system

Abstract

The system according to this embodiment aims to efficiently support the management and sale of inherited real estate properties. [Solution] The system according to the embodiment comprises a reception unit, an evaluation unit, a proposal unit, and a support unit. The reception unit receives information on real estate properties from users. The evaluation unit analyzes the information received by the reception unit and evaluates the condition of the property. The proposal unit proposes the optimal management and sales plan based on the information evaluated by the evaluation unit. The support unit assists with necessary legal procedures and document preparation based on the plan proposed by the proposal unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that procedures are complicated and time-consuming in the management and sale of inherited real estate properties.

[0005] The system according to the embodiment aims to efficiently support the management and sale of inherited real estate properties.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, an evaluation unit, a proposal unit, and a support unit. The reception unit receives information on real estate properties from users. The evaluation unit analyzes the information received by the reception unit and evaluates the condition of the property. The proposal unit proposes the optimal management and sales plan based on the information evaluated by the evaluation unit. The support unit assists with necessary legal procedures and document preparation based on the plan proposed by the proposal unit. [Effects of the Invention]

[0007] The system according to this embodiment can efficiently support the management and sale of inherited real estate properties. [Brief explanation of the drawing]

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

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

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

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

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

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

[0014] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F controls communication between a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The real estate management support system according to an embodiment of the present invention is an autonomous AI agent that supports the management and sale of real estate properties inherited as an inheritance. This real estate management support system is an AI agent that allows users to entrust complex management tasks and procedures to the AI ​​and receive suggestions for the optimal timing and method of sale. For example, the real estate management support system receives information about the real estate property inherited by the user. For example, if the property is located in Tokyo, has an area of ​​100 square meters, and is 20 years old, this information is entered. This information is entered into the real estate management support system. Next, the real estate management support system analyzes the entered information and evaluates the condition of the property. The real estate management support system evaluates the value of the property based on past transaction data and market data. For example, it calculates the current value of the property by considering past transaction prices in the same area and current market trends. This allows for an accurate evaluation of the property's condition. Furthermore, the real estate management support system proposes an optimal management and sales plan based on the evaluation results. For example, if the value of the property is trending upward, it suggests holding it for a certain period before selling it. Also, if the value of the property is trending downward, it suggests selling it early. This allows the user to know the optimal timing and method of sale. Furthermore, the real estate management support system automatically assists with necessary legal procedures and document creation. For example, it can automate complex procedures such as inheritance registration and the creation of sales contracts. This allows users to complete necessary procedures without hassle. As a result, users can entrust complex management tasks and procedures to AI and receive suggestions for the optimal timing and method of sale. For example, by properly managing inherited real estate properties and selling them at the optimal time, users can make effective use of their assets. In addition, the real estate management support system reduces the burden on users by automatically assisting with legal procedures and document creation. As a result, the real estate management support system can efficiently support the management and sale of real estate properties inherited as part of an estate.

[0029] The real estate management support system according to this embodiment comprises a reception unit, an evaluation unit, a proposal unit, and a support unit. The reception unit receives information about real estate properties from users. The information about real estate properties received from users includes, but is not limited to, the property's location, area, age, and surrounding infrastructure. The reception unit provides, for example, an interface for inputting the property's location. The reception unit may also provide a field for inputting the property's area. Furthermore, the reception unit may provide an option for inputting the property's age. For example, the reception unit provides an interface for selecting the property's location on a map. The property's area can be entered in square meters. The property's age can be selected in calendar format. The evaluation unit analyzes the information received by the reception unit and evaluates the property's condition. The evaluation unit evaluates the property's value based on, for example, past transaction data and market data. The evaluation unit calculates the property's current value by, for example, obtaining past transaction prices from a database. The evaluation unit can also analyze market trends and predict the property's future value. For example, the valuation department calculates the average transaction price in the same area based on past transaction data. Market trends are analyzed considering the current balance of supply and demand. The future value of the property is predicted based on past data and current market trends. The proposal department proposes the optimal management and sales plan based on the information evaluated by the valuation department. For example, if the value of the property is on an upward trend, the proposal department may propose holding it for a certain period before selling it. For example, if the value of the property is on a downward trend, the proposal department may also propose selling it early. Furthermore, if the value of the property is stable, the proposal department may also propose operating it as a rental property. For example, if the value of the property is on an upward trend, the proposal department may propose setting a holding period and selling it after that period has elapsed. If the value of the property is on a downward trend, losses can be minimized by selling it early. If the value of the property is stable, stable income can be obtained by operating it as a rental property. The support department assists with the necessary legal procedures and document preparation based on the plan proposed by the proposal department.The support department automatically assists with legal procedures such as inheritance registration and the creation of sales contracts. For example, the support department provides a system for automating inheritance registration procedures. The support department can also provide tools for automating the creation of sales contracts. For example, the support department provides an interface for completing inheritance registration procedures online. Sales contracts can be automatically generated based on templates. As a result, the real estate management support system according to this embodiment allows users to entrust complex management tasks and procedures to AI and receive suggestions for the optimal timing and method of sale.

[0030] The reception desk receives information about real estate properties from users. This information may include, but is not limited to, the property's location, area, age, and surrounding infrastructure. For example, the reception desk may provide an interface for entering the property's location. It may also provide a field for entering the property's area. Furthermore, it may provide an option for entering the property's age. For example, the reception desk may provide an interface for selecting the property's location on a map. The property's area can be entered in square meters. The property's age can be selected using a calendar format. The reception desk provides an intuitive and user-friendly interface to allow users to easily enter information. For example, when selecting the property's location on a map, it includes zoom-in / zoom-out and pin-placing functions to specify the exact location. The property area input field allows input in square meters as well as tsubo (a traditional Japanese unit of area), enhancing user convenience. The age input option allows direct input of the number of years in addition to a calendar format, providing flexible input methods. Furthermore, the reception department provides input fields for surrounding infrastructure information, allowing users to enter details such as the nearest train station, bus stop, school, hospital, and supermarket. This enables the comprehensive collection of detailed property information, which can then be used for subsequent evaluations and proposals. The reception department automatically saves the entered information to a database and can share it with other departments as needed. For example, the entered property information can be made accessible to the evaluation and proposal departments, ensuring a smooth flow of information. The reception department also includes a check function to verify the accuracy of the entered information, preventing input errors and inaccuracies. This allows the reception department to collect information from users efficiently and accurately, improving the overall reliability of the system.

[0031] The valuation department analyzes the information received by the reception department and evaluates the property's condition. For example, the valuation department evaluates the property's value based on past transaction data and market data. For example, the valuation department retrieves past transaction prices from a database and calculates the property's current value. The valuation department can also analyze market trends and predict the property's future value. For example, the valuation department calculates the average transaction price in the same area based on past transaction data. Market trends are analyzed considering the current balance of supply and demand. The property's future value is predicted based on past data and current market trends. The valuation department uses AI to perform data analysis and evaluate the property's value with high accuracy. Specifically, the AI ​​learns from past transaction data and calculates the property's current value considering factors such as the property's location, area, age, and surrounding infrastructure. Furthermore, the AI ​​analyzes market trends in real time and predicts the property's future value considering the balance of supply and demand, economic indicators, and regional development plans. For example, the valuation department calculates the average transaction price in the same area based on past transaction data and evaluates the property's current value. Market trends are analyzed considering the current balance of supply and demand, and future price fluctuations are predicted. The future value of a property is predicted based on historical data and current market trends, and can be presented to the user with specific numbers and graphs. The evaluation department provides these evaluation results to the user and creates a detailed report on the property's value. The report includes the property's current value, future value predictions, and the data and analysis results that form the basis of the evaluation, allowing the user to accurately understand the property's situation. Furthermore, based on the evaluation results, the evaluation department can also propose advice and improvements to enhance the property's value. For example, they may suggest renovations or improvements to increase the property's value, or provide information on improving surrounding infrastructure. In this way, the evaluation department can help users maximize the value of their properties and enhance the overall value of the system.

[0032] The proposal department proposes the optimal management and sales plan based on information evaluated by the evaluation department. For example, if the property's value is on an upward trend, the proposal department may propose holding the property for a certain period before selling it. If the property's value is on a downward trend, the proposal department may also propose selling it early. Furthermore, if the property's value is stable, the proposal department may propose operating it as a rental property. For example, if the property's value is on an upward trend, the proposal department may propose setting a holding period and selling it after that period has elapsed. If the property's value is on a downward trend, selling it early can minimize losses. If the property's value is stable, operating it as a rental property can generate stable income. The proposal department uses AI to automatically generate the optimal management and sales plan based on data provided by the evaluation department. Specifically, the AI ​​analyzes the fluctuation patterns of the property's value and market trends, and proposes the most effective timing for selling or renting the property. For example, if the property's value is on an upward trend, the AI ​​calculates the optimal holding period based on past data and proposes selling it after that period has elapsed. If the property's value is on a downward trend, the AI ​​proposes a plan to minimize losses by selling it early. If the property's value is stable, the AI ​​proposes a plan to generate stable income by operating it as a rental property. The proposal department presents these proposals to the user along with specific figures and simulation results, helping the user make the best decision. For example, if the property's value is on an upward trend, the proposal department simulates and presents to the user the expected income during the holding period and the profit after sale. If the property's value is on a downward trend, it shows the effect of avoiding losses through early sale with specific figures. If the property's value is stable, it presents detailed income forecasts and operating costs from rental management, helping the user understand the benefits of rental management. In this way, the proposal department can provide the user with the optimal management and sales plan and help maximize the value of the property.

[0033] The Support Department assists with necessary legal procedures and document creation based on the plan proposed by the Proposal Department. For example, the Support Department automatically assists with legal procedures such as inheritance registration and the creation of sales contracts. For instance, the Support Department provides a system for automating inheritance registration procedures. It can also provide tools for automating the creation of sales contracts. For example, the Support Department provides an interface for completing inheritance registration procedures online. Sales contracts can be automatically generated based on templates. The Support Department utilizes AI to streamline legal procedures and document creation, reducing the burden on users. Specifically, AI automatically collects information necessary for legal procedures and generates appropriate documents. For example, in inheritance registration procedures, AI automatically creates the necessary documents based on information provided by the user and provides an interface for online submission. In sales contract creation, AI automatically generates contract content based on templates, allowing users to review and modify it. By performing these procedures quickly and accurately, the Support Department enables users to manage their real estate smoothly without being bogged down in cumbersome procedures. Furthermore, the support department constantly updates the latest laws, regulations, and guidelines regarding legal procedures and document preparation, providing users with accurate information. For example, if there are legal changes, the AI ​​automatically updates the system and generates documents that comply with the new regulations. This ensures that the support department helps users always carry out procedures in compliance with the latest laws and regulations. The support department also responds quickly to user inquiries and support requests, providing necessary information and advice. For example, if a user has any questions or concerns about a procedure, the AI ​​automatically provides answers and can escalate to experts if necessary. This allows the support department to help users manage their properties with peace of mind and improves the overall reliability and usability of the system.

[0034] The reception desk can receive detailed information about a property, such as its location, area, age, and surrounding infrastructure. The reception desk can, for example, provide an interface for inputting the property's location. For example, the reception desk can provide an interface for selecting the property's location on a map. The reception desk can also provide a field for inputting the property's area. For example, the property's area can be entered in square meters. Furthermore, the reception desk can provide an option for inputting the property's age. For example, the property's age can be selected in a calendar format. This improves the accuracy of the evaluation by receiving detailed information about the property. Detailed information includes, but is not limited to, the property's location, area, age, and surrounding infrastructure. Some or all of the processing described above in the reception desk may be performed using, for example, AI, or not using AI. For example, the reception desk can input an interface for inputting the property's location into a generating AI, and have the generating AI perform the generation of the interface.

[0035] The valuation unit can assess the value of a property based on past transaction data and market data. For example, the valuation unit can obtain past transaction prices from a database and calculate the current value of the property. For example, the valuation unit can calculate the average transaction price in the same area based on past transaction data. The valuation unit can also analyze market trends and predict the future value of a property. For example, the valuation unit can analyze market trends considering the current balance of supply and demand. Furthermore, the valuation unit can predict the future value of a property based on past data and current market trends. For example, the valuation unit can determine whether the value of a property is trending upward or downward based on past transaction data. This allows for an accurate assessment of the property's value by evaluating it based on past transaction data and market data. Past transaction data includes, but is not limited to, transaction prices, transaction dates, and transaction conditions. Market data includes, but is not limited to, market price trends and the balance of supply and demand. Some or all of the above-described processes in the valuation unit may be performed using, for example, AI, or not using AI. For example, the evaluation unit can input past transaction data into a generating AI and have the AI ​​perform an evaluation of the property's value.

[0036] The proposal department can suggest the optimal timing and method of sale based on the evaluation results. For example, if the value of the property is on an upward trend, the proposal department may suggest holding it for a certain period before selling it. For example, if the value of the property is on an upward trend, the proposal department may suggest setting a holding period and selling it after that period has elapsed. The proposal department may also suggest selling the property early if its value is on a downward trend. For example, if the value of the property is on a downward trend, the proposal department may suggest selling it early to minimize losses. Furthermore, if the value of the property is stable, the proposal department may suggest operating it as a rental property to obtain stable income. For example, if the value of the property is stable, the proposal department may suggest operating it as a rental property to obtain stable income. In this way, by suggesting the optimal timing and method of sale based on the evaluation results, users can learn the optimal sales strategy. Optimal timing of sale includes, but is not limited to, factors such as season, economic conditions, and the balance of supply and demand. Optimal sales methods include, but are not limited to, auctions, direct sales, and brokered sales. Some or all of the above processing in the proposal department may be performed using, for example, AI, or not using AI. For example, the proposal department can input evaluation results into a generation AI and have the AI ​​generate suggestions for the optimal sales timing and method.

[0037] The support unit can automatically assist with legal procedures such as inheritance registration and the creation of sales contracts. For example, the support unit can provide a system for automating inheritance registration procedures. For example, the support unit can provide an interface for completing inheritance registration procedures online. The support unit can also provide tools for automating the creation of sales contracts. For example, the support unit can automatically generate sales contracts based on templates. Furthermore, the support unit can also assist in the creation of documents necessary for legal procedures. For example, the support unit can provide a list of necessary documents and automatically create them based on that list. This reduces the burden on users by automating legal procedures. Legal procedures include, but are not limited to, inheritance registration and the creation of sales contracts. Some or all of the above processes in the support unit may be performed using AI, for example, or not using AI. For example, the support unit can input the inheritance registration procedure into a generating AI and have the generating AI perform the procedure assistance.

[0038] The reception desk can analyze past property information input history and suggest the optimal input method. For example, the reception desk can automatically display property information that the user has frequently entered in the past as a suggestion. For example, the reception desk can retrieve property information that the user has entered in the past from the database and display it as an input suggestion. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. For example, if the reception desk has used voice input in the past, it will prioritize suggesting voice input. Furthermore, the reception desk can predict and suggest property information to be used during a specific time period based on the user's past input history. For example, based on property information that the user has entered during a specific time period in the past, the reception desk can predict and suggest information to be entered during the same time period. In this way, by analyzing past input history, the reception desk can suggest the optimal input method to the user. The optimal input method includes, but is not limited to, the order of input items and the selection of input formats. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input past property information input history into a generating AI and have the AI ​​suggest the optimal input method.

[0039] The input field can be customized based on the user's current living situation and areas of interest when entering property information. For example, if the user enters their family structure, the input field will prioritize entering property information suitable for families. For example, if the user enters their family structure, the input field will provide fields for entering property information suitable for families. The input field can also prioritize entering property information related to hobbies and areas of interest if the user enters their hobbies or areas of interest. For example, if the user enters their hobbies or areas of interest, the input field will provide options for entering property information related to those hobbies or areas of interest. Furthermore, if the user enters their current living situation (e.g., job change or moving), the input field can be customized accordingly. For example, if the user enters their job change or moving, the input field will provide an interface for entering property information accordingly. This improves the efficiency of data entry by customizing input fields according to the user's living situation and areas of interest. Current living situation includes, but is not limited to, family structure, occupation, and income. Areas of interest include, but are not limited to, hobbies and topics of interest. Some or all of the above-described processes in the reception area may be performed using AI, for example, or without AI. For example, the reception area can input data on the user's current living situation and areas of interest into a generating AI and have the generating AI customize the input items.

[0040] The reception system can prioritize inputting highly relevant information when a user enters property information, taking into account the user's geographical location. For example, if the user enters their current location, the reception system will prioritize inputting property information relevant to that area. For example, if the user enters their current location, the reception system will provide a field for entering property information relevant to that area. The reception system can also prioritize inputting property information relevant to a specific area if the user specifies one. For example, if the reception system specifies one area, the reception system will provide an option for entering property information relevant to that area. Furthermore, if the user is using the app while on the move, the reception system can prioritize inputting highly relevant property information based on their current location. For example, if the user is using the app while on the move, the reception system will provide an interface for entering highly relevant property information based on their current location. This allows for the priority input of highly relevant information by considering the user's geographical location. Geographical location information includes, but is not limited to, GPS data and address information. Highly relevant information includes, but is not limited to, information on nearby facilities and transportation access. Some or all of the above-described processes in the reception area may be performed using AI, for example, or without AI. For example, the reception area can input the user's geographical location information into a generating AI and cause the generating AI to prioritize inputting highly relevant information.

[0041] The reception desk can analyze the user's social media activity and input relevant information when entering property information. For example, the reception desk can input relevant property information based on information shared by the user on social media. For example, the reception desk can retrieve information shared by the user on social media from a database and input relevant property information. The reception desk can also input relevant property information based on information of accounts followed by the user on social media. For example, the reception desk can provide an option to input relevant property information based on information of accounts followed by the user on social media. Furthermore, the reception desk can input relevant property information based on information the user has shown interest in on social media. For example, the reception desk can provide an interface to input relevant property information based on information the user has shown interest in on social media. This allows for efficient input of relevant information by analyzing the user's social media activity. Social media activity includes, but is not limited to, posts, follower count, and likes. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's social media activity data into a generating AI and have the generating AI input the relevant information.

[0042] The valuation unit can improve the accuracy of its valuations by considering the interrelationships between properties during the valuation process. For example, the valuation unit can calculate the value of the property being valued based on the transaction prices of neighboring properties. For example, the valuation unit can obtain the transaction prices of neighboring properties from a database and calculate the value of the property being valued. The valuation unit can also compare the valuation results of properties in the same area and adjust the value of the property being valued. For example, the valuation unit can adjust the value of the property being valued based on the valuation results of properties in the same area. Furthermore, the valuation unit can predict the future value of the property being valued by considering the interrelationships between properties. For example, the valuation unit predicts the future value of the property being valued based on the interrelationships between properties. This improves the accuracy of the valuation by considering the interrelationships between properties. Interrelationships between properties include, but are not limited to, the influence of neighboring properties and regional characteristics. Some or all of the above-described processes in the valuation unit may be performed using, for example, AI, or not using AI. For example, the valuation unit can input interrelationship data between properties into a generating AI and have the generating AI perform the task of improving the accuracy of the valuation.

[0043] The valuation unit can perform valuations while considering the attribute information of the property owner. For example, if the property owner is elderly, the valuation unit can perform valuations while considering the possibility of future sale. For example, if the property owner is elderly, the valuation unit can perform valuations while considering the possibility of future sale. Furthermore, if the property owner is a corporation, the valuation unit can perform valuations while considering the financial situation of the corporation. For example, if the property owner is a corporation, the valuation unit can perform valuations while considering the agreement status among the owners. For example, if the property owner is a corporation, the valuation unit can perform valuations while considering the agreement status among the owners. For example, if the property owner is a corporation, the valuation unit can perform valuations while considering the agreement status among the owners. This improves the accuracy of the valuation by considering the attribute information of the property owner. The attribute information of the owner includes, but is not limited to, age, occupation, and income. Some or all of the above processing in the valuation unit may be performed using, for example, AI, or not using AI. For example, the valuation unit can input the attribute information of the property owner into a generating AI and have the generating AI perform the valuation.

[0044] The evaluation unit can perform evaluations while considering the geographical distribution of properties. For example, if a property is located in an urban area, the evaluation unit will perform the evaluation while considering the market trends of the urban area. For example, if a property is located in an urban area, the evaluation unit will perform the evaluation based on the market trends of the urban area. Furthermore, if a property is located in a suburban area, the evaluation unit can perform the evaluation while considering the market trends of the suburban area. For example, if a property is located in a rural area, the evaluation unit will perform the evaluation based on the market trends of the rural area. In this way, the accuracy of the evaluation is improved by considering the geographical distribution of properties. Geographical distribution includes, but is not limited to, regional characteristics and fluctuations in land prices. Some or all of the above processing in the evaluation unit may be performed using, for example, AI, or not using AI. For example, the evaluation unit can input geographical distribution data of properties into a generating AI and have the generating AI perform the evaluation.

[0045] The evaluation unit can improve the accuracy of its evaluation by referring to relevant literature on the property during the evaluation process. For example, the evaluation unit may perform an evaluation by referring to past transaction data on the property. For example, the evaluation unit may obtain past transaction data on the property from a database and perform an evaluation. The evaluation unit may also perform an evaluation by referring to market reports on the property. For example, the evaluation unit may perform an evaluation based on market reports on the property. Furthermore, the evaluation unit may also perform an evaluation by referring to expert opinions on the property. For example, the evaluation unit may perform an evaluation based on expert opinions on the property. This improves the accuracy of the evaluation by referring to relevant literature on the property. Relevant literature includes, but is not limited to, academic papers, industry reports, and news articles. Some or all of the above processing in the evaluation unit may be performed using, for example, AI, or not using AI. For example, the evaluation unit may input relevant literature data on the property into a generating AI and have the generating AI perform the evaluation.

[0046] The proposal department can adjust the level of detail in a proposal based on the importance of the property. For example, the proposal department will provide a detailed proposal for important properties. The proposal department can also provide a simplified proposal for less important properties. Furthermore, the proposal department can adjust the level of detail in a stepwise manner according to the importance of the property. For example, the proposal department will adjust the level of detail in a stepwise manner according to the importance of the property. This improves the efficiency of proposals by adjusting the level of detail according to the importance of the property. The importance of a property includes, but is not limited to, the value of the property, the level of demand, and the location. The level of detail in a proposal includes, but is not limited to, the depth, specificity, and amount of information of the proposal. Some or all of the above processing in the proposal department may be performed using, for example, AI, or not using AI. For example, the proposal department can input property importance data into the generation AI and have the generation AI adjust the level of detail in the proposals.

[0047] The proposal unit can apply different proposal algorithms depending on the property category when making a proposal. For example, for residential properties, the proposal unit can make proposals that take into account trends in the residential market. For example, for residential properties, the proposal unit can make proposals based on trends in the residential market. The proposal unit can also make proposals that take into account trends in the commercial market for commercial properties. For example, for commercial properties, the proposal unit can make proposals based on trends in the commercial market. Furthermore, the proposal unit can also make proposals that take into account trends in the investment market for investment properties. For example, for investment properties, the proposal unit can make proposals based on trends in the investment market. This allows for the provision of optimal proposals according to the property category. Property categories include, but are not limited to, residential, commercial, and industrial land. Proposal algorithms include, but are not limited to, recommendation systems and optimization algorithms. Some or all of the above processing in the proposal unit may be performed using, for example, AI, or not using AI. For example, the proposal unit can input property category data into a generating AI and have the generating AI execute the application of the proposal algorithm.

[0048] The proposal department can determine the priority of proposals based on the submission timing of the properties. For example, the proposal department will prioritize proposals for properties with earlier submission dates. For example, the proposal department will prioritize providing proposal content for properties with earlier submission dates. The proposal department can also postpone proposals for properties with later submission dates. For example, the proposal department will postpone providing proposal content for properties with later submission dates. Furthermore, the proposal department can adjust the priority of proposals in stages according to the submission dates. For example, the proposal department will adjust the priority of proposal content in stages according to the submission dates. This allows for efficient proposals by determining the priority of proposals based on the submission timing of the properties. The submission timing of a property includes, but is not limited to, the submission date, submission time, and submission timing. The priority of proposals includes, but is not limited to, importance, urgency, and relevance. Some or all of the above processing in the proposal department may be performed using, for example, AI, or not using AI. For example, the proposal department can input data on the submission timing of properties into a generating AI and have the AI ​​determine the priority of proposals.

[0049] The proposal department can adjust the order of proposals based on the relevance of the properties when submitting proposals. For example, the proposal department will prioritize proposals for properties with high relevance. For example, the proposal department will prioritize providing proposals for properties with high relevance. The proposal department can also postpone proposals for properties with low relevance. For example, the proposal department will postpone providing proposals for properties with low relevance. Furthermore, the proposal department can adjust the order of proposals in stages according to the relevance of the properties. For example, the proposal department will adjust the order of proposals in stages according to the relevance of the properties. This allows for efficient proposals by adjusting the order of proposals based on the relevance of the properties. Property relevance includes, but is not limited to, proximity of properties and similarity of use. Proposal order includes, but is not limited to, order of importance, relevance, and submission date. Some or all of the above processing in the proposal department may be performed using, for example, AI, or not using AI. For example, the proposal department can input property relevance data into the generation AI and have the generation AI adjust the order of the proposals.

[0050] The support unit can optimize its support algorithm by referring to past legal procedure data during support. For example, the support unit can propose the optimal support method based on past legal procedure data. For example, the support unit can obtain past legal procedure data from a database and propose the optimal support method. The support unit can also analyze past legal procedure data, extract common problems, and improve its support algorithm. For example, the support unit can analyze past legal procedure data, extract common problems, and improve its support algorithm. Furthermore, the support unit can refer to past legal procedure data to provide the user with the optimal support method. For example, the support unit provides the user with the optimal support method based on past legal procedure data. This optimizes the support algorithm by referring to past legal procedure data, improving the accuracy of support. Past legal procedure data includes, but is not limited to, the success rate of procedures and the time taken for procedures. Support algorithms include, but are not limited to, optimization algorithms and machine learning algorithms. Some or all of the above processing in the support unit may be performed using, for example, AI, or not using AI. For example, the support department can input past legal procedure data into a generating AI and have the generating AI optimize the support algorithm.

[0051] The support department can provide support while considering the attribute information of the property owner. For example, if the property owner is elderly, the support department can provide support methods tailored to the elderly. The support department can also provide support methods tailored to corporations if the property owner is a corporation. Furthermore, if there are multiple property owners, the support department can provide support while considering the agreement status among the owners. This improves the accuracy of support by considering the attribute information of the property owner. The attribute information of the owner includes, but is not limited to, age, occupation, and income. Some or all of the above processing in the support department may be performed using, for example, AI, or not using AI. For example, the support department can input the attribute information of the property owner into a generating AI and have the generating AI perform the support.

[0052] The support department can select the most appropriate legal procedure when providing support, taking into account the geographical distribution of the property. For example, if the property is located in an urban area, the support department can provide the legal procedure for urban areas. For example, if the property is located in an urban area, the support department can provide the legal procedure for urban areas. For example, if the property is located in a suburban area, the support department can provide the legal procedure for suburban areas. For example, if the property is located in a rural area, the support department can provide the legal procedure for rural areas. For example, if the property is located in a rural area, the support department can provide the legal procedure for rural areas. In this way, the most appropriate legal procedure can be selected by considering the geographical distribution of the property. Geographical distribution includes, but is not limited to, regional characteristics and fluctuations in land prices. Legal procedures include, but are not limited to, regional legal requirements and procedural steps. Some or all of the above processing by the support department may be performed using, for example, AI, or not using AI. For example, the support department can input geographical distribution data of properties into a generating AI and have the AI ​​select the appropriate legal procedures.

[0053] The support department can improve the accuracy of its support by referring to relevant literature on the property during the support process. For example, the support department can provide support by referring to past legal proceedings data related to the property. For example, the support department can obtain past legal proceedings data related to the property from a database and provide support. The support department can also provide support by referring to market reports related to the property. For example, the support department can provide support based on market reports related to the property. Furthermore, the support department can provide support by referring to expert opinions on the property. For example, the support department can provide support based on expert opinions on the property. This improves the accuracy of the support by referring to relevant literature on the property. Relevant literature includes, but is not limited to, academic papers, industry reports, and news articles. Some or all of the above processing in the support department may be performed using, for example, AI, or not using AI. For example, the support department can input relevant literature data for the property into a generating AI and have the generating AI perform the support.

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

[0055] The real estate management support system can further analyze the user's past behavioral history to provide optimal suggestions. For example, the suggestion department can analyze what properties the user has selected in the past and suggest similar properties. It can also analyze what sales methods the user has selected in the past and suggest similar methods. Furthermore, it can analyze when the user has sold properties in the past and suggest the optimal timing for selling. This improves the accuracy of suggestions by providing optimal suggestions based on the user's past behavioral history. Behavioral history includes, but is not limited to, property selection history, sales method selection history, and sales timing selection history.

[0056] The real estate management support system can further customize its suggestions by taking into account the user's current living situation. For example, if the user enters their family structure, the suggestion section can prioritize suggesting properties suitable for families. It can also suggest properties that match the user's plans for changing jobs or moving. Furthermore, if the user has specific hobbies or areas of interest, it can suggest properties related to those. This customization of suggestions according to the user's living situation increases their likelihood of acceptance. Living situation includes, but is not limited to, family structure, occupation, hobbies, and areas of interest.

[0057] The real estate management support system can further customize its suggestions by taking into account the user's geographical location. For example, if the user enters their current location, the suggestion section can prioritize suggesting properties related to that area. It can also suggest properties related to a specific area if the user specifies one. Furthermore, if the user is using the app while on the move, it can suggest highly relevant properties based on their current location. This allows the system to prioritize suggesting highly relevant information by considering the user's geographical location. Geographical location information includes, but is not limited to, GPS data and address information.

[0058] The real estate management support system can further analyze users' social media activity and suggest relevant information. For example, the suggestion function can suggest relevant properties based on information shared by users on social media. It can also suggest relevant properties based on information about accounts that users follow on social media. Furthermore, it can suggest relevant properties based on information that users have shown interest in on social media. In this way, relevant information can be efficiently suggested by analyzing users' social media activity. Social media activity includes, but is not limited to, posts, follower count, and likes.

[0059] The real estate management support system can further optimize its support algorithms by referencing the user's past legal procedure data. For example, the support department can propose the optimal support method based on past legal procedure data. It can also analyze past legal procedure data, extract common problems, and improve the support algorithm. Furthermore, it can provide the user with the most suitable support method by referring to past legal procedure data. This optimizes the support algorithm and improves the accuracy of support by referring to past legal procedure data. Past legal procedure data includes, but is not limited to, the success rate of procedures and the time taken for procedures.

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

[0061] Step 1: The reception desk receives information about real estate properties from users. This information includes the property's location, area, age, and surrounding infrastructure. The reception desk provides an interface for entering the property's location, a field for entering the property's area, and an option for entering the property's age. For example, there may be an interface for selecting the property's location on a map, a field for entering the property's area in square meters, and an option for selecting the property's age in a calendar format. Step 2: The evaluation department analyzes the information received by the reception department and assesses the property's condition. The evaluation department evaluates the property's value based on past transaction data and market data, and calculates the property's current value by retrieving past transaction prices from the database. It also analyzes market trends and predicts the property's future value. For example, it calculates the average transaction price in the same area, analyzes market trends considering the current balance of supply and demand, and predicts the property's future value based on past data and current market trends. Step 3: The proposal department proposes the optimal management and sales plan based on the information evaluated by the evaluation department. If the property's value is on an upward trend, the proposal department will propose holding it for a certain period before selling it; if the property's value is on a downward trend, it will propose selling it early. If the property's value is stable, it will propose operating it as a rental property. For example, it may propose setting a holding period and selling it after that period has elapsed, minimizing losses by selling it early, or obtaining stable income by operating it as a rental property. Step 4: The support department assists with necessary legal procedures and document preparation based on the plan proposed by the proposal department. The support department provides automated support for legal procedures such as inheritance registration and the creation of sales contracts, and provides systems for automating inheritance registration procedures and tools for automating the creation of sales contracts. For example, there is an interface for completing inheritance registration procedures online and a tool for creating sales contracts that are automatically generated based on templates.

[0062] (Example of form 2) The real estate management support system according to an embodiment of the present invention is an autonomous AI agent that supports the management and sale of real estate properties inherited as an inheritance. This real estate management support system is an AI agent that allows users to entrust complex management tasks and procedures to the AI ​​and receive suggestions for the optimal timing and method of sale. For example, the real estate management support system receives information about the real estate property inherited by the user. For example, if the property is located in Tokyo, has an area of ​​100 square meters, and is 20 years old, this information is entered. This information is entered into the real estate management support system. Next, the real estate management support system analyzes the entered information and evaluates the condition of the property. The real estate management support system evaluates the value of the property based on past transaction data and market data. For example, it calculates the current value of the property by considering past transaction prices in the same area and current market trends. This allows for an accurate evaluation of the property's condition. Furthermore, the real estate management support system proposes an optimal management and sales plan based on the evaluation results. For example, if the value of the property is trending upward, it suggests holding it for a certain period before selling it. Also, if the value of the property is trending downward, it suggests selling it early. This allows the user to know the optimal timing and method of sale. Furthermore, the real estate management support system automatically assists with necessary legal procedures and document creation. For example, it can automate complex procedures such as inheritance registration and the creation of sales contracts. This allows users to complete necessary procedures without hassle. As a result, users can entrust complex management tasks and procedures to AI and receive suggestions for the optimal timing and method of sale. For example, by properly managing inherited real estate properties and selling them at the optimal time, users can make effective use of their assets. In addition, the real estate management support system reduces the burden on users by automatically assisting with legal procedures and document creation. As a result, the real estate management support system can efficiently support the management and sale of real estate properties inherited as part of an estate.

[0063] The real estate management support system according to this embodiment comprises a reception unit, an evaluation unit, a proposal unit, and a support unit. The reception unit receives information about real estate properties from users. The information about real estate properties received from users includes, but is not limited to, the property's location, area, age, and surrounding infrastructure. The reception unit provides, for example, an interface for inputting the property's location. The reception unit may also provide a field for inputting the property's area. Furthermore, the reception unit may provide an option for inputting the property's age. For example, the reception unit provides an interface for selecting the property's location on a map. The property's area can be entered in square meters. The property's age can be selected in calendar format. The evaluation unit analyzes the information received by the reception unit and evaluates the property's condition. The evaluation unit evaluates the property's value based on, for example, past transaction data and market data. The evaluation unit calculates the property's current value by, for example, obtaining past transaction prices from a database. The evaluation unit can also analyze market trends and predict the property's future value. For example, the valuation department calculates the average transaction price in the same area based on past transaction data. Market trends are analyzed considering the current balance of supply and demand. The future value of the property is predicted based on past data and current market trends. The proposal department proposes the optimal management and sales plan based on the information evaluated by the valuation department. For example, if the value of the property is on an upward trend, the proposal department may propose holding it for a certain period before selling it. For example, if the value of the property is on a downward trend, the proposal department may also propose selling it early. Furthermore, if the value of the property is stable, the proposal department may also propose operating it as a rental property. For example, if the value of the property is on an upward trend, the proposal department may propose setting a holding period and selling it after that period has elapsed. If the value of the property is on a downward trend, losses can be minimized by selling it early. If the value of the property is stable, stable income can be obtained by operating it as a rental property. The support department assists with the necessary legal procedures and document preparation based on the plan proposed by the proposal department.The support department automatically assists with legal procedures such as inheritance registration and the creation of sales contracts. For example, the support department provides a system for automating inheritance registration procedures. The support department can also provide tools for automating the creation of sales contracts. For example, the support department provides an interface for completing inheritance registration procedures online. Sales contracts can be automatically generated based on templates. As a result, the real estate management support system according to this embodiment allows users to entrust complex management tasks and procedures to AI and receive suggestions for the optimal timing and method of sale.

[0064] The reception desk receives information about real estate properties from users. This information may include, but is not limited to, the property's location, area, age, and surrounding infrastructure. For example, the reception desk may provide an interface for entering the property's location. It may also provide a field for entering the property's area. Furthermore, it may provide an option for entering the property's age. For example, the reception desk may provide an interface for selecting the property's location on a map. The property's area can be entered in square meters. The property's age can be selected using a calendar format. The reception desk provides an intuitive and user-friendly interface to allow users to easily enter information. For example, when selecting the property's location on a map, it includes zoom-in / zoom-out and pin-placing functions to specify the exact location. The property area input field allows input in square meters as well as tsubo (a traditional Japanese unit of area), enhancing user convenience. The age input option allows direct input of the number of years in addition to a calendar format, providing flexible input methods. Furthermore, the reception department provides input fields for surrounding infrastructure information, allowing users to enter details such as the nearest train station, bus stop, school, hospital, and supermarket. This enables the comprehensive collection of detailed property information, which can then be used for subsequent evaluations and proposals. The reception department automatically saves the entered information to a database and can share it with other departments as needed. For example, the entered property information can be made accessible to the evaluation and proposal departments, ensuring a smooth flow of information. The reception department also includes a check function to verify the accuracy of the entered information, preventing input errors and inaccuracies. This allows the reception department to collect information from users efficiently and accurately, improving the overall reliability of the system.

[0065] The valuation department analyzes the information received by the reception department and evaluates the property's condition. For example, the valuation department evaluates the property's value based on past transaction data and market data. For example, the valuation department retrieves past transaction prices from a database and calculates the property's current value. The valuation department can also analyze market trends and predict the property's future value. For example, the valuation department calculates the average transaction price in the same area based on past transaction data. Market trends are analyzed considering the current balance of supply and demand. The property's future value is predicted based on past data and current market trends. The valuation department uses AI to perform data analysis and evaluate the property's value with high accuracy. Specifically, the AI ​​learns from past transaction data and calculates the property's current value considering factors such as the property's location, area, age, and surrounding infrastructure. Furthermore, the AI ​​analyzes market trends in real time and predicts the property's future value considering the balance of supply and demand, economic indicators, and regional development plans. For example, the valuation department calculates the average transaction price in the same area based on past transaction data and evaluates the property's current value. Market trends are analyzed considering the current balance of supply and demand, and future price fluctuations are predicted. The future value of a property is predicted based on historical data and current market trends, and can be presented to the user with specific numbers and graphs. The evaluation department provides these evaluation results to the user and creates a detailed report on the property's value. The report includes the property's current value, future value predictions, and the data and analysis results that form the basis of the evaluation, allowing the user to accurately understand the property's situation. Furthermore, based on the evaluation results, the evaluation department can also propose advice and improvements to enhance the property's value. For example, they may suggest renovations or improvements to increase the property's value, or provide information on improving surrounding infrastructure. In this way, the evaluation department can help users maximize the value of their properties and enhance the overall value of the system.

[0066] The proposal department proposes the optimal management and sales plan based on information evaluated by the evaluation department. For example, if the property's value is on an upward trend, the proposal department may propose holding the property for a certain period before selling it. If the property's value is on a downward trend, the proposal department may also propose selling it early. Furthermore, if the property's value is stable, the proposal department may propose operating it as a rental property. For example, if the property's value is on an upward trend, the proposal department may propose setting a holding period and selling it after that period has elapsed. If the property's value is on a downward trend, selling it early can minimize losses. If the property's value is stable, operating it as a rental property can generate stable income. The proposal department uses AI to automatically generate the optimal management and sales plan based on data provided by the evaluation department. Specifically, the AI ​​analyzes the fluctuation patterns of the property's value and market trends, and proposes the most effective timing for selling or renting the property. For example, if the property's value is on an upward trend, the AI ​​calculates the optimal holding period based on past data and proposes selling it after that period has elapsed. If the property's value is on a downward trend, the AI ​​proposes a plan to minimize losses by selling it early. If the property's value is stable, the AI ​​proposes a plan to generate stable income by operating it as a rental property. The proposal department presents these proposals to the user along with specific figures and simulation results, helping the user make the best decision. For example, if the property's value is on an upward trend, the proposal department simulates and presents to the user the expected income during the holding period and the profit after sale. If the property's value is on a downward trend, it shows the effect of avoiding losses through early sale with specific figures. If the property's value is stable, it presents detailed income forecasts and operating costs from rental management, helping the user understand the benefits of rental management. In this way, the proposal department can provide the user with the optimal management and sales plan and help maximize the value of the property.

[0067] The Support Department assists with necessary legal procedures and document creation based on the plan proposed by the Proposal Department. For example, the Support Department automatically assists with legal procedures such as inheritance registration and the creation of sales contracts. For instance, the Support Department provides a system for automating inheritance registration procedures. It can also provide tools for automating the creation of sales contracts. For example, the Support Department provides an interface for completing inheritance registration procedures online. Sales contracts can be automatically generated based on templates. The Support Department utilizes AI to streamline legal procedures and document creation, reducing the burden on users. Specifically, AI automatically collects information necessary for legal procedures and generates appropriate documents. For example, in inheritance registration procedures, AI automatically creates the necessary documents based on information provided by the user and provides an interface for online submission. In sales contract creation, AI automatically generates contract content based on templates, allowing users to review and modify it. By performing these procedures quickly and accurately, the Support Department enables users to manage their real estate smoothly without being bogged down in cumbersome procedures. Furthermore, the support department constantly updates the latest laws, regulations, and guidelines regarding legal procedures and document preparation, providing users with accurate information. For example, if there are legal changes, the AI ​​automatically updates the system and generates documents that comply with the new regulations. This ensures that the support department helps users always carry out procedures in compliance with the latest laws and regulations. The support department also responds quickly to user inquiries and support requests, providing necessary information and advice. For example, if a user has any questions or concerns about a procedure, the AI ​​automatically provides answers and can escalate to experts if necessary. This allows the support department to help users manage their properties with peace of mind and improves the overall reliability and usability of the system.

[0068] The reception desk can receive detailed information about a property, such as its location, area, age, and surrounding infrastructure. The reception desk can, for example, provide an interface for inputting the property's location. For example, the reception desk can provide an interface for selecting the property's location on a map. The reception desk can also provide a field for inputting the property's area. For example, the property's area can be entered in square meters. Furthermore, the reception desk can provide an option for inputting the property's age. For example, the property's age can be selected in a calendar format. This improves the accuracy of the evaluation by receiving detailed information about the property. Detailed information includes, but is not limited to, the property's location, area, age, and surrounding infrastructure. Some or all of the processing described above in the reception desk may be performed using, for example, AI, or not using AI. For example, the reception desk can input an interface for inputting the property's location into a generating AI, and have the generating AI perform the generation of the interface.

[0069] The valuation unit can assess the value of a property based on past transaction data and market data. For example, the valuation unit can obtain past transaction prices from a database and calculate the current value of the property. For example, the valuation unit can calculate the average transaction price in the same area based on past transaction data. The valuation unit can also analyze market trends and predict the future value of a property. For example, the valuation unit can analyze market trends considering the current balance of supply and demand. Furthermore, the valuation unit can predict the future value of a property based on past data and current market trends. For example, the valuation unit can determine whether the value of a property is trending upward or downward based on past transaction data. This allows for an accurate assessment of the property's value by evaluating it based on past transaction data and market data. Past transaction data includes, but is not limited to, transaction prices, transaction dates, and transaction conditions. Market data includes, but is not limited to, market price trends and the balance of supply and demand. Some or all of the above-described processes in the valuation unit may be performed using, for example, AI, or not using AI. For example, the evaluation unit can input past transaction data into a generating AI and have the AI ​​perform an evaluation of the property's value.

[0070] The proposal department can suggest the optimal timing and method of sale based on the evaluation results. For example, if the value of the property is on an upward trend, the proposal department may suggest holding it for a certain period before selling it. For example, if the value of the property is on an upward trend, the proposal department may suggest setting a holding period and selling it after that period has elapsed. The proposal department may also suggest selling the property early if its value is on a downward trend. For example, if the value of the property is on a downward trend, the proposal department may suggest selling it early to minimize losses. Furthermore, if the value of the property is stable, the proposal department may suggest operating it as a rental property to obtain stable income. For example, if the value of the property is stable, the proposal department may suggest operating it as a rental property to obtain stable income. In this way, by suggesting the optimal timing and method of sale based on the evaluation results, users can learn the optimal sales strategy. Optimal timing of sale includes, but is not limited to, factors such as season, economic conditions, and the balance of supply and demand. Optimal sales methods include, but are not limited to, auctions, direct sales, and brokered sales. Some or all of the above processing in the proposal department may be performed using, for example, AI, or not using AI. For example, the proposal department can input evaluation results into a generation AI and have the AI ​​generate suggestions for the optimal sales timing and method.

[0071] The support unit can automatically assist with legal procedures such as inheritance registration and the creation of sales contracts. For example, the support unit can provide a system for automating inheritance registration procedures. For example, the support unit can provide an interface for completing inheritance registration procedures online. The support unit can also provide tools for automating the creation of sales contracts. For example, the support unit can automatically generate sales contracts based on templates. Furthermore, the support unit can also assist in the creation of documents necessary for legal procedures. For example, the support unit can provide a list of necessary documents and automatically create them based on that list. This reduces the burden on users by automating legal procedures. Legal procedures include, but are not limited to, inheritance registration and the creation of sales contracts. Some or all of the above processes in the support unit may be performed using AI, for example, or not using AI. For example, the support unit can input the inheritance registration procedure into a generating AI and have the generating AI perform the procedure assistance.

[0072] The reception desk can estimate the user's emotions and adjust the property information input interface based on those emotions. For example, if the user is stressed, the reception desk can provide a simple and intuitive interface and minimize the input steps. For example, if the user is stressed, the reception desk can reduce the number of input fields and provide an interface that only requires the minimum necessary information. Also, if the user is relaxed, the reception desk can provide detailed input options and suggest customizable input methods. For example, if the user is relaxed, the reception desk can provide options for entering detailed information and an interface that allows the user to freely select input fields. Furthermore, if the user is in a hurry, the reception desk can prioritize voice input to allow for quick input of property information. For example, if the user is in a hurry, the reception desk can provide an interface that allows for quick information entry using voice input. This reduces user stress by adjusting the input interface according to the user's emotions. User emotions include, but are not limited to, stress, relaxation, and urgency. The input interface includes, but is not limited to, the display order of input fields and changes to the input method. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception unit may be performed using AI or not using AI. For example, the reception unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0073] The reception desk can analyze past property information input history and suggest the optimal input method. For example, the reception desk can automatically display property information that the user has frequently entered in the past as a suggestion. For example, the reception desk can retrieve property information that the user has entered in the past from the database and display it as an input suggestion. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. For example, if the reception desk has used voice input in the past, it will prioritize suggesting voice input. Furthermore, the reception desk can predict and suggest property information to be used during a specific time period based on the user's past input history. For example, based on property information that the user has entered during a specific time period in the past, the reception desk can predict and suggest information to be entered during the same time period. In this way, by analyzing past input history, the reception desk can suggest the optimal input method to the user. The optimal input method includes, but is not limited to, the order of input items and the selection of input formats. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input past property information input history into a generating AI and have the AI ​​suggest the optimal input method.

[0074] The input field can be customized based on the user's current living situation and areas of interest when entering property information. For example, if the user enters their family structure, the input field will prioritize entering property information suitable for families. For example, if the user enters their family structure, the input field will provide fields for entering property information suitable for families. The input field can also prioritize entering property information related to hobbies and areas of interest if the user enters their hobbies or areas of interest. For example, if the user enters their hobbies or areas of interest, the input field will provide options for entering property information related to those hobbies or areas of interest. Furthermore, if the user enters their current living situation (e.g., job change or moving), the input field can be customized accordingly. For example, if the user enters their job change or moving, the input field will provide an interface for entering property information accordingly. This improves the efficiency of data entry by customizing input fields according to the user's living situation and areas of interest. Current living situation includes, but is not limited to, family structure, occupation, and income. Areas of interest include, but are not limited to, hobbies and topics of interest. Some or all of the above-described processes in the reception area may be performed using AI, for example, or without AI. For example, the reception area can input data on the user's current living situation and areas of interest into a generating AI and have the generating AI customize the input items.

[0075] The reception desk can estimate the user's emotions and determine the priority of property information to be entered based on those emotions. For example, if the user is stressed, the reception desk can prioritize important information and postpone detailed information. For example, if the user is stressed, the reception desk can provide an interface that prioritizes important information and postpones detailed information. Also, if the user is relaxed, the reception desk can prioritize detailed information and suggest a customizable input method. For example, if the user is relaxed, the reception desk can provide an interface that prioritizes detailed information and suggests a customizable input method. Furthermore, if the user is in a hurry, the reception desk can prioritize the most important information and complete the input quickly. For example, if the user is in a hurry, the reception desk can provide an interface that prioritizes the most important information and completes the input quickly. This improves input efficiency by determining input priorities according to the user's emotions. User emotions include, but are not limited to, stress, relaxation, and urgency. Prioritization of property information includes, but is not limited to, importance, urgency, and relevance. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception unit may be performed using AI or not using AI. For example, the reception unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0076] The reception system can prioritize inputting highly relevant information when a user enters property information, taking into account the user's geographical location. For example, if the user enters their current location, the reception system will prioritize inputting property information relevant to that area. For example, if the user enters their current location, the reception system will provide a field for entering property information relevant to that area. The reception system can also prioritize inputting property information relevant to a specific area if the user specifies one. For example, if the reception system specifies one area, the reception system will provide an option for entering property information relevant to that area. Furthermore, if the user is using the app while on the move, the reception system can prioritize inputting highly relevant property information based on their current location. For example, if the user is using the app while on the move, the reception system will provide an interface for entering highly relevant property information based on their current location. This allows for the priority input of highly relevant information by considering the user's geographical location. Geographical location information includes, but is not limited to, GPS data and address information. Highly relevant information includes, but is not limited to, information on nearby facilities and transportation access. Some or all of the above-described processes in the reception area may be performed using AI, for example, or without AI. For example, the reception area can input the user's geographical location information into a generating AI and cause the generating AI to prioritize inputting highly relevant information.

[0077] The reception desk can analyze the user's social media activity and input relevant information when entering property information. For example, the reception desk can input relevant property information based on information shared by the user on social media. For example, the reception desk can retrieve information shared by the user on social media from a database and input relevant property information. The reception desk can also input relevant property information based on information of accounts followed by the user on social media. For example, the reception desk can provide an option to input relevant property information based on information of accounts followed by the user on social media. Furthermore, the reception desk can input relevant property information based on information the user has shown interest in on social media. For example, the reception desk can provide an interface to input relevant property information based on information the user has shown interest in on social media. This allows for efficient input of relevant information by analyzing the user's social media activity. Social media activity includes, but is not limited to, posts, follower count, and likes. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's social media activity data into a generating AI and have the generating AI input the relevant information.

[0078] The evaluation unit can estimate the user's emotions and adjust the property evaluation criteria based on the estimated emotions. For example, if the user is stressed, the evaluation unit can provide simple and intuitive evaluation criteria. For example, if the user is stressed, the evaluation unit can reduce the number of evaluation items and perform the evaluation based on only the minimum necessary information. Also, if the user is relaxed, the evaluation unit can provide detailed evaluation criteria and suggest a customizable evaluation method. For example, if the user is relaxed, the evaluation unit can provide detailed evaluation items and an interface that allows the user to freely select evaluation criteria. Furthermore, if the user is in a hurry, the evaluation unit can provide a simplified evaluation criteria for quick evaluation. For example, if the user is in a hurry, the evaluation unit can provide an interface that allows for quick evaluation using simplified evaluation criteria. This improves the accuracy of the evaluation by adjusting the evaluation criteria according to the user's emotions. User emotions include, but are not limited to, stress, relaxation, and urgency. Property evaluation criteria include, but are not limited to, weighting of evaluation items and changes in evaluation methods. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generative 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 processing described above in the evaluation unit may be performed using AI, or not using AI. For example, the evaluation unit may input user emotion data into the generative AI and have the generative AI perform emotion estimation.

[0079] The valuation unit can improve the accuracy of its valuations by considering the interrelationships between properties during the valuation process. For example, the valuation unit can calculate the value of the property being valued based on the transaction prices of neighboring properties. For example, the valuation unit can obtain the transaction prices of neighboring properties from a database and calculate the value of the property being valued. The valuation unit can also compare the valuation results of properties in the same area and adjust the value of the property being valued. For example, the valuation unit can adjust the value of the property being valued based on the valuation results of properties in the same area. Furthermore, the valuation unit can predict the future value of the property being valued by considering the interrelationships between properties. For example, the valuation unit predicts the future value of the property being valued based on the interrelationships between properties. This improves the accuracy of the valuation by considering the interrelationships between properties. Interrelationships between properties include, but are not limited to, the influence of neighboring properties and regional characteristics. Some or all of the above-described processes in the valuation unit may be performed using, for example, AI, or not using AI. For example, the valuation unit can input interrelationship data between properties into a generating AI and have the generating AI perform the task of improving the accuracy of the valuation.

[0080] The valuation unit can perform valuations while considering the attribute information of the property owner. For example, if the property owner is elderly, the valuation unit can perform valuations while considering the possibility of future sale. For example, if the property owner is elderly, the valuation unit can perform valuations while considering the possibility of future sale. Furthermore, if the property owner is a corporation, the valuation unit can perform valuations while considering the financial situation of the corporation. For example, if the property owner is a corporation, the valuation unit can perform valuations while considering the agreement status among the owners. For example, if the property owner is a corporation, the valuation unit can perform valuations while considering the agreement status among the owners. For example, if the property owner is a corporation, the valuation unit can perform valuations while considering the agreement status among the owners. This improves the accuracy of the valuation by considering the attribute information of the property owner. The attribute information of the owner includes, but is not limited to, age, occupation, and income. Some or all of the above processing in the valuation unit may be performed using, for example, AI, or not using AI. For example, the valuation unit can input the attribute information of the property owner into a generating AI and have the generating AI perform the valuation.

[0081] The evaluation unit can estimate the user's emotions and adjust the display method of the evaluation results based on the estimated user emotions. For example, if the user is nervous, the evaluation unit can provide a simple and highly visible display method. For example, if the user is nervous, the evaluation unit can display the evaluation results in a simple and highly visible format. The evaluation unit can also provide a display method that includes detailed information if the user is relaxed. For example, if the user is relaxed, the evaluation unit can display the evaluation results in a format that includes detailed information. Furthermore, if the user is in a hurry, the evaluation unit can provide a concise display method. For example, if the user is in a hurry, the evaluation unit can display the evaluation results in a concise format. By adjusting the display method according to the user's emotions, the understanding of the evaluation results is deepened. User emotions include, but are not limited to, nervousness, relaxation, and urgency. Display methods for evaluation results include, but are not limited to, graph displays, text displays, and interactive displays. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generative 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 processing described above in the evaluation unit may be performed using AI, or not using AI. For example, the evaluation unit may input user emotion data into the generative AI and have the generative AI perform emotion estimation.

[0082] The evaluation unit can perform evaluations while considering the geographical distribution of properties. For example, if a property is located in an urban area, the evaluation unit will perform the evaluation while considering the market trends of the urban area. For example, if a property is located in an urban area, the evaluation unit will perform the evaluation based on the market trends of the urban area. Furthermore, if a property is located in a suburban area, the evaluation unit can perform the evaluation while considering the market trends of the suburban area. For example, if a property is located in a rural area, the evaluation unit will perform the evaluation based on the market trends of the rural area. In this way, the accuracy of the evaluation is improved by considering the geographical distribution of properties. Geographical distribution includes, but is not limited to, regional characteristics and fluctuations in land prices. Some or all of the above processing in the evaluation unit may be performed using, for example, AI, or not using AI. For example, the evaluation unit can input geographical distribution data of properties into a generating AI and have the generating AI perform the evaluation.

[0083] The evaluation unit can improve the accuracy of its evaluation by referring to relevant literature on the property during the evaluation process. For example, the evaluation unit may perform an evaluation by referring to past transaction data on the property. For example, the evaluation unit may obtain past transaction data on the property from a database and perform an evaluation. The evaluation unit may also perform an evaluation by referring to market reports on the property. For example, the evaluation unit may perform an evaluation based on market reports on the property. Furthermore, the evaluation unit may also perform an evaluation by referring to expert opinions on the property. For example, the evaluation unit may perform an evaluation based on expert opinions on the property. This improves the accuracy of the evaluation by referring to relevant literature on the property. Relevant literature includes, but is not limited to, academic papers, industry reports, and news articles. Some or all of the above processing in the evaluation unit may be performed using, for example, AI, or not using AI. For example, the evaluation unit may input relevant literature data on the property into a generating AI and have the generating AI perform the evaluation.

[0084] The suggestion function can estimate the user's emotions and adjust the presentation of suggestions based on those emotions. For example, if the user is nervous, the suggestion function can provide a simple and highly visible suggestion. For example, if the user is nervous, the suggestion function can display the suggestion in a simple and highly visible format. The suggestion function can also provide a suggestion that includes detailed information if the user is relaxed. For example, if the user is relaxed, the suggestion function can display the suggestion in a format that includes detailed information. Furthermore, if the user is in a hurry, the suggestion function can provide a concise suggestion. For example, if the user is in a hurry, the suggestion function can display the suggestion in a concise format. By adjusting the presentation of suggestions according to the user's emotions, the understanding of the suggestions is enhanced. User emotions include, but are not limited to, nervousness, relaxation, and urgency. Presentation methods for suggestions include, but are not limited to, text, visual, and audio formats. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. The generative 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 processing described above in the proposed unit may be performed using AI, or not using AI. For example, the proposed unit may input user emotion data into the generative AI and have the generative AI perform emotion estimation.

[0085] The proposal department can adjust the level of detail in a proposal based on the importance of the property. For example, the proposal department will provide a detailed proposal for important properties. The proposal department can also provide a simplified proposal for less important properties. Furthermore, the proposal department can adjust the level of detail in a stepwise manner according to the importance of the property. For example, the proposal department will adjust the level of detail in a stepwise manner according to the importance of the property. This improves the efficiency of proposals by adjusting the level of detail according to the importance of the property. The importance of a property includes, but is not limited to, the value of the property, the level of demand, and the location. The level of detail in a proposal includes, but is not limited to, the depth, specificity, and amount of information of the proposal. Some or all of the above processing in the proposal department may be performed using, for example, AI, or not using AI. For example, the proposal department can input property importance data into the generation AI and have the generation AI adjust the level of detail in the proposals.

[0086] The proposal unit can apply different proposal algorithms depending on the property category when making a proposal. For example, for residential properties, the proposal unit can make proposals that take into account trends in the residential market. For example, for residential properties, the proposal unit can make proposals based on trends in the residential market. The proposal unit can also make proposals that take into account trends in the commercial market for commercial properties. For example, for commercial properties, the proposal unit can make proposals based on trends in the commercial market. Furthermore, the proposal unit can also make proposals that take into account trends in the investment market for investment properties. For example, for investment properties, the proposal unit can make proposals based on trends in the investment market. This allows for the provision of optimal proposals according to the property category. Property categories include, but are not limited to, residential, commercial, and industrial land. Proposal algorithms include, but are not limited to, recommendation systems and optimization algorithms. Some or all of the above processing in the proposal unit may be performed using, for example, AI, or not using AI. For example, the proposal unit can input property category data into a generating AI and have the generating AI execute the application of the proposal algorithm.

[0087] The suggestion section can estimate the user's emotions and adjust the length of the suggestion based on the estimated emotions. For example, if the user is nervous, the suggestion section can provide a short, concise suggestion. For example, if the user is nervous, the suggestion section can display the suggestion in a short, concise format. The suggestion section can also provide a longer suggestion with more detailed explanations if the user is relaxed. For example, if the user is relaxed, the suggestion section can display the suggestion in a longer format with more detailed explanations. Furthermore, if the user is in a hurry, the suggestion section can provide a quick and concise suggestion. For example, if the user is in a hurry, the suggestion section can display the suggestion in a quick and concise format. By adjusting the length of the suggestion according to the user's emotions, the user's understanding of the suggestion is enhanced. User emotions include, but are not limited to, nervousness, relaxation, and urgency. The length of the suggestion includes, but is not limited to, summarizing the suggestion or omitting detailed explanations. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. The generative 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 processing described above in the proposed unit may be performed using AI, or not using AI. For example, the proposed unit may input user emotion data into the generative AI and have the generative AI perform emotion estimation.

[0088] The proposal department can determine the priority of proposals based on the submission timing of the properties. For example, the proposal department will prioritize proposals for properties with earlier submission dates. For example, the proposal department will prioritize providing proposal content for properties with earlier submission dates. The proposal department can also postpone proposals for properties with later submission dates. For example, the proposal department will postpone providing proposal content for properties with later submission dates. Furthermore, the proposal department can adjust the priority of proposals in stages according to the submission dates. For example, the proposal department will adjust the priority of proposal content in stages according to the submission dates. This allows for efficient proposals by determining the priority of proposals based on the submission timing of the properties. The submission timing of a property includes, but is not limited to, the submission date, submission time, and submission timing. The priority of proposals includes, but is not limited to, importance, urgency, and relevance. Some or all of the above processing in the proposal department may be performed using, for example, AI, or not using AI. For example, the proposal department can input data on the submission timing of properties into a generating AI and have the AI ​​determine the priority of proposals.

[0089] The proposal department can adjust the order of proposals based on the relevance of the properties when submitting proposals. For example, the proposal department will prioritize proposals for properties with high relevance. For example, the proposal department will prioritize providing proposals for properties with high relevance. The proposal department can also postpone proposals for properties with low relevance. For example, the proposal department will postpone providing proposals for properties with low relevance. Furthermore, the proposal department can adjust the order of proposals in stages according to the relevance of the properties. For example, the proposal department will adjust the order of proposals in stages according to the relevance of the properties. This allows for efficient proposals by adjusting the order of proposals based on the relevance of the properties. Property relevance includes, but is not limited to, proximity of properties and similarity of use. Proposal order includes, but is not limited to, order of importance, relevance, and submission date. Some or all of the above processing in the proposal department may be performed using, for example, AI, or not using AI. For example, the proposal department can input property relevance data into the generation AI and have the generation AI adjust the order of the proposals.

[0090] The support unit can estimate the user's emotions and adjust the method of legal procedure support based on those emotions. For example, if the user is nervous, the support unit can provide simple and intuitive support. For instance, if the user is nervous, the support unit can reduce the steps of the legal procedure and provide support based on only the minimum necessary information. Furthermore, if the user is relaxed, the support unit can provide detailed support and suggest customizable support methods. For example, if the user is relaxed, the support unit can provide detailed steps of the legal procedure and an interface that allows the user to freely choose the order of the procedure. Additionally, if the user is in a hurry, the support unit can provide simplified support methods to quickly complete the legal procedure. For example, if the user is in a hurry, the support unit can provide an interface that allows for quick completion of the procedure using simplified steps. This reduces the user's burden by adjusting the method of legal procedure support according to the user's emotions. User emotions include, but are not limited to, nervousness, relaxation, and urgency. Methods of legal procedure support include, but are not limited to, steps of the procedure and guidance on necessary documents. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the support unit may be performed using AI, or not using AI. For example, the support unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0091] The support unit can optimize its support algorithm by referring to past legal procedure data during support. For example, the support unit can propose the optimal support method based on past legal procedure data. For example, the support unit can obtain past legal procedure data from a database and propose the optimal support method. The support unit can also analyze past legal procedure data, extract common problems, and improve its support algorithm. For example, the support unit can analyze past legal procedure data, extract common problems, and improve its support algorithm. Furthermore, the support unit can refer to past legal procedure data to provide the user with the optimal support method. For example, the support unit provides the user with the optimal support method based on past legal procedure data. This optimizes the support algorithm by referring to past legal procedure data, improving the accuracy of support. Past legal procedure data includes, but is not limited to, the success rate of procedures and the time taken for procedures. Support algorithms include, but are not limited to, optimization algorithms and machine learning algorithms. Some or all of the above processing in the support unit may be performed using, for example, AI, or not using AI. For example, the support department can input past legal procedure data into a generating AI and have the generating AI optimize the support algorithm.

[0092] The support department can provide support while considering the attribute information of the property owner. For example, if the property owner is elderly, the support department can provide support methods tailored to the elderly. The support department can also provide support methods tailored to corporations if the property owner is a corporation. Furthermore, if there are multiple property owners, the support department can provide support while considering the agreement status among the owners. This improves the accuracy of support by considering the attribute information of the property owner. The attribute information of the owner includes, but is not limited to, age, occupation, and income. Some or all of the above processing in the support department may be performed using, for example, AI, or not using AI. For example, the support department can input the attribute information of the property owner into a generating AI and have the generating AI perform the support.

[0093] The support unit can estimate the user's emotions and determine the priority of legal procedures based on those estimated emotions. For example, if the user is nervous, the support unit will prioritize assisting with important procedures. For example, if the user is nervous, the support unit will prioritize assisting with important procedures. For example, if the user is relaxed, the support unit will prioritize assisting with detailed procedures. For example, if the user is relaxed, the support unit will prioritize assisting with detailed procedures. For example, if the user is in a hurry, the support unit will prioritize assisting with procedures that can be completed quickly. For example, if the user is in a hurry, the support unit will prioritize assisting with procedures that can be completed quickly. This allows for efficient procedures by determining the priority of legal procedures according to the user's emotions. User emotions include, but are not limited to, nervousness, relaxation, and urgency. Prioritization of legal procedures includes, but are not limited to, importance, urgency, and relevance. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generative AI may be, but is not limited to, text-generating AI (e.g., LLM) or multimodal generative AI. Some or all of the processing described above in the support unit may be performed using AI, or not using AI. For example, the support unit may input user emotion data into the generative AI and have the generative AI perform emotion estimation.

[0094] The support department can select the most appropriate legal procedure when providing support, taking into account the geographical distribution of the property. For example, if the property is located in an urban area, the support department can provide the legal procedure for urban areas. For example, if the property is located in an urban area, the support department can provide the legal procedure for urban areas. For example, if the property is located in a suburban area, the support department can provide the legal procedure for suburban areas. For example, if the property is located in a rural area, the support department can provide the legal procedure for rural areas. For example, if the property is located in a rural area, the support department can provide the legal procedure for rural areas. In this way, the most appropriate legal procedure can be selected by considering the geographical distribution of the property. Geographical distribution includes, but is not limited to, regional characteristics and fluctuations in land prices. Legal procedures include, but are not limited to, regional legal requirements and procedural steps. Some or all of the above processing by the support department may be performed using, for example, AI, or not using AI. For example, the support department can input geographical distribution data of properties into a generating AI and have the AI ​​select the appropriate legal procedures.

[0095] The support department can improve the accuracy of its support by referring to relevant literature on the property during the support process. For example, the support department can provide support by referring to past legal proceedings data related to the property. For example, the support department can obtain past legal proceedings data related to the property from a database and provide support. The support department can also provide support by referring to market reports related to the property. For example, the support department can provide support based on market reports related to the property. Furthermore, the support department can provide support by referring to expert opinions on the property. For example, the support department can provide support based on expert opinions on the property. This improves the accuracy of the support by referring to relevant literature on the property. Relevant literature includes, but is not limited to, academic papers, industry reports, and news articles. Some or all of the above processing in the support department may be performed using, for example, AI, or not using AI. For example, the support department can input relevant literature data for the property into a generating AI and have the generating AI perform the support.

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

[0097] The real estate management support system can further estimate the user's emotions and adjust its suggestions based on those emotions. For example, if the user is stressed, the suggestion section can provide simple and intuitive suggestions, delaying detailed information. If the user is relaxed, it can provide suggestions with detailed information and offer options for the user to choose from. Furthermore, if the user is in a hurry, it can provide suggestions quickly and prioritize important information. By adjusting suggestions according to the user's emotions, the likelihood of suggestions being accepted is increased. Emotion estimation can be achieved using, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0098] The real estate management support system can further analyze the user's past behavioral history to provide optimal suggestions. For example, the suggestion department can analyze what properties the user has selected in the past and suggest similar properties. It can also analyze what sales methods the user has selected in the past and suggest similar methods. Furthermore, it can analyze when the user has sold properties in the past and suggest the optimal timing for selling. This improves the accuracy of suggestions by providing optimal suggestions based on the user's past behavioral history. Behavioral history includes, but is not limited to, property selection history, sales method selection history, and sales timing selection history.

[0099] The real estate management support system can further estimate the user's emotions and adjust the display method of the evaluation results based on the estimated emotions. For example, if the user is stressed, the evaluation unit can provide a simple and highly visible display method. If the user is relaxed, it can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, it can provide a display method that gets straight to the point. By adjusting the display method of the evaluation results according to the user's emotions, the understanding of the evaluation results is deepened. Emotion estimation is achieved using, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0100] The real estate management support system can further customize its suggestions by taking into account the user's current living situation. For example, if the user enters their family structure, the suggestion section can prioritize suggesting properties suitable for families. It can also suggest properties that match the user's plans for changing jobs or moving. Furthermore, if the user has specific hobbies or areas of interest, it can suggest properties related to those. This customization of suggestions according to the user's living situation increases their likelihood of acceptance. Living situation includes, but is not limited to, family structure, occupation, hobbies, and areas of interest.

[0101] The real estate management support system can further estimate the user's emotions and adjust the method of legal procedure support based on those emotions. For example, if the user is stressed, the support system can provide a simple and intuitive method of assistance. If the user is relaxed, it can provide a detailed method of assistance and an interface that allows the user to freely choose the order of procedures. Furthermore, if the user is in a hurry, it can provide a simplified method of assistance to quickly complete the legal procedure. In this way, the burden on the user can be reduced by adjusting the method of legal procedure support according to the user's emotions. Emotion estimation can be achieved using, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0102] The real estate management support system can further customize its suggestions by taking into account the user's geographical location. For example, if the user enters their current location, the suggestion section can prioritize suggesting properties related to that area. It can also suggest properties related to a specific area if the user specifies one. Furthermore, if the user is using the app while on the move, it can suggest highly relevant properties based on their current location. This allows the system to prioritize suggesting highly relevant information by considering the user's geographical location. Geographical location information includes, but is not limited to, GPS data and address information.

[0103] The real estate management support system can further estimate the user's emotions and adjust the length of suggestions based on those emotions. For example, if the user is stressed, the suggestion section can provide short, to-the-point suggestions. If the user is relaxed, it can provide longer suggestions with more detailed explanations. Furthermore, if the user is in a hurry, it can provide quick and concise suggestions. By adjusting the length of suggestions according to the user's emotions, the user's understanding of the suggestions is enhanced. Emotion estimation can be achieved using, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0104] The real estate management support system can further analyze users' social media activity and suggest relevant information. For example, the suggestion function can suggest relevant properties based on information shared by users on social media. It can also suggest relevant properties based on information about accounts that users follow on social media. Furthermore, it can suggest relevant properties based on information that users have shown interest in on social media. In this way, relevant information can be efficiently suggested by analyzing users' social media activity. Social media activity includes, but is not limited to, posts, follower count, and likes.

[0105] The real estate management support system can further estimate the user's emotions and prioritize legal procedures based on those emotions. For example, if the user is stressed, the support system can prioritize assisting with important procedures. If the user is relaxed, it can prioritize assisting with detailed procedures. Furthermore, if the user is in a hurry, it can prioritize assisting with procedures that can be completed quickly. This enables efficient procedures by prioritizing legal procedures according to the user's emotions. Emotion estimation is achieved using, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0106] The real estate management support system can further optimize its support algorithms by referencing the user's past legal procedure data. For example, the support department can propose the optimal support method based on past legal procedure data. It can also analyze past legal procedure data, extract common problems, and improve the support algorithm. Furthermore, it can provide the user with the most suitable support method by referring to past legal procedure data. This optimizes the support algorithm and improves the accuracy of support by referring to past legal procedure data. Past legal procedure data includes, but is not limited to, the success rate of procedures and the time taken for procedures.

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

[0108] Step 1: The reception desk receives information about real estate properties from users. This information includes the property's location, area, age, and surrounding infrastructure. The reception desk provides an interface for entering the property's location, a field for entering the property's area, and an option for entering the property's age. For example, there may be an interface for selecting the property's location on a map, a field for entering the property's area in square meters, and an option for selecting the property's age in a calendar format. Step 2: The evaluation department analyzes the information received by the reception department and assesses the property's condition. The evaluation department evaluates the property's value based on past transaction data and market data, and calculates the property's current value by retrieving past transaction prices from the database. It also analyzes market trends and predicts the property's future value. For example, it calculates the average transaction price in the same area, analyzes market trends considering the current balance of supply and demand, and predicts the property's future value based on past data and current market trends. Step 3: The proposal department proposes the optimal management and sales plan based on the information evaluated by the evaluation department. If the property's value is on an upward trend, the proposal department will propose holding it for a certain period before selling it; if the property's value is on a downward trend, it will propose selling it early. If the property's value is stable, it will propose operating it as a rental property. For example, it may propose setting a holding period and selling it after that period has elapsed, minimizing losses by selling it early, or obtaining stable income by operating it as a rental property. Step 4: The support department assists with necessary legal procedures and document preparation based on the plan proposed by the proposal department. The support department provides automated support for legal procedures such as inheritance registration and the creation of sales contracts, and provides systems for automating inheritance registration procedures and tools for automating the creation of sales contracts. For example, there is an interface for completing inheritance registration procedures online and a tool for creating sales contracts that are automatically generated based on templates.

[0109] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0110] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0111] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0112] Each of the multiple elements described above, including the reception unit, evaluation unit, proposal unit, and support unit, is implemented by, for example, at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14 and provides an interface for receiving information on real estate properties from users. The evaluation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and evaluates the value of the property based on past transaction data and market data. The proposal unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and proposes the optimal management and sales plan based on the evaluation results. The support unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and assists with necessary legal procedures and document creation. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

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

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

[0115] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

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

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

[0118] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0119] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0120] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0121] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0122] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0123] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0124] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0125] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0126] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0127] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0128] Each of the multiple elements described above, including the reception unit, evaluation unit, proposal unit, and support unit, is implemented by, for example, at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart glasses 214 and provides an interface for receiving information on real estate properties from the user. The evaluation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and evaluates the value of the property based on past transaction data and market data. The proposal unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and proposes the optimal management and sales plan based on the evaluation results. The support unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and assists with necessary legal procedures and document creation. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

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

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

[0131] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

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

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

[0134] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0135] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0136] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0137] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0138] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0139] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0140] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

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

[0142] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0143] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0144] Each of the multiple elements described above, including the reception unit, evaluation unit, proposal unit, and support unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the headset terminal 314 and provides an interface for receiving information on real estate properties from the user. The evaluation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and evaluates the value of the property based on past transaction data and market data. The proposal unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and proposes the optimal management and sales plan based on the evaluation results. The support unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and assists with necessary legal procedures and document creation. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

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

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

[0147] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

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

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

[0150] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0151] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0152] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0153] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0154] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0155] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0156] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0157] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0158] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0159] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0160] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0161] Each of the multiple elements described above, including the reception unit, evaluation unit, proposal unit, and support unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the robot 414 and provides an interface for receiving information on real estate properties from users. The evaluation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and evaluates the value of the property based on past transaction data and market data. The proposal unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and proposes the optimal management and sales plan based on the evaluation results. The support unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and assists with necessary legal procedures and document creation. The correspondence between each unit and the devices and control units is not limited to the examples described above and can be changed in various ways.

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

[0163] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0164] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0165] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0166] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0167] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0168] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0169] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

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

[0171] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0172] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0173] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

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

[0175] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0176] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0177] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0178] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0179] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0180] (Note 1) A reception desk that receives information on real estate properties from users, An evaluation unit analyzes the information received by the reception unit and evaluates the condition of the property, Based on the information evaluated by the aforementioned evaluation unit, the proposal unit proposes the optimal management and sales plan. The system includes a support unit that assists with necessary legal procedures and document preparation based on the plan proposed by the aforementioned proposal unit. A system characterized by the following features. (Note 2) The aforementioned reception unit is We accept detailed information such as the property's location, area, year of construction, and surrounding infrastructure. The system described in Appendix 1, characterized by the features described herein. (Note 3) The evaluation unit, The value of a property is assessed based on past transaction data and market data. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned proposal section is, Based on the evaluation results, we propose the optimal sales timing and method. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned support unit, It automatically assists with legal procedures such as inheritance registration and the creation of sales contracts. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reception unit is The system estimates the user's emotions and adjusts the property information input interface based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is We analyze past property information entry history and propose the optimal input method. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is When entering property information, the input fields are customized based on the user's current living situation and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is The system estimates the user's emotions and determines the priority of property information to be entered based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is When entering property information, the system prioritizes inputting highly relevant information, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When entering property information, the system analyzes the user's social media activity and inputs relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 12) The evaluation unit, The system estimates user sentiment and adjusts property evaluation criteria based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 13) The evaluation unit, When evaluating properties, consider the interrelationships between them to improve the accuracy of the evaluation. The system described in Appendix 1, characterized by the features described herein. (Note 14) The evaluation unit, When evaluating a property, the owner's attribute information will be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 15) The evaluation unit, The system estimates the user's emotions and adjusts how the evaluation results are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The evaluation unit, When evaluating properties, the geographical distribution of those properties should be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 17) The evaluation unit, During the evaluation process, we improve the accuracy of the evaluation by referring to relevant literature on the property. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned proposal section is, It estimates the user's emotions and adjusts the way suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned proposal section is, When making a proposal, adjust the level of detail based on the importance of the property. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned proposal section is, When making a proposal, different proposal algorithms are applied depending on the property category. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned proposal section is, It estimates the user's emotions and adjusts the length of the suggestion based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned proposal section is, When submitting proposals, we will prioritize them based on when the properties were submitted. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned proposal section is, When making proposals, adjust the order of proposals based on the relevance of the properties. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned support unit, We estimate the user's emotions and adjust how we support legal proceedings based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned support unit, During support, the support algorithm is optimized by referring to past legal procedure data. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned support unit, When providing support, we will take into account the attribute information of the property owner. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned support unit, The system estimates user sentiment and determines the priority of legal proceedings based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned support unit, When providing support, we select the most appropriate legal procedure considering the geographical distribution of the properties. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned support unit, When providing support, we refer to relevant literature on the property to improve the accuracy of the support. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

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

Claims

1. A reception desk that receives information on real estate properties from users, An evaluation unit analyzes the information received by the reception unit and evaluates the condition of the property, Based on the information evaluated by the aforementioned evaluation unit, the proposal unit proposes the optimal management and sales plan. The system includes a support unit that assists with necessary legal procedures and document preparation based on the plan proposed by the aforementioned proposal unit. A system characterized by the following features.

2. The aforementioned reception unit is We accept detailed information such as the property's location, area, year of construction, and surrounding infrastructure. The system according to feature 1.

3. The evaluation unit, The value of a property is assessed based on past transaction data and market data. The system according to feature 1.

4. The aforementioned proposal section is, Based on the evaluation results, we propose the optimal sales timing and method. The system according to feature 1.

5. The aforementioned support unit, It automatically assists with legal procedures such as inheritance registration and the creation of sales contracts. The system according to feature 1.

6. The aforementioned reception unit is The system estimates the user's emotions and adjusts the property information input interface based on those estimated emotions. The system according to feature 1.

7. The aforementioned reception unit is We analyze past property information entry history and propose the optimal input method. The system according to feature 1.

8. The aforementioned reception unit is When entering property information, the input fields are customized based on the user's current living situation and areas of interest. The system according to feature 1.

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