system

Abstract

The system according to this embodiment aims to collect and analyze information about vacant houses and propose the optimal solution. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, and a proposal unit. The collection unit collects information about vacant houses. The analysis unit analyzes the information collected by the collection unit. The proposal unit proposes the optimal solution based on the analysis results obtained by the analysis unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there was a problem that information about vacant houses was not sufficiently collected and analyzed efficiently, and optimal solutions were not sufficiently proposed.

[0005] The system according to the embodiment aims to collect and analyze information about vacant houses and propose an optimal solution.

Means for Solving the Problems

[0006] The system according to the embodiment includes a collection unit, an analysis unit, and a proposal unit. The collection unit collects information about vacant houses. The analysis unit analyzes the information collected by the collection unit. The proposal unit proposes an optimal solution based on the analysis result obtained by the analysis unit.

Effects of the Invention

[0007] The system according to this embodiment can collect and analyze information about vacant houses and propose optimal solutions. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

[0014] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 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. Also, the reception device 38, the output device 40, and the camera 42 are connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) An embodiment of the present invention provides a vacant house management system that collects information on vacant houses, has an AI agent analyze and judge the information, and provides the optimal answer to the person making the inquiry. This vacant house management system collects information on vacant houses, has an AI agent analyze the information, and proposes the optimal solution to issues such as vacant house management, decline in asset value, deterioration of local safety and scenery, and inheritance problems. For example, the vacant house management system collects detailed data such as the location of the vacant house, the condition of the building, and information about the owner. For example, it investigates the location and condition of the vacant house and confirms the owner's information. This allows for a detailed understanding of the vacant house. Next, the vacant house management system has an AI agent analyze the collected information. The AI ​​agent analyzes the collected data and proposes the optimal solution to issues such as vacant house management, decline in asset value, deterioration of local safety and scenery, and inheritance problems. For example, regarding vacant house management, it proposes regular inspections and cleaning services, and regarding decline in asset value, it offers options such as renovation, rental, or sale. This provides the optimal solution for vacant house owners, local governments, real estate agents, and local residents. Furthermore, the vacant house management system offers options such as renovation, rental, and sale. For example, it can propose renovating vacant houses and using them as rental properties, or suggest the best way to sell them. This promotes the effective use of vacant houses and is expected to revitalize the community. The vacant house management system also proposes management services such as regular inspections and cleaning services for vacant houses, and the promotion of local events and community activities. For example, by providing regular inspections and cleaning services for vacant houses, management of vacant houses becomes easier, preventing deterioration of local safety and scenery. Furthermore, promoting local events and community activities contributes to the revitalization of the community. In this way, by utilizing an AI agent, it is possible to analyze and judge information about vacant houses and provide the best possible answers to those who inquire. This provides optimal solutions for vacant house owners, local governments, real estate agents, and local residents, and contributes to solving the vacant house problem. In this way, the vacant house management system can efficiently collect, analyze, and propose information about vacant houses.

[0029] The vacant house management system according to this embodiment comprises a collection unit, an analysis unit, and a proposal unit. The collection unit collects information about vacant houses. The collection unit collects detailed data such as the location of the vacant house, the condition of the building, and information about the owner. The collection unit investigates the location and condition of the vacant house and verifies the owner's information. The collection unit obtains the location of the vacant house from a map database and verifies the condition of the building using on-site surveys or drones. The collection unit obtains owner information from a public real estate registration database. The analysis unit analyzes the information collected by the collection unit. The analysis unit analyzes the collected data and proposes optimal solutions to issues such as vacant house management, decline in asset value, deterioration of local safety and landscape, and inheritance problems. For example, regarding vacant house management, the analysis unit proposes regular inspections and cleaning services. For example, regarding decline in asset value, the analysis unit provides options such as renovation, rental, or sale. For example, regarding deterioration of local safety and landscape, the analysis unit proposes promoting local events and community activities. The proposal department proposes optimal solutions based on the analysis results obtained by the analysis department. For example, the proposal department may propose renovating vacant houses and using them as rental properties, or propose the best way to sell them. For example, the proposal department may propose providing regular inspection and cleaning services for vacant houses. For example, the proposal department may propose promoting local events and community activities. In this way, the vacant house management system according to the embodiment can efficiently collect, analyze, and propose information about vacant houses.

[0030] The collection department collects information about vacant houses. For example, it collects detailed data such as the location of the vacant house, the condition of the building, and information about the owner. Specifically, it obtains the location of the vacant house from a map database and verifies the condition of the building through on-site surveys and drones. The map database provides up-to-date geographical information and is used to pinpoint the exact location of the vacant house. During on-site surveys, professional surveyors actually visit the vacant house and meticulously record the condition of the building's exterior and interior. Using drones allows for efficient surveys of hard-to-reach locations and large areas. Drones are equipped with high-resolution cameras and infrared sensors that can detect building deterioration and temperature anomalies. Owner information is obtained from the official real estate registry database. This database includes the owner's name, contact information, and ownership history, allowing for accurate ownership information. The collection department centrally manages this information and stores it in the database. The database serves as a foundation for efficiently searching, updating, and sharing the collected information. Furthermore, the collection department regularly updates the data to maintain up-to-date information. For example, regular on-site inspections and drone-based re-inspections can be conducted to reflect changes in the building's condition and ownership information. This allows the data collection unit to provide accurate and up-to-date information on vacant properties, improving the overall reliability of the system.

[0031] The Analysis Department analyzes the information collected by the Collection Department. For example, the Analysis Department analyzes the collected data and proposes optimal solutions to issues such as vacant property management, declining property values, deterioration of local safety and scenery, and inheritance problems. Specifically, regarding vacant property management, they propose regular inspections and cleaning services. Regular inspections check the deterioration and safety of the building and carry out necessary repairs and maintenance. Cleaning services clean the interior and surrounding areas of the building to maintain hygiene. Regarding declining property values, they offer options such as renovation, rental, and sale. Renovation involves improving the interior and exterior of the building to increase property value. Rental allows vacant properties to be used as rental properties to generate income. For sales, they propose the optimal sales method and aim for a quick and high-priced sale. Regarding deterioration of local safety and scenery, they propose promoting local events and community activities. Local events promote interaction among residents and enhance a sense of community. Community activities improve local safety and scenery through vacant property management and beautification activities. Regarding inheritance problems, they collaborate with specialized lawyers and real estate consultants to support appropriate inheritance procedures. This allows the analysis department to propose optimal solutions to various issues related to vacant houses based on the collected data, thereby supporting the effective utilization of vacant houses and the revitalization of local communities.

[0032] The proposal department proposes optimal solutions based on the analysis results obtained by the analysis department. For example, the proposal department may propose renovating vacant houses for use as rental properties or suggesting the best methods for selling them. Specifically, when renovating, they consider the building's structure and design to provide a renovation plan that maximizes asset value. When using the property as a rental, they propose interiors and facilities tailored to the target tenant group to enhance competitiveness in the rental market. When selling, they introduce appropriate real estate agents and conduct appraisals of the selling price and market trends. Furthermore, the proposal department proposes providing regular inspections and cleaning services for vacant houses. During regular inspections, specialized technicians check the building's deterioration and safety and propose necessary repairs and maintenance. Cleaning services include cleaning the interior and surrounding areas of the building to maintain hygiene. When proposing the promotion of local events and community activities, they propose specific event planning and operation methods to encourage participation from local residents. For example, they may hold workshops or market events using vacant houses to revitalize the area. In addition, for community activities, they propose activities to deepen interaction among residents and beautification activities in the area. This allows the proposal department to propose concrete and feasible solutions based on the analysis department's results, thereby supporting the effective utilization of vacant houses and the revitalization of local communities.

[0033] The proposal department can offer options such as renovation, rental, or sale. For example, the proposal department may propose renovating a vacant house and using it as a rental property. For example, the proposal department may propose the best way to sell a vacant house. For example, the proposal department may propose specific details such as the type of renovation, rental conditions, and sales method for a vacant house. By offering options such as renovation, rental, and sale, the effective use of vacant houses can be promoted. Some or all of the above processing in the proposal department may be performed using, for example, a generation AI, or not. For example, the proposal department can input the type of renovation, rental conditions, and sales method for a vacant house into a generation AI, and the generation AI can generate the best proposal.

[0034] The proposal department can propose regular inspections and cleaning services for vacant properties. For example, the proposal department can specifically propose the frequency of regular inspections and the scope of cleaning services for vacant properties. For example, the proposal department can propose the content of regular inspections and the details of cleaning services for vacant properties. For example, the proposal department can propose that regular inspections be conducted once a month and that cleaning services be conducted both inside and outside the building. By proposing regular inspections and cleaning services for vacant properties, the management of vacant properties becomes easier. Some or all of the above processing in the proposal department may be performed using, for example, a generative AI, or without using a generative AI. For example, the proposal department can input the content of regular inspections and the details of cleaning services for vacant properties into a generative AI, and the generative AI can generate an optimal proposal.

[0035] The proposal department can propose the promotion of local events and community activities. For example, the proposal department can specifically propose the types of local events and the content of community activities. For example, the proposal department can propose the frequency of local events and the details of community activities. For example, the proposal department can propose local cleaning activities and crime prevention patrols as types of local events, and interaction events with local residents as content of community activities. By proposing the promotion of local events and community activities, the region can be revitalized. Some or all of the above processing in the proposal department may be performed using, for example, a generative AI, or not using a generative AI. For example, the proposal department can input the types of local events and the content of community activities into a generative AI, and the generative AI can generate the most suitable proposal.

[0036] The data collection unit can collect detailed data such as the location of vacant properties, the condition of the buildings, and information about the owners. For example, the data collection unit can obtain the location of vacant properties from a map database and verify the condition of the buildings using on-site surveys or drones. For example, the data collection unit can obtain information about the owners from an official real estate registration database. For example, the data collection unit can accurately pinpoint the location of vacant properties using GPS data and record the condition of the buildings with photos and videos. For example, the data collection unit can automatically obtain information about the owners from an online database. By collecting detailed data about vacant properties, it is possible to accurately understand information about them. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the data collection unit can input the location of vacant properties, the condition of the buildings, and information about the owners into a generative AI, which can then collect the data.

[0037] The data collection unit can analyze the past usage history of vacant properties and select the most suitable information collection method. For example, if a vacant property was previously used as a rental property, the data collection unit collects rental history and feedback from past tenants. For example, if a vacant property has been renovated in the past, the data collection unit collects detailed information about that renovation. For example, if a vacant property has been sold in the past, the data collection unit collects market price and sales conditions at the time of sale. By analyzing the past usage history of vacant properties, the data collection unit can select the most suitable information collection method. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit can input the past usage history of vacant properties into a generative AI, which can then select the most suitable information collection method.

[0038] The data collection unit can simultaneously collect information on the surrounding environment and local safety when collecting data on vacant houses. For example, the data collection unit can collect information on the crime rate and location of police stations around the vacant house. For example, the data collection unit can collect information on public facilities such as schools and hospitals around the vacant house. For example, the data collection unit can collect information on commercial facilities and transportation access around the vacant house. By simultaneously collecting information on the surrounding environment and local safety, more comprehensive information can be provided. Some or all of the above processing in the data collection unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the data collection unit can input information on the surrounding environment and local safety of the vacant house into a generating AI, and the generating AI can collect the information.

[0039] The data collection unit can also collect information on local infrastructure and public transportation when collecting data on vacant houses. For example, the data collection unit can collect information on infrastructure such as electricity, water, and gas around the vacant house. For example, the data collection unit can collect information on the location and operation status of bus stops and train stations around the vacant house. For example, the data collection unit can collect information on road conditions and parking lots around the vacant house. By collecting information on local infrastructure and public transportation, more comprehensive information can be provided. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit can input information on infrastructure and public transportation around the vacant house into a generative AI, and the generative AI can collect the information.

[0040] The collection unit can collect opinions and feedback from local residents when collecting information on vacant houses. For example, the collection unit can conduct surveys of residents around vacant houses to collect opinions and feedback. For example, the collection unit can analyze social media and blog posts of residents around vacant houses to collect opinions and feedback. For example, the collection unit can investigate the participation of residents around vacant houses in community activities and events to collect opinions and feedback. By collecting opinions and feedback from local residents, more comprehensive information can be provided. Some or all of the above processing in the collection unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the collection unit can input the opinions and feedback of local residents into a generative AI, and the generative AI can collect the information.

[0041] The analysis unit can improve the accuracy of its analysis by referring to the past sales and rental history of vacant properties during the analysis process. For example, the analysis unit can refer to the past sales history of vacant properties to analyze their current market value. For example, the analysis unit can refer to the past rental history of vacant properties to analyze rental demand and fluctuations in rent. For example, the analysis unit can integrate the past sales and rental history of vacant properties to analyze their overall asset value. This improves the accuracy of the analysis by referring to the past sales and rental history of vacant properties. Some or all of the above processes in the analysis unit may be performed using, for example, a generating AI, or not using a generating AI. For example, the analysis unit can input the past sales and rental history of vacant properties into a generating AI, which can then improve the accuracy of the analysis.

[0042] The analysis unit can perform its analysis while considering the economic conditions and demographics of the area surrounding the vacant house. For example, the analysis unit can perform its analysis while considering the economic growth rate and unemployment rate of the area surrounding the vacant house. For example, the analysis unit can perform its analysis while considering the population increase / decrease and age structure of the area surrounding the vacant house. For example, the analysis unit can perform its analysis while considering the housing demand and supply situation of the area surrounding the vacant house. This makes it possible to perform a more accurate analysis by considering the economic conditions and demographics of the area surrounding the vacant house. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or it may be performed without using a generative AI. For example, the analysis unit can input the economic conditions and demographics of the area surrounding the vacant house into a generative AI, and the generative AI can perform the analysis.

[0043] The analysis unit can perform analyses while considering the energy efficiency and environmental impact of vacant houses. For example, the analysis unit can perform analyses while considering the energy consumption and utility costs of vacant houses. For example, the analysis unit can perform analyses while considering the insulation performance and presence of energy-saving equipment of vacant houses. For example, the analysis unit can perform analyses while considering the environmental impact and CO2 emissions of vacant houses. This makes it possible to perform more accurate analyses by considering the energy efficiency and environmental impact of vacant houses. Some or all of the above processing in the analysis unit may be performed using, for example, a generating AI, or it may be performed without using a generating AI. For example, the analysis unit can input data on the energy consumption and environmental impact of vacant houses into a generating AI, and the generating AI can perform the analysis.

[0044] The analysis unit can perform its analysis while considering information about educational and medical institutions in the area surrounding the vacant house. For example, the analysis unit can perform its analysis while considering information about schools and daycare centers in the area surrounding the vacant house. For example, the analysis unit can perform its analysis while considering information about hospitals and clinics in the area surrounding the vacant house. For example, the analysis unit can perform its analysis while considering the evaluation and reputation of educational and medical institutions in the area surrounding the vacant house. This makes it possible to perform a more accurate analysis by considering information about educational and medical institutions in the area surrounding the vacant house. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or it may be performed without using a generative AI. For example, the analysis unit can input information about educational and medical institutions in the area surrounding the vacant house into a generative AI, and the generative AI can perform the analysis.

[0045] The proposal department can provide simulations of renovation costs and rental income for vacant properties when making proposals. For example, the proposal department can simulate the renovation costs of vacant properties in detail and make a proposal. For example, the proposal department can simulate the rental income of vacant properties and make a highly profitable proposal. For example, the proposal department can simulate the rental income of vacant properties after renovation and make an optimal proposal. By providing simulations of renovation costs and rental income for vacant properties, more specific proposals become possible. Some or all of the above processing in the proposal department may be performed using, for example, a generative AI, or not using a generative AI. For example, the proposal department can input data on renovation costs and rental income for vacant properties into a generative AI, and the generative AI can perform the simulation.

[0046] The proposal unit can provide a market price forecast for the sale of a vacant house when making a proposal. For example, the proposal unit can forecast the current market price of a vacant house and make a sale proposal. For example, the proposal unit can forecast the future market price of a vacant house and make a suggestion for the timing of the sale. For example, the proposal unit can simulate the market price at the time of sale of a vacant house and propose the optimal selling method. By providing a market price forecast for the sale of a vacant house, more specific proposals become possible. Some or all of the above processing in the proposal unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the proposal unit can input market price data for vacant houses into a generative AI, and the generative AI can perform a market price forecast.

[0047] The proposal department can provide design proposals and interior coordination for renovated vacant houses at the time of proposal. For example, the proposal department can provide detailed design proposals for renovated vacant houses. For example, the proposal department can propose interior coordination for vacant houses to provide an attractive space. For example, the proposal department can provide proposals that integrate the design and interior coordination for renovated vacant houses. This makes it possible to provide more concrete proposals by providing design proposals and interior coordination for renovated vacant houses. Some or all of the above processing in the proposal department may be performed using, for example, a generative AI, or not using a generative AI. For example, the proposal department can input data on the design and interior coordination for renovated vacant houses into a generative AI, and the generative AI can make proposals.

[0048] The proposal department can offer vacant property rental management services and sales support services when making proposals. For example, the proposal department can propose detailed vacant property rental management services to reduce the burden of management. For example, the proposal department can propose vacant property sales support services to facilitate the sales process. For example, the proposal department can make proposals that integrate vacant property rental management services and sales support services. This allows for more specific proposals by offering vacant property rental management services and sales support services. Some or all of the above processing in the proposal department may be performed using, for example, a generation AI, or not. For example, the proposal department can input data on vacant property rental management services and sales support services into a generation AI, which can then make proposals.

[0049] The proposal department can make optimal proposals when proposing renovations by referring to past renovation cases. For example, the proposal department can make optimal renovation proposals for similar vacant houses based on past renovation cases. For example, the proposal department can propose effective renovation plans by referring to successful past renovation cases. For example, the proposal department can make suggestions to avoid risks by referring to unsuccessful past renovation cases. In this way, more specific proposals become possible by referring to past renovation cases. Some or all of the above processing in the proposal department may be performed using, for example, a generation AI, or not using a generation AI. For example, the proposal department can input data on past renovation cases into a generation AI, and the generation AI can make optimal proposals.

[0050] The proposal department can make rental proposals while considering the trends in the local rental market. For example, the proposal department can make rental proposals while considering the balance of supply and demand in the local rental market. For example, the proposal department can propose the optimal rent setting while considering the rental market rate in the local rental market. For example, the proposal department can propose an attractive rental plan while considering the trends in the local rental market. This makes it possible to make more specific proposals by considering the trends in the local rental market. Some or all of the above processing in the proposal department may be performed using, for example, a generation AI, or not using a generation AI. For example, the proposal department can input local rental market data into a generation AI, and the generation AI can make proposals.

[0051] The proposal department can make optimal proposals when proposing periodic inspections by referring to past inspection history. For example, the proposal department can make optimal inspection proposals for similar vacant houses based on past inspection history. For example, the proposal department can propose effective inspection plans by referring to successful examples from past inspection history. For example, the proposal department can make suggestions to avoid risks by referring to unsuccessful examples from past inspection history. In this way, more specific proposals become possible by referring to past inspection history. Some or all of the above processing in the proposal department may be performed using, for example, a generation AI, or not using a generation AI. For example, the proposal department can input data from past inspection history into a generation AI, and the generation AI can make optimal proposals.

[0052] The proposal department can consider the evaluation of local cleaning companies when proposing cleaning services. For example, the proposal department can propose a highly reliable cleaning company based on the evaluation of local cleaning companies. For example, the proposal department can propose an optimal cleaning plan by considering the past performance of local cleaning companies. For example, the proposal department can make the best proposal by comparing the fees and services of local cleaning companies. This makes it possible to make more specific proposals by considering the evaluation of local cleaning companies. Some or all of the above processes in the proposal department may be performed using, for example, a generation AI, or not using a generation AI. For example, the proposal department can input evaluation data of local cleaning companies into a generation AI, and the generation AI can make the best proposal.

[0053] The proposal department can make optimal proposals for local events by referring to feedback from participants of past events. For example, the proposal department can make optimal proposals for similar events based on feedback from participants of past local events. For example, the proposal department can propose effective event plans by referring to successful examples of past local events. For example, the proposal department can make suggestions to avoid risks by referring to unsuccessful examples of past local events. This makes it possible to make more specific proposals by referring to feedback from participants of past events. Some or all of the above processing in the proposal department may be performed using, for example, a generative AI, or not using a generative AI. For example, the proposal department can input feedback data from participants of past events into a generative AI, which can then make optimal proposals.

[0054] The proposal department can consider the opinions of local residents when proposing community activities. For example, the proposal department can propose community activities that meet the needs of residents based on their opinions. For example, the proposal department can propose optimal community activities by considering the past participation of local residents. For example, the proposal department can propose effective community activities by referring to feedback from local residents. This makes it possible to make more specific proposals by considering the opinions of local residents. Some or all of the above processing in the proposal department may be performed using, for example, a generative AI, or not using a generative AI. For example, the proposal department can input local residents' opinion data into a generative AI, which can then make optimal proposals.

[0055] The data collection unit can collect information on nearby transportation access and commercial facilities when collecting location information for vacant houses. For example, when collecting location information for vacant houses, the data collection unit can collect information on the location of the nearest train station or bus stop. For example, when collecting location information for vacant houses, the data collection unit can collect information on nearby commercial facilities and restaurants. For example, when collecting location information for vacant houses, the data collection unit can collect information on nearby parks and recreational facilities. By collecting information on nearby transportation access and commercial facilities, more comprehensive information can be provided. Some or all of the above processing in the data collection unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the data collection unit can input the location information of vacant houses and information on nearby transportation access and commercial facilities into a generating AI, and the generating AI can collect the information.

[0056] The data collection unit can collect past repair and renovation history when collecting information on the condition of vacant buildings. For example, when collecting information on the condition of vacant buildings, the data collection unit can collect detailed information on past repair history. For example, when collecting information on the condition of vacant buildings, the data collection unit can collect detailed information on past renovation history. For example, when collecting information on the condition of vacant buildings, the data collection unit can collect data to assess the need for repairs or renovations. By collecting past repair and renovation history, more comprehensive information can be provided. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit can input data on the condition of vacant buildings and past repair and renovation history into a generative AI, which can then collect the information.

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

[0058] The vacant property management system can also include a communication department. This department provides functions to facilitate communication with vacant property owners and local residents. For example, it can send regular emails and notifications to vacant property owners, reporting on the condition and management status of the property. It can also collect feedback and opinions from local residents and incorporate them into vacant property management. Furthermore, it can share information on local events and community activities, promoting local revitalization. This allows the vacant property management system to strengthen collaboration with vacant property owners and local residents, resulting in more effective vacant property management.

[0059] The vacant property management system can also be equipped with a security unit. The security unit provides functions to enhance security measures for vacant properties. For example, the security unit can collect data from surveillance cameras and sensors installed in vacant properties and notify the owner or the police if suspicious activity is detected. The security unit can also analyze crime rates and public safety information in the area surrounding the vacant property and propose security measures. Furthermore, the security unit can arrange for the installation and maintenance of security equipment in vacant properties. In this way, the vacant property management system can enhance security measures for vacant properties and increase the sense of security for owners and local residents.

[0060] The vacant property management system can also include an energy management unit. This unit provides functions to optimize the energy consumption of vacant properties. For example, it can monitor the electricity, gas, and water usage of vacant properties and suggest ways to reduce unnecessary energy consumption. It can also evaluate the effectiveness of energy-saving equipment installed in vacant properties and propose further energy-saving measures. Furthermore, it can provide renovation plans to improve the energy efficiency of vacant properties. In this way, the vacant property management system can optimize the energy consumption of vacant properties and reduce their environmental impact.

[0061] The vacant property management system can also include a health management department. This department provides functions to assess the health impact of the vacant property's environment and propose improvement measures. For example, it can monitor indoor air quality, humidity, and temperature, and issue warnings if harmful elements are detected. It can also investigate the occurrence of mold and mites in vacant properties and propose countermeasures. Furthermore, it can recommend the use of health-conscious materials and equipment during vacant property renovations. In this way, the vacant property management system can maintain a healthy environment in vacant properties and protect the health of the residents.

[0062] The vacant property management system can also include an education department. This department can provide knowledge and skills related to vacant property management to vacant property owners and local residents. For example, the education department can hold online courses and workshops on vacant property management methods and renovation procedures. Furthermore, the education department can provide the latest information and technologies related to vacant property management, supporting owners and local residents in effectively managing their properties. In addition, the education department can collaborate with local schools and community centers to provide educational programs on vacant property management. This allows the vacant property management system to improve the knowledge and skills of vacant property owners and local residents, contributing to the resolution of the vacant property problem.

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

[0064] Step 1: The collection unit collects information about vacant houses. The collection unit collects detailed data such as the location of the vacant house, the condition of the building, and information about the owner. The collection unit investigates the location and condition of the vacant house and verifies the owner's information. The collection unit obtains the location of the vacant house from a map database and verifies the condition of the building using on-site surveys or drones. The collection unit obtains the owner's information from an official real estate registration database. Step 2: The Analysis Department analyzes the information collected by the Collection Department. For example, the Analysis Department analyzes the collected data and proposes optimal solutions to issues such as vacant property management, declining property values, deterioration of local safety and scenery, and inheritance problems. For example, regarding vacant property management, the Analysis Department might propose regular inspections and cleaning services. For example, regarding declining property values, the Analysis Department might offer options such as renovation, rental, or sale. For example, regarding deterioration of local safety and scenery, the Analysis Department might propose promoting local events and community activities. Step 3: The proposal department proposes the optimal solution based on the analysis results obtained by the analysis department. For example, the proposal department may propose renovating vacant houses and using them as rental properties, or propose the best way to sell them. For example, the proposal department may propose providing regular inspection and cleaning services for vacant houses. For example, the proposal department may propose promoting local events and community activities.

[0065] (Example of form 2) An embodiment of the present invention provides a vacant house management system that collects information on vacant houses, has an AI agent analyze and judge the information, and provides the optimal answer to the person making the inquiry. This vacant house management system collects information on vacant houses, has an AI agent analyze the information, and proposes the optimal solution to issues such as vacant house management, decline in asset value, deterioration of local safety and scenery, and inheritance problems. For example, the vacant house management system collects detailed data such as the location of the vacant house, the condition of the building, and information about the owner. For example, it investigates the location and condition of the vacant house and confirms the owner's information. This allows for a detailed understanding of the vacant house. Next, the vacant house management system has an AI agent analyze the collected information. The AI ​​agent analyzes the collected data and proposes the optimal solution to issues such as vacant house management, decline in asset value, deterioration of local safety and scenery, and inheritance problems. For example, regarding vacant house management, it proposes regular inspections and cleaning services, and regarding decline in asset value, it offers options such as renovation, rental, or sale. This provides the optimal solution for vacant house owners, local governments, real estate agents, and local residents. Furthermore, the vacant house management system offers options such as renovation, rental, and sale. For example, it can propose renovating vacant houses and using them as rental properties, or suggest the best way to sell them. This promotes the effective use of vacant houses and is expected to revitalize the community. The vacant house management system also proposes management services such as regular inspections and cleaning services for vacant houses, and the promotion of local events and community activities. For example, by providing regular inspections and cleaning services for vacant houses, management of vacant houses becomes easier, preventing deterioration of local safety and scenery. Furthermore, promoting local events and community activities contributes to the revitalization of the community. In this way, by utilizing an AI agent, it is possible to analyze and judge information about vacant houses and provide the best possible answers to those who inquire. This provides optimal solutions for vacant house owners, local governments, real estate agents, and local residents, and contributes to solving the vacant house problem. In this way, the vacant house management system can efficiently collect, analyze, and propose information about vacant houses.

[0066] The vacant house management system according to this embodiment comprises a collection unit, an analysis unit, and a proposal unit. The collection unit collects information about vacant houses. The collection unit collects detailed data such as the location of the vacant house, the condition of the building, and information about the owner. The collection unit investigates the location and condition of the vacant house and verifies the owner's information. The collection unit obtains the location of the vacant house from a map database and verifies the condition of the building using on-site surveys or drones. The collection unit obtains owner information from a public real estate registration database. The analysis unit analyzes the information collected by the collection unit. The analysis unit analyzes the collected data and proposes optimal solutions to issues such as vacant house management, decline in asset value, deterioration of local safety and landscape, and inheritance problems. For example, regarding vacant house management, the analysis unit proposes regular inspections and cleaning services. For example, regarding decline in asset value, the analysis unit provides options such as renovation, rental, or sale. For example, regarding deterioration of local safety and landscape, the analysis unit proposes promoting local events and community activities. The proposal department proposes optimal solutions based on the analysis results obtained by the analysis department. For example, the proposal department may propose renovating vacant houses and using them as rental properties, or propose the best way to sell them. For example, the proposal department may propose providing regular inspection and cleaning services for vacant houses. For example, the proposal department may propose promoting local events and community activities. In this way, the vacant house management system according to the embodiment can efficiently collect, analyze, and propose information about vacant houses.

[0067] The collection department collects information about vacant houses. For example, it collects detailed data such as the location of the vacant house, the condition of the building, and information about the owner. Specifically, it obtains the location of the vacant house from a map database and verifies the condition of the building through on-site surveys and drones. The map database provides up-to-date geographical information and is used to pinpoint the exact location of the vacant house. During on-site surveys, professional surveyors actually visit the vacant house and meticulously record the condition of the building's exterior and interior. Using drones allows for efficient surveys of hard-to-reach locations and large areas. Drones are equipped with high-resolution cameras and infrared sensors that can detect building deterioration and temperature anomalies. Owner information is obtained from the official real estate registry database. This database includes the owner's name, contact information, and ownership history, allowing for accurate ownership information. The collection department centrally manages this information and stores it in the database. The database serves as a foundation for efficiently searching, updating, and sharing the collected information. Furthermore, the collection department regularly updates the data to maintain up-to-date information. For example, regular on-site inspections and drone-based re-inspections can be conducted to reflect changes in the building's condition and ownership information. This allows the data collection unit to provide accurate and up-to-date information on vacant properties, improving the overall reliability of the system.

[0068] The Analysis Department analyzes the information collected by the Collection Department. For example, the Analysis Department analyzes the collected data and proposes optimal solutions to issues such as vacant property management, declining property values, deterioration of local safety and scenery, and inheritance problems. Specifically, regarding vacant property management, they propose regular inspections and cleaning services. Regular inspections check the deterioration and safety of the building and carry out necessary repairs and maintenance. Cleaning services clean the interior and surrounding areas of the building to maintain hygiene. Regarding declining property values, they offer options such as renovation, rental, and sale. Renovation involves improving the interior and exterior of the building to increase property value. Rental allows vacant properties to be used as rental properties to generate income. For sales, they propose the optimal sales method and aim for a quick and high-priced sale. Regarding deterioration of local safety and scenery, they propose promoting local events and community activities. Local events promote interaction among residents and enhance a sense of community. Community activities improve local safety and scenery through vacant property management and beautification activities. Regarding inheritance problems, they collaborate with specialized lawyers and real estate consultants to support appropriate inheritance procedures. This allows the analysis department to propose optimal solutions to various issues related to vacant houses based on the collected data, thereby supporting the effective utilization of vacant houses and the revitalization of local communities.

[0069] The proposal department proposes optimal solutions based on the analysis results obtained by the analysis department. For example, the proposal department may propose renovating vacant houses for use as rental properties or suggesting the best methods for selling them. Specifically, when renovating, they consider the building's structure and design to provide a renovation plan that maximizes asset value. When using the property as a rental, they propose interiors and facilities tailored to the target tenant group to enhance competitiveness in the rental market. When selling, they introduce appropriate real estate agents and conduct appraisals of the selling price and market trends. Furthermore, the proposal department proposes providing regular inspections and cleaning services for vacant houses. During regular inspections, specialized technicians check the building's deterioration and safety and propose necessary repairs and maintenance. Cleaning services include cleaning the interior and surrounding areas of the building to maintain hygiene. When proposing the promotion of local events and community activities, they propose specific event planning and operation methods to encourage participation from local residents. For example, they may hold workshops or market events using vacant houses to revitalize the area. In addition, for community activities, they propose activities to deepen interaction among residents and beautification activities in the area. This allows the proposal department to propose concrete and feasible solutions based on the analysis department's results, thereby supporting the effective utilization of vacant houses and the revitalization of local communities.

[0070] The proposal department can offer options such as renovation, rental, or sale. For example, the proposal department may propose renovating a vacant house and using it as a rental property. For example, the proposal department may propose the best way to sell a vacant house. For example, the proposal department may propose specific details such as the type of renovation, rental conditions, and sales method for a vacant house. By offering options such as renovation, rental, and sale, the effective use of vacant houses can be promoted. Some or all of the above processing in the proposal department may be performed using, for example, a generation AI, or not. For example, the proposal department can input the type of renovation, rental conditions, and sales method for a vacant house into a generation AI, and the generation AI can generate the best proposal.

[0071] The proposal department can propose regular inspections and cleaning services for vacant properties. For example, the proposal department can specifically propose the frequency of regular inspections and the scope of cleaning services for vacant properties. For example, the proposal department can propose the content of regular inspections and the details of cleaning services for vacant properties. For example, the proposal department can propose that regular inspections be conducted once a month and that cleaning services be conducted both inside and outside the building. By proposing regular inspections and cleaning services for vacant properties, the management of vacant properties becomes easier. Some or all of the above processing in the proposal department may be performed using, for example, a generative AI, or without using a generative AI. For example, the proposal department can input the content of regular inspections and the details of cleaning services for vacant properties into a generative AI, and the generative AI can generate an optimal proposal.

[0072] The proposal department can propose the promotion of local events and community activities. For example, the proposal department can specifically propose the types of local events and the content of community activities. For example, the proposal department can propose the frequency of local events and the details of community activities. For example, the proposal department can propose local cleaning activities and crime prevention patrols as types of local events, and interaction events with local residents as content of community activities. By proposing the promotion of local events and community activities, the region can be revitalized. Some or all of the above processing in the proposal department may be performed using, for example, a generative AI, or not using a generative AI. For example, the proposal department can input the types of local events and the content of community activities into a generative AI, and the generative AI can generate the most suitable proposal.

[0073] The data collection unit can collect detailed data such as the location of vacant properties, the condition of the buildings, and information about the owners. For example, the data collection unit can obtain the location of vacant properties from a map database and verify the condition of the buildings using on-site surveys or drones. For example, the data collection unit can obtain information about the owners from an official real estate registration database. For example, the data collection unit can accurately pinpoint the location of vacant properties using GPS data and record the condition of the buildings with photos and videos. For example, the data collection unit can automatically obtain information about the owners from an online database. By collecting detailed data about vacant properties, it is possible to accurately understand information about them. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the data collection unit can input the location of vacant properties, the condition of the buildings, and information about the owners into a generative AI, which can then collect the data.

[0074] The information collection unit can estimate the emotions of the person making the inquiry and determine the priority of the information to collect based on the estimated emotions. For example, if the person making the inquiry is feeling anxious, the information collection unit will prioritize collecting information related to safety. For example, if the person making the inquiry is interested, the information collection unit will prioritize collecting information related to the asset value of vacant houses and the possibilities of renovation. For example, if the person making the inquiry is in a hurry, the information collection unit will prioritize collecting basic information that can be obtained quickly. This allows for the provision of more appropriate information by prioritizing information based on the emotions of the person making the inquiry. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the information collection unit may be performed using a generative AI, or not using a generative AI. For example, the information collection unit can input the emotions of the person making the inquiry into a generative AI, which can estimate the emotions and determine the priority of the information.

[0075] The data collection unit can analyze the past usage history of vacant properties and select the most suitable information collection method. For example, if a vacant property was previously used as a rental property, the data collection unit collects rental history and feedback from past tenants. For example, if a vacant property has been renovated in the past, the data collection unit collects detailed information about that renovation. For example, if a vacant property has been sold in the past, the data collection unit collects market price and sales conditions at the time of sale. By analyzing the past usage history of vacant properties, the data collection unit can select the most suitable information collection method. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit can input the past usage history of vacant properties into a generative AI, which can then select the most suitable information collection method.

[0076] The data collection unit can simultaneously collect information on the surrounding environment and local safety when collecting data on vacant houses. For example, the data collection unit can collect information on the crime rate and location of police stations around the vacant house. For example, the data collection unit can collect information on public facilities such as schools and hospitals around the vacant house. For example, the data collection unit can collect information on commercial facilities and transportation access around the vacant house. By simultaneously collecting information on the surrounding environment and local safety, more comprehensive information can be provided. Some or all of the above processing in the data collection unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the data collection unit can input information on the surrounding environment and local safety of the vacant house into a generating AI, and the generating AI can collect the information.

[0077] The data collection unit can estimate the emotions of the person making the inquiry and adjust the level of detail of the information it collects based on the estimated emotions. For example, if the person making the inquiry is feeling anxious, the data collection unit can provide detailed information to reassure them. For example, if the person making the inquiry is interested, the data collection unit can provide specific data and examples to maintain their interest. For example, if the person making the inquiry is in a hurry, the data collection unit can provide concise and to-the-point information. This allows for the provision of more appropriate information by adjusting the level of detail of the information based on the emotions of the person making the inquiry. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using a generative AI, or not using a generative AI. For example, the data collection unit can input the emotions of the person making the inquiry into a generative AI, which can estimate the emotions and adjust the level of detail of the information.

[0078] The data collection unit can also collect information on local infrastructure and public transportation when collecting data on vacant houses. For example, the data collection unit can collect information on infrastructure such as electricity, water, and gas around the vacant house. For example, the data collection unit can collect information on the location and operation status of bus stops and train stations around the vacant house. For example, the data collection unit can collect information on road conditions and parking lots around the vacant house. By collecting information on local infrastructure and public transportation, more comprehensive information can be provided. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit can input information on infrastructure and public transportation around the vacant house into a generative AI, and the generative AI can collect the information.

[0079] The collection unit can collect opinions and feedback from local residents when collecting information on vacant houses. For example, the collection unit can conduct surveys of residents around vacant houses to collect opinions and feedback. For example, the collection unit can analyze social media and blog posts of residents around vacant houses to collect opinions and feedback. For example, the collection unit can investigate the participation of residents around vacant houses in community activities and events to collect opinions and feedback. By collecting opinions and feedback from local residents, more comprehensive information can be provided. Some or all of the above processing in the collection unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the collection unit can input the opinions and feedback of local residents into a generative AI, and the generative AI can collect the information.

[0080] The analysis unit can estimate the emotions of the person making the inquiry and adjust the presentation of the analysis results based on the estimated emotions. For example, if the person making the inquiry is feeling anxious, the analysis unit will provide the analysis results in a way that provides reassurance. For example, if the person making the inquiry is interested, the analysis unit will provide the analysis results using detailed data and graphs. For example, if the person making the inquiry is in a hurry, the analysis unit will provide concise and to-the-point analysis results. By adjusting the presentation of the analysis results based on the emotions of the person making the inquiry, more appropriate information can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using a generative AI, or not using a generative AI. For example, the analysis unit can input the emotions of the person making the inquiry into a generative AI, which will estimate the emotions and adjust the presentation of the analysis results.

[0081] The analysis unit can improve the accuracy of its analysis by referring to the past sales and rental history of vacant properties during the analysis process. For example, the analysis unit can refer to the past sales history of vacant properties to analyze their current market value. For example, the analysis unit can refer to the past rental history of vacant properties to analyze rental demand and fluctuations in rent. For example, the analysis unit can integrate the past sales and rental history of vacant properties to analyze their overall asset value. This improves the accuracy of the analysis by referring to the past sales and rental history of vacant properties. Some or all of the above processes in the analysis unit may be performed using, for example, a generating AI, or not using a generating AI. For example, the analysis unit can input the past sales and rental history of vacant properties into a generating AI, which can then improve the accuracy of the analysis.

[0082] The analysis unit can perform its analysis while considering the economic conditions and demographics of the area surrounding the vacant house. For example, the analysis unit can perform its analysis while considering the economic growth rate and unemployment rate of the area surrounding the vacant house. For example, the analysis unit can perform its analysis while considering the population increase / decrease and age structure of the area surrounding the vacant house. For example, the analysis unit can perform its analysis while considering the housing demand and supply situation of the area surrounding the vacant house. This makes it possible to perform a more accurate analysis by considering the economic conditions and demographics of the area surrounding the vacant house. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or it may be performed without using a generative AI. For example, the analysis unit can input the economic conditions and demographics of the area surrounding the vacant house into a generative AI, and the generative AI can perform the analysis.

[0083] The analysis unit can estimate the emotions of the person making the inquiry and prioritize the analysis results based on those estimated emotions. For example, if the person making the inquiry is feeling anxious, the analysis unit will prioritize providing analysis results related to safety. For example, if the person making the inquiry is interested, the analysis unit will prioritize providing analysis results related to asset value and renovation possibilities. For example, if the person making the inquiry is in a hurry, the analysis unit will prioritize providing basic analysis results that can be obtained quickly. This allows for the provision of more appropriate information by prioritizing the analysis results based on the emotions of the person making the inquiry. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using, for example, generative AI, or not using generative AI. For example, the analysis unit can input the emotions of the person making the inquiry into a generative AI, which can estimate the emotions and determine the priority of the analysis results.

[0084] The analysis unit can perform analyses while considering the energy efficiency and environmental impact of vacant houses. For example, the analysis unit can perform analyses while considering the energy consumption and utility costs of vacant houses. For example, the analysis unit can perform analyses while considering the insulation performance and presence of energy-saving equipment of vacant houses. For example, the analysis unit can perform analyses while considering the environmental impact and CO2 emissions of vacant houses. This makes it possible to perform more accurate analyses by considering the energy efficiency and environmental impact of vacant houses. Some or all of the above processing in the analysis unit may be performed using, for example, a generating AI, or it may be performed without using a generating AI. For example, the analysis unit can input data on the energy consumption and environmental impact of vacant houses into a generating AI, and the generating AI can perform the analysis.

[0085] The analysis unit can perform its analysis while considering information about educational and medical institutions in the area surrounding the vacant house. For example, the analysis unit can perform its analysis while considering information about schools and daycare centers in the area surrounding the vacant house. For example, the analysis unit can perform its analysis while considering information about hospitals and clinics in the area surrounding the vacant house. For example, the analysis unit can perform its analysis while considering the evaluation and reputation of educational and medical institutions in the area surrounding the vacant house. This makes it possible to perform a more accurate analysis by considering information about educational and medical institutions in the area surrounding the vacant house. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or it may be performed without using a generative AI. For example, the analysis unit can input information about educational and medical institutions in the area surrounding the vacant house into a generative AI, and the generative AI can perform the analysis.

[0086] The proposal unit can estimate the emotions of the person making the inquiry and adjust the way the proposal is presented based on the estimated emotions. For example, if the person making the inquiry is feeling anxious, the proposal unit will present the proposal in a way that provides reassurance. For example, if the person making the inquiry is interested, the proposal unit will present the proposal using detailed data and graphs. For example, if the person making the inquiry is in a hurry, the proposal unit will present a concise and to-the-point proposal. In this way, by adjusting the way the proposal is presented based on the emotions of the person making the inquiry, more appropriate information can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the proposal unit may be performed using a generative AI, or not using a generative AI. For example, the proposal unit can input the emotions of the person making the inquiry into a generative AI, which will estimate the emotions and adjust the way the proposal is presented.

[0087] The proposal department can provide simulations of renovation costs and rental income for vacant properties when making proposals. For example, the proposal department can simulate the renovation costs of vacant properties in detail and make a proposal. For example, the proposal department can simulate the rental income of vacant properties and make a highly profitable proposal. For example, the proposal department can simulate the rental income of vacant properties after renovation and make an optimal proposal. By providing simulations of renovation costs and rental income for vacant properties, more specific proposals become possible. Some or all of the above processing in the proposal department may be performed using, for example, a generative AI, or not using a generative AI. For example, the proposal department can input data on renovation costs and rental income for vacant properties into a generative AI, and the generative AI can perform the simulation.

[0088] The proposal unit can provide a market price forecast for the sale of a vacant house when making a proposal. For example, the proposal unit can forecast the current market price of a vacant house and make a sale proposal. For example, the proposal unit can forecast the future market price of a vacant house and make a suggestion for the timing of the sale. For example, the proposal unit can simulate the market price at the time of sale of a vacant house and propose the optimal selling method. By providing a market price forecast for the sale of a vacant house, more specific proposals become possible. Some or all of the above processing in the proposal unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the proposal unit can input market price data for vacant houses into a generative AI, and the generative AI can perform a market price forecast.

[0089] The proposal department can estimate the emotions of the person making the inquiry and determine the priority of proposals based on those estimated emotions. For example, if the person making the inquiry is feeling anxious, the proposal department will prioritize proposals related to safety. For example, if the person making the inquiry is interested, the proposal department will prioritize proposals related to asset value or renovation possibilities. For example, if the person making the inquiry is in a hurry, the proposal department will prioritize basic proposals that can be obtained quickly. This allows for the provision of more appropriate information by prioritizing proposals based on the emotions of the person making the inquiry. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the proposal department may be performed using, for example, generative AI, or not using generative AI. For example, the proposal department can input the emotions of the person making the inquiry into a generative AI, which will estimate the emotions and determine the priority of proposals.

[0090] The proposal department can provide design proposals and interior coordination for renovated vacant houses at the time of proposal. For example, the proposal department can provide detailed design proposals for renovated vacant houses. For example, the proposal department can propose interior coordination for vacant houses to provide an attractive space. For example, the proposal department can provide proposals that integrate the design and interior coordination for renovated vacant houses. This makes it possible to provide more concrete proposals by providing design proposals and interior coordination for renovated vacant houses. Some or all of the above processing in the proposal department may be performed using, for example, a generative AI, or not using a generative AI. For example, the proposal department can input data on the design and interior coordination for renovated vacant houses into a generative AI, and the generative AI can make proposals.

[0091] The proposal department can offer vacant property rental management services and sales support services when making proposals. For example, the proposal department can propose detailed vacant property rental management services to reduce the burden of management. For example, the proposal department can propose vacant property sales support services to facilitate the sales process. For example, the proposal department can make proposals that integrate vacant property rental management services and sales support services. This allows for more specific proposals by offering vacant property rental management services and sales support services. Some or all of the above processing in the proposal department may be performed using, for example, a generation AI, or not. For example, the proposal department can input data on vacant property rental management services and sales support services into a generation AI, which can then make proposals.

[0092] The proposal department can estimate the emotions of the person making the inquiry and adjust the renovation proposal based on those estimated emotions. For example, if the person making the inquiry is feeling anxious, the proposal department will make a renovation proposal that will provide reassurance. For example, if the person making the inquiry is interested, the proposal department will propose a detailed renovation plan. For example, if the person making the inquiry is in a hurry, the proposal department will make a renovation proposal that can be implemented quickly. In this way, by adjusting the renovation proposal based on the emotions of the person making the inquiry, more appropriate information can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the proposal department may be performed using a generative AI, or not using a generative AI. For example, the proposal department can input the emotions of the person making the inquiry into a generative AI, the generative AI can estimate the emotions, and adjust the renovation proposal.

[0093] The proposal department can make optimal proposals when proposing renovations by referring to past renovation cases. For example, the proposal department can make optimal renovation proposals for similar vacant houses based on past renovation cases. For example, the proposal department can propose effective renovation plans by referring to successful past renovation cases. For example, the proposal department can make suggestions to avoid risks by referring to unsuccessful past renovation cases. In this way, more specific proposals become possible by referring to past renovation cases. Some or all of the above processing in the proposal department may be performed using, for example, a generation AI, or not using a generation AI. For example, the proposal department can input data on past renovation cases into a generation AI, and the generation AI can make optimal proposals.

[0094] The proposal unit can estimate the emotions of the person making the inquiry and adjust the rental proposal based on those emotions. For example, if the person making the inquiry is feeling anxious, the proposal unit will make a rental proposal that provides reassurance. For example, if the person making the inquiry is interested, the proposal unit will propose a detailed rental plan. For example, if the person making the inquiry is in a hurry, the proposal unit will make a rental proposal that can be implemented quickly. In this way, by adjusting the rental proposal based on the emotions of the person making the inquiry, more appropriate information can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the proposal unit may be performed using generative AI, or not using generative AI. For example, the proposal unit can input the emotions of the person making the inquiry into a generative AI, which will estimate the emotions and adjust the rental proposal.

[0095] The proposal department can make rental proposals while considering the trends in the local rental market. For example, the proposal department can make rental proposals while considering the balance of supply and demand in the local rental market. For example, the proposal department can propose the optimal rent setting while considering the rental market rate in the local rental market. For example, the proposal department can propose an attractive rental plan while considering the trends in the local rental market. This makes it possible to make more specific proposals by considering the trends in the local rental market. Some or all of the above processing in the proposal department may be performed using, for example, a generation AI, or not using a generation AI. For example, the proposal department can input local rental market data into a generation AI, and the generation AI can make proposals.

[0096] The suggestion unit can estimate the emotions of the person making the inquiry and adjust the frequency of periodic checks based on the estimated emotions. For example, if the person making the inquiry is feeling anxious, the suggestion unit will suggest more frequent periodic checks. For example, if the person making the inquiry is interested, the suggestion unit will make a suggestion that includes detailed check content. For example, if the person making the inquiry is in a hurry, the suggestion unit will suggest a check plan that can be implemented quickly. This allows for the provision of more appropriate information by adjusting the frequency of periodic checks based on the emotions of the person making the inquiry. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using a generative AI, or not using a generative AI. For example, the suggestion unit can input the emotions of the person making the inquiry into a generative AI, which will estimate the emotions and adjust the frequency of periodic checks.

[0097] The proposal department can make optimal proposals when proposing periodic inspections by referring to past inspection history. For example, the proposal department can make optimal inspection proposals for similar vacant houses based on past inspection history. For example, the proposal department can propose effective inspection plans by referring to successful examples from past inspection history. For example, the proposal department can make suggestions to avoid risks by referring to unsuccessful examples from past inspection history. In this way, more specific proposals become possible by referring to past inspection history. Some or all of the above processing in the proposal department may be performed using, for example, a generation AI, or not using a generation AI. For example, the proposal department can input data from past inspection history into a generation AI, and the generation AI can make optimal proposals.

[0098] The proposal department can estimate the emotions of the person making the inquiry and adjust the content of the cleaning service based on those estimated emotions. For example, if the person making the inquiry is feeling anxious, the proposal department will propose a cleaning service that will provide a sense of security. For example, if the person making the inquiry is interested, the proposal department will propose a detailed cleaning plan. For example, if the person making the inquiry is in a hurry, the proposal department will propose a cleaning service that can be performed quickly. In this way, by adjusting the content of the cleaning service based on the emotions of the person making the inquiry, more appropriate information can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the proposal department may be performed using generative AI, or not using generative AI. For example, the proposal department can input the emotions of the person making the inquiry into a generative AI, which will estimate the emotions and adjust the content of the cleaning service.

[0099] The proposal department can consider the evaluation of local cleaning companies when proposing cleaning services. For example, the proposal department can propose a highly reliable cleaning company based on the evaluation of local cleaning companies. For example, the proposal department can propose an optimal cleaning plan by considering the past performance of local cleaning companies. For example, the proposal department can make the best proposal by comparing the fees and services of local cleaning companies. This makes it possible to make more specific proposals by considering the evaluation of local cleaning companies. Some or all of the above processes in the proposal department may be performed using, for example, a generation AI, or not using a generation AI. For example, the proposal department can input evaluation data of local cleaning companies into a generation AI, and the generation AI can make the best proposal.

[0100] The proposal department can estimate the emotions of the person making the inquiry and adjust the content of local events based on those estimated emotions. For example, if the person making the inquiry is feeling anxious, the proposal department will propose local events that will provide a sense of security. For example, if the person making the inquiry is interested, the proposal department will propose a detailed local event plan. For example, if the person making the inquiry is in a hurry, the proposal department will propose local events that can be implemented quickly. This allows for the provision of more appropriate information by adjusting the content of local events based on the emotions of the person making the inquiry. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the proposal department may be performed using a generative AI, or not using a generative AI. For example, the proposal department can input the emotions of the person making the inquiry into a generative AI, which will estimate the emotions and adjust the content of local events.

[0101] The proposal department can make optimal proposals for local events by referring to feedback from participants of past events. For example, the proposal department can make optimal proposals for similar events based on feedback from participants of past local events. For example, the proposal department can propose effective event plans by referring to successful examples of past local events. For example, the proposal department can make suggestions to avoid risks by referring to unsuccessful examples of past local events. This makes it possible to make more specific proposals by referring to feedback from participants of past events. Some or all of the above processing in the proposal department may be performed using, for example, a generative AI, or not using a generative AI. For example, the proposal department can input feedback data from participants of past events into a generative AI, which can then make optimal proposals.

[0102] The suggestion unit can estimate the emotions of the person making the inquiry and adjust the content of community activities based on those estimated emotions. For example, if the person making the inquiry is feeling anxious, the suggestion unit can suggest community activities that provide a sense of security. For example, if the person making the inquiry is interested, the suggestion unit can suggest a detailed community activity plan. For example, if the person making the inquiry is in a hurry, the suggestion unit can suggest community activities that can be implemented quickly. This allows for the provision of more appropriate information by adjusting the content of community activities based on the emotions of the person making the inquiry. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using a generative AI, or not using a generative AI. For example, the suggestion unit can input the emotions of the person making the inquiry into a generative AI, which can estimate the emotions and adjust the content of community activities.

[0103] The proposal department can consider the opinions of local residents when proposing community activities. For example, the proposal department can propose community activities that meet the needs of residents based on their opinions. For example, the proposal department can propose optimal community activities by considering the past participation of local residents. For example, the proposal department can propose effective community activities by referring to feedback from local residents. This makes it possible to make more specific proposals by considering the opinions of local residents. Some or all of the above processing in the proposal department may be performed using, for example, a generative AI, or not using a generative AI. For example, the proposal department can input local residents' opinion data into a generative AI, which can then make optimal proposals.

[0104] The data collection unit can estimate the emotions of the person making the inquiry and determine the priority of the data to collect based on the estimated emotions. For example, if the person making the inquiry is feeling anxious, the data collection unit will prioritize collecting data related to safety. For example, if the person making the inquiry is interested, the data collection unit will prioritize collecting data related to property value and renovation possibilities. For example, if the person making the inquiry is in a hurry, the data collection unit will prioritize collecting basic data that can be obtained quickly. This allows for the provision of more appropriate information by prioritizing the data to collect based on the emotions of the person making the inquiry. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using a generative AI, or not using a generative AI. For example, the data collection unit can input the emotions of the person making the inquiry into a generative AI, which will estimate the emotions and determine the priority of the data to collect.

[0105] The data collection unit can collect information on nearby transportation access and commercial facilities when collecting location information for vacant houses. For example, when collecting location information for vacant houses, the data collection unit can collect information on the location of the nearest train station or bus stop. For example, when collecting location information for vacant houses, the data collection unit can collect information on nearby commercial facilities and restaurants. For example, when collecting location information for vacant houses, the data collection unit can collect information on nearby parks and recreational facilities. By collecting information on nearby transportation access and commercial facilities, more comprehensive information can be provided. Some or all of the above processing in the data collection unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the data collection unit can input the location information of vacant houses and information on nearby transportation access and commercial facilities into a generating AI, and the generating AI can collect the information.

[0106] The data collection unit can estimate the emotions of the person making the inquiry and adjust the level of detail of the data collected based on the estimated emotions. For example, if the person making the inquiry is feeling anxious, the data collection unit can provide detailed data to reassure them. For example, if the person making the inquiry is interested, the data collection unit can provide specific data and examples to maintain their interest. For example, if the person making the inquiry is in a hurry, the data collection unit can provide concise and to-the-point data. This allows for the provision of more appropriate information by adjusting the level of detail of the data collected based on the emotions of the person making the inquiry. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using a generative AI, or not. For example, the data collection unit can input the emotions of the person making the inquiry into a generative AI, which can estimate the emotions and adjust the level of detail of the data collected.

[0107] The data collection unit can collect past repair and renovation history when collecting information on the condition of vacant buildings. For example, when collecting information on the condition of vacant buildings, the data collection unit can collect detailed information on past repair history. For example, when collecting information on the condition of vacant buildings, the data collection unit can collect detailed information on past renovation history. For example, when collecting information on the condition of vacant buildings, the data collection unit can collect data to assess the need for repairs or renovations. By collecting past repair and renovation history, more comprehensive information can be provided. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit can input data on the condition of vacant buildings and past repair and renovation history into a generative AI, which can then collect the information.

[0108] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0109] The vacant property management system can also include a communication department. This department provides functions to facilitate communication with vacant property owners and local residents. For example, it can send regular emails and notifications to vacant property owners, reporting on the condition and management status of the property. It can also collect feedback and opinions from local residents and incorporate them into vacant property management. Furthermore, it can share information on local events and community activities, promoting local revitalization. This allows the vacant property management system to strengthen collaboration with vacant property owners and local residents, resulting in more effective vacant property management.

[0110] The vacant property management system can also be equipped with a security unit. The security unit provides functions to enhance security measures for vacant properties. For example, the security unit can collect data from surveillance cameras and sensors installed in vacant properties and notify the owner or the police if suspicious activity is detected. The security unit can also analyze crime rates and public safety information in the area surrounding the vacant property and propose security measures. Furthermore, the security unit can arrange for the installation and maintenance of security equipment in vacant properties. In this way, the vacant property management system can enhance security measures for vacant properties and increase the sense of security for owners and local residents.

[0111] The vacant property management system can also include an energy management unit. This unit provides functions to optimize the energy consumption of vacant properties. For example, it can monitor the electricity, gas, and water usage of vacant properties and suggest ways to reduce unnecessary energy consumption. It can also evaluate the effectiveness of energy-saving equipment installed in vacant properties and propose further energy-saving measures. Furthermore, it can provide renovation plans to improve the energy efficiency of vacant properties. In this way, the vacant property management system can optimize the energy consumption of vacant properties and reduce their environmental impact.

[0112] The vacant property management system can also include a health management department. This department provides functions to assess the health impact of the vacant property's environment and propose improvement measures. For example, it can monitor indoor air quality, humidity, and temperature, and issue warnings if harmful elements are detected. It can also investigate the occurrence of mold and mites in vacant properties and propose countermeasures. Furthermore, it can recommend the use of health-conscious materials and equipment during vacant property renovations. In this way, the vacant property management system can maintain a healthy environment in vacant properties and protect the health of the residents.

[0113] The vacant property management system can also include an education department. This department can provide knowledge and skills related to vacant property management to vacant property owners and local residents. For example, the education department can hold online courses and workshops on vacant property management methods and renovation procedures. Furthermore, the education department can provide the latest information and technologies related to vacant property management, supporting owners and local residents in effectively managing their properties. In addition, the education department can collaborate with local schools and community centers to provide educational programs on vacant property management. This allows the vacant property management system to improve the knowledge and skills of vacant property owners and local residents, contributing to the resolution of the vacant property problem.

[0114] The information collection unit can estimate the emotions of the person making the inquiry and determine the priority of the information to collect based on the estimated emotions. For example, if the person making the inquiry is feeling anxious, the information collection unit will prioritize collecting information related to safety. For example, if the person making the inquiry is interested, the information collection unit will prioritize collecting information related to the asset value of vacant houses and the possibilities of renovation. For example, if the person making the inquiry is in a hurry, the information collection unit will prioritize collecting basic information that can be obtained quickly. This allows for the provision of more appropriate information by prioritizing information based on the emotions of the person making the inquiry. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the information collection unit may be performed using a generative AI, or not using a generative AI. For example, the information collection unit can input the emotions of the person making the inquiry into a generative AI, which can estimate the emotions and determine the priority of the information.

[0115] The analysis unit can estimate the emotions of the person making the inquiry and adjust the presentation of the analysis results based on the estimated emotions. For example, if the person making the inquiry is feeling anxious, the analysis unit will provide the analysis results in a way that provides reassurance. For example, if the person making the inquiry is interested, the analysis unit will provide the analysis results using detailed data and graphs. For example, if the person making the inquiry is in a hurry, the analysis unit will provide concise and to-the-point analysis results. By adjusting the presentation of the analysis results based on the emotions of the person making the inquiry, more appropriate information can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using a generative AI, or not using a generative AI. For example, the analysis unit can input the emotions of the person making the inquiry into a generative AI, which will estimate the emotions and adjust the presentation of the analysis results.

[0116] The proposal unit can estimate the emotions of the person making the inquiry and adjust the way the proposal is presented based on the estimated emotions. For example, if the person making the inquiry is feeling anxious, the proposal unit will present the proposal in a way that provides reassurance. For example, if the person making the inquiry is interested, the proposal unit will present the proposal using detailed data and graphs. For example, if the person making the inquiry is in a hurry, the proposal unit will present a concise and to-the-point proposal. In this way, by adjusting the way the proposal is presented based on the emotions of the person making the inquiry, more appropriate information can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the proposal unit may be performed using a generative AI, or not using a generative AI. For example, the proposal unit can input the emotions of the person making the inquiry into a generative AI, which will estimate the emotions and adjust the way the proposal is presented.

[0117] The proposal department can estimate the emotions of the person making the inquiry and determine the priority of proposals based on those estimated emotions. For example, if the person making the inquiry is feeling anxious, the proposal department will prioritize proposals related to safety. For example, if the person making the inquiry is interested, the proposal department will prioritize proposals related to asset value or renovation possibilities. For example, if the person making the inquiry is in a hurry, the proposal department will prioritize basic proposals that can be obtained quickly. This allows for the provision of more appropriate information by prioritizing proposals based on the emotions of the person making the inquiry. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the proposal department may be performed using, for example, generative AI, or not using generative AI. For example, the proposal department can input the emotions of the person making the inquiry into a generative AI, which will estimate the emotions and determine the priority of proposals.

[0118] The proposal department can estimate the emotions of the person making the inquiry and adjust the content of the cleaning service based on those estimated emotions. For example, if the person making the inquiry is feeling anxious, the proposal department will propose a cleaning service that will provide a sense of security. For example, if the person making the inquiry is interested, the proposal department will propose a detailed cleaning plan. For example, if the person making the inquiry is in a hurry, the proposal department will propose a cleaning service that can be performed quickly. In this way, by adjusting the content of the cleaning service based on the emotions of the person making the inquiry, more appropriate information can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the proposal department may be performed using generative AI, or not using generative AI. For example, the proposal department can input the emotions of the person making the inquiry into a generative AI, which will estimate the emotions and adjust the content of the cleaning service.

[0119] The following briefly describes the processing flow for example form 2.

[0120] Step 1: The collection unit collects information about vacant houses. The collection unit collects detailed data such as the location of the vacant house, the condition of the building, and information about the owner. The collection unit investigates the location and condition of the vacant house and verifies the owner's information. The collection unit obtains the location of the vacant house from a map database and verifies the condition of the building using on-site surveys or drones. The collection unit obtains the owner's information from an official real estate registration database. Step 2: The Analysis Department analyzes the information collected by the Collection Department. For example, the Analysis Department analyzes the collected data and proposes optimal solutions to issues such as vacant property management, declining property values, deterioration of local safety and scenery, and inheritance problems. For example, regarding vacant property management, the Analysis Department might propose regular inspections and cleaning services. For example, regarding declining property values, the Analysis Department might offer options such as renovation, rental, or sale. For example, regarding deterioration of local safety and scenery, the Analysis Department might propose promoting local events and community activities. Step 3: The proposal department proposes the optimal solution based on the analysis results obtained by the analysis department. For example, the proposal department may propose renovating vacant houses and using them as rental properties, or propose the best way to sell them. For example, the proposal department may propose providing regular inspection and cleaning services for vacant houses. For example, the proposal department may propose promoting local events and community activities.

[0121] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0122] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0123] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0124] Each of the multiple elements described above, including the collection unit, analysis unit, and proposal unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit uses the camera 42 and communication I / F 44 of the smart device 14 to investigate the location and condition of vacant houses and confirm the owner's information. The analysis unit is implemented in the identification processing unit 290 of the data processing unit 12, for example, to analyze the collected data and propose the optimal solution. The proposal unit is implemented in the control unit 46A of the smart device 14, for example, to provide the user with options such as renovation, rental, or sale. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0125] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0126] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0127] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0128] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0129] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0130] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0131] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0132] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0133] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0134] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0135] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0136] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0137] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0138] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0139] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0140] Each of the multiple elements described above, including the collection unit, analysis unit, and proposal unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit uses the camera 42 and communication I / F 44 of the smart glasses 214 to investigate the location and condition of vacant houses and confirm the owner's information. The analysis unit is implemented in the identification processing unit 290 of the data processing unit 12, for example, to analyze the collected data and propose the optimal solution. The proposal unit is implemented in the control unit 46A of the smart glasses 214, for example, to provide the user with options such as renovation, rental, or sale. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0141] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0142] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0143] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0144] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0145] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0146] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0147] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0148] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0149] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0150] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0151] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0152] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0153] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0154] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0155] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0156] Each of the multiple elements described above, including the collection unit, analysis unit, and proposal unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit uses the camera 42 and communication I / F 44 of the headset terminal 314 to investigate the location and condition of vacant houses and confirm the owner's information. The analysis unit is implemented in the identification processing unit 290 of the data processing unit 12, for example, to analyze the collected data and propose the optimal solution. The proposal unit is implemented in the control unit 46A of the headset terminal 314, for example, to provide the user with options such as renovation, rental, or sale. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0157] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0158] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0159] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0160] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0161] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0162] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0163] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0164] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0165] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0166] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0167] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0168] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0169] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0170] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0171] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0172] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0173] Each of the multiple elements described above, including the collection unit, analysis unit, and proposal unit, is implemented in at least one of the following: the robot 414 and the data processing unit 12. For example, the collection unit uses the camera 42 and communication I / F 44 of the robot 414 to investigate the location and condition of vacant houses and confirm the owner's information. The analysis unit is implemented in the identification processing unit 290 of the data processing unit 12, for example, to analyze the collected data and propose the optimal solution. The proposal unit is implemented in the control unit 46A of the robot 414, for example, to provide the user with options such as renovation, rental, or sale. The correspondence between each unit and the devices and control units is not limited to the examples described above and can be modified in various ways.

[0174] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0175] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0176] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0177] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0178] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0179] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0180] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0181] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0182] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0183] 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.

[0184] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0185] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0186] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0187] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0188] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0189] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0190] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0191] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0192] (Note 1) A collection department that collects information about vacant houses, An analysis unit analyzes the information collected by the aforementioned collection unit, A proposal unit that proposes the optimal solution based on the analysis results obtained by the aforementioned analysis unit, Equipped with A system characterized by the following features. (Note 2) The aforementioned proposal section is, We offer options for renovation, rental, and sale. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned proposal section is, We propose regular inspection and cleaning services for vacant properties. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned proposal section is, Propose promoting local events and community activities. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned collection unit is Collect detailed data such as the location of vacant properties, the condition of the buildings, and information about the owners. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned collection unit is The system estimates the emotions of the person making the inquiry and determines the priority of the information to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is Analyze the past usage history of vacant properties and select the most suitable information gathering method. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is When collecting information on vacant houses, we also collect information on the surrounding environment and local safety. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is The system estimates the emotions of the person making the inquiry and adjusts the level of detail of the information collected based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is When collecting information on vacant houses, we also gather information on the local infrastructure and public transportation. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting information about vacant houses, we gather opinions and feedback from local residents. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit is We estimate the emotions of the person making the inquiry and adjust the way the analysis results are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit is During analysis, we refer to past sales and rental history of vacant properties to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit is During the analysis, the economic conditions and demographic trends of the surrounding area of ​​the vacant house will be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit is The system estimates the emotions of the person making the inquiry and prioritizes the analysis results based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit is During the analysis, the energy efficiency and environmental impact of vacant houses will be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit is During the analysis, information on educational and medical institutions in the surrounding area of ​​the vacant property will be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned proposal section is, We estimate the emotions of the person making the inquiry and adjust the way the proposal is expressed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned proposal section is, When making a proposal, we will provide simulations of the renovation costs and rental income of vacant properties. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned proposal section is, When making a proposal, we will provide a market price forecast for the sale of the vacant property. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned proposal section is, The system estimates the emotions of the person making the inquiry and determines the priority of proposals based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned proposal section is, When making a proposal, we offer design suggestions and interior coordination for the renovated vacant house. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned proposal section is, When making a proposal, we offer services such as rental management services and sales support services for vacant properties. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned proposal section is, We estimate the emotions of the person making the inquiry and adjust the renovation proposal based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 25) The aforementioned proposal section is, When proposing renovations, we refer to past renovation examples to make the best possible proposals. The system described in Appendix 2, characterized by the features described herein. (Note 26) The aforementioned proposal section is, The system estimates the emotions of the person making the inquiry and adjusts the rental proposal based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 27) The aforementioned proposal section is, When proposing rental properties, we take into account the trends in the local rental market. The system described in Appendix 2, characterized by the features described herein. (Note 28) The aforementioned proposal section is, The system estimates the emotions of the person making the inquiry and adjusts the frequency of regular inspections based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 29) The aforementioned proposal section is, When proposing periodic inspections, we refer to past inspection history to make the most appropriate recommendations. The system described in Appendix 3, characterized by the features described herein. (Note 30) The aforementioned proposal section is, We estimate the emotions of the person making the inquiry and adjust the cleaning service based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 31) The aforementioned proposal section is, When proposing cleaning services, we take into account the evaluation of local cleaning companies. The system described in Appendix 3, characterized by the features described herein. (Note 32) The aforementioned proposal section is, The system estimates the emotions of those who make inquiries and adjusts the content of local events based on those estimated emotions. The system described in Appendix 4, characterized by the features described herein. (Note 33) The aforementioned proposal section is, When proposing local events, we refer to feedback from past event participants to make the best possible proposals. The system described in Appendix 4, characterized by the features described herein. (Note 34) The aforementioned proposal section is, The system estimates the emotions of the person making the inquiry and adjusts the content of community activities based on those estimated emotions. The system described in Appendix 4, characterized by the features described herein. (Note 35) The aforementioned proposal section is, When proposing community activities, we will take into consideration the opinions of local residents. The system described in Appendix 4, characterized by the features described herein. (Note 36) The aforementioned collection unit is The system estimates the emotions of the person making the inquiry and determines the priority of the data to collect based on those estimated emotions. The system described in Appendix 5, characterized by the features described herein. (Note 37) The aforementioned collection unit is When collecting information on the location of vacant houses, we also collect information on nearby transportation access and commercial facilities. The system described in Appendix 5, characterized by the features described herein. (Note 38) The aforementioned collection unit is The system estimates the emotions of the person making the inquiry and adjusts the level of detail in the data collected based on those estimated emotions. The system described in Appendix 5, characterized by the features described herein. (Note 39) The aforementioned collection unit is When collecting information on the condition of vacant buildings, we also collect information on past repair and renovation history. The system described in Appendix 5, characterized by the features described herein. [Explanation of symbols]

[0193] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A collection department that collects information about vacant houses, An analysis unit analyzes the information collected by the aforementioned collection unit, A proposal unit that proposes the optimal solution based on the analysis results obtained by the aforementioned analysis unit, Equipped with A system characterized by the following features.

2. The aforementioned proposal section is, We offer options for renovation, rental, and sale. The system according to feature 1.

3. The aforementioned proposal section is, We propose regular inspection and cleaning services for vacant properties. The system according to feature 1.

4. The aforementioned proposal section is, Propose promoting local events and community activities. The system according to feature 1.

5. The aforementioned collection unit is Collect detailed data such as the location of vacant properties, the condition of the buildings, and information about the owners. The system according to feature 1.

6. The aforementioned collection unit is The system estimates the emotions of the person making the inquiry and determines the priority of the information to collect based on those estimated emotions. The system according to feature 1.

7. The aforementioned collection unit is Analyze the past usage history of vacant properties and select the most suitable information gathering method. The system according to feature 1.

8. The aforementioned collection unit is When collecting information on vacant houses, we also collect information on the surrounding environment and local safety. The system according to feature 1.

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