system

Abstract

The system according to this embodiment aims to propose diverse ways to utilize vacant houses, thereby revitalizing local communities and improving profitability. [Solution] The system according to the embodiment comprises a data collection unit, an analysis unit, a proposal unit, and a simulation unit. The data collection unit collects data from the area surrounding vacant houses. The analysis unit analyzes the data collected by the data collection unit and generates a regional analysis report. The proposal unit proposes various utilization methods other than rental based on the report generated by the analysis unit. The simulation unit simulates the profitability of the utilization methods proposed by the proposal unit and evaluates the risks.

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, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, the methods for effectively utilizing vacant houses are limited, and there are problems in activating the local area and ensuring profitability.

[0005] The system according to the embodiment aims to propose various utilization methods for vacant houses and improve the activation of the local area and profitability.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, a proposal unit, and a simulation unit. The data collection unit collects data from the area surrounding vacant houses. The analysis unit analyzes the data collected by the data collection unit and generates a regional analysis report. The proposal unit proposes various utilization methods other than rental based on the report generated by the analysis unit. The simulation unit simulates the profitability of the utilization methods proposed by the proposal unit and evaluates the risks. [Effects of the Invention]

[0007] The system according to this embodiment proposes diverse ways to utilize vacant houses, thereby contributing to regional revitalization and improved profitability. [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 numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F controls communication between a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), 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 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also 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) The vacant house utilization proposal system according to an embodiment of the present invention is a system that uses AI to thoroughly analyze the surrounding environment of a vacant house and proposes a variety of utilization methods other than rental. The vacant house utilization proposal system analyzes data such as population dynamics, commercial facilities, and transportation access around the vacant house and generates a regional analysis report. Next, the vacant house utilization proposal system proposes a variety of utilization methods other than rental. For example, it proposes specific utilization ideas such as a cafe, co-working space, or short-term rental. The vacant house utilization proposal system also simulates the profitability of each utilization method and evaluates the risks. This promotes the effective utilization of vacant houses and regional revitalization. For example, the vacant house utilization proposal system analyzes data such as population dynamics, commercial facilities, and transportation access around the vacant house. In this process, the vacant house utilization proposal system collects and analyzes detailed regional data. For example, as population dynamics of the surrounding area, it collects data such as age groups, number of households, and population increase / decrease. As data on commercial facilities, it collects the types and number of stores in the surrounding area, business hours, etc. Furthermore, as data on transportation access, it collects the location of the nearest station or bus stop, and the operating status, etc. This enables a detailed analysis of the region. Next, the vacant house utilization proposal system proposes a variety of utilization methods other than rental. For example, the system proposes specific utilization ideas such as cafes, co-working spaces, and short-term rentals. This allows owners of vacant properties to consider diverse uses other than renting. The vacant property utilization proposal system also simulates the profitability of each utilization method and assesses the risks. For example, it simulates the profitability of using the property as a cafe, evaluating initial investment, operating costs, and expected revenue. This allows owners of vacant properties to minimize investment risk. This promotes the effective use of vacant properties and regional revitalization. For example, using a vacant property as a cafe can revitalize local economic activity and address the problem of population decline. Also, using a vacant property as a co-working space can provide a place to work in the community and support local economic activity. Furthermore, using a vacant property as short-term rentals can increase the number of tourists and support the local tourism industry.In this way, the vacant house utilization proposal system can promote the effective use of vacant houses and regional revitalization by thoroughly analyzing the surrounding environment of vacant houses and proposing diverse utilization methods other than rental. This can lead to the resolution of the vacant house problem and the sustainable development of the region.

[0029] The vacant house utilization proposal system according to this embodiment comprises a data collection unit, an analysis unit, a proposal unit, and a simulation unit. The data collection unit collects data about the area surrounding the vacant house. For example, the data collection unit collects data such as population dynamics, commercial facilities, and transportation access around the vacant house. For example, as population dynamics of the surrounding area, the data collection unit collects data such as age groups, number of households, and population increase / decrease. The data collection unit can also collect data on commercial facilities, such as the types and number of stores in the surrounding area and their operating hours. Furthermore, the data collection unit can collect data on transportation access, such as the location of the nearest train station or bus stop and their operating status. For example, the data collection unit can collect population dynamics data about the surrounding area and obtain information such as age groups, number of households, and population increase / decrease. The data collection unit can also collect data on commercial facilities and obtain information such as the types and number of stores in the surrounding area and their operating hours. Furthermore, the data collection unit can collect data on transportation access and obtain information such as the location of the nearest train station or bus stop and their operating status. The analysis unit analyzes the data collected by the data collection unit and generates a regional analysis report. The analysis department can, for example, analyze collected demographic data to understand trends such as the age distribution, number of households, and population changes in the region. The analysis department can also analyze collected commercial facility data to understand trends such as the types and number of surrounding stores and their operating hours. Furthermore, the analysis department can analyze collected transportation access data to understand trends such as the location and operating status of the nearest train stations and bus stops. For example, the analysis department can analyze collected demographic data to understand trends such as the age distribution, number of households, and population changes in the region. Furthermore, the analysis department can analyze collected commercial facility data to understand trends such as the types and number of surrounding stores and their operating hours. Furthermore, the analysis department can analyze collected transportation access data to understand trends such as the location and operating status of the nearest train stations and bus stops. The proposal department, based on the reports generated by the analysis department, proposes diverse uses other than rental. For example, the proposal department can propose specific use ideas such as a cafe, co-working space, or short-term rental. For example, the proposal department can propose use as a cafe and demonstrate cafe operation methods and profitability.Furthermore, the proposal department can propose ways to utilize the space as a co-working space and demonstrate its operation and profitability. In addition, the proposal department can propose ways to utilize the space as a short-term rental and demonstrate its operation and profitability. For example, the proposal department can propose ways to utilize the space as a cafe and demonstrate its operation and profitability. Furthermore, the proposal department can propose ways to utilize the space as a co-working space and demonstrate its operation and profitability. Furthermore, the proposal department can propose ways to utilize the space as a short-term rental and demonstrate its operation and profitability. The simulation department simulates the profitability of the utilization methods proposed by the proposal department and evaluates the risks. For example, the simulation department can simulate the profitability of using the space as a cafe and evaluate initial investment, operating costs, and expected revenue. Furthermore, the simulation department can simulate the profitability of using the space as a co-working space and evaluate initial investment, operating costs, and expected revenue. Furthermore, the simulation department can simulate the profitability of using the space as a short-term rental and evaluate initial investment, operating costs, and expected revenue. For example, the simulation unit can simulate the profitability of using the property as a cafe and evaluate initial investment, operating costs, and projected revenue. It can also simulate the profitability of using the property as a co-working space and evaluate initial investment, operating costs, and projected revenue. Furthermore, it can simulate the profitability of using the property as a short-term rental (Airbnb) and evaluate initial investment, operating costs, and projected revenue. As a result, the vacant property utilization proposal system according to this embodiment can promote the effective use of vacant properties and regional revitalization.

[0030] The data collection unit collects data about the area surrounding vacant houses. For example, the unit collects data such as population dynamics, commercial facilities, and transportation access around the vacant houses. Specifically, the unit collects data on surrounding population dynamics, including age groups, number of households, and population changes. This can utilize government statistics and local resident registration information. The unit can also collect data on commercial facilities, such as the types and number of stores in the area and their operating hours. This could involve using the official websites of commercial facilities or databases of local chambers of commerce. Furthermore, the unit can collect data on transportation access, such as the location and operating status of the nearest train stations and bus stops. This can involve utilizing public transportation operation information and map data. For example, the unit can collect surrounding population dynamics data to obtain information such as age groups, number of households, and population changes. It can also collect data on commercial facilities to obtain information such as the types and number of stores in the area and their operating hours. Furthermore, it can collect data on transportation access to obtain information such as the location and operating status of the nearest train stations and bus stops. This allows the data collection unit to comprehensively collect detailed data about the surrounding area of ​​vacant houses, providing the basic information necessary for subsequent analysis and proposals.

[0031] The analysis unit analyzes data collected by the collection unit and generates regional analysis reports. For example, the analysis unit can analyze collected demographic data to understand trends in regional age distribution, household numbers, and population changes. Specifically, it utilizes AI-based data analysis methods to statistically process the collected data. For instance, it can graph the distribution of age groups and changes in household numbers to visually represent regional demographic trends. Furthermore, the analysis unit can analyze collected commercial facility data to understand trends in surrounding stores, such as types, numbers, and operating hours. This includes analyzing the distribution and operating patterns of different store types to reveal the characteristics of regional commercial activity. Additionally, the analysis unit can analyze collected transportation access data to understand trends in the location and operation status of the nearest train stations and bus stops. This includes evaluating the usage and accessibility of transportation to understand the current state of regional transportation infrastructure. Furthermore, the analysis unit can analyze the collected transportation access data to understand trends such as the location of the nearest train stations and bus stops, and their operating status. This allows the analysis unit to conduct a detailed analysis of regional characteristics based on the collected data and provide useful information for utilizing vacant properties.

[0032] The proposal department proposes diverse uses other than rental based on reports generated by the analysis department. For example, the proposal department can propose specific use ideas such as a cafe, co-working space, or short-term rental. Specifically, the proposal department can propose uses as a cafe and demonstrate the cafe's operation and profitability. This includes detailed operational plans such as menu composition, pricing, and marketing strategies. The proposal department can also propose uses as a co-working space and demonstrate its operation and profitability. This includes operational plans such as setting usage fees, equipment layout, and identifying target users. Furthermore, the proposal department can propose uses as short-term rental and demonstrate its operation and profitability. This includes setting accommodation fees, implementing a reservation system, and planning cleaning and maintenance. This allows the proposal department to concretely demonstrate diverse ways to utilize vacant houses and provide owners with feasible options.

[0033] The Simulation Department simulates the profitability of the proposed usage methods and assesses the risks. For example, the Simulation Department can simulate the profitability of using the space as a cafe, evaluating initial investment, operating costs, and projected revenue. Specifically, the Simulation Department calculates in detail the equipment investment required to open a cafe, as well as labor costs and material costs for operation, and evaluates profitability by comparing these with projected revenue. The Simulation Department can also simulate the profitability of using the space as a co-working space, evaluating initial investment, operating costs, and projected revenue. This involves considering equipment installation costs, utility costs, cleaning costs, etc., and evaluating profitability based on revenue from users. Furthermore, the Simulation Department can simulate the profitability of using the space as a short-term rental, evaluating initial investment, operating costs, and projected revenue. This involves calculating in detail the renovation costs of the accommodation, cleaning costs, and reservation system implementation costs, and evaluates profitability by comparing these with revenue obtained from accommodation fees. Furthermore, the simulation department can simulate the profitability of using the space as a co-working space, evaluating initial investment, operating costs, and projected revenue. It can also simulate the profitability of using the space as a short-term rental (Airbnb), evaluating initial investment, operating costs, and projected revenue. This allows the simulation department to thoroughly evaluate the feasibility of proposed uses and provide owners with information that considers the balance between risk and profit.

[0034] The data collection unit can collect data on population dynamics, commercial facilities, and transportation access around vacant houses. For example, the data collection unit can collect data on surrounding population dynamics such as age groups, number of households, and population changes. It can also collect data on commercial facilities such as the types and number of stores in the vicinity and their operating hours. Furthermore, it can collect data on transportation access such as the location of the nearest train station or bus stop and their operating status. For example, the data collection unit can collect surrounding population dynamics data and obtain information such as age groups, number of households, and population changes. It can also collect data on commercial facilities and obtain information such as the types and number of stores in the vicinity and their operating hours. Furthermore, it can collect data on transportation access and obtain information such as the location of the nearest train station or bus stop and their operating status. By collecting detailed data around vacant houses, the accuracy of regional analysis reports is improved. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can input population dynamics data around vacant houses into AI, and the AI ​​can analyze and collect the data.

[0035] The analysis unit can analyze the data collected by the collection unit and generate a regional analysis report. For example, the analysis unit can analyze collected demographic data to understand trends such as the age distribution, number of households, and population growth / decrease in the region. It can also analyze collected commercial facility data to understand trends such as the types and number of surrounding stores and their operating hours. Furthermore, the analysis unit can analyze collected transportation access data to understand trends such as the location of the nearest train stations and bus stops and their operating status. This allows for a detailed analysis of the region by analyzing the collected data. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generation AI, or without using a generation AI. For example, the analysis unit can input the collected data into a generating AI, which can then analyze the data and generate a regional analysis report.

[0036] The proposal department can propose specific utilization ideas such as a cafe, co-working space, or short-term rental. For example, the proposal department can propose a use as a cafe and show how to operate and make it profitable. It can also propose a use as a co-working space and show how to operate and make it profitable. Furthermore, the proposal department can propose a use as a short-term rental and show how to operate and make it profitable. For example, the proposal department can propose a use as a cafe and show how to operate and make it profitable. It can also propose a use as a co-working space and show how to operate and make it profitable. Furthermore, the proposal department can propose a use as a short-term rental and show how to operate and make it profitable. This allows owners of vacant properties to consider a variety of uses other than renting by proposing specific utilization ideas. Some or all of the above processing in the proposal department may be performed using, for example, a generative AI, or not. For example, the proposal department can input a use as a cafe into a generative AI, and the generative AI can propose how to operate and make it profitable.

[0037] The simulation unit can simulate the profitability of proposed uses and assess the risks. For example, the simulation unit can simulate the profitability of using the space as a cafe and assess initial investment, operating costs, and expected revenue. It can also simulate the profitability of using the space as a co-working space and assess initial investment, operating costs, and expected revenue. Furthermore, the simulation unit can simulate the profitability of using the space as a short-term rental and assess initial investment, operating costs, and expected revenue. By simulating profitability and assessing risks, investment risk can be minimized. Some or all of the above processing in the simulation unit may be performed using AI, for example, or without AI. For example, the simulation unit can input the profitability of the proposed usage method into the AI, which can then simulate the profitability and assess the risks.

[0038] The data collection unit can analyze past data collection history and select the optimal collection method. For example, the data collection unit can select the most efficient collection method from past data collection history to improve the accuracy of data collection. Furthermore, the data collection unit can determine the priority of data to be collected based on past data collection history, and prioritize the collection of important data. In addition, the data collection unit can analyze past data collection history to identify areas for improvement in the collection method and optimize the collection process. For example, the data collection unit can select the most efficient collection method from past data collection history to improve the accuracy of data collection. Furthermore, the data collection unit can determine the priority of data to be collected based on past data collection history, and prioritize the collection of important data. Furthermore, the data collection unit can analyze past data collection history to identify areas for improvement in the collection method and optimize the collection process. This allows for improved data collection accuracy through analysis of past data collection history. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input past data collection history into AI, which can then select the optimal collection method.

[0039] The data collection unit can filter the data to be collected based on specific events or seasons. For example, the data collection unit can prioritize the collection of data related to a particular season, taking into account seasonal demographic changes. Furthermore, the data collection unit can collect data related to local events when they are held and analyze the state of local revitalization. In addition, the data collection unit can filter the data to be collected based on specific events or seasons, collecting only the necessary data. This allows for the efficient collection of only the necessary data by filtering the data based on specific events or seasons. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data related to specific events or seasons into AI, which can then filter and collect the data.

[0040] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information during data collection. For example, the data collection unit can prioritize the collection of data on surrounding population dynamics and commercial facilities based on the user's current location. Furthermore, the data collection unit can prioritize the collection of the nearest transportation access data based on the user's geographical location information. In addition, the data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information and generate a regional analysis report. For example, the data collection unit can prioritize the collection of data on surrounding population dynamics and commercial facilities based on the user's current location. Furthermore, the data collection unit can prioritize the collection of the nearest transportation access data based on the user's geographical location information. Furthermore, the data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information and generate a regional analysis report. This allows for the efficient collection of highly relevant data by considering the user's geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into AI, which can then prioritize the collection of highly relevant data.

[0041] The data collection unit can analyze users' social media activity and collect relevant data during data collection. For example, the data collection unit can analyze users' social media activity and collect data on local trends and popular spots. Furthermore, the data collection unit can collect local event information based on users' social media activity and reflect it in local analysis reports. In addition, the data collection unit can consider users' social media activity, collect relevant data, and conduct detailed local analysis. For example, the data collection unit can analyze users' social media activity and collect data on local trends and popular spots. Furthermore, the data collection unit can collect local event information based on users' social media activity and reflect it in local analysis reports. Furthermore, the data collection unit can consider users' social media activity, collect relevant data, and conduct detailed local analysis. This allows for the efficient collection of data on local trends and popular spots by analyzing users' social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input user social media activity data into AI, which can then collect relevant data.

[0042] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit can perform a detailed analysis on important data to generate a highly accurate report. Conversely, the analysis unit can perform a simplified analysis on less important data to efficiently generate a report. Furthermore, the analysis unit can adjust the level of detail of the analysis based on the importance of the data to generate an optimal report. For example, the analysis unit can perform a detailed analysis on important data to generate a highly accurate report. Conversely, the analysis unit can perform a simplified analysis on less important data to efficiently generate a report. Furthermore, the analysis unit can adjust the level of detail of the analysis based on the importance of the data to generate an optimal report. This allows for efficient report generation by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the data into the AI, and the AI ​​can adjust the level of detail of the analysis.

[0043] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply a demographic analysis algorithm to demographic data. It can also apply a commercial analysis algorithm to commercial facility data. Furthermore, it can apply a traffic analysis algorithm to transportation access data. By applying different analysis algorithms depending on the data category, highly accurate reports can be generated. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data category into the AI, which can then apply an appropriate analysis algorithm.

[0044] The analysis unit can determine the priority of analysis based on the data collection period during analysis. For example, the analysis unit can prioritize the analysis of the latest data and generate the latest regional analysis report. The analysis unit can also perform analysis with an emphasis on the latest data while referring to past data. Furthermore, the analysis unit can determine the priority of analysis based on the data collection period and generate reports efficiently. For example, the analysis unit can prioritize the analysis of the latest data and generate the latest regional analysis report. Furthermore, the analysis unit can perform analysis with an emphasis on the latest data while referring to past data. Furthermore, the analysis unit can determine the priority of analysis based on the data collection period and generate reports efficiently. This allows for efficient report generation by determining the priority of analysis based on the data collection period. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data collection period into the AI, and the AI ​​can determine the priority of analysis.

[0045] The analysis unit can adjust the order of analysis based on the relevance of the data during analysis. For example, the analysis unit can prioritize the analysis of highly relevant data to generate a highly accurate report. Furthermore, the analysis unit can efficiently generate reports by adjusting the order of analysis based on the relevance of the data. In addition, the analysis unit can postpone the analysis of less relevant data and prioritize the analysis of important data. For example, the analysis unit can prioritize the analysis of highly relevant data to generate a highly accurate report. Furthermore, the analysis unit can efficiently generate reports by adjusting the order of analysis based on the relevance of the data. In this way, by adjusting the order of analysis based on the relevance of the data, reports can be generated efficiently. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance of the data into the AI, and the AI ​​can adjust the order of analysis.

[0046] The proposal department can adjust the level of detail in its proposals based on the importance of the application methods. For example, the proposal department can provide detailed proposals for important application methods, offering highly accurate information. Conversely, it can provide simplified proposals for less important application methods, efficiently delivering information. Furthermore, the proposal department can adjust the level of detail in its proposals based on the importance of the application methods to provide optimal information. For example, the proposal department can provide detailed proposals for important application methods, offering highly accurate information. Conversely, it can provide simplified proposals for less important application methods, efficiently delivering information. Furthermore, the proposal department can adjust the level of detail in its proposals based on the importance of the application methods, providing optimal information. This allows for efficient information delivery by adjusting the level of detail in proposals based on the importance of the application methods. Some or all of the above processing in the proposal department may be performed using AI, for example, or without AI. For example, the proposal department can input the importance of the application methods into the AI, which can then adjust the level of detail in the proposals.

[0047] The proposal function can apply different proposal algorithms depending on the category of use when making a proposal. For example, for use as a cafe, the proposal function can apply a proposal algorithm specialized in cafe management. Similarly, for use as a co-working space, the proposal function can apply a proposal algorithm specialized in co-working space management. Furthermore, for use as a short-term rental, the proposal function can apply a proposal algorithm specialized in short-term rental management. By applying different proposal algorithms depending on the category of use, the proposal function can provide highly accurate information. Some or all of the above processing in the proposal function may be performed using AI, for example, or without AI. For example, the proposal function can input the category of use into the AI, and the AI ​​can apply an appropriate proposal algorithm.

[0048] The proposal department can determine the priority of proposals based on the submission deadline for each application method. For example, the proposal department can prioritize proposals for applications with an approaching submission deadline. Furthermore, it can provide detailed proposals for applications with ample time before the submission deadline. In addition, the proposal department can efficiently provide information by determining the priority of proposals based on the submission deadline. Some or all of the above processing in the proposal department may be performed using AI, or not. For example, the proposal department can input the submission deadlines for each application method into the AI, which can then determine the priority of the proposals.

[0049] The proposal department can adjust the order of proposals based on the relevance of their applications. For example, the proposal department can prioritize highly relevant applications to provide accurate information. Furthermore, the proposal department can efficiently provide information by adjusting the order of proposals based on the relevance of their applications. In addition, the proposal department can postpone less relevant applications and prioritize important applications. For example, the proposal department can prioritize highly relevant applications to provide accurate information. Furthermore, the proposal department can efficiently provide information by adjusting the order of proposals based on the relevance of their applications. In this way, information can be efficiently provided by adjusting the order of proposals based on the relevance of their applications. Some or all of the above processing in the proposal department may be performed using AI, for example, or not. For example, the proposal department can input the relevance of the applications into the AI, and the AI ​​can adjust the order of the proposals.

[0050] The simulation unit can select the optimal simulation method by referring to past simulation data during the simulation. For example, the simulation unit can select the most efficient simulation method based on past simulation data. Furthermore, the simulation unit can improve the accuracy of the simulation by referring to past simulation data. In addition, the simulation unit can analyze past simulation data to identify areas for improvement in the simulation method and optimize it. For example, the simulation unit can select the most efficient simulation method based on past simulation data. Furthermore, the simulation unit can improve the accuracy of the simulation by referring to past simulation data. Furthermore, the simulation unit can analyze past simulation data to identify areas for improvement in the simulation method and optimize it. This allows for improved simulation accuracy by referring to past simulation data. Some or all of the above-described processes in the simulation unit may be performed using AI, for example, or without AI. For example, the simulation unit can input past simulation data into AI, which can then select the optimal simulation method.

[0051] The simulation unit can adjust the simulation parameters based on specific market trends and economic indicators during the simulation. For example, the simulation unit can adjust the simulation parameters based on the latest market trends to provide highly accurate results. Furthermore, the simulation unit can adjust the simulation parameters based on economic indicators to perform risk assessments. In addition, the simulation unit can optimize the simulation parameters by considering market trends and economic indicators. This allows for the provision of highly accurate results by adjusting the simulation parameters based on market trends and economic indicators. Some or all of the above-described processes in the simulation unit may be performed using AI, for example, or without AI. For example, the simulation unit can input market trends and economic indicators into the AI, which can then adjust the simulation parameters.

[0052] The simulation unit can select the optimal simulation method by considering the user's geographical location information during the simulation. For example, the simulation unit can perform a simulation that takes into account regional market trends based on the user's current location. Furthermore, the simulation unit can select the optimal simulation method based on the user's geographical location information and provide highly accurate results. In addition, the simulation unit can perform highly relevant simulations by considering the user's geographical location information. For example, the simulation unit can perform a simulation that takes into account regional market trends based on the user's current location. Furthermore, the simulation unit can select the optimal simulation method based on the user's geographical location information and provide highly accurate results. Furthermore, the simulation unit can perform highly relevant simulations by considering the user's geographical location information. This allows for highly relevant simulations to be performed by considering the user's geographical location information. Some or all of the above processing in the simulation unit may be performed using AI, for example, or without AI. For example, the simulation unit can input the user's geographical location information into AI, and the AI ​​can select the optimal simulation method.

[0053] The simulation unit can analyze the user's social media activity during a simulation and propose simulation methods. For example, the simulation unit can analyze the user's social media activity and perform simulations that take into account local trends and popular spots. Furthermore, the simulation unit can propose the optimal simulation method based on the user's social media activity. In addition, the simulation unit can perform highly relevant simulations by considering the user's social media activity. For example, the simulation unit can analyze the user's social media activity and perform simulations that take into account local trends and popular spots. Furthermore, the simulation unit can propose the optimal simulation method based on the user's social media activity. In addition, the simulation unit can perform highly relevant simulations by considering the user's social media activity. This allows for simulations that take into account local trends and popular spots by analyzing the user's social media activity. Some or all of the above processing in the simulation unit may be performed using AI, for example, or without AI. For example, the simulation unit can input the user's social media activity data into AI, which can then propose the optimal simulation method.

[0054] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0055] The vacant house utilization proposal system can also include a community collaboration department. This department can facilitate collaboration with local residents and businesses, and collect feedback on how to utilize vacant houses. For example, the department can hold workshops with local residents to share ideas for utilizing vacant houses and incorporate residents' opinions. Furthermore, through cooperation with local businesses, the department can provide the resources and support necessary for utilizing vacant houses. In addition, the department can participate in local events and activities to widely publicize methods for utilizing vacant houses. This strengthens collaboration with local residents and businesses, enabling the entire community to support the utilization of vacant houses.

[0056] The vacant house utilization proposal system can also include an environmental assessment department. This department can evaluate the environmental impact of vacant houses and propose sustainable utilization methods. For example, it can evaluate the energy consumption and carbon dioxide emissions of vacant houses and propose eco-friendly utilization methods. Furthermore, it can recommend the introduction of renewable energy and the use of energy-saving technologies in the renovation of vacant houses. In addition, it can propose utilization methods that consider the local natural environment and ecosystem, thereby promoting environmental protection. This minimizes the environmental impact of vacant house utilization and contributes to the realization of a sustainable local community.

[0057] The vacant house utilization proposal system can also include a cultural preservation department. This department can evaluate the historical value and cultural significance of vacant houses and propose methods for their preservation and utilization. For example, it can investigate the architectural style and historical background of vacant houses and assess their value as cultural properties. Furthermore, it can propose utilization methods that reflect local traditions and culture, thereby protecting local cultural heritage. Additionally, it can propose using vacant houses as exhibition spaces or museums showcasing local history and culture. This allows for the protection of the historical value and cultural significance of vacant houses and the transmission of local cultural heritage to future generations.

[0058] The vacant house utilization proposal system can also include a health promotion department. This department can propose ways to utilize vacant houses as facilities for health promotion. For example, it could propose using vacant houses as fitness centers or yoga studios, supporting the health maintenance of local residents. It could also propose using vacant houses as health consultation centers or rehabilitation facilities, enhancing local medical services. Furthermore, it could propose using vacant houses as venues for health events and workshops, raising health awareness among local residents. In this way, vacant houses can be utilized as facilities for health promotion, supporting the health of local residents.

[0059] The vacant house utilization proposal system can also include an education support department. This department can propose ways to utilize vacant houses as educational spaces. For example, it could propose using vacant houses as community learning centers or libraries, providing learning opportunities for local residents. It could also propose using vacant houses as venues for children's learning classes or after-school programs, supporting children's learning. Furthermore, it could propose using vacant houses as educational facilities for learning about local history and culture, enriching local educational resources. This allows vacant houses to be utilized as educational spaces, providing learning opportunities for local residents.

[0060] The following briefly describes the processing flow for example form 1.

[0061] Step 1: The data collection unit collects data about the area surrounding the vacant house. Specifically, the data collection unit collects data such as population dynamics, commercial facilities, and transportation access around the vacant house. For example, it collects population dynamics data such as age groups, number of households, and population changes; commercial facility data such as the types and number of nearby stores and their operating hours; and transportation access data such as the location of the nearest station or bus stop and its operating status. Step 2: The analysis unit analyzes the data collected by the collection unit and generates a regional analysis report. Specifically, the analysis unit analyzes the collected demographic data, commercial facility data, and transportation access data to understand the trends in each. Step 3: Based on the report generated by the analysis department, the proposal department proposes various uses other than rental. Specifically, the proposal department proposes concrete use ideas such as a cafe, co-working space, and short-term rental, and shows the operation methods and profitability of each. Step 4: The simulation department simulates the profitability of the proposed usage methods and assesses the risks. Specifically, the simulation department simulates the profitability of using the space as a cafe, co-working space, and short-term rental property, and evaluates initial investment, operating costs, and projected revenue.

[0062] (Example of form 2) The vacant house utilization proposal system according to an embodiment of the present invention is a system that uses AI to thoroughly analyze the surrounding environment of a vacant house and proposes a variety of utilization methods other than rental. The vacant house utilization proposal system analyzes data such as population dynamics, commercial facilities, and transportation access around the vacant house and generates a regional analysis report. Next, the vacant house utilization proposal system proposes a variety of utilization methods other than rental. For example, it proposes specific utilization ideas such as a cafe, co-working space, or short-term rental. The vacant house utilization proposal system also simulates the profitability of each utilization method and evaluates the risks. This promotes the effective utilization of vacant houses and regional revitalization. For example, the vacant house utilization proposal system analyzes data such as population dynamics, commercial facilities, and transportation access around the vacant house. In this process, the vacant house utilization proposal system collects and analyzes detailed regional data. For example, as population dynamics of the surrounding area, it collects data such as age groups, number of households, and population increase / decrease. As data on commercial facilities, it collects the types and number of stores in the surrounding area, business hours, etc. Furthermore, as data on transportation access, it collects the location of the nearest station or bus stop, and the operating status, etc. This enables a detailed analysis of the region. Next, the vacant house utilization proposal system proposes a variety of utilization methods other than rental. For example, the system proposes specific utilization ideas such as cafes, co-working spaces, and short-term rentals. This allows owners of vacant properties to consider diverse uses other than renting. The vacant property utilization proposal system also simulates the profitability of each utilization method and assesses the risks. For example, it simulates the profitability of using the property as a cafe, evaluating initial investment, operating costs, and expected revenue. This allows owners of vacant properties to minimize investment risk. This promotes the effective use of vacant properties and regional revitalization. For example, using a vacant property as a cafe can revitalize local economic activity and address the problem of population decline. Also, using a vacant property as a co-working space can provide a place to work in the community and support local economic activity. Furthermore, using a vacant property as short-term rentals can increase the number of tourists and support the local tourism industry.In this way, the vacant house utilization proposal system can promote the effective use of vacant houses and regional revitalization by thoroughly analyzing the surrounding environment of vacant houses and proposing diverse utilization methods other than rental. This can lead to the resolution of the vacant house problem and the sustainable development of the region.

[0063] The vacant house utilization proposal system according to this embodiment comprises a data collection unit, an analysis unit, a proposal unit, and a simulation unit. The data collection unit collects data about the area surrounding the vacant house. For example, the data collection unit collects data such as population dynamics, commercial facilities, and transportation access around the vacant house. For example, as population dynamics of the surrounding area, the data collection unit collects data such as age groups, number of households, and population increase / decrease. The data collection unit can also collect data on commercial facilities, such as the types and number of stores in the surrounding area and their operating hours. Furthermore, the data collection unit can collect data on transportation access, such as the location of the nearest train station or bus stop and their operating status. For example, the data collection unit can collect population dynamics data about the surrounding area and obtain information such as age groups, number of households, and population increase / decrease. The data collection unit can also collect data on commercial facilities and obtain information such as the types and number of stores in the surrounding area and their operating hours. Furthermore, the data collection unit can collect data on transportation access and obtain information such as the location of the nearest train station or bus stop and their operating status. The analysis unit analyzes the data collected by the data collection unit and generates a regional analysis report. The analysis department can, for example, analyze collected demographic data to understand trends such as the age distribution, number of households, and population changes in the region. The analysis department can also analyze collected commercial facility data to understand trends such as the types and number of surrounding stores and their operating hours. Furthermore, the analysis department can analyze collected transportation access data to understand trends such as the location and operating status of the nearest train stations and bus stops. For example, the analysis department can analyze collected demographic data to understand trends such as the age distribution, number of households, and population changes in the region. Furthermore, the analysis department can analyze collected commercial facility data to understand trends such as the types and number of surrounding stores and their operating hours. Furthermore, the analysis department can analyze collected transportation access data to understand trends such as the location and operating status of the nearest train stations and bus stops. The proposal department, based on the reports generated by the analysis department, proposes diverse uses other than rental. For example, the proposal department can propose specific use ideas such as a cafe, co-working space, or short-term rental. For example, the proposal department can propose use as a cafe and demonstrate cafe operation methods and profitability.Furthermore, the proposal department can propose ways to utilize the space as a co-working space and demonstrate its operation and profitability. In addition, the proposal department can propose ways to utilize the space as a short-term rental and demonstrate its operation and profitability. For example, the proposal department can propose ways to utilize the space as a cafe and demonstrate its operation and profitability. Furthermore, the proposal department can propose ways to utilize the space as a co-working space and demonstrate its operation and profitability. Furthermore, the proposal department can propose ways to utilize the space as a short-term rental and demonstrate its operation and profitability. The simulation department simulates the profitability of the utilization methods proposed by the proposal department and evaluates the risks. For example, the simulation department can simulate the profitability of using the space as a cafe and evaluate initial investment, operating costs, and expected revenue. Furthermore, the simulation department can simulate the profitability of using the space as a co-working space and evaluate initial investment, operating costs, and expected revenue. Furthermore, the simulation department can simulate the profitability of using the space as a short-term rental and evaluate initial investment, operating costs, and expected revenue. For example, the simulation unit can simulate the profitability of using the property as a cafe and evaluate initial investment, operating costs, and projected revenue. It can also simulate the profitability of using the property as a co-working space and evaluate initial investment, operating costs, and projected revenue. Furthermore, it can simulate the profitability of using the property as a short-term rental (Airbnb) and evaluate initial investment, operating costs, and projected revenue. As a result, the vacant property utilization proposal system according to this embodiment can promote the effective use of vacant properties and regional revitalization.

[0064] The data collection unit collects data about the area surrounding vacant houses. For example, the unit collects data such as population dynamics, commercial facilities, and transportation access around the vacant houses. Specifically, the unit collects data on surrounding population dynamics, including age groups, number of households, and population changes. This can utilize government statistics and local resident registration information. The unit can also collect data on commercial facilities, such as the types and number of stores in the area and their operating hours. This could involve using the official websites of commercial facilities or databases of local chambers of commerce. Furthermore, the unit can collect data on transportation access, such as the location and operating status of the nearest train stations and bus stops. This can involve utilizing public transportation operation information and map data. For example, the unit can collect surrounding population dynamics data to obtain information such as age groups, number of households, and population changes. It can also collect data on commercial facilities to obtain information such as the types and number of stores in the area and their operating hours. Furthermore, it can collect data on transportation access to obtain information such as the location and operating status of the nearest train stations and bus stops. This allows the data collection unit to comprehensively collect detailed data about the surrounding area of ​​vacant houses, providing the basic information necessary for subsequent analysis and proposals.

[0065] The analysis unit analyzes data collected by the collection unit and generates regional analysis reports. For example, the analysis unit can analyze collected demographic data to understand trends in regional age distribution, household numbers, and population changes. Specifically, it utilizes AI-based data analysis methods to statistically process the collected data. For instance, it can graph the distribution of age groups and changes in household numbers to visually represent regional demographic trends. Furthermore, the analysis unit can analyze collected commercial facility data to understand trends in surrounding stores, such as types, numbers, and operating hours. This includes analyzing the distribution and operating patterns of different store types to reveal the characteristics of regional commercial activity. Additionally, the analysis unit can analyze collected transportation access data to understand trends in the location and operation status of the nearest train stations and bus stops. This includes evaluating the usage and accessibility of transportation to understand the current state of regional transportation infrastructure. Furthermore, the analysis unit can analyze the collected transportation access data to understand trends such as the location of the nearest train stations and bus stops, and their operating status. This allows the analysis unit to conduct a detailed analysis of regional characteristics based on the collected data and provide useful information for utilizing vacant properties.

[0066] The proposal department proposes diverse uses other than rental based on reports generated by the analysis department. For example, the proposal department can propose specific use ideas such as a cafe, co-working space, or short-term rental. Specifically, the proposal department can propose uses as a cafe and demonstrate the cafe's operation and profitability. This includes detailed operational plans such as menu composition, pricing, and marketing strategies. The proposal department can also propose uses as a co-working space and demonstrate its operation and profitability. This includes operational plans such as setting usage fees, equipment layout, and identifying target users. Furthermore, the proposal department can propose uses as short-term rental and demonstrate its operation and profitability. This includes setting accommodation fees, implementing a reservation system, and planning cleaning and maintenance. This allows the proposal department to concretely demonstrate diverse ways to utilize vacant houses and provide owners with feasible options.

[0067] The Simulation Department simulates the profitability of the proposed usage methods and assesses the risks. For example, the Simulation Department can simulate the profitability of using the space as a cafe, evaluating initial investment, operating costs, and projected revenue. Specifically, the Simulation Department calculates in detail the equipment investment required to open a cafe, as well as labor costs and material costs for operation, and evaluates profitability by comparing these with projected revenue. The Simulation Department can also simulate the profitability of using the space as a co-working space, evaluating initial investment, operating costs, and projected revenue. This involves considering equipment installation costs, utility costs, cleaning costs, etc., and evaluating profitability based on revenue from users. Furthermore, the Simulation Department can simulate the profitability of using the space as a short-term rental, evaluating initial investment, operating costs, and projected revenue. This involves calculating in detail the renovation costs of the accommodation, cleaning costs, and reservation system implementation costs, and evaluates profitability by comparing these with revenue obtained from accommodation fees. Furthermore, the simulation department can simulate the profitability of using the space as a co-working space, evaluating initial investment, operating costs, and projected revenue. It can also simulate the profitability of using the space as a short-term rental (Airbnb), evaluating initial investment, operating costs, and projected revenue. This allows the simulation department to thoroughly evaluate the feasibility of proposed uses and provide owners with information that considers the balance between risk and profit.

[0068] The data collection unit can collect data on population dynamics, commercial facilities, and transportation access around vacant houses. For example, the data collection unit can collect data on surrounding population dynamics such as age groups, number of households, and population changes. It can also collect data on commercial facilities such as the types and number of stores in the vicinity and their operating hours. Furthermore, it can collect data on transportation access such as the location of the nearest train station or bus stop and their operating status. For example, the data collection unit can collect surrounding population dynamics data and obtain information such as age groups, number of households, and population changes. It can also collect data on commercial facilities and obtain information such as the types and number of stores in the vicinity and their operating hours. Furthermore, it can collect data on transportation access and obtain information such as the location of the nearest train station or bus stop and their operating status. By collecting detailed data around vacant houses, the accuracy of regional analysis reports is improved. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can input population dynamics data around vacant houses into AI, and the AI ​​can analyze and collect the data.

[0069] The analysis unit can analyze the data collected by the collection unit and generate a regional analysis report. For example, the analysis unit can analyze collected demographic data to understand trends such as the age distribution, number of households, and population growth / decrease in the region. It can also analyze collected commercial facility data to understand trends such as the types and number of surrounding stores and their operating hours. Furthermore, the analysis unit can analyze collected transportation access data to understand trends such as the location of the nearest train stations and bus stops and their operating status. This allows for a detailed analysis of the region by analyzing the collected data. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generation AI, or without using a generation AI. For example, the analysis unit can input the collected data into a generating AI, which can then analyze the data and generate a regional analysis report.

[0070] The proposal department can propose specific utilization ideas such as a cafe, co-working space, or short-term rental. For example, the proposal department can propose a use as a cafe and show how to operate and make it profitable. It can also propose a use as a co-working space and show how to operate and make it profitable. Furthermore, the proposal department can propose a use as a short-term rental and show how to operate and make it profitable. For example, the proposal department can propose a use as a cafe and show how to operate and make it profitable. It can also propose a use as a co-working space and show how to operate and make it profitable. Furthermore, the proposal department can propose a use as a short-term rental and show how to operate and make it profitable. This allows owners of vacant properties to consider a variety of uses other than renting by proposing specific utilization ideas. Some or all of the above processing in the proposal department may be performed using, for example, a generative AI, or not. For example, the proposal department can input a use as a cafe into a generative AI, and the generative AI can propose how to operate and make it profitable.

[0071] The simulation unit can simulate the profitability of proposed uses and assess the risks. For example, the simulation unit can simulate the profitability of using the space as a cafe and assess initial investment, operating costs, and expected revenue. It can also simulate the profitability of using the space as a co-working space and assess initial investment, operating costs, and expected revenue. Furthermore, the simulation unit can simulate the profitability of using the space as a short-term rental and assess initial investment, operating costs, and expected revenue. By simulating profitability and assessing risks, investment risk can be minimized. Some or all of the above processing in the simulation unit may be performed using AI, for example, or without AI. For example, the simulation unit can input the profitability of the proposed usage method into the AI, which can then simulate the profitability and assess the risks.

[0072] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can reduce the frequency of data collection to lessen the user's burden. Also, if the user is relaxed, the data collection unit can collect detailed data and generate a more accurate report. Furthermore, if the user is in a hurry, the data collection unit can collect data quickly and generate a report in a short time. For example, if the user is stressed, the data collection unit can reduce the frequency of data collection to lessen the user's burden. Also, if the user is relaxed, the data collection unit can collect detailed data and generate a more accurate report. Furthermore, if the user is in a hurry, the data collection unit can collect data quickly and generate a report in a short time. In this way, the user's burden can be reduced by adjusting the timing of data collection according to the user's emotions. 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 processing described above in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input user emotion data into a generating AI, which can estimate the emotion and adjust the timing of data collection.

[0073] The data collection unit can analyze past data collection history and select the optimal collection method. For example, the data collection unit can select the most efficient collection method from past data collection history to improve the accuracy of data collection. Furthermore, the data collection unit can determine the priority of data to be collected based on past data collection history, and prioritize the collection of important data. In addition, the data collection unit can analyze past data collection history to identify areas for improvement in the collection method and optimize the collection process. For example, the data collection unit can select the most efficient collection method from past data collection history to improve the accuracy of data collection. Furthermore, the data collection unit can determine the priority of data to be collected based on past data collection history, and prioritize the collection of important data. Furthermore, the data collection unit can analyze past data collection history to identify areas for improvement in the collection method and optimize the collection process. This allows for improved data collection accuracy through analysis of past data collection history. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input past data collection history into AI, which can then select the optimal collection method.

[0074] The data collection unit can filter the data to be collected based on specific events or seasons. For example, the data collection unit can prioritize the collection of data related to a particular season, taking into account seasonal demographic changes. Furthermore, the data collection unit can collect data related to local events when they are held and analyze the state of local revitalization. In addition, the data collection unit can filter the data to be collected based on specific events or seasons, collecting only the necessary data. This allows for the efficient collection of only the necessary data by filtering the data based on specific events or seasons. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data related to specific events or seasons into AI, which can then filter and collect the data.

[0075] The data collection unit can estimate the user's emotions and prioritize the data to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit can prioritize collecting only important data, reducing the user's burden. If the user is relaxed, the data collection unit can prioritize collecting detailed data, generating a highly accurate report. Furthermore, if the user is in a hurry, the data collection unit can prioritize collecting data that can be collected quickly, generating a report in a short time. For example, if the user is stressed, the data collection unit can prioritize collecting only important data, reducing the user's burden. If the user is relaxed, the data collection unit can prioritize collecting detailed data, generating a highly accurate report. Furthermore, if the user is in a hurry, the data collection unit can prioritize collecting data that can be collected quickly, generating a report in a short time. In this way, the user's burden can be reduced by prioritizing the data to be collected according to the user's emotions. 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 processing described above in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input user emotion data into a generating AI, which can then estimate the emotion and determine the priority of the data to be collected.

[0076] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information during data collection. For example, the data collection unit can prioritize the collection of data on surrounding population dynamics and commercial facilities based on the user's current location. Furthermore, the data collection unit can prioritize the collection of the nearest transportation access data based on the user's geographical location information. In addition, the data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information and generate a regional analysis report. For example, the data collection unit can prioritize the collection of data on surrounding population dynamics and commercial facilities based on the user's current location. Furthermore, the data collection unit can prioritize the collection of the nearest transportation access data based on the user's geographical location information. Furthermore, the data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information and generate a regional analysis report. This allows for the efficient collection of highly relevant data by considering the user's geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into AI, which can then prioritize the collection of highly relevant data.

[0077] The data collection unit can analyze users' social media activity and collect relevant data during data collection. For example, the data collection unit can analyze users' social media activity and collect data on local trends and popular spots. Furthermore, the data collection unit can collect local event information based on users' social media activity and reflect it in local analysis reports. In addition, the data collection unit can consider users' social media activity, collect relevant data, and conduct detailed local analysis. For example, the data collection unit can analyze users' social media activity and collect data on local trends and popular spots. Furthermore, the data collection unit can collect local event information based on users' social media activity and reflect it in local analysis reports. Furthermore, the data collection unit can consider users' social media activity, collect relevant data, and conduct detailed local analysis. This allows for the efficient collection of data on local trends and popular spots by analyzing users' social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input user social media activity data into AI, which can then collect relevant data.

[0078] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is tense, the analysis unit can provide simple and easy-to-understand analysis results. If the user is relaxed, the analysis unit can provide detailed analysis results to deepen the user's understanding. Furthermore, if the user is in a hurry, the analysis unit can provide concise analysis results to quickly convey information. For example, if the user is tense, the analysis unit can provide simple and easy-to-understand analysis results. If the user is relaxed, the analysis unit can provide detailed analysis results to deepen the user's understanding. Furthermore, if the user is in a hurry, the analysis unit can provide concise analysis results to quickly convey information. In this way, by adjusting the presentation of the analysis according to the user's emotions, the user's understanding can be deepened. 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-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into a generating AI, which can then estimate the emotion and adjust the method of expressing the analysis.

[0079] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit can perform a detailed analysis on important data to generate a highly accurate report. Conversely, the analysis unit can perform a simplified analysis on less important data to efficiently generate a report. Furthermore, the analysis unit can adjust the level of detail of the analysis based on the importance of the data to generate an optimal report. For example, the analysis unit can perform a detailed analysis on important data to generate a highly accurate report. Conversely, the analysis unit can perform a simplified analysis on less important data to efficiently generate a report. Furthermore, the analysis unit can adjust the level of detail of the analysis based on the importance of the data to generate an optimal report. This allows for efficient report generation by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the data into the AI, and the AI ​​can adjust the level of detail of the analysis.

[0080] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply a demographic analysis algorithm to demographic data. It can also apply a commercial analysis algorithm to commercial facility data. Furthermore, it can apply a traffic analysis algorithm to transportation access data. By applying different analysis algorithms depending on the data category, highly accurate reports can be generated. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data category into the AI, which can then apply an appropriate analysis algorithm.

[0081] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is nervous, the analysis unit can provide a simple and highly visible display method. If the user is relaxed, the analysis unit can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the analysis unit can provide a display method that gets straight to the point. For example, if the user is nervous, the analysis unit can provide a simple and highly visible display method. If the user is relaxed, the analysis unit can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the analysis unit can provide a display method that gets straight to the point. By adjusting the display method of the analysis results according to the user's emotions, the user's understanding can be deepened. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The 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 analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into a generating AI, which can then estimate the emotion and adjust how the analysis results are displayed.

[0082] The analysis unit can determine the priority of analysis based on the data collection period during analysis. For example, the analysis unit can prioritize the analysis of the latest data and generate the latest regional analysis report. The analysis unit can also perform analysis with an emphasis on the latest data while referring to past data. Furthermore, the analysis unit can determine the priority of analysis based on the data collection period and generate reports efficiently. For example, the analysis unit can prioritize the analysis of the latest data and generate the latest regional analysis report. Furthermore, the analysis unit can perform analysis with an emphasis on the latest data while referring to past data. Furthermore, the analysis unit can determine the priority of analysis based on the data collection period and generate reports efficiently. This allows for efficient report generation by determining the priority of analysis based on the data collection period. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data collection period into the AI, and the AI ​​can determine the priority of analysis.

[0083] The analysis unit can adjust the order of analysis based on the relevance of the data during analysis. For example, the analysis unit can prioritize the analysis of highly relevant data to generate a highly accurate report. Furthermore, the analysis unit can efficiently generate reports by adjusting the order of analysis based on the relevance of the data. In addition, the analysis unit can postpone the analysis of less relevant data and prioritize the analysis of important data. For example, the analysis unit can prioritize the analysis of highly relevant data to generate a highly accurate report. Furthermore, the analysis unit can efficiently generate reports by adjusting the order of analysis based on the relevance of the data. In this way, by adjusting the order of analysis based on the relevance of the data, reports can be generated efficiently. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance of the data into the AI, and the AI ​​can adjust the order of analysis.

[0084] The suggestion section can estimate the user's emotions and adjust the way suggestions are presented based on those emotions. For example, if the user is nervous, the suggestion section can provide simple and highly visible suggestions. If the user is relaxed, the suggestion section can provide detailed suggestions to deepen the user's understanding. Furthermore, if the user is in a hurry, the suggestion section can provide concise suggestions to quickly convey information. For example, if the user is nervous, the suggestion section can provide simple and highly visible suggestions. If the user is relaxed, the suggestion section can provide detailed suggestions to deepen the user's understanding. Furthermore, if the user is in a hurry, the suggestion section can provide concise suggestions to quickly convey information. This allows for a deeper understanding of the user by adjusting the way suggestions are presented according to their emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the suggestion section may be performed using AI, for example, or without AI. For example, the proposal unit can input user emotion data into a generating AI, which can then estimate the emotion and adjust the way the proposal is expressed.

[0085] The proposal department can adjust the level of detail in its proposals based on the importance of the application methods. For example, the proposal department can provide detailed proposals for important application methods, offering highly accurate information. Conversely, it can provide simplified proposals for less important application methods, efficiently delivering information. Furthermore, the proposal department can adjust the level of detail in its proposals based on the importance of the application methods to provide optimal information. For example, the proposal department can provide detailed proposals for important application methods, offering highly accurate information. Conversely, it can provide simplified proposals for less important application methods, efficiently delivering information. Furthermore, the proposal department can adjust the level of detail in its proposals based on the importance of the application methods, providing optimal information. This allows for efficient information delivery by adjusting the level of detail in proposals based on the importance of the application methods. Some or all of the above processing in the proposal department may be performed using AI, for example, or without AI. For example, the proposal department can input the importance of the application methods into the AI, which can then adjust the level of detail in the proposals.

[0086] The proposal function can apply different proposal algorithms depending on the category of use when making a proposal. For example, for use as a cafe, the proposal function can apply a proposal algorithm specialized in cafe management. Similarly, for use as a co-working space, the proposal function can apply a proposal algorithm specialized in co-working space management. Furthermore, for use as a short-term rental, the proposal function can apply a proposal algorithm specialized in short-term rental management. By applying different proposal algorithms depending on the category of use, the proposal function can provide highly accurate information. Some or all of the above processing in the proposal function may be performed using AI, for example, or without AI. For example, the proposal function can input the category of use into the AI, and the AI ​​can apply an appropriate proposal algorithm.

[0087] The suggestion section can estimate the user's emotions and adjust the length of the suggestion based on the estimated emotions. For example, if the user is nervous, the suggestion section can provide a short, to-the-point suggestion. If the user is relaxed, the suggestion section can provide a longer suggestion with more detailed explanations. Furthermore, if the user is in a hurry, the suggestion section can provide a short suggestion to quickly convey the information. For example, if the user is nervous, the suggestion section can provide a short, to-the-point suggestion. If the user is relaxed, the suggestion section can provide a longer suggestion with more detailed explanations. Furthermore, if the user is in a hurry, the suggestion section can provide a short suggestion to quickly convey the information. By adjusting the length of the suggestion according to the user's emotions, the user's understanding can be enhanced. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the suggestion section may be performed using AI, for example, or without AI. For example, the suggestion unit can input user emotion data into a generating AI, which can then estimate the emotion and adjust the length of the suggestion.

[0088] The proposal department can determine the priority of proposals based on the submission deadline for each application method. For example, the proposal department can prioritize proposals for applications with an approaching submission deadline. Furthermore, it can provide detailed proposals for applications with ample time before the submission deadline. In addition, the proposal department can efficiently provide information by determining the priority of proposals based on the submission deadline. Some or all of the above processing in the proposal department may be performed using AI, or not. For example, the proposal department can input the submission deadlines for each application method into the AI, which can then determine the priority of the proposals.

[0089] The proposal department can adjust the order of proposals based on the relevance of their applications. For example, the proposal department can prioritize highly relevant applications to provide accurate information. Furthermore, the proposal department can efficiently provide information by adjusting the order of proposals based on the relevance of their applications. In addition, the proposal department can postpone less relevant applications and prioritize important applications. For example, the proposal department can prioritize highly relevant applications to provide accurate information. Furthermore, the proposal department can efficiently provide information by adjusting the order of proposals based on the relevance of their applications. In this way, information can be efficiently provided by adjusting the order of proposals based on the relevance of their applications. Some or all of the above processing in the proposal department may be performed using AI, for example, or not. For example, the proposal department can input the relevance of the applications into the AI, and the AI ​​can adjust the order of the proposals.

[0090] The simulation unit can estimate the user's emotions and adjust the simulation method based on the estimated emotions. For example, if the user is nervous, the simulation unit can provide simple and easy-to-understand simulation results. If the user is relaxed, the simulation unit can provide detailed simulation results to deepen the user's understanding. Furthermore, if the user is in a hurry, the simulation unit can provide concise simulation results to quickly convey information. For example, if the user is nervous, the simulation unit can provide simple and easy-to-understand simulation results. If the user is relaxed, the simulation unit can provide detailed simulation results to deepen the user's understanding. Furthermore, if the user is in a hurry, the simulation unit can provide concise simulation results to quickly convey information. In this way, by adjusting the simulation method according to the user's emotions, the user's understanding can be deepened. 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-described processes in the simulation unit may be performed using AI, for example, or without AI. For example, the simulation unit can input user emotion data into a generating AI, which can then estimate the emotion and adjust the simulation method.

[0091] The simulation unit can select the optimal simulation method by referring to past simulation data during the simulation. For example, the simulation unit can select the most efficient simulation method based on past simulation data. Furthermore, the simulation unit can improve the accuracy of the simulation by referring to past simulation data. In addition, the simulation unit can analyze past simulation data to identify areas for improvement in the simulation method and optimize it. For example, the simulation unit can select the most efficient simulation method based on past simulation data. Furthermore, the simulation unit can improve the accuracy of the simulation by referring to past simulation data. Furthermore, the simulation unit can analyze past simulation data to identify areas for improvement in the simulation method and optimize it. This allows for improved simulation accuracy by referring to past simulation data. Some or all of the above-described processes in the simulation unit may be performed using AI, for example, or without AI. For example, the simulation unit can input past simulation data into AI, which can then select the optimal simulation method.

[0092] The simulation unit can adjust the simulation parameters based on specific market trends and economic indicators during the simulation. For example, the simulation unit can adjust the simulation parameters based on the latest market trends to provide highly accurate results. Furthermore, the simulation unit can adjust the simulation parameters based on economic indicators to perform risk assessments. In addition, the simulation unit can optimize the simulation parameters by considering market trends and economic indicators. This allows for the provision of highly accurate results by adjusting the simulation parameters based on market trends and economic indicators. Some or all of the above-described processes in the simulation unit may be performed using AI, for example, or without AI. For example, the simulation unit can input market trends and economic indicators into the AI, which can then adjust the simulation parameters.

[0093] The simulation unit can estimate the user's emotions and determine the priority of simulations based on the estimated emotions. For example, if the user is tense, the simulation unit can prioritize important simulations to reduce the user's burden. Also, if the user is relaxed, the simulation unit can prioritize detailed simulations to provide highly accurate results. Furthermore, if the user is in a hurry, the simulation unit can perform simulations quickly to provide results in a short time. In this way, the user's burden can be reduced by determining the priority of simulations according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the simulation unit may be performed using AI, for example, or without AI. For example, the simulation unit can input user emotion data into a generating AI, which can then estimate the emotion and determine the simulation priorities.

[0094] The simulation unit can select the optimal simulation method by considering the user's geographical location information during the simulation. For example, the simulation unit can perform a simulation that takes into account regional market trends based on the user's current location. Furthermore, the simulation unit can select the optimal simulation method based on the user's geographical location information and provide highly accurate results. In addition, the simulation unit can perform highly relevant simulations by considering the user's geographical location information. For example, the simulation unit can perform a simulation that takes into account regional market trends based on the user's current location. Furthermore, the simulation unit can select the optimal simulation method based on the user's geographical location information and provide highly accurate results. Furthermore, the simulation unit can perform highly relevant simulations by considering the user's geographical location information. This allows for highly relevant simulations to be performed by considering the user's geographical location information. Some or all of the above processing in the simulation unit may be performed using AI, for example, or without AI. For example, the simulation unit can input the user's geographical location information into AI, and the AI ​​can select the optimal simulation method.

[0095] The simulation unit can analyze the user's social media activity during a simulation and propose simulation methods. For example, the simulation unit can analyze the user's social media activity and perform simulations that take into account local trends and popular spots. Furthermore, the simulation unit can propose the optimal simulation method based on the user's social media activity. In addition, the simulation unit can perform highly relevant simulations by considering the user's social media activity. For example, the simulation unit can analyze the user's social media activity and perform simulations that take into account local trends and popular spots. Furthermore, the simulation unit can propose the optimal simulation method based on the user's social media activity. In addition, the simulation unit can perform highly relevant simulations by considering the user's social media activity. This allows for simulations that take into account local trends and popular spots by analyzing the user's social media activity. Some or all of the above processing in the simulation unit may be performed using AI, for example, or without AI. For example, the simulation unit can input the user's social media activity data into AI, which can then propose the optimal simulation method.

[0096] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0097] The vacant house utilization proposal system can also include a community collaboration department. This department can facilitate collaboration with local residents and businesses, and collect feedback on how to utilize vacant houses. For example, the department can hold workshops with local residents to share ideas for utilizing vacant houses and incorporate residents' opinions. Furthermore, through cooperation with local businesses, the department can provide the resources and support necessary for utilizing vacant houses. In addition, the department can participate in local events and activities to widely publicize methods for utilizing vacant houses. This strengthens collaboration with local residents and businesses, enabling the entire community to support the utilization of vacant houses.

[0098] The vacant house utilization proposal system can also include an environmental assessment department. This department can evaluate the environmental impact of vacant houses and propose sustainable utilization methods. For example, it can evaluate the energy consumption and carbon dioxide emissions of vacant houses and propose eco-friendly utilization methods. Furthermore, it can recommend the introduction of renewable energy and the use of energy-saving technologies in the renovation of vacant houses. In addition, it can propose utilization methods that consider the local natural environment and ecosystem, thereby promoting environmental protection. This minimizes the environmental impact of vacant house utilization and contributes to the realization of a sustainable local community.

[0099] The vacant house utilization proposal system can also include a cultural preservation department. This department can evaluate the historical value and cultural significance of vacant houses and propose methods for their preservation and utilization. For example, it can investigate the architectural style and historical background of vacant houses and assess their value as cultural properties. Furthermore, it can propose utilization methods that reflect local traditions and culture, thereby protecting local cultural heritage. Additionally, it can propose using vacant houses as exhibition spaces or museums showcasing local history and culture. This allows for the protection of the historical value and cultural significance of vacant houses and the transmission of local cultural heritage to future generations.

[0100] The vacant house utilization proposal system can also include a health promotion department. This department can propose ways to utilize vacant houses as facilities for health promotion. For example, it could propose using vacant houses as fitness centers or yoga studios, supporting the health maintenance of local residents. It could also propose using vacant houses as health consultation centers or rehabilitation facilities, enhancing local medical services. Furthermore, it could propose using vacant houses as venues for health events and workshops, raising health awareness among local residents. In this way, vacant houses can be utilized as facilities for health promotion, supporting the health of local residents.

[0101] The vacant house utilization proposal system can also include an education support department. This department can propose ways to utilize vacant houses as educational spaces. For example, it could propose using vacant houses as community learning centers or libraries, providing learning opportunities for local residents. It could also propose using vacant houses as venues for children's learning classes or after-school programs, supporting children's learning. Furthermore, it could propose using vacant houses as educational facilities for learning about local history and culture, enriching local educational resources. This allows vacant houses to be utilized as educational spaces, providing learning opportunities for local residents.

[0102] The vacant house utilization proposal system can also be equipped with an emotion estimation unit. The emotion estimation unit can estimate the user's emotions and adjust the proposal content based on the estimated emotions. For example, if the user is feeling stressed, the emotion estimation unit can suggest ways to use the vacant house in a relaxing manner. If the user is excited, the emotion estimation unit can suggest ways to use the vacant house in an active manner. Furthermore, if the user is depressed, the emotion estimation unit can suggest ways to use the vacant house as a healing space. In this way, the system can propose the most suitable way to use the vacant house according to the user's emotions, thereby increasing user satisfaction.

[0103] The vacant house utilization proposal system can also be equipped with an emotion estimation unit. This unit can estimate the user's emotions and adjust the timing of proposals based on those emotions. For example, if the user is relaxed, the emotion estimation unit can provide detailed proposals, allowing the user ample time to consider them. If the user is in a hurry, the unit can provide concise and to-the-point proposals, delivering information quickly. Furthermore, if the user is stressed, the unit can reduce the frequency of proposals, lessening the user's burden. This allows for proposals to be delivered at the optimal timing according to the user's emotions, thereby increasing user satisfaction.

[0104] The vacant house utilization proposal system can also be equipped with an emotion estimation unit. The emotion estimation unit can estimate the user's emotions and customize the proposal content based on the estimated emotions. For example, if the user is excited, the emotion estimation unit can suggest ways to utilize the vacant house for active activities. If the user is relaxed, the emotion estimation unit can suggest ways to utilize it in a quiet environment. Furthermore, if the user is depressed, the emotion estimation unit can suggest ways to utilize it as a healing space. This allows the system to propose the most suitable way to utilize the vacant house according to the user's emotions, thereby increasing user satisfaction.

[0105] The vacant house utilization proposal system can also be equipped with an emotion estimation unit. This unit can estimate the user's emotions and adjust the format of the proposals based on the estimated emotions. For example, if the user is feeling stressed, the emotion estimation unit can provide simple and highly visual proposals. If the user is relaxed, it can provide proposals containing detailed information. Furthermore, if the user is in a hurry, it can provide concise and to-the-point proposals. This allows the system to provide proposals in the most appropriate format for the user's emotions, thereby deepening the user's understanding.

[0106] The vacant house utilization proposal system can also be equipped with an emotion estimation unit. This unit can estimate the user's emotions and determine the priority of proposals based on those emotions. For example, if the user is feeling stressed, the emotion estimation unit can prioritize important proposals to reduce the user's burden. If the user is relaxed, the emotion estimation unit can prioritize detailed proposals to deepen the user's understanding. Furthermore, if the user is in a hurry, the emotion estimation unit can prioritize short proposals to provide information quickly. This allows for proposals to be prioritized according to the user's emotions, thereby increasing user satisfaction.

[0107] The following briefly describes the processing flow for example form 2.

[0108] Step 1: The data collection unit collects data about the area surrounding the vacant house. Specifically, the data collection unit collects data such as population dynamics, commercial facilities, and transportation access around the vacant house. For example, it collects population dynamics data such as age groups, number of households, and population changes; commercial facility data such as the types and number of nearby stores and their operating hours; and transportation access data such as the location of the nearest station or bus stop and its operating status. Step 2: The analysis unit analyzes the data collected by the collection unit and generates a regional analysis report. Specifically, the analysis unit analyzes the collected demographic data, commercial facility data, and transportation access data to understand the trends in each. Step 3: Based on the report generated by the analysis department, the proposal department proposes various uses other than rental. Specifically, the proposal department proposes concrete use ideas such as a cafe, co-working space, and short-term rental, and shows the operation methods and profitability of each. Step 4: The simulation department simulates the profitability of the proposed usage methods and assesses the risks. Specifically, the simulation department simulates the profitability of using the space as a cafe, co-working space, and short-term rental property, and evaluates initial investment, operating costs, and projected revenue.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] Each of the multiple elements described above, including the collection unit, analysis unit, proposal unit, and simulation unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects data around the vacant house using the camera 42 and communication I / F 44 of the smart device 14 and transmits it to the data processing unit 12 via the control unit 46A. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the collected data to generate a regional analysis report. The proposal unit is implemented in the specific processing unit 290 of the data processing unit 12 and proposes various utilization methods other than rental based on the analysis results. The simulation unit is implemented in the specific processing unit 290 of the data processing unit 12 and simulates the profitability of the proposed utilization methods and evaluates the risks. 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.

[0113] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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).

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.).

[0125] 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.

[0126] 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.

[0127] 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.

[0128] Each of the multiple elements described above, including the data collection unit, analysis unit, proposal unit, and simulation unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit collects data around the vacant house using the camera 42 and communication I / F 44 of the smart glasses 214 and transmits it to the data processing unit 12 via the control unit 46A. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and analyzes the collected data to generate a regional analysis report. The proposal unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and proposes various utilization methods other than rental based on the analysis results. The simulation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and simulates the profitability of the proposed utilization methods and evaluates the risks. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0129] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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).

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.).

[0141] 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.

[0142] 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.

[0143] 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.

[0144] Each of the multiple elements described above, including the data collection unit, analysis unit, proposal unit, and simulation unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the data collection unit collects data around the vacant house using the camera 42 and communication I / F 44 of the headset terminal 314 and transmits it to the data processing unit 12 via the control unit 46A. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the collected data to generate a regional analysis report. The proposal unit is implemented in the specific processing unit 290 of the data processing unit 12 and proposes various utilization methods other than rental based on the analysis results. The simulation unit is implemented in the specific processing unit 290 of the data processing unit 12 and simulates the profitability of the proposed utilization methods and evaluates the risks. 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.

[0145] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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).

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.).

[0158] 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.

[0159] 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.

[0160] 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.

[0161] Each of the multiple elements described above, including the collection unit, analysis unit, proposal unit, and simulation unit, is implemented, for example, by at least one of the robot 414 and the data processing unit 12. For example, the collection unit collects data around the vacant house using the camera 42 and communication I / F 44 of the robot 414 and transmits it to the data processing unit 12 by the control unit 46A. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and analyzes the collected data to generate a regional analysis report. The proposal unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and proposes various utilization methods other than rental based on the analysis results. The simulation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and simulates the profitability of the proposed utilization methods and evaluates the risks. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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."

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] (Note 1) A data collection unit that collects data on the area around vacant houses, An analysis unit analyzes the data collected by the aforementioned collection unit and generates a regional analysis report, Based on the report generated by the aforementioned analysis unit, the proposal unit proposes various uses other than rental, The system includes a simulation unit that simulates the profitability of the proposed utilization method and evaluates the risks. A system characterized by the following features. (Note 2) The aforementioned collection unit is We collect data on population trends, commercial facilities, and transportation access around vacant houses. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, The data collected by the aforementioned collection unit is analyzed, and a regional analysis report is generated. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned proposal section is, We propose specific utilization ideas such as cafes, co-working spaces, and short-term rentals. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned simulation unit, Simulate the profitability of the proposed usage methods and assess the risks. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection 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 past data collection history and select the optimal collection method. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is When collecting data, filter the data to be collected based on specific events or seasons. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is During data collection, the system prioritizes the collection of highly relevant data, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is During data collection, the system analyzes users' social media activity and collects relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, During analysis, adjust the level of detail based on the importance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the data category. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, During analysis, the priority of the analysis is determined based on when the data was collected. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, adjust the order of analysis based on the relevance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned proposal section is, It estimates the user's emotions and adjusts the way suggestions are presented 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, adjust the level of detail based on the importance of how the proposal will be used. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned proposal section is, When making a proposal, different proposal algorithms are applied depending on the category of the intended use. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned proposal section is, It estimates the user's emotions and adjusts the length of the suggestion based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned proposal section is, When submitting a proposal, the priority of the proposal will be determined based on the timing of the submission of the application method. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned proposal section is, When making proposals, adjust the order of proposals based on the relevance of their intended uses. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned simulation unit, It estimates the user's emotions and adjusts the simulation method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned simulation unit, During the simulation, the optimal simulation method is selected by referring to past simulation data. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned simulation unit, During the simulation, adjust the simulation parameters based on specific market trends and economic indicators. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned simulation unit, It estimates the user's emotions and determines the priority of simulations based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned simulation unit, During the simulation, the optimal simulation method is selected by considering the user's geographical location information. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned simulation unit, During the simulation, we analyze the user's social media activity and propose simulation methods. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0181] 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 data collection unit that collects data on the area around vacant houses, An analysis unit analyzes the data collected by the aforementioned collection unit and generates a regional analysis report, Based on the report generated by the aforementioned analysis unit, the proposal unit proposes various uses other than rental, The system includes a simulation unit that simulates the profitability of the proposed utilization method and evaluates the risks. A system characterized by the following features.

2. The aforementioned collection unit is We collect data on population trends, commercial facilities, and transportation access around vacant houses. The system according to feature 1.

3. The aforementioned analysis unit, The data collected by the aforementioned collection unit is analyzed, and a regional analysis report is generated. The system according to feature 1.

4. The aforementioned proposal section is, We propose specific utilization ideas such as cafes, co-working spaces, and short-term rentals. The system according to feature 1.

5. The aforementioned simulation unit, Simulate the profitability of the proposed usage methods and assess the risks. The system according to feature 1.

6. The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system according to feature 1.

7. The aforementioned collection unit is Analyze past data collection history and select the optimal collection method. The system according to feature 1.

8. The aforementioned collection unit is When collecting data, filter the data to be collected based on specific events or seasons. The system according to feature 1.

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