System

Abstract

To provide a system according to an embodiment that simply carries out the procedure for the reuse of a vacant house.SOLUTION: A system comprises a receiving unit, an analysis unit, a proposal unit, and a procedure unit. The receiving unit receives input of vacant house information. The analysis unit analyzes the information input to the receiving unit. The proposal unit carries out a proposal for reuse on the basis of an analysis result obtained by the analysis unit. The procedure unit carries out a procedure associated with the reuse proposed by the proposal unit.SELECTED DRAWING: Figure 1

Description

[Technical field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including a description and related instruction sentence regarding the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] With conventional technology, the reuse and procedures for vacant houses were complicated, making it difficult for owners to respond appropriately.

[0005] The system of the embodiment aims to simplify the reuse and procedures for vacant houses. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, a proposal unit, and a procedure unit. The reception unit inputs vacant house information. The analysis unit analyzes the information input by the reception unit. The proposal unit makes a proposal for reuse based on the analysis result obtained by the analysis unit. The procedure unit performs procedures associated with the reuse proposed by the proposal unit. Effect of the Invention

[0007] The system according to the embodiment makes it easy to reuse vacant houses and carry out the procedures for doing so. [Brief description of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Diagram 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0009] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

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

[0012] In the following embodiments, a signed RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by the processor.

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

[0014] In the following embodiments, a communication I / F (Interface) with a code is an interface including a communication processor and an antenna. The communication I / F controls communication between multiple computers. An example of a communication standard applied to the communication I / F is a wireless communication standard 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." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. In addition, in this specification, the same idea as "A and / or B" is also applied when three or more things are expressed by connecting them with "and / or."

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

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

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

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (e.g., a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., voice and / or text). Display 40A displays visible information such as text and images according to instructions from processor 46. Speaker 40B outputs voice according to instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a Complementary Metal-Oxide-Semiconductor (CMOS) image sensor or a Charge Coupled Device (CCD) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

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

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

[0028] (Example 1) The vacant house management system according to the embodiment of the present invention is a system that centrally manages the matching of reuse proposals, tax support information, and vacant house utilization applicants in response to the problem of an increase in vacant house owners. In this system, by inputting vacant house information, AI analyzes the value, regional characteristics, and demand and market trends of the vacant house, and provides reuse proposals and tax support information. For example, when information such as the location, age, and area of ​​the vacant house is input, AI performs analysis taking into account the demographics, economic situation, and neighboring facilities of the area. Based on the analysis results, reuse proposals and tax support information are provided. For example, proposals to renovate vacant houses and use them as rental properties, and information on tax preferential treatment when selling vacant houses are provided. In addition, an environment is provided in which the necessary procedures can be easily performed based on these proposals and information. For example, a function is provided to support collaboration with renovation companies and real estate companies and to centrally manage the progress of procedures. This is expected to solve the vacant house problem and contribute to the revitalization of the local community and the fulfillment of housing demand. This will enable the vacant house management system to centrally manage reuse proposals, tax support information, and matching with people wanting to use vacant houses, in response to the issue of an increasing number of vacant house owners.

[0029] The vacant house management system according to the embodiment includes a reception unit, an analysis unit, a proposal unit, and a procedure unit. The reception unit inputs vacant house information. The vacant house information includes, for example, location, age, area, owner information, etc., but is not limited to such examples. The reception unit, for example, saves the vacant house information input by the user in a database. The reception unit can also transmit the information input by the user to the analysis unit in real time. The analysis unit uses AI to analyze the information input by the reception unit. The analysis is performed based on, for example, information such as the location, age, and area of ​​the vacant house, but is not limited to such examples. The analysis unit performs the analysis taking into account, for example, the demographics and economic situation of the area, neighboring facilities, etc. The analysis unit uses AI to analyze the value of the vacant house, regional characteristics, and demand and market trends. The proposal unit makes a proposal for reuse based on the analysis results obtained by the analysis unit. The proposal provides, for example, a proposal to renovate the vacant house and use it as a rental property, and information on tax incentives when selling the vacant house, but is not limited to such examples. The proposal unit proposes an optimal reuse method using AI. The procedure unit performs procedures associated with the reuse proposed by the proposal unit. The procedures, for example, support collaboration with renovation companies and real estate agents, and provide a function for centrally managing the progress of the procedures, but are not limited to such examples. The procedure unit manages the progress of the procedures using AI. As a result, the vacant house management system according to the embodiment can centrally manage everything from inputting vacant house information to analysis, proposals, and procedures.

[0030] The reception unit inputs vacant house information. The vacant house information includes, for example, location, age, area, owner information, etc., but is not limited to such examples. The reception unit, for example, saves the vacant house information input by the user in a database. Specifically, the user inputs vacant house information through a dedicated web form or mobile app, and the information is automatically saved in the database. The input information is sent to the analysis unit in real time, so analysis begins immediately. The reception unit also has a field validation function to check the accuracy of the information input by the user. For example, the input field for the location has an automatic completion function for postal code and address, and the input field for the age and area is designed to accept only numerical values ​​within an appropriate range. In addition, the owner information is safely stored using encryption technology from the viewpoint of privacy protection. Furthermore, the reception unit also has a function to periodically update the information input by the user, so that, for example, when the owner changes or the state of the vacant house changes, the user can easily update the information. As a result, the reception unit always holds the latest vacant house information, and the analysis unit and proposal unit can perform analysis and proposals based on accurate data.

[0031] The analysis unit uses AI to analyze the information input by the reception unit. The analysis is performed based on information such as the location, age, and area of ​​the vacant house, but is not limited to such examples. Specifically, the AI ​​uses a machine learning algorithm to analyze the value of the vacant house, local characteristics, and demand and market trends. For example, if the vacant house is located in an urban area, the possibility of reusing the vacant house is evaluated taking into account the surrounding population density, transportation access, and the presence or absence of nearby commercial facilities. In addition, based on the age and area, the cost and period of renovation are predicted and a specific plan for reuse is drawn up. Furthermore, the analysis unit performs analysis taking into account the demographics, economic situation, and neighboring facilities of the area. For example, if the population of the area is on the rise, the demand for rental property in the area is likely to increase, so it is determined that a proposal to use the vacant house as a rental property is effective. In addition, if the economic situation is good, the selling price of the vacant house may be high, so the sale may be recommended. The analysis unit comprehensively analyzes this information and derives the optimal method of reusing the vacant house. In addition, the analysis unit can use past data and statistical information to perform long-term market trend and risk assessments. This allows the analysis unit to provide highly accurate analysis results regarding the reuse of vacant houses, providing a foundation for the proposal unit to make specific and feasible proposals.

[0032] The proposal unit makes a proposal for reuse based on the analysis results obtained by the analysis unit. The proposal may, for example, be a proposal to renovate a vacant house and use it as a rental property, or provide information on tax benefits when selling a vacant house, but is not limited to such examples. Specifically, the proposal unit uses AI to propose the optimal reuse method. For example, if the location of the vacant house is near a university or company, it may propose using it as a shared house for students or employees. Also, if it is near a tourist destination, it may propose using it as a private lodging facility. Furthermore, the proposal unit provides detailed explanations of the specific renovation plan, cost estimates, and necessary procedures. For example, it presents the details of the construction work required for renovation, the period, and a breakdown of the costs, so that the user can have a concrete image. In addition, when proposing a sale, it provides details of the current market price, the fees for sale, and tax benefits, and supports the user in making the best decision. The proposal unit can also make multiple proposals according to the user's needs and wishes. For example, it makes proposals for both renovation and sale, and compares the advantages and disadvantages of each, allowing the user to make the best choice. This enables the proposal unit to propose specific and feasible reuse methods to the user, thereby promoting the effective use of vacant houses.

[0033] The procedure unit performs procedures associated with the reuse proposed by the proposal unit. The procedures include, for example, supporting collaboration with renovation companies and real estate agents, and providing a function for centrally managing the progress of the procedures, but are not limited to such examples. Specifically, the procedure unit uses AI to manage the progress of the procedures. For example, it monitors in real time the contract procedures with the renovation company, the progress of the construction work, and the progress of the sales contract with the real estate agent, and notifies the user. The procedure unit also creates and submits necessary documents, and manages the schedule of various procedures. For example, it supports the creation of permission applications and contracts required for renovation, and manages the submission deadlines to ensure that the procedures proceed smoothly. Furthermore, the procedure unit provides appropriate advice and support for questions and problems that the user may have as the procedures proceed. For example, it provides expert opinions for problems that arise during the renovation process and legal questions regarding real estate transactions, allowing the user to proceed with the procedures with peace of mind. Through these functions, the procedure unit supports the user to smoothly proceed with the procedures to realize the proposed reuse method. This enables the procedure unit to play an important role in enabling users to smoothly proceed with the reuse of vacant houses, improving the efficiency and reliability of the entire system.

[0034] The analysis unit can analyze information on the location, age, and area of ​​the vacant house, and perform analysis based on the demographics and economic situation of the area, and the facilities in the vicinity. The analysis unit, for example, analyzes information on the location, age, and area of ​​the vacant house. For example, the address and geographic coordinates can be used to clarify the specific range and form of the location. In addition, the construction confirmation date and renovation date can be used to clarify the specific calculation method and standard of the age. Furthermore, the total floor area and the site area can be used to clarify the specific measurement method and standard of the area. The analysis unit performs analysis based on the demographics and economic situation of the area, and the facilities in the vicinity. For example, the age distribution and the number of households can be used to clarify the specific data and analysis method of the demographics. In addition, the average income and the unemployment rate can be used to clarify the specific indicators and data of the economic situation. Furthermore, schools, hospitals, commercial facilities, etc. can be used to clarify the specific type and range of the facilities in the vicinity. As a result, by performing an analysis taking into account the characteristics of the area, more appropriate reuse proposals can be made. A part or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input information such as the location, age, and area of ​​vacant houses into the AI, and perform analysis based on the local demographics, economic situation, and nearby facilities.

[0035] The proposal unit can make a proposal to renovate a vacant house and use it as a rental property. For example, the proposal unit makes a proposal to renovate a vacant house and use it as a rental property. In order to clarify the specific content and standards of the renovation, the scope of the renovation and the materials to be used can be considered. For example, the proposal unit makes a proposal to renovate the interior of the vacant house to increase its appeal as a rental property. The proposal unit can also propose exterior renovation and equipment updates. Furthermore, the proposal unit can consider the rent and contract period in order to clarify the conditions and standards of the rental property after renovation. For example, the proposal unit makes a proposal to set the rent of the rental property after renovation based on the market price. The proposal unit can also propose the period and conditions of the rental contract. This makes it possible to propose the use of the property as a rental property through renovation. Part or all of the above-mentioned processing in the proposal unit may be performed, for example, using AI, or may be performed without using AI. For example, the proposal unit can input a renovation plan for the vacant house into AI and make a proposal to use it as a rental property.

[0036] The proposal unit can provide information on tax preferential treatment when selling a vacant house. The proposal unit, for example, provides information on tax preferential treatment when selling a vacant house. In order to clarify the specific contents and application conditions of the tax preferential treatment, the amount of tax reduction and the application period can be considered. For example, the proposal unit presents the amount of tax reduction applied when selling a vacant house. The proposal unit can also explain the application period and conditions of the tax preferential treatment. Furthermore, the proposal unit can provide the procedure method and necessary documents for receiving the tax preferential treatment. For example, the proposal unit guides the application documents and procedural steps for receiving the tax preferential treatment. As a result, the provision of information on the tax preferential treatment promotes the sale of the vacant house. Part or all of the above-mentioned processing in the proposal unit may be performed, for example, using AI, or may be performed without using AI. For example, the proposal unit can input the selling plan of the vacant house into AI and provide information on the tax preferential treatment.

[0037] The procedure section can provide a function to support collaboration with renovation companies and real estate agents and manage the progress of the procedure. The procedure section supports collaboration with renovation companies and real estate agents, for example. In order to clarify the specific method and criteria of collaboration, the type of collaborating company and the procedure of collaboration can be considered. For example, the procedure section supports collaboration with renovation companies and manages the progress of the renovation plan. The procedure section can also support collaboration with real estate agents and manage the progress of the sale procedure of the vacant house. Furthermore, the procedure section provides a function to centrally manage the progress of the procedure. In order to clarify the specific management method and criteria of the progress, the frequency of progress reports and management tools can be considered. For example, the procedure section monitors the progress of the procedure in real time and reports the progress. The procedure section can also visualize the progress of the procedure and provide it to the user. As a result, the procedure progresses smoothly by centrally managing the progress of the procedure. A part or all of the above-mentioned processing in the procedure section may be performed, for example, using AI, or may be performed without using AI. For example, the procedures department can input collaboration information with renovation contractors and real estate agents into the AI ​​and manage the progress of procedures.

[0038] The reception unit can analyze the user's past vacant house information input history and select an appropriate input method. The reception unit, for example, analyzes the user's past vacant house information input history. In order to clarify the specific content of the input history and the analysis method, past input data and analysis algorithms can be taken into consideration. For example, the reception unit automatically displays information that the user frequently inputs in the past as candidates. The reception unit can also preferentially suggest input methods (such as voice and text) that the user has used in the past. Furthermore, the reception unit can predict and suggest an input method to be used in a specific time period from the user's past input history. In this way, the most suitable input method can be provided to the user by analyzing the past input history. Part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input the user's past input data into AI and select the most suitable input method.

[0039] The reception unit can perform filtering based on the user's current living situation and field of interest when inputting vacant house information. For example, the reception unit performs filtering based on the user's current living situation and field of interest when inputting vacant house information. In order to clarify the specific content of the living situation and the filtering method, the family structure and income situation can be taken into consideration. For example, if the user lives with his / her family, the reception unit can preferentially display vacant house information for families. In addition, if the user is busy with work, the reception unit can also preferentially display vacant house information with easy procedures. Furthermore, if the user enjoys gardening as a hobby, the reception unit can also preferentially display vacant house information with a garden. In order to clarify the specific content of the field of interest and the filtering method, hobbies and topics of interest can be taken into consideration. As a result, more appropriate vacant house information can be input by providing information according to the user's living situation and field of interest. Part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input data on the user's living situation and field of interest into AI and perform filtering.

[0040] The reception unit can preferentially input information with high relevance based on the geographical location information of the user when inputting vacant house information. For example, when inputting vacant house information, the reception unit preferentially inputs information with high relevance in consideration of the geographical location information of the user. In order to clarify a specific method of acquiring and using the geographical location information, GPS data and location information services can be considered. For example, the reception unit preferentially inputs vacant house information close to the user's current location. In addition, the reception unit can also preferentially input vacant house information in an area that the user frequently visits. Furthermore, the reception unit can also preferentially input vacant house information in an area in which the user is interested. In order to clarify a specific criterion or selection method for information with high relevance, a relevance evaluation criterion or a selection algorithm can be considered. As a result, by considering the geographical location information, information with high relevance for the user can be preferentially input. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input the geographical location information of the user to AI and preferentially input information with high relevance.

[0041] The reception unit can analyze the social media activity of the user and input related information when inputting vacant house information. The reception unit, for example, analyzes the social media activity of the user when inputting vacant house information. In order to clarify the specific content and analysis method of the social media activity, the post content and the number of followers can be considered. For example, the reception unit preferentially inputs vacant house information of a place shared by the user on social media. In addition, the reception unit can also preferentially input vacant house information of an area followed by the user on social media. Furthermore, the reception unit can also preferentially input vacant house information related to a topic in which the user showed interest on social media. In order to clarify the specific criteria and selection method of the related information, the relevance evaluation criteria and selection algorithm can be considered. As a result, by analyzing the social media activity, it is possible to provide information related to the user. Part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input the social media data of the user to AI and input related information.

[0042] The analysis unit can set the level of detail of the analysis based on the importance of the vacant house at the time of analysis. For example, the analysis unit adjusts the level of detail of the analysis based on the importance of the vacant house at the time of analysis. In order to clarify the specific evaluation criteria and method of the importance of the vacant house, the location conditions and the condition of the building can be considered. For example, the analysis unit provides detailed analysis results for important vacant houses. In addition, the analysis unit can provide concise analysis results for vacant houses that are not so important. Furthermore, the analysis unit can gradually adjust the level of detail of the analysis according to the importance of the vacant house. In order to clarify the specific method and criteria for setting the level of detail, the depth of the analysis and the evaluation criteria of the level of detail can be considered. As a result, by adjusting the level of detail of the analysis according to the importance of the vacant house, appropriate analysis results can be provided. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the importance data of the vacant house into AI and adjust the level of detail of the analysis.

[0043] The analysis unit can apply different analysis algorithms depending on the category of the vacant house during the analysis. For example, the analysis unit applies different analysis algorithms depending on the category of the vacant house during the analysis. In order to clarify the specific classification criteria and method of the vacant house category, a house, a commercial facility, a public facility, etc. can be considered. For example, the analysis unit applies an analysis algorithm that takes into account the trend of the housing market for a residential vacant house. In addition, the analysis unit can apply an analysis algorithm that takes into account the demand of commercial facilities for a commercial vacant house. Furthermore, the analysis unit can apply an analysis algorithm that takes into account the trend of the agricultural market for a farmland vacant house. In order to clarify the specific type and implementation method of the analysis algorithm, a machine learning algorithm or a statistical analysis can be considered. As a result, by applying an analysis algorithm according to the category of the vacant house, a more accurate analysis result can be provided. A part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input vacant house category data into AI and apply different analysis algorithms.

[0044] The analysis unit can set the priority of the analysis based on the input time of the vacant house at the time of analysis. For example, the analysis unit determines the priority of the analysis based on the input time of the vacant house at the time of analysis. In order to clarify the specific evaluation criteria and method of the input time, the evaluation criteria of the timing and the time of input can be considered. For example, the analysis unit preferentially analyzes the vacant house information that has been input recently. In addition, the analysis unit can also preferentially analyze the vacant house information that has been left for a long time. Furthermore, the analysis unit can gradually adjust the priority of the analysis according to the input time of the vacant house. In order to clarify the specific method and criteria for setting the priority, the evaluation criteria and setting process of the priority can be considered. As a result, by determining the priority of the analysis based on the input time of the vacant house, efficient analysis is possible. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the input time data of the vacant house into AI and determine the priority of the analysis.

[0045] The analysis unit can adjust the order of analysis based on the relevance of vacant houses during analysis. The analysis unit, for example, adjusts the order of analysis based on the relevance of vacant houses during analysis. In order to clarify specific evaluation criteria and methods of relevance, evaluation criteria and evaluation algorithms of relevance can be considered. For example, the analysis unit preferentially analyzes important vacant house information. The analysis unit can also preferentially analyze highly relevant vacant house information. Furthermore, the analysis unit can adjust the order of analysis stepwise according to the relevance of vacant houses. In order to clarify specific methods and criteria for adjusting the order, the order of analysis and adjustment criteria can be considered. As a result, important information can be preferentially analyzed by adjusting the order of analysis based on the relevance of vacant houses. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input relevance data of vacant houses into AI and adjust the order of analysis.

[0046] The suggestion unit can set the level of detail of the proposal based on the importance of the vacant house at the time of proposal. For example, the suggestion unit adjusts the level of detail of the proposal based on the importance of the vacant house at the time of proposal. In order to clarify the specific evaluation criteria and method of the importance of the vacant house, the location conditions and the condition of the building can be considered. For example, the suggestion unit provides a detailed proposal for an important vacant house. In addition, the suggestion unit can provide a concise proposal for a vacant house that is not so important. Furthermore, the suggestion unit can gradually adjust the level of detail of the proposal according to the importance of the vacant house. In order to clarify the specific method and criteria for setting the level of detail, the depth of the proposal and the evaluation criteria of the level of detail can be considered. As a result, an appropriate proposal can be provided by adjusting the level of detail of the proposal according to the importance of the vacant house. Part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI or may be performed without using AI. For example, the suggestion unit can input the importance data of the vacant house into AI and adjust the level of detail of the proposal.

[0047] The proposal unit can apply different proposal algorithms depending on the category of the vacant house at the time of proposal. For example, the proposal unit applies different proposal algorithms depending on the category of the vacant house at the time of proposal. In order to clarify the specific classification criteria and method of the category of the vacant house, a house, a commercial facility, a public facility, etc. can be considered. For example, the proposal unit applies a proposal algorithm that takes into account the trend of the housing market for a residential vacant house. In addition, the proposal unit can also apply a proposal algorithm that takes into account the demand of commercial facilities for a commercial vacant house. Furthermore, the proposal unit can apply a proposal algorithm that takes into account the trend of the agricultural market for a farmland vacant house. In order to clarify the specific type and implementation method of the proposal algorithm, a machine learning algorithm or a statistical analysis can be considered. As a result, by applying a proposal algorithm according to the category of the vacant house, a more accurate proposal can be provided. A part or all of the above-mentioned processing in the proposal unit may be performed, for example, using AI, or may be performed without using AI. For example, the proposal unit can input vacant house category data into AI and apply different proposal algorithms.

[0048] The proposal unit can set the priority of the proposal based on the input time of the vacant house at the time of proposal. For example, the proposal unit determines the priority of the proposal based on the input time of the vacant house at the time of proposal. In order to clarify the specific evaluation criteria and method of the input time, the evaluation criteria of the timing and the time of input can be considered. For example, the proposal unit preferentially proposes the vacant house information that has been input recently. In addition, the proposal unit can also preferentially propose the vacant house information that has been left for a long time. Furthermore, the proposal unit can adjust the priority of the proposal step by step according to the input time of the vacant house. In order to clarify the specific method and criteria for setting the priority, the evaluation criteria and setting process of the priority can be considered. As a result, efficient proposals can be made by determining the priority of the proposal based on the input time of the vacant house. Part or all of the above-mentioned processing in the proposal unit may be performed, for example, using AI, or may be performed without using AI. For example, the proposal unit can input the input time data of the vacant house into AI and determine the priority of the proposal.

[0049] The proposal unit can adjust the order of proposals based on the relevance of vacant houses at the time of proposal. For example, the proposal unit adjusts the order of proposals based on the relevance of vacant houses at the time of proposal. In order to clarify specific evaluation criteria and methods of relevance, evaluation criteria and evaluation algorithms of relevance can be considered. For example, the proposal unit preferentially proposes important vacant house information. The proposal unit can also preferentially propose highly relevant vacant house information. Furthermore, the proposal unit can adjust the order of proposals stepwise according to the relevance of vacant houses. In order to clarify specific methods and criteria for adjusting the order, the order of proposals and adjustment criteria can be considered. As a result, important information can be preferentially proposed by adjusting the order of proposals based on the relevance of vacant houses. Part or all of the above-mentioned processing in the proposal unit may be performed, for example, using AI, or may be performed without using AI. For example, the proposal unit can input relevance data of vacant houses into AI and adjust the order of proposals.

[0050] The procedure unit can select an appropriate procedure method by analyzing the user's past procedure history at the time of the procedure. The procedure unit, for example, analyzes the user's past procedure history at the time of the procedure. In order to clarify the specific content of the procedure history and the analysis method, past procedure data and analysis algorithms can be taken into consideration. For example, the procedure unit preferentially proposes a procedure method used by the user in the past. In addition, the procedure unit can predict and propose a procedure method to be used in a specific time period from the user's past procedure history. Furthermore, the procedure unit can analyze the user's past procedure history and propose the most efficient procedure method. In this way, the optimal procedure method can be provided to the user by analyzing the past procedure history. Part or all of the above-mentioned processing in the procedure unit may be performed, for example, using AI or may be performed without using AI. For example, the procedure unit can input the user's past procedure data into AI and select an appropriate procedure method.

[0051] The procedure unit can customize the procedure means based on the user's current living situation at the time of the procedure. For example, the procedure unit customizes the procedure means based on the user's current living situation at the time of the procedure. In order to clarify the specific content of the living situation and the customization method, the family structure and income situation can be taken into consideration. For example, if the user is busy with work, the procedure unit can propose a procedure method that can be completed online. In addition, if the user is elderly, the procedure unit can also propose face-to-face procedure support. Furthermore, if the user lives with his / her family, the procedure unit can also propose a method that allows the whole family to complete the procedure. This allows more appropriate procedures to be performed by providing a procedure means according to the user's living situation. Part or all of the above-mentioned processing in the procedure unit may be performed, for example, using AI or may be performed without using AI. For example, the procedure unit can input the user's living situation data into AI and customize the procedure means.

[0052] The procedure unit can select an appropriate procedure method based on the geographical location information of the user at the time of the procedure. For example, the procedure unit selects an appropriate procedure method by considering the geographical location information of the user at the time of the procedure. In order to clarify the specific acquisition method and use method of the geographical location information, GPS data and location information services can be considered. For example, the procedure unit preferentially suggests a procedure location close to the user's current location. In addition, the procedure unit can also preferentially suggest a procedure location in an area that the user frequently visits. Furthermore, the procedure unit can also preferentially suggest a procedure location in an area in which the user is interested. In this way, by considering the geographical location information, it is possible to provide the optimal procedure method for the user. Part or all of the above-mentioned processing in the procedure unit may be performed, for example, using AI, or may be performed without using AI. For example, the procedure unit can input the user's geographical location information into AI and select an appropriate procedure method.

[0053] The procedure unit can analyze the social media activity of the user at the time of the procedure and suggest a procedure means. The procedure unit, for example, analyzes the social media activity of the user at the time of the procedure. In order to clarify the specific content and analysis method of the social media activity, the post content and the number of followers can be taken into consideration. For example, the procedure unit preferentially suggests a procedure means for a location shared by the user on social media. The procedure unit can also preferentially suggest a procedure means for an area that the user follows on social media. Furthermore, the procedure unit can also preferentially suggest a procedure means related to a topic in which the user showed interest on social media. In this way, by analyzing the social media activity, a procedure means related to the user can be provided. Part or all of the above-mentioned processing in the procedure unit may be performed, for example, using AI, or may be performed without using AI. For example, the procedure unit can input the user's social media data into AI and suggest a procedure means.

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

[0055] The reception unit can provide an automatic input completion function based on the user's past input data. For example, it can automatically complete vacant house information previously input by the user, reducing the effort required for input. The reception unit can also learn the user's input patterns and present predicted candidates the next time the user inputs information. Furthermore, the reception unit can automatically input part of the input based on information previously input by the user. This makes the user's input work more efficient and helps prevent input errors.

[0056] The analysis unit can analyze the energy efficiency of vacant houses and make proposals for improving energy efficiency. For example, the analysis unit can analyze the insulation performance and energy consumption of vacant houses and propose adding insulation materials or installing energy-saving equipment. The analysis unit can also propose installing a solar power generation system or an energy management system. Furthermore, the analysis unit can present the cost reduction effects of improving energy efficiency. This can be expected to improve the energy efficiency of vacant houses, reduce environmental impact and cut costs.

[0057] The proposal department can make proposals to utilize the vacant house as a community space. For example, the proposal department can make proposals to utilize the vacant house as a local meeting place or event space. The proposal department can also make proposals to utilize the vacant house as a play area or study space for local children. Furthermore, the proposal department can also make proposals to utilize the vacant house as a place for local elderly people to interact. In this way, the vacant house contributes to the revitalization of the local community and is utilized as a place for local residents to interact.

[0058] The Proposal Department can make a proposal to use the vacant house as an art gallery. For example, the Proposal Department can make a proposal to use the vacant house as an exhibition space for local artists. The Proposal Department can also make a proposal to use the vacant house as a venue for art events and workshops. Furthermore, the Proposal Department can make a proposal to use the vacant house as a base for art projects. In this way, the vacant house becomes a base for local cultural activities and contributes to the promotion of local culture.

[0059] The procedure section can provide a dashboard that visualizes the user's procedure progress. For example, the procedure section can display the status of the procedure currently in progress by the user in real time. The procedure section can also display the progress of each step of the procedure in graphs and charts. Furthermore, the procedure section can display the planned completion date of the procedure and the deadline for the next step. This allows the user to grasp the progress of the procedure at a glance and prevent delays in the procedure.

[0060] The process flow of the first embodiment will be briefly described below.

[0061] Step 1: The reception unit inputs vacant house information. The vacant house information includes, for example, the location, the age of the building, the area, and the owner information. The reception unit can also store the vacant house information input by the user in a database and transmit it to the analysis unit in real time. Step 2: The analysis unit uses AI to analyze the information entered by the reception unit. The analysis is based on information such as the location, age, and area of ​​the vacant house, and takes into account the local demographics, economic situation, and nearby facilities. The analysis unit analyzes the value of the vacant house, local characteristics, and demand and market trends. Step 3: The proposal unit makes a proposal for reuse based on the analysis results obtained by the analysis unit. For example, the proposal may suggest renovating the vacant house and using it as a rental property, or provide information on tax incentives when selling the vacant house. The proposal unit uses AI to propose the optimal reuse method. Step 4: The Procedures Department carries out the procedures associated with the reuse proposed by the Proposal Department. For example, the Procedures Department supports collaboration with renovation companies and real estate agents, and provides a function to centrally manage the progress of the procedures. The Procedures Department uses AI to manage the progress of the procedures.

[0062] (Example 2) The vacant house management system according to the embodiment of the present invention is a system that centrally manages the matching of reuse proposals, tax support information, and vacant house utilization applicants in response to the problem of an increase in vacant house owners. In this system, by inputting vacant house information, AI analyzes the value, regional characteristics, and demand and market trends of the vacant house, and provides reuse proposals and tax support information. For example, when information such as the location, age, and area of ​​the vacant house is input, AI performs analysis taking into account the demographics, economic situation, and neighboring facilities of the area. Based on the analysis results, reuse proposals and tax support information are provided. For example, proposals to renovate vacant houses and use them as rental properties, and information on tax preferential treatment when selling vacant houses are provided. In addition, an environment is provided in which the necessary procedures can be easily performed based on these proposals and information. For example, a function is provided to support collaboration with renovation companies and real estate companies and to centrally manage the progress of procedures. This is expected to solve the vacant house problem and contribute to the revitalization of the local community and the fulfillment of housing demand. This will enable the vacant house management system to centrally manage reuse proposals, tax support information, and matching with people wanting to use vacant houses, in response to the issue of an increasing number of vacant house owners.

[0063] The vacant house management system according to the embodiment includes a reception unit, an analysis unit, a proposal unit, and a procedure unit. The reception unit inputs vacant house information. The vacant house information includes, for example, location, age, area, owner information, etc., but is not limited to such examples. The reception unit, for example, saves the vacant house information input by the user in a database. The reception unit can also transmit the information input by the user to the analysis unit in real time. The analysis unit uses AI to analyze the information input by the reception unit. The analysis is performed based on, for example, information such as the location, age, and area of ​​the vacant house, but is not limited to such examples. The analysis unit performs the analysis taking into account, for example, the demographics and economic situation of the area, neighboring facilities, etc. The analysis unit uses AI to analyze the value of the vacant house, regional characteristics, and demand and market trends. The proposal unit makes a proposal for reuse based on the analysis results obtained by the analysis unit. The proposal provides, for example, a proposal to renovate the vacant house and use it as a rental property, and information on tax incentives when selling the vacant house, but is not limited to such examples. The proposal unit proposes an optimal reuse method using AI. The procedure unit performs procedures associated with the reuse proposed by the proposal unit. The procedures, for example, support collaboration with renovation companies and real estate agents, and provide a function for centrally managing the progress of the procedures, but are not limited to such examples. The procedure unit manages the progress of the procedures using AI. As a result, the vacant house management system according to the embodiment can centrally manage everything from inputting vacant house information to analysis, proposals, and procedures.

[0064] The reception unit inputs vacant house information. The vacant house information includes, for example, location, age, area, owner information, etc., but is not limited to such examples. The reception unit, for example, saves the vacant house information input by the user in a database. Specifically, the user inputs vacant house information through a dedicated web form or mobile app, and the information is automatically saved in the database. The input information is sent to the analysis unit in real time, so analysis begins immediately. The reception unit also has a field validation function to check the accuracy of the information input by the user. For example, the input field for the location has an automatic completion function for postal code and address, and the input field for the age and area is designed to accept only numerical values ​​within an appropriate range. In addition, the owner information is safely stored using encryption technology from the viewpoint of privacy protection. Furthermore, the reception unit also has a function to periodically update the information input by the user, so that, for example, when the owner changes or the state of the vacant house changes, the user can easily update the information. As a result, the reception unit always holds the latest vacant house information, and the analysis unit and proposal unit can perform analysis and proposals based on accurate data.

[0065] The analysis unit uses AI to analyze the information input by the reception unit. The analysis is performed based on information such as the location, age, and area of ​​the vacant house, but is not limited to such examples. Specifically, the AI ​​uses a machine learning algorithm to analyze the value of the vacant house, local characteristics, and demand and market trends. For example, if the vacant house is located in an urban area, the possibility of reusing the vacant house is evaluated taking into account the surrounding population density, transportation access, and the presence or absence of nearby commercial facilities. In addition, based on the age and area, the cost and period of renovation are predicted and a specific plan for reuse is drawn up. Furthermore, the analysis unit performs analysis taking into account the demographics, economic situation, and neighboring facilities of the area. For example, if the population of the area is on the rise, the demand for rental property in the area is likely to increase, so it is determined that a proposal to use the vacant house as a rental property is effective. In addition, if the economic situation is good, the selling price of the vacant house may be high, so the sale may be recommended. The analysis unit comprehensively analyzes this information and derives the optimal method of reusing the vacant house. In addition, the analysis unit can use past data and statistical information to perform long-term market trend and risk assessments. This allows the analysis unit to provide highly accurate analysis results regarding the reuse of vacant houses, providing a foundation for the proposal unit to make specific and feasible proposals.

[0066] The proposal unit makes a proposal for reuse based on the analysis results obtained by the analysis unit. The proposal may, for example, be a proposal to renovate a vacant house and use it as a rental property, or provide information on tax benefits when selling a vacant house, but is not limited to such examples. Specifically, the proposal unit uses AI to propose the optimal reuse method. For example, if the location of the vacant house is near a university or company, it may propose using it as a shared house for students or employees. Also, if it is near a tourist destination, it may propose using it as a private lodging facility. Furthermore, the proposal unit provides detailed explanations of the specific renovation plan, cost estimates, and necessary procedures. For example, it presents the details of the construction work required for renovation, the period, and a breakdown of the costs, so that the user can have a concrete image. In addition, when proposing a sale, it provides details of the current market price, the fees for sale, and tax benefits, and supports the user in making the best decision. The proposal unit can also make multiple proposals according to the user's needs and wishes. For example, it makes proposals for both renovation and sale, and compares the advantages and disadvantages of each, allowing the user to make the best choice. This enables the proposal unit to propose specific and feasible reuse methods to the user, thereby promoting the effective use of vacant houses.

[0067] The procedure unit performs procedures associated with the reuse proposed by the proposal unit. The procedures include, for example, supporting collaboration with renovation companies and real estate agents, and providing a function for centrally managing the progress of the procedures, but are not limited to such examples. Specifically, the procedure unit uses AI to manage the progress of the procedures. For example, it monitors in real time the contract procedures with the renovation company, the progress of the construction work, and the progress of the sales contract with the real estate agent, and notifies the user. The procedure unit also creates and submits necessary documents, and manages the schedule of various procedures. For example, it supports the creation of permission applications and contracts required for renovation, and manages the submission deadlines to ensure that the procedures proceed smoothly. Furthermore, the procedure unit provides appropriate advice and support for questions and problems that the user may have as the procedures proceed. For example, it provides expert opinions for problems that arise during the renovation process and legal questions regarding real estate transactions, allowing the user to proceed with the procedures with peace of mind. Through these functions, the procedure unit supports the user to smoothly proceed with the procedures to realize the proposed reuse method. This enables the procedure unit to play an important role in enabling users to smoothly proceed with the reuse of vacant houses, improving the efficiency and reliability of the entire system.

[0068] The analysis unit can analyze information on the location, age, and area of ​​the vacant house, and perform analysis based on the demographics and economic situation of the area, and the facilities in the vicinity. The analysis unit, for example, analyzes information on the location, age, and area of ​​the vacant house. For example, the address and geographic coordinates can be used to clarify the specific range and form of the location. In addition, the construction confirmation date and renovation date can be used to clarify the specific calculation method and standard of the age. Furthermore, the total floor area and the site area can be used to clarify the specific measurement method and standard of the area. The analysis unit performs analysis based on the demographics and economic situation of the area, and the facilities in the vicinity. For example, the age distribution and the number of households can be used to clarify the specific data and analysis method of the demographics. In addition, the average income and the unemployment rate can be used to clarify the specific indicators and data of the economic situation. Furthermore, schools, hospitals, commercial facilities, etc. can be used to clarify the specific type and range of the facilities in the vicinity. As a result, by performing an analysis taking into account the characteristics of the area, more appropriate reuse proposals can be made. A part or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input information such as the location, age, and area of ​​vacant houses into the AI, and perform analysis based on the local demographics, economic situation, and nearby facilities.

[0069] The proposal unit can make a proposal to renovate a vacant house and use it as a rental property. For example, the proposal unit makes a proposal to renovate a vacant house and use it as a rental property. In order to clarify the specific content and standards of the renovation, the scope of the renovation and the materials to be used can be considered. For example, the proposal unit makes a proposal to renovate the interior of the vacant house to increase its appeal as a rental property. The proposal unit can also propose exterior renovation and equipment updates. Furthermore, the proposal unit can consider the rent and contract period in order to clarify the conditions and standards of the rental property after renovation. For example, the proposal unit makes a proposal to set the rent of the rental property after renovation based on the market price. The proposal unit can also propose the period and conditions of the rental contract. This makes it possible to propose the use of the property as a rental property through renovation. Part or all of the above-mentioned processing in the proposal unit may be performed, for example, using AI, or may be performed without using AI. For example, the proposal unit can input a renovation plan for the vacant house into AI and make a proposal to use it as a rental property.

[0070] The proposal unit can provide information on tax preferential treatment when selling a vacant house. The proposal unit, for example, provides information on tax preferential treatment when selling a vacant house. In order to clarify the specific contents and application conditions of the tax preferential treatment, the amount of tax reduction and the application period can be considered. For example, the proposal unit presents the amount of tax reduction applied when selling a vacant house. The proposal unit can also explain the application period and conditions of the tax preferential treatment. Furthermore, the proposal unit can provide the procedure method and necessary documents for receiving the tax preferential treatment. For example, the proposal unit guides the application documents and procedural steps for receiving the tax preferential treatment. As a result, the provision of information on the tax preferential treatment promotes the sale of the vacant house. Part or all of the above-mentioned processing in the proposal unit may be performed, for example, using AI, or may be performed without using AI. For example, the proposal unit can input the selling plan of the vacant house into AI and provide information on the tax preferential treatment.

[0071] The procedure section can provide a function to support collaboration with renovation companies and real estate agents and manage the progress of the procedure. The procedure section supports collaboration with renovation companies and real estate agents, for example. In order to clarify the specific method and criteria of collaboration, the type of collaborating company and the procedure of collaboration can be considered. For example, the procedure section supports collaboration with renovation companies and manages the progress of the renovation plan. The procedure section can also support collaboration with real estate agents and manage the progress of the sale procedure of the vacant house. Furthermore, the procedure section provides a function to centrally manage the progress of the procedure. In order to clarify the specific management method and criteria of the progress, the frequency of progress reports and management tools can be considered. For example, the procedure section monitors the progress of the procedure in real time and reports the progress. The procedure section can also visualize the progress of the procedure and provide it to the user. As a result, the procedure progresses smoothly by centrally managing the progress of the procedure. A part or all of the above-mentioned processing in the procedure section may be performed, for example, using AI, or may be performed without using AI. For example, the procedures department can input collaboration information with renovation contractors and real estate agents into the AI ​​and manage the progress of procedures.

[0072] The reception unit can estimate the user's emotions and adjust the input timing of the vacant house information based on the estimated user's emotions. The reception unit, for example, estimates the user's emotions. In order to clarify a specific method and criteria for estimating emotions, the algorithm to be used and the type of emotion can be considered. For example, the reception unit analyzes the user's facial expression and voice to estimate the emotion. The reception unit can also collect the user's biometric data (heart rate and skin electrodermal activity) with a sensor and estimate the emotion. Furthermore, the reception unit adjusts the input timing of the vacant house information based on the estimated user's emotions. In order to clarify a specific method and criteria for adjusting the input timing, a determination criterion for the frequency and timing of input can be considered. For example, when the user is feeling stressed, the reception unit simplifies the input procedure so that the input can be completed in a short time. In addition, when the user is relaxed, the reception unit can provide detailed input options so that the user can input information until he or she is satisfied. Furthermore, when the user is in a hurry, the reception unit can prioritize voice input and enable the user to input vacant house information quickly. In this way, the input timing is adjusted according to the user's emotions, thereby reducing the burden on the user. The estimation of emotions is realized, for example, by using an emotion estimation function using an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the reception unit may be performed, for example, using an AI or may be performed without using an AI. For example, the reception unit may input the user's facial expression data to the generation AI and cause the generation AI to perform emotion estimation.

[0073] The reception unit can analyze the user's past vacant house information input history and select an appropriate input method. The reception unit, for example, analyzes the user's past vacant house information input history. In order to clarify the specific content of the input history and the analysis method, past input data and analysis algorithms can be taken into consideration. For example, the reception unit automatically displays information that the user frequently inputs in the past as candidates. The reception unit can also preferentially suggest input methods (such as voice and text) that the user has used in the past. Furthermore, the reception unit can predict and suggest an input method to be used in a specific time period from the user's past input history. In this way, the most suitable input method can be provided to the user by analyzing the past input history. Part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input the user's past input data into AI and select the most suitable input method.

[0074] The reception unit can perform filtering based on the user's current living situation and field of interest when inputting vacant house information. For example, the reception unit performs filtering based on the user's current living situation and field of interest when inputting vacant house information. In order to clarify the specific content of the living situation and the filtering method, the family structure and income situation can be taken into consideration. For example, if the user lives with his / her family, the reception unit can preferentially display vacant house information for families. In addition, if the user is busy with work, the reception unit can also preferentially display vacant house information with easy procedures. Furthermore, if the user enjoys gardening as a hobby, the reception unit can also preferentially display vacant house information with a garden. In order to clarify the specific content of the field of interest and the filtering method, hobbies and topics of interest can be taken into consideration. As a result, more appropriate vacant house information can be input by providing information according to the user's living situation and field of interest. Part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input data on the user's living situation and field of interest into AI and perform filtering.

[0075] The reception unit can estimate the user's emotion and determine the priority of the vacant house information to be input based on the estimated user's emotion. The reception unit, for example, estimates the user's emotion. In order to clarify the specific method and criteria for estimating the emotion, the algorithm to be used and the type of emotion can be considered. For example, the reception unit analyzes the user's facial expression and voice to estimate the emotion. The reception unit can also collect the user's biometric data (heart rate and skin electrodermal activity) with a sensor and estimate the emotion. Furthermore, the reception unit determines the priority of the vacant house information to be input based on the estimated user's emotion. In order to clarify the specific method and criteria for determining the priority, the evaluation criteria and the determination process of the priority can be considered. For example, the reception unit allows the user to input important information with priority when the user is feeling stressed. The reception unit can also allow the user to input detailed information when the user is relaxed. Furthermore, the reception unit can also allow the user to input the minimum amount of information with priority when the user is in a hurry. This enables efficient information input by determining the priority of the input information according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit may input the user's facial expression data to the generation AI and cause the generation AI to estimate emotions.

[0076] The reception unit can preferentially input information with high relevance based on the geographical location information of the user when inputting vacant house information. For example, when inputting vacant house information, the reception unit preferentially inputs information with high relevance in consideration of the geographical location information of the user. In order to clarify a specific method of acquiring and using the geographical location information, GPS data and location information services can be considered. For example, the reception unit preferentially inputs vacant house information close to the user's current location. In addition, the reception unit can also preferentially input vacant house information in an area that the user frequently visits. Furthermore, the reception unit can also preferentially input vacant house information in an area in which the user is interested. In order to clarify a specific criterion or selection method for information with high relevance, a relevance evaluation criterion or a selection algorithm can be considered. As a result, by considering the geographical location information, information with high relevance for the user can be preferentially input. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input the geographical location information of the user to AI and preferentially input information with high relevance.

[0077] The reception unit can analyze the social media activity of the user and input related information when inputting vacant house information. The reception unit, for example, analyzes the social media activity of the user when inputting vacant house information. In order to clarify the specific content and analysis method of the social media activity, the post content and the number of followers can be considered. For example, the reception unit preferentially inputs vacant house information of a place shared by the user on social media. In addition, the reception unit can also preferentially input vacant house information of an area followed by the user on social media. Furthermore, the reception unit can also preferentially input vacant house information related to a topic in which the user showed interest on social media. In order to clarify the specific criteria and selection method of the related information, the relevance evaluation criteria and selection algorithm can be considered. As a result, by analyzing the social media activity, it is possible to provide information related to the user. Part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input the social media data of the user to AI and input related information.

[0078] The analysis unit can estimate the user's emotion and adjust the expression method of the analysis based on the estimated user's emotion. The analysis unit, for example, estimates the user's emotion. In order to clarify the specific method and criteria for estimating the emotion, the algorithm to be used and the type of emotion can be considered. For example, the analysis unit analyzes the user's facial expression and voice to estimate the emotion. The analysis unit can also collect the user's biometric data (heart rate and skin electrodermal activity) with a sensor and estimate the emotion. Furthermore, the analysis unit adjusts the expression method of the analysis based on the estimated user's emotion. In order to clarify the specific method and criteria for adjusting the expression method, the expression style and the adjustment criteria can be considered. For example, the analysis unit provides a detailed analysis result when the user is relaxed. In addition, the analysis unit can provide a concise analysis result that covers the main points when the user is in a hurry. Furthermore, the analysis unit can provide a visually easy-to-understand analysis result when the user is feeling stressed. In this way, by adjusting the expression method of the analysis according to the user's emotion, it is possible to provide an analysis result that is easy for the user to understand. The estimation of emotions is realized, for example, by using an emotion estimation function using an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using an AI or may be performed without using an AI. For example, the analysis unit may input the user's facial expression data to the generation AI and cause the generation AI to estimate emotions.

[0079] The analysis unit can set the level of detail of the analysis based on the importance of the vacant house at the time of analysis. For example, the analysis unit adjusts the level of detail of the analysis based on the importance of the vacant house at the time of analysis. In order to clarify the specific evaluation criteria and method of the importance of the vacant house, the location conditions and the condition of the building can be considered. For example, the analysis unit provides detailed analysis results for important vacant houses. In addition, the analysis unit can provide concise analysis results for vacant houses that are not so important. Furthermore, the analysis unit can gradually adjust the level of detail of the analysis according to the importance of the vacant house. In order to clarify the specific method and criteria for setting the level of detail, the depth of the analysis and the evaluation criteria of the level of detail can be considered. As a result, by adjusting the level of detail of the analysis according to the importance of the vacant house, appropriate analysis results can be provided. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the importance data of the vacant house into AI and adjust the level of detail of the analysis.

[0080] The analysis unit can apply different analysis algorithms depending on the category of the vacant house during the analysis. For example, the analysis unit applies different analysis algorithms depending on the category of the vacant house during the analysis. In order to clarify the specific classification criteria and method of the vacant house category, a house, a commercial facility, a public facility, etc. can be considered. For example, the analysis unit applies an analysis algorithm that takes into account the trend of the housing market for a residential vacant house. In addition, the analysis unit can apply an analysis algorithm that takes into account the demand of commercial facilities for a commercial vacant house. Furthermore, the analysis unit can apply an analysis algorithm that takes into account the trend of the agricultural market for a farmland vacant house. In order to clarify the specific type and implementation method of the analysis algorithm, a machine learning algorithm or a statistical analysis can be considered. As a result, by applying an analysis algorithm according to the category of the vacant house, a more accurate analysis result can be provided. A part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input vacant house category data into AI and apply different analysis algorithms.

[0081] The analysis unit can estimate the user's emotion and adjust the length of the analysis based on the estimated user's emotion. The analysis unit, for example, estimates the user's emotion. In order to clarify the specific method and criteria for estimating the emotion, the algorithm to be used and the type of emotion can be considered. For example, the analysis unit analyzes the user's facial expression and voice to estimate the emotion. The analysis unit can also collect the user's biometric data (heart rate and skin electrodermal activity) with a sensor and estimate the emotion. Furthermore, the analysis unit adjusts the length of the analysis based on the estimated user's emotion. In order to clarify the specific method and criteria for adjusting the length, the level of detail of the analysis and the criteria for adjustment can be considered. For example, the analysis unit provides a short and to the point analysis result when the user is in a hurry. The analysis unit can also provide a detailed analysis result when the user is relaxed. Furthermore, the analysis unit can provide a visually easy-to-understand analysis result when the user is feeling stressed. As a result, by adjusting the length of the analysis according to the user's emotion, it is possible to provide an appropriate analysis result for the user. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit may input the user's facial expression data to the generation AI and cause the generation AI to estimate emotions.

[0082] The analysis unit can set the priority of the analysis based on the input time of the vacant house at the time of analysis. For example, the analysis unit determines the priority of the analysis based on the input time of the vacant house at the time of analysis. In order to clarify the specific evaluation criteria and method of the input time, the evaluation criteria of the timing and the time of input can be considered. For example, the analysis unit preferentially analyzes the vacant house information that has been input recently. In addition, the analysis unit can also preferentially analyze the vacant house information that has been left for a long time. Furthermore, the analysis unit can gradually adjust the priority of the analysis according to the input time of the vacant house. In order to clarify the specific method and criteria for setting the priority, the evaluation criteria and setting process of the priority can be considered. As a result, by determining the priority of the analysis based on the input time of the vacant house, efficient analysis is possible. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the input time data of the vacant house into AI and determine the priority of the analysis.

[0083] The analysis unit can adjust the order of analysis based on the relevance of vacant houses during analysis. The analysis unit, for example, adjusts the order of analysis based on the relevance of vacant houses during analysis. In order to clarify specific evaluation criteria and methods of relevance, evaluation criteria and evaluation algorithms of relevance can be considered. For example, the analysis unit preferentially analyzes important vacant house information. The analysis unit can also preferentially analyze highly relevant vacant house information. Furthermore, the analysis unit can adjust the order of analysis stepwise according to the relevance of vacant houses. In order to clarify specific methods and criteria for adjusting the order, the order of analysis and adjustment criteria can be considered. As a result, important information can be preferentially analyzed by adjusting the order of analysis based on the relevance of vacant houses. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input relevance data of vacant houses into AI and adjust the order of analysis.

[0084] The suggestion unit can estimate the user's emotion and adjust the expression method of the proposal based on the estimated user's emotion. The suggestion unit, for example, estimates the user's emotion. In order to clarify the specific method and criteria for estimating the emotion, the algorithm to be used and the type of emotion can be considered. For example, the suggestion unit analyzes the user's facial expression and voice to estimate the emotion. The suggestion unit can also collect the user's biometric data (heart rate and skin electrodermal activity) with a sensor and estimate the emotion. Furthermore, the suggestion unit adjusts the expression method of the proposal based on the estimated user's emotion. In order to clarify the specific method and criteria for adjusting the expression method, the expression style and the adjustment criteria can be considered. For example, the suggestion unit provides a detailed proposal when the user is relaxed. In addition, the suggestion unit can provide a concise proposal that covers the main points when the user is in a hurry. Furthermore, the suggestion unit can provide a visually easy-to-understand proposal when the user is feeling stressed. As a result, it is possible to provide a proposal that is easy for the user to understand by adjusting the expression method of the proposal according to the user's emotion. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit may input the user's facial expression data to the generation AI and cause the generation AI to estimate emotions.

[0085] The suggestion unit can set the level of detail of the proposal based on the importance of the vacant house at the time of proposal. For example, the suggestion unit adjusts the level of detail of the proposal based on the importance of the vacant house at the time of proposal. In order to clarify the specific evaluation criteria and method of the importance of the vacant house, the location conditions and the condition of the building can be considered. For example, the suggestion unit provides a detailed proposal for an important vacant house. In addition, the suggestion unit can provide a concise proposal for a vacant house that is not so important. Furthermore, the suggestion unit can gradually adjust the level of detail of the proposal according to the importance of the vacant house. In order to clarify the specific method and criteria for setting the level of detail, the depth of the proposal and the evaluation criteria of the level of detail can be considered. As a result, an appropriate proposal can be provided by adjusting the level of detail of the proposal according to the importance of the vacant house. Part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI or may be performed without using AI. For example, the suggestion unit can input the importance data of the vacant house into AI and adjust the level of detail of the proposal.

[0086] The proposal unit can apply different proposal algorithms depending on the category of the vacant house at the time of proposal. For example, the proposal unit applies different proposal algorithms depending on the category of the vacant house at the time of proposal. In order to clarify the specific classification criteria and method of the category of the vacant house, a house, a commercial facility, a public facility, etc. can be considered. For example, the proposal unit applies a proposal algorithm that takes into account the trend of the housing market for a residential vacant house. In addition, the proposal unit can also apply a proposal algorithm that takes into account the demand of commercial facilities for a commercial vacant house. Furthermore, the proposal unit can apply a proposal algorithm that takes into account the trend of the agricultural market for a farmland vacant house. In order to clarify the specific type and implementation method of the proposal algorithm, a machine learning algorithm or a statistical analysis can be considered. As a result, by applying a proposal algorithm according to the category of the vacant house, a more accurate proposal can be provided. A part or all of the above-mentioned processing in the proposal unit may be performed, for example, using AI, or may be performed without using AI. For example, the proposal unit can input vacant house category data into AI and apply different proposal algorithms.

[0087] The suggestion unit can estimate the user's emotion and adjust the length of the proposal based on the estimated user's emotion. The suggestion unit, for example, estimates the user's emotion. In order to clarify a specific method and criteria for estimating the emotion, the algorithm to be used and the type of emotion can be considered. For example, the suggestion unit analyzes the user's facial expression and voice to estimate the emotion. The suggestion unit can also collect the user's biometric data (heart rate and skin electrodermal activity) with a sensor and estimate the emotion. Furthermore, the suggestion unit adjusts the length of the proposal based on the estimated user's emotion. In order to clarify a specific method and criteria for adjusting the length, the level of detail of the proposal and the adjustment criteria can be considered. For example, the suggestion unit provides a short and to the point proposal when the user is in a hurry. The suggestion unit can also provide a detailed proposal when the user is relaxed. Furthermore, the suggestion unit can provide a visually easy-to-understand proposal when the user is feeling stressed. In this way, the length of the proposal can be adjusted according to the user's emotion, thereby providing a proposal that is appropriate for the user. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit may input the user's facial expression data to the generation AI and cause the generation AI to estimate emotions.

[0088] The proposal unit can set the priority of the proposal based on the input time of the vacant house at the time of proposal. For example, the proposal unit determines the priority of the proposal based on the input time of the vacant house at the time of proposal. In order to clarify the specific evaluation criteria and method of the input time, the evaluation criteria of the timing and the time of input can be considered. For example, the proposal unit preferentially proposes the vacant house information that has been input recently. In addition, the proposal unit can also preferentially propose the vacant house information that has been left for a long time. Furthermore, the proposal unit can adjust the priority of the proposal step by step according to the input time of the vacant house. In order to clarify the specific method and criteria for setting the priority, the evaluation criteria and setting process of the priority can be considered. As a result, efficient proposals can be made by determining the priority of the proposal based on the input time of the vacant house. Part or all of the above-mentioned processing in the proposal unit may be performed, for example, using AI, or may be performed without using AI. For example, the proposal unit can input the input time data of the vacant house into AI and determine the priority of the proposal.

[0089] The proposal unit can adjust the order of proposals based on the relevance of vacant houses at the time of proposal. For example, the proposal unit adjusts the order of proposals based on the relevance of vacant houses at the time of proposal. In order to clarify specific evaluation criteria and methods of relevance, evaluation criteria and evaluation algorithms of relevance can be considered. For example, the proposal unit preferentially proposes important vacant house information. The proposal unit can also preferentially propose highly relevant vacant house information. Furthermore, the proposal unit can adjust the order of proposals stepwise according to the relevance of vacant houses. In order to clarify specific methods and criteria for adjusting the order, the order of proposals and adjustment criteria can be considered. As a result, important information can be preferentially proposed by adjusting the order of proposals based on the relevance of vacant houses. Part or all of the above-mentioned processing in the proposal unit may be performed, for example, using AI, or may be performed without using AI. For example, the proposal unit can input relevance data of vacant houses into AI and adjust the order of proposals.

[0090] The procedure unit can estimate the user's emotion and adjust the procedure method based on the estimated user's emotion. The procedure unit, for example, estimates the user's emotion. In order to clarify the specific method and criteria for estimating the emotion, the algorithm to be used and the type of emotion can be considered. For example, the procedure unit analyzes the user's facial expression and voice to estimate the emotion. The procedure unit can also collect the user's biometric data (heart rate and skin electrodermal activity) with a sensor and estimate the emotion. Furthermore, the procedure unit adjusts the procedure method based on the estimated user's emotion. In order to clarify the specific method and criteria for adjusting the procedure method, the procedure unit can consider the procedure steps and adjustment criteria. For example, the procedure unit provides a detailed procedure method when the user is relaxed. Also, the procedure unit can provide a concise procedure method when the user is in a hurry. Furthermore, the procedure unit can provide a visually easy-to-understand procedure method when the user is feeling stressed. In this way, the procedure method can be adjusted according to the user's emotion, thereby providing an appropriate procedure for the user. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the procedure unit may be performed, for example, using AI, or may be performed without using AI. For example, the procedure unit may input the user's facial expression data to the generation AI and cause the generation AI to estimate emotions.

[0091] The procedure unit can select an appropriate procedure method by analyzing the user's past procedure history at the time of the procedure. The procedure unit, for example, analyzes the user's past procedure history at the time of the procedure. In order to clarify the specific content of the procedure history and the analysis method, past procedure data and analysis algorithms can be taken into consideration. For example, the procedure unit preferentially proposes a procedure method used by the user in the past. In addition, the procedure unit can predict and propose a procedure method to be used in a specific time period from the user's past procedure history. Furthermore, the procedure unit can analyze the user's past procedure history and propose the most efficient procedure method. In this way, the optimal procedure method can be provided to the user by analyzing the past procedure history. Part or all of the above-mentioned processing in the procedure unit may be performed, for example, using AI or may be performed without using AI. For example, the procedure unit can input the user's past procedure data into AI and select an appropriate procedure method.

[0092] The procedure unit can customize the procedure means based on the user's current living situation at the time of the procedure. For example, the procedure unit customizes the procedure means based on the user's current living situation at the time of the procedure. In order to clarify the specific content of the living situation and the customization method, the family structure and income situation can be taken into consideration. For example, if the user is busy with work, the procedure unit can propose a procedure method that can be completed online. In addition, if the user is elderly, the procedure unit can also propose face-to-face procedure support. Furthermore, if the user lives with his / her family, the procedure unit can also propose a method that allows the whole family to complete the procedure. This allows more appropriate procedures to be performed by providing a procedure means according to the user's living situation. Part or all of the above-mentioned processing in the procedure unit may be performed, for example, using AI or may be performed without using AI. For example, the procedure unit can input the user's living situation data into AI and customize the procedure means.

[0093] The procedure unit can estimate the user's emotions and determine the priority of procedures based on the estimated user's emotions. The procedure unit, for example, estimates the user's emotions. In order to clarify the specific method and criteria for estimating emotions, the algorithm to be used and the type of emotions can be considered. For example, the procedure unit analyzes the user's facial expressions and voice to estimate emotions. The procedure unit can also collect the user's biometric data (heart rate and skin electrodermal activity) with a sensor and estimate emotions. Furthermore, the procedure unit determines the priority of procedures based on the estimated user's emotions. In order to clarify the specific method and criteria for determining the priority, the procedure unit can consider the evaluation criteria and decision process of the priority. For example, the procedure unit prioritizes important procedures when the user is in a hurry. Also, the procedure unit can prioritize detailed procedures when the user is relaxed. Furthermore, the procedure unit can prioritize simple procedures when the user is feeling stressed. This enables efficient procedures by determining the priority of procedures according to the user's emotions. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the procedure unit may be performed, for example, using AI, or may be performed without using AI. For example, the procedure unit may input the user's facial expression data to the generation AI and cause the generation AI to estimate emotions.

[0094] The procedure unit can select an appropriate procedure method based on the geographical location information of the user at the time of the procedure. For example, the procedure unit selects an appropriate procedure method by considering the geographical location information of the user at the time of the procedure. In order to clarify the specific acquisition method and use method of the geographical location information, GPS data and location information services can be considered. For example, the procedure unit preferentially suggests a procedure location close to the user's current location. In addition, the procedure unit can also preferentially suggest a procedure location in an area that the user frequently visits. Furthermore, the procedure unit can also preferentially suggest a procedure location in an area in which the user is interested. In this way, by considering the geographical location information, it is possible to provide the optimal procedure method for the user. Part or all of the above-mentioned processing in the procedure unit may be performed, for example, using AI, or may be performed without using AI. For example, the procedure unit can input the user's geographical location information into AI and select an appropriate procedure method.

[0095] The procedure unit can analyze the social media activity of the user at the time of the procedure and suggest a procedure means. The procedure unit, for example, analyzes the social media activity of the user at the time of the procedure. In order to clarify the specific content and analysis method of the social media activity, the post content and the number of followers can be taken into consideration. For example, the procedure unit preferentially suggests a procedure means for a location shared by the user on social media. The procedure unit can also preferentially suggest a procedure means for an area that the user follows on social media. Furthermore, the procedure unit can also preferentially suggest a procedure means related to a topic in which the user showed interest on social media. In this way, by analyzing the social media activity, a procedure means related to the user can be provided. Part or all of the above-mentioned processing in the procedure unit may be performed, for example, using AI, or may be performed without using AI. For example, the procedure unit can input the user's social media data into AI and suggest a procedure means.

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

[0097] The reception unit can provide an automatic input completion function based on the user's past input data. For example, it can automatically complete vacant house information previously input by the user, reducing the effort required for input. The reception unit can also learn the user's input patterns and present predicted candidates the next time the user inputs information. Furthermore, the reception unit can automatically input part of the input based on information previously input by the user. This makes the user's input work more efficient and helps prevent input errors.

[0098] The analysis unit can analyze the energy efficiency of vacant houses and make proposals for improving energy efficiency. For example, the analysis unit can analyze the insulation performance and energy consumption of vacant houses and propose adding insulation materials or installing energy-saving equipment. The analysis unit can also propose installing a solar power generation system or an energy management system. Furthermore, the analysis unit can present the cost reduction effects of improving energy efficiency. This can be expected to improve the energy efficiency of vacant houses, reduce environmental impact and cut costs.

[0099] The proposal department can make proposals to utilize the vacant house as a community space. For example, the proposal department can make proposals to utilize the vacant house as a local meeting place or event space. The proposal department can also make proposals to utilize the vacant house as a play area or study space for local children. Furthermore, the proposal department can also make proposals to utilize the vacant house as a place for local elderly people to interact. In this way, the vacant house contributes to the revitalization of the local community and is utilized as a place for local residents to interact.

[0100] The Proposal Department can make a proposal to use the vacant house as an art gallery. For example, the Proposal Department can make a proposal to use the vacant house as an exhibition space for local artists. The Proposal Department can also make a proposal to use the vacant house as a venue for art events and workshops. Furthermore, the Proposal Department can make a proposal to use the vacant house as a base for art projects. In this way, the vacant house becomes a base for local cultural activities and contributes to the promotion of local culture.

[0101] The procedure section can provide a dashboard that visualizes the user's procedure progress. For example, the procedure section can display the status of the procedure currently in progress by the user in real time. The procedure section can also display the progress of each step of the procedure in graphs and charts. Furthermore, the procedure section can display the planned completion date of the procedure and the deadline for the next step. This allows the user to grasp the progress of the procedure at a glance and prevent delays in the procedure.

[0102] The reception unit can estimate the user's emotion and customize the input interface based on the estimated user's emotion. For example, if the user is feeling stressed, a simple and intuitive interface can be provided. Also, if the user is relaxed, detailed input options can be provided. Furthermore, if the user is in a hurry, voice input and auto-completion functions can be provided with priority. In this way, an optimal input interface according to the user's emotion is provided, reducing the burden on the user.

[0103] The analysis unit can estimate the user's emotions and adjust the way in which the analysis results are presented based on the estimated user's emotions. For example, if the user is feeling stressed, the analysis results can be presented using visually easy-to-understand graphs and charts. If the user is relaxed, a detailed text report can be provided. Furthermore, if the user is in a hurry, a concise analysis result that summarizes the main points can be provided. This provides an optimal way to present the analysis results according to the user's emotions, facilitating the user's understanding.

[0104] The suggestion unit can estimate the user's emotions and adjust the suggested content based on the estimated user's emotions. For example, when the user is stressed, a simple and easy-to-implement suggestion can be provided. When the user is relaxed, detailed suggested content can be provided. Furthermore, when the user is in a hurry, a suggestion that can be implemented in a short time can be provided. In this way, optimal suggested content according to the user's emotions can be provided, improving the user's satisfaction.

[0105] The procedure unit can estimate the user's emotions and adjust the method of supporting the procedure based on the estimated user's emotions. For example, if the user is feeling stressed, the procedure steps can be simplified and support can be strengthened. Also, if the user is relaxed, a detailed procedure guide can be provided. Furthermore, if the user is in a hurry, support can be provided to allow the user to complete the procedure quickly. In this way, optimal procedure support according to the user's emotions is provided, helping the procedure proceed smoothly.

[0106] The procedure unit can estimate the user's emotions and adjust the method of notifying the progress of the procedure based on the estimated user's emotions. For example, if the user is stressed, the progress can be notified briefly and the required action can be clarified. If the user is relaxed, detailed progress can be notified. Furthermore, if the user is in a hurry, only important progress can be notified. In this way, the optimal notification method according to the user's emotions can be provided to effectively communicate the progress of the procedure.

[0107] The process flow of the second embodiment will be briefly described below.

[0108] Step 1: The reception unit inputs vacant house information. The vacant house information includes, for example, the location, the age of the building, the area, and the owner information. The reception unit can also store the vacant house information input by the user in a database and transmit it to the analysis unit in real time. Step 2: The analysis unit uses AI to analyze the information entered by the reception unit. The analysis is based on information such as the location, age, and area of ​​the vacant house, and takes into account the local demographics, economic situation, and nearby facilities. The analysis unit analyzes the value of the vacant house, local characteristics, and demand and market trends. Step 3: The proposal unit makes a proposal for reuse based on the analysis results obtained by the analysis unit. For example, the proposal may suggest renovating the vacant house and using it as a rental property, or provide information on tax incentives when selling the vacant house. The proposal unit uses AI to propose the optimal reuse method. Step 4: The Procedures Department carries out the procedures associated with the reuse proposed by the Proposal Department. For example, the Procedures Department supports collaboration with renovation companies and real estate agents, and provides a function to centrally manage the progress of the procedures. The Procedures Department uses AI to manage the progress of the procedures.

[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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0110] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0111] Furthermore, the above-described processing by the data processing system 10 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0112] For example, the reception unit can input vacant house information using the reception device 38 of the smart device 14. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12, and uses AI to analyze the value of vacant houses, regional characteristics, and demand and market trends. For example, the proposal unit is realized by the specific processing unit 290 of the data processing device 12, and provides reuse proposals and tax support information based on the analysis results. For example, the procedure unit is realized by the control unit 46A of the smart device 14, and provides a function to support collaboration with renovation companies and real estate agents and to centrally manage the progress of procedures. The correspondence between each unit and the device or control unit is not limited to the above example, and various changes are possible.

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

[0114] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0115] 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0116] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0117] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0118] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0119] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0120] Fig. 4 shows an example of main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0121] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0122] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0123] In the smart glasses 214, the 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 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0124] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0126] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may be executed 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 required for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0128] For example, the reception unit can input vacant house information using the microphone 238 of the smart glasses 214. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12, and uses AI to analyze the value of vacant houses, regional characteristics, and demand and market trends. For example, the proposal unit is realized by the specific processing unit 290 of the data processing device 12, and provides reuse proposals and tax support information based on the analysis results. For example, the procedure unit is realized by the control unit 46A of the smart glasses 214, and provides a function to support collaboration with renovation companies and real estate agents and to centrally manage the progress of procedures. The correspondence between each unit and the device or control unit is not limited to the above example, and various changes are possible.

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

[0130] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0131] 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0132] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0133] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0134] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0135] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0136] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0137] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0138] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0139] In the headset type terminal 314, the 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 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0140] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0141] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0142] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0144] For example, the reception unit can input vacant house information using the microphone 238 of the headset terminal 314. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12, and uses AI to analyze the value of vacant houses, regional characteristics, and demand and market trends. For example, the proposal unit is realized by the specific processing unit 290 of the data processing device 12, and provides reuse proposals and tax support information based on the analysis results. For example, the procedure unit is realized by the control unit 46A of the headset terminal 314, and provides a function to support collaboration with renovation companies and real estate agents and to centrally manage the progress of procedures. The correspondence between each unit and the device or control unit is not limited to the above example, and various changes are possible.

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

[0146] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0147] 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0148] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0149] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0150] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0151] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0152] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0153] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0154] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0155] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0156] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0157] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[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 control target 443 to output the result of the specific processing. The microphone 238 acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0159] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may be executed 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 required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0161] For example, the reception unit can input vacant house information using the microphone 238 of the robot 414. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12, and uses AI to analyze the value of vacant houses, regional characteristics, and demand and market trends. For example, the proposal unit is realized by the specific processing unit 290 of the data processing device 12, and provides reuse proposals and tax support information based on the analysis results. For example, the procedure unit is realized by the control unit 46A of the robot 414, and provides a function to support collaboration with renovation companies and real estate agents and to centrally manage the progress of procedures. The correspondence between each unit and the device or control unit is not limited to the above example, and various changes are possible.

[0162] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0163] FIG. 9 is a diagram showing an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0164] These emotions are distributed in the 3 o'clock direction of emotion map 400 and usually fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0165] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out you go on emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0166] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0167] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this 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 positive emotion around "desire" on the response side. In other words, this 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 the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in Fig. 10. Fig. 10 shows an example in which multiple emotions, "relief," "calm," and "encouraging," have similar emotion values.

[0169] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers, including computer 22.

[0170] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable computer-readable non-transitory storage medium such as a Universal Serial Bus (USB) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0171] In addition, 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 upon request from the data processing device 12.

[0172] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0173] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0174] The hardware resource that executes the specific process may be one of these various processors, or may be a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0175] As an example of a configuration using one processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a configuration using a processor that realizes the functions of the entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0176] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0177] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0178] The above description and illustrations are detailed descriptions of the parts related to the technology of the present disclosure, and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, function, action, and effect is an example of the configuration, function, action, and effect of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0179] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0180] (Appendix 1) A reception section for inputting vacant house information; an analysis unit that analyzes the information input by the reception unit; a proposal unit that proposes reuse based on the analysis result obtained by the analysis unit; A procedure unit that performs a procedure associated with the reuse proposed by the proposal unit. A system characterized by: (Appendix 2) The analysis unit is Analyze information on the location, age, and area of ​​vacant houses, and perform analysis based on local demographics, economic conditions, and nearby facilities. 2. The system of claim 1, (Appendix 3) The suggestion unit, Proposal to renovate vacant houses and use them as rental properties 2. The system of claim 1, (Appendix 4) The suggestion unit, Providing information on tax benefits when selling vacant homes 2. The system of claim 1, (Appendix 5) The procedure division comprises: Supports collaboration with renovation contractors and real estate agents and provides the ability to manage the progress of procedures 2. The system of claim 1, (Appendix 6) The reception unit is To estimate a user's emotions and adjust the timing of inputting vacant house information based on the estimated user's emotions 2. The system of claim 1, (Appendix 7) The reception unit is Analyze the user's past vacant house information input history and select the appropriate input method 2. The system of claim 1, (Appendix 8) The reception unit is Filtering available property information based on the user's current living situation and interests 2. The system of claim 1, (Appendix 9) The reception unit is The system estimates the user's emotions and determines the priority of vacant house information to be input based on the estimated user emotions. 2. The system of claim 1, (Appendix 10) The reception unit is When entering vacant house information, the system prioritizes information that is more relevant based on the user's geographic location. 2. The system of claim 1, (Appendix 11) The reception unit is When entering vacant property information, analyze the user's social media activity and enter relevant information 2. The system of claim 1, (Appendix 12) The analysis unit is Infer user emotions and adjust the presentation of analytics based on the inferred user emotions 2. The system of claim 1, (Appendix 13) The analysis unit is During analysis, set the level of detail for the analysis based on the importance of vacant homes 2. The system of claim 1, (Appendix 14) The analysis unit is During analysis, apply different analysis algorithms depending on the vacant property category 2. The system of claim 1, (Appendix 15) The analysis unit is Infer user sentiment and adjust the length of the analysis based on the inferred user sentiment 2. The system of claim 1, (Appendix 16) The analysis unit is During analysis, prioritize analysis based on when vacancies were entered 2. The system of claim 1, (Appendix 17) The analysis unit is Adjust the analysis order based on vacant relevance at solve time 2. The system of claim 1, (Appendix 18) The suggestion unit, Infer user emotions and adjust the presentation of suggestions based on the inferred user emotions 2. The system of claim 1, (Appendix 19) The suggestion unit, When making a proposal, set the level of detail for the proposal based on the importance of the vacant house 2. The system of claim 1, (Appendix 20) The suggestion unit, When making suggestions, different suggestion algorithms are applied depending on the category of vacant property. 2. The system of claim 1, (Appendix 21) The suggestion unit, Infer user sentiment and adjust the length of suggestions based on the estimated user sentiment 2. The system of claim 1, (Appendix 22) The suggestion unit, When making a proposal, prioritize the proposal based on when the vacant property was entered 2. The system of claim 1, (Appendix 23) The suggestion unit, When making suggestions, adjust the order of suggestions based on vacant property relevance 2. The system of claim 1, (Appendix 24) The procedure division comprises: Inferring user emotions and adjusting procedural methods based on the estimated user emotions 2. The system of claim 1, (Appendix 25) The procedure division comprises: When processing a transaction, the system analyzes the user's past transaction history and selects the appropriate transaction method. 2. The system of claim 1, (Appendix 26) The procedure division comprises: At checkout, customize the checkout experience based on the user's current life situation 2. The system of claim 1, (Appendix 27) The procedure division comprises: Estimate user's emotions and prioritize procedures based on the estimated user's emotions 2. The system of claim 1, (Appendix 28) The procedure division comprises: At the time of transaction, the appropriate transaction method is selected based on the user's geographic location. 2. The system of claim 1, (Appendix 29) The procedure division comprises: At the time of checkout, the system analyzes the user's social media activity to suggest checkout options 2. The system of claim 1, [Explanation of symbols]

[0181] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A reception section for inputting vacant house information; an analysis unit that analyzes the information input by the reception unit; a proposal unit that proposes reuse based on the analysis result obtained by the analysis unit; A procedure unit that performs a procedure associated with the reuse proposed by the proposal unit. A system characterized by:

2. The analysis unit is Analyze information on the location, age, and area of ​​vacant houses, and perform analysis based on local demographics, economic conditions, and nearby facilities.

2. The system of claim 1 .

3. The suggestion unit, Propose to renovate vacant houses and use them as rental properties 2. The system of claim 1 .

4. The suggestion unit, Providing information on tax benefits when selling vacant homes 2. The system of claim 1 .

5. The procedure division comprises: Supports collaboration with renovation contractors and real estate agents and provides the ability to manage the progress of procedures 2. The system of claim 1 .

6. The reception unit is To estimate a user's emotions and adjust the timing of inputting vacant house information based on the estimated user's emotions 2. The system of claim 1 .

7. The reception unit is Analyze the user's past vacant house information input history and select the appropriate input method 2. The system of claim 1 .

8. The reception unit is Filtering available property information based on the user's current living situation and interests 2. The system of claim 1 .

9. The reception unit is The system estimates the user's emotions and determines the priority of vacant house information to be input based on the estimated user emotions.

2. The system of claim 1 .

10. The reception unit is When entering vacant house information, the system prioritizes information that is more relevant based on the user's geographic location.

2. The system of claim 1 .

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