, the generation AI in the communication promotion unit collects residents' schedules and suggests the optimal date, time, and location based on that. For example, it selects a date and time that most residents can attend and plans an event. The communication promotion unit also takes residents' schedules into consideration and suggests the optimal date, time, and location. For example, it proposes the most convenient date and time based on residents' calendar information. The communication promotion unit also takes residents' schedules into consideration and suggests the optimal date, time, and location. For example, it selects a location where most residents can easily gather and plans an event. This makes it possible to suggest the optimal date, time, and location based on residents' schedules.

[0085] The communication promotion unit uses the emotion estimation function to monitor the emotions of residents when they participate in an event in real time, thereby encouraging participation. For example, the communication promotion unit uses the emotion estimation function to monitor the emotions of residents when they participate in an event in real time. For example, if a resident expresses positive emotions toward an event, the unit plans an event that incorporates elements that will elicit those emotions. The communication promotion unit also monitors the emotions of residents when they participate in an event in real time, thereby encouraging participation. For example, if a resident expresses anxiety about an event, the generation AI provides information that gives them a sense of security. The communication promotion unit also uses the emotion estimation function to monitor the emotions of residents when they participate in an event in real time, thereby encouraging participation. For example, if a resident expresses positive emotions toward an event, the unit plans an event that incorporates elements that will elicit those emotions. In this way, the communication promotion unit can monitor the emotions of residents in real time and encourage participation in an event.

[0086] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0087] The renovation platform can further include an energy efficiency improvement department. The energy efficiency improvement department makes suggestions to optimize energy efficiency in residents' renovation plans. For example, by optimizing the selection of insulation materials and window placement, the efficiency of heating and cooling can be improved. The energy efficiency improvement department also analyzes residents' electricity consumption data and proposes optimal energy management systems. For example, it can support the introduction of solar power generation systems and the selection of energy-efficient home appliances. Furthermore, the energy efficiency improvement department makes energy efficiency suggestions tailored to residents' lifestyles. For example, for residents who spend a lot of time at home during the day, it can make suggestions to reduce daytime electricity consumption.

[0088] The question reception unit can further include a health management proposal unit. The health management proposal unit makes renovation proposals that take into account the health condition of residents. For example, for residents with allergies, it can propose the use of materials that suppress allergens or the introduction of an air purification system. The health management proposal unit also analyzes residents' fitness data and provides renovation proposals to create an environment that is easy to exercise in. For example, it can propose the installation of a home gym or the provision of an exercise space. Furthermore, the health management proposal unit makes proposals to create a comfortable sleeping environment based on residents' sleep data. For example, it can propose wall materials with high sound insulation and appropriate lighting placement.

[0089] The question receiving unit can further include a disaster prevention measures proposal unit. The disaster prevention measures proposal unit makes renovation proposals to improve the safety of residents' homes. For example, it provides renovation proposals that incorporate earthquake reinforcement and fire prevention measures. The disaster prevention measures proposal unit also analyzes disaster risks in the residents' areas and proposes disaster prevention measures based on the results. For example, it proposes renovation proposals that incorporate waterproofing measures in areas with a high risk of flooding. Furthermore, the disaster prevention measures proposal unit provides renovation proposals that take residents' evacuation routes into consideration. For example, it can propose floor plans that make evacuation easier and the provision of emergency supply storage space.

[0090] The question receiving unit may further include an entertainment suggestion unit. The entertainment suggestion unit makes renovation suggestions based on the resident's hobbies and preferences. For example, it may provide suggestions for installing a home theater or renovating a game room. The entertainment suggestion unit may also analyze the resident's preferences for music and movies and make suggestions for audio and video equipment based on that analysis. For example, it may suggest speaker placement and projector installation to maximize acoustic effects. Furthermore, the entertainment suggestion unit may take into account the resident's family composition and make suggestions for entertainment spaces that the whole family can enjoy. For example, it may suggest the installation of a playroom for children or a board game space that the whole family can enjoy.

[0091] The question reception unit can further use the emotion estimation function to analyze the stress level of residents and propose renovation ideas that will help them relax. For example, if a resident is feeling stressed, it can propose renovation ideas that use colors and materials that have a relaxing effect. The question reception unit can also monitor the stress level of residents in real time and make proposals to reduce stress. For example, it can propose renovation ideas that incorporate music and scents that will help residents relax. Furthermore, the question reception unit can analyze the stress level of residents and propose interior and furniture arrangements that will have a relaxing effect. For example, it can propose relaxing sofa and lighting arrangements.

[0092] The question reception unit can further analyze the happiness levels of residents using the emotion estimation function and propose renovation proposals to improve happiness levels. For example, it can provide renovation proposals using designs and colors that make residents feel happy. The question reception unit can also monitor the happiness levels of residents in real time and make proposals to improve happiness levels. For example, it can propose interior and furniture layouts that make residents feel happy. The question reception unit can also analyze the happiness levels of residents and provide renovation proposals to bring out a sense of happiness. For example, it can propose the creation of spaces where residents can relax or enjoy their hobbies.

[0093] The question reception unit can further use the emotion estimation function to analyze residents' anxieties and propose renovation plans to alleviate their anxieties. For example, it can provide renovation plans that eliminate elements that cause anxiety to residents. The question reception unit can also monitor residents' anxieties in real time and make proposals to alleviate their anxieties. For example, it can propose renovation plans that use colors and materials that make residents feel at ease. The question reception unit can also analyze residents' anxieties and propose interior and furniture layouts to alleviate their anxieties. For example, it can propose lighting and furniture layouts that make residents feel at ease.

[0094] The design proposal unit can further analyze residents' preferences using emotion estimation functions and propose the most popular design proposals. For example, it can provide new design proposals based on design elements that residents have previously preferred. The design proposal unit can also monitor residents' preferences in real time and propose the most popular design proposals. For example, it can provide design proposals that use residents' preferred colors and materials. The design proposal unit can also analyze residents' preferences and propose the most popular interior and furniture arrangements. For example, it can provide design proposals that reflect residents' preferred styles and themes.

[0095] The design proposal unit can further use the emotion estimation function to analyze resident satisfaction and propose the design proposal that will provide the highest level of satisfaction. For example, it can provide new design proposals based on design elements that have previously satisfied residents. The design proposal unit can also monitor resident satisfaction in real time and propose the design proposal that will provide the highest level of satisfaction. For example, it can provide design proposals that use colors and materials that residents find satisfying. The design proposal unit can also analyze resident satisfaction and propose the interior and furniture arrangement that will provide the highest level of satisfaction. For example, it can provide design proposals that reflect styles and themes that residents find satisfying.

[0096] The design proposal department can further include an eco-friendly proposal department. The eco-friendly proposal department proposes renovation proposals that are environmentally friendly. For example, it provides renovation proposals that use renewable materials and highly energy-efficient equipment. The eco-friendly proposal department also makes proposals to raise residents' environmental awareness. For example, it proposes the introduction of recyclable materials and energy-efficient home appliances. Furthermore, the eco-friendly proposal department provides eco-friendly renovation proposals that are tailored to the residents' lifestyles. For example, it can propose the installation of a home vegetable garden space or the introduction of a rainwater utilization system.

[0097] The processing flow of the second embodiment will be briefly explained below.

[0098] Step 1: The question reception unit accepts questions and requests from residents regarding renovations. For example, if a resident inputs a question such as, "What kind of design would be good for renovating the living room?", the question reception unit will accept the question. The question reception unit will also analyze the resident's past renovation history and provide customized renovation information that is best suited to each individual resident. For example, it will analyze the types of renovations that have been carried out in the past, the materials used, and design trends, and provide information that matches the resident's preferences. Furthermore, the question reception unit will learn about the resident's lifestyle and hobbies and preferences, and make renovation suggestions based on that. For example, if a resident enjoys the outdoors, it will suggest renovation ideas for the garden or balcony. Step 2: The design proposal unit proposes renovation design proposals based on the questions and requests received by the question reception unit. For example, if a resident inputs a request such as "I want to renovate my kitchen in a modern style," the design proposal unit generates specific design proposals and presents them to the resident. The design proposal unit also considers the resident's family composition and future life events to propose design proposals from a long-term perspective. For example, it proposes a design proposal that allows rooms to be divided in the future, anticipating the growth of children. Furthermore, the design proposal unit uses an emotion estimation function to analyze the emotions that residents have toward the design proposals and prioritizes presenting design proposals that elicit the most positive reactions. For example, it analyzes the facial expressions and voice of residents when they view the design proposals and prioritizes presenting design proposals that elicit a positive reaction. Step 3: The contractor selection unit selects the most suitable contractor based on the design proposal proposed by the design proposal unit. For example, if a resident inputs a request to "be introduced to a reliable contractor," the contractor selection unit will suggest multiple contractors based on their past performance and evaluations. The contractor selection unit also considers the resident's budget and schedule to select the most suitable contractor. For example, it will suggest a contractor that can provide high-quality construction within the budget. Furthermore, the contractor selection unit uses an emotion estimation function to analyze the residents' emotions toward the contractor and prioritize recommending reliable contractors. For example, if the resident expresses positive emotions toward a contractor's proposal, it will prioritize recommending that contractor. Step 4: The feedback collection unit collects feedback from residents after the renovation is completed. For example, when a resident asks, "Are you satisfied with the results of the renovation?", the feedback collection unit prompts the resident to enter their opinions and thoughts. The feedback collection unit also analyzes the resident feedback, extracts common problems and areas for improvement, and shares them. For example, it extracts problems pointed out by multiple residents and proposes solutions to address them. Furthermore, the feedback collection unit uses an emotion estimation function to analyze the resident's feelings toward the feedback and prioritizes sharing positive feedback. For example, if a resident expresses positive feelings toward the renovation, it prioritizes sharing that feedback. Step 5: The Communication Promotion Department promotes communication between residents and supports event planning within the community. For example, if a resident inputs a request such as "I want to hold a workshop on renovation," the Communication Promotion Department will suggest the optimal date, time, and location and support event planning. The Communication Promotion Department also analyzes residents' interests and suggests the most suitable event based on that. For example, if a resident is interested in DIY, it will suggest a DIY workshop. Furthermore, the Communication Promotion Department uses an emotion estimation function to analyze residents' emotions toward events and prioritizes planning events that elicit positive emotions. For example, if residents express positive emotions toward an event, it will plan an event that incorporates elements that elicit those emotions.

[0099] 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.

[0100] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<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.

[0101] 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.

[0102] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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).

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0112] 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. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0113] 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.

[0114] 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.

[0115] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0116] 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.

[0117] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0118] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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).

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0127] 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 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0128] 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.

[0129] 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.

[0130] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0131] 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.

[0132] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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).

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0143] In the robot 414, the processor 46 performs the identification process. 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. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0144] 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.

[0145] 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.

[0146] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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).

[0152] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0153] 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."

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0166] 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 question reception desk that accepts questions or requests from residents regarding the renovation; a design proposal unit that proposes a renovation design plan based on the questions or requests received by the question reception unit; a contractor selection unit that selects an optimal contractor based on the design proposal proposed by the design proposal unit; a feedback collection department that collects feedback from residents after the renovation is completed; and a communication promotion department that promotes communication between residents and supports the planning of events within the community. A system characterized by:

2. The question receiving unit Analyze the residents' feelings toward the renovation and provide information that elicits positive feelings preferentially.

2. The system of claim 1.

3. The design proposal section Propose design proposals from a long-term perspective, taking into consideration the family structure or future life events of the residents.

2. The system of claim 1.

4. The contractor selection unit Analyzing the contractors' past project data and recommending the contractor that best suits the residents' needs 2. The system of claim 1.

5. The feedback collection unit: Analyze the residents' feedback, extract common problems and areas for improvement, and share them.

2. The system of claim 1.

6. The communication promotion unit Analyze the feelings of the residents toward the event and prioritize planning the event that will elicit positive feelings.

2. The system of claim 1.

7. The design proposal section Analyze the residents' feelings toward the design proposals and prioritize the design proposals that evoke the most positive reactions.

2. The system of claim 1.

8. The contractor selection unit Analyzing the feelings of the residents toward the construction companies and preferentially recommending reliable companies 2. The system of claim 1.

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