System

Abstract

An object of a system according to an embodiment is to eliminate a lack of information and determination of an appropriate price in real estate transactions.SOLUTION: A system includes a property introduction sentence creation part, a property proposal part, a market price information provision part, an interior proposal part, an investment determination support part, and a contract creation part. A property introduction sentence creation part receives property information as an input and automatically creates a property introduction sentence. The property proposal unit receives the user's preferences and conditions as input, and proposes an optimal property. A market price information providing part analyzes past transaction data and a market trend and provides the proper price of the property. The interior proposal unit proposes an interior based on the floor plan of the property and the preference of the user. The investment analysis support unit evaluates the investment value of the property and supports the investment analysis. The contract document creation unit automatically creates a contract document necessary for a transaction.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technology has room for improvement, as it is difficult to determine the appropriate price and there is a lack of information in real estate transactions.

[0005] The system according to the embodiment aims to resolve the lack of information and the difficulty in determining the appropriate price in real estate transactions. [Means for solving the problem]

[0006] The system according to the embodiment comprises a property description creation unit, a property proposal unit, a market price information providing unit, an interior proposal unit, an investment decision support unit, and a contract creation unit. The property description creation unit receives property information as input and automatically creates a property description. The property proposal unit receives the user's preferences and conditions as input and proposes the most suitable property. The market price information providing unit analyzes past transaction data and market trends and provides a fair price for the property. The interior proposal unit proposes interior design based on the property's layout and the user's preferences. The investment decision support unit evaluates the investment value of the property and supports investment decisions. The contract creation unit automatically creates the contract required for the transaction. [Effects of the Invention]

[0007] The system according to the embodiment can resolve the lack of information and the determination of fair prices in real estate transactions. [Brief explanation of the drawings]

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

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

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

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

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

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

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

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

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

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

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

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

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

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

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

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

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

[0028] (Example 1) The AI ​​system according to an embodiment of the present invention is a system that resolves the problems of insufficient information and human resources, and the difficulty of determining the appropriate price in real estate transactions. This system uses generative AI to create property descriptions, propose properties that suit the user's preferences, provide market price information, propose interior design, support investment decisions, and draft contracts. As a result, the AI ​​system resolves the problems of insufficient information and human resources, and the difficulty of determining the appropriate price in real estate transactions, enabling efficient and effective transactions.

[0029] The AI ​​system according to the embodiment includes a property description creation unit, a property proposal unit, a market price information provision unit, an interior proposal unit, an investment decision support unit, and a contract creation unit. The property description creation unit receives property information as input and automatically creates a property description. For example, the property description creation unit receives information such as the property's location, layout, price, and surrounding environment as input, and the generation AI generates a description such as, "This property is located in a quiet residential area, has a spacious living room, and the latest amenities." The property proposal unit receives a user's preferences and conditions as input and proposes optimal properties. For example, when a user inputs conditions such as "3LDK, near the station, pets allowed," the property proposal unit generates a list of properties that meet the conditions and proposes them to the user. The market price information provision unit analyzes past transaction data and market trends to provide the appropriate price for a property. For example, when the market price information provision unit inputs data related to a specific area or property type, the generation AI analyzes the average price and price trends for that area and property type and provides them to the user. The interior proposal unit proposes interior designs based on the property's layout and the user's preferences. For example, if the user inputs, "I like modern designs," the interior proposal unit proposes furniture and interior layouts that match that preference. The investment decision support unit evaluates the investment value of a property and supports investment decisions. For example, the investment decision support unit analyzes the property's profitability and the possibility of future value increases, and the generation AI provides an evaluation such as, "This property is expected to be highly profitable." The contract creation unit automatically creates contracts required for transactions. For example, the contract creation unit generates contracts by inputting the necessary information based on templates for property sales and rental contracts. As a result, the AI ​​system according to the embodiment can resolve issues such as a lack of information and personnel, and the difficulty of determining appropriate prices in real estate transactions, thereby realizing efficient and effective transactions.

[0030] The property description creation unit can create detailed descriptions that include information about the property's history and past owners. For example, the AI ​​generation unit collects information about the property's past owners and creates a description based on that information. For example, it generates a detailed description such as, "This property was designed by a famous architect and was once home to a famous artist." The AI ​​generation unit also researches the property's construction year and renovation history and incorporates that information into the description. For example, it provides information such as, "This property was built in the 1920s and was most recently fully renovated in 2015." The AI ​​generation unit also researches the property's historical background and the history of the area and incorporates that information into the description. For example, it generates a description such as, "This property is located in an area with a preserved historic streetscape and is surrounded by many cultural assets." This provides detailed information about the property, deepening the user's understanding.

[0031] The property description creation unit can incorporate information about the culture and events in the area surrounding the property into the description. For example, the generation AI in the property description creation unit collects information about cultural facilities and events in the area surrounding the property and reflects that information in the description. For example, the unit generates a description such as, "This property is close to an art museum and a theater, and many cultural events are held there throughout the year." The generation AI in the property description creation unit also researches seasonal event information in the area surrounding the property and incorporates that information into the description. For example, the unit provides information such as, "This property is located in an area where cherry blossom festivals are held in the spring and fireworks displays are held in the summer." The generation AI in the property description creation unit also researches the history and traditional events in the area surrounding the property and reflects that information in the description. For example, the unit generates a description such as, "This property is located in an area where long-standing traditional festivals are held, allowing you to feel the history of the area." This makes it easier to attract users' interest by providing information about the area surrounding the property.

[0032] The property description creation unit automatically generates property descriptions in multiple languages, making it possible to accommodate international users. For example, the generation AI in the property description creation unit automatically generates property descriptions in multiple languages, such as English, French, and Chinese. For example, it provides "This property is located in a quiet residential area and features a spacious living room and the latest amenities" in each language. The generation AI in the property description creation unit also posts property descriptions on a multilingual platform and collects feedback from international users. For example, it reflects user opinions based on the descriptions in each language. The generation AI in the property description creation unit also customizes property descriptions to suit different cultures and regional characteristics and provides them in each language. For example, it generates descriptions that take cultural backgrounds and regional characteristics into account. This expands the sales channels of properties by catering to international users.

[0033] The property description creation unit can incorporate 3D tour and virtual reality links into the property description. For example, the generation AI in the property description creation unit incorporates a 3D tour link into the property description, allowing users to virtually tour the property. For example, the generation AI generates a description such as, "For more information about this property, please take a look at this 3D tour." The property description creation unit also adds a virtual reality link to the property description, allowing users to experience the property using a VR device. For example, the generation AI generates a description such as, "To experience this property in virtual reality, please use this link." The property description creation unit also incorporates a link to an interactive 3D model into the property description, allowing users to freely explore the property details. For example, the generation AI generates a description such as, "You can view a 3D model of this property here." This allows users to virtually tour the property.

[0034] The property suggestion unit analyzes a user's past search history and browsing history to make more accurate property suggestions. For example, the generation AI in the property suggestion unit analyzes a user's past search history and suggests properties based on similar search conditions. For example, it lists the most suitable properties based on the conditions the user has searched for in the past. The property suggestion unit also analyzes a user's browsing history and suggests new properties based on the characteristics of the properties they have viewed. For example, it analyzes the characteristics of properties the user has viewed in the past and suggests properties with similar characteristics. The property suggestion unit also integrates the user's search history and browsing history to make more accurate property suggestions. For example, it combines search conditions and browsing history to suggest properties that best match the user's preferences. This makes use of the user's past behavioral data to make more accurate property suggestions.

[0035] The property suggestion unit can suggest properties based on the user's lifestyle and hobbies. For example, the generation AI collects information about the user's lifestyle and suggests properties based on that information. For example, for a user who likes the outdoors, the generation AI suggests properties with parks or nature nearby. The property suggestion unit also analyzes data about the user's hobbies and suggests properties based on that. For example, for a user whose hobby is cooking, the generation AI suggests properties with spacious kitchens. The property suggestion unit also performs an integrated analysis of the user's lifestyle and hobbies and suggests the most suitable property. For example, for a user who has a pet, the generation AI suggests properties that allow pets. In this way, property suggestions are made that match the user's lifestyle and hobbies.

[0036] The property suggestion unit can analyze a user's social media posts and suggest properties that match their preferences. For example, the generation AI in the property suggestion unit analyzes a user's social media posts and identifies the user's preferences from the content of the posts. For example, it suggests optimal properties based on posts about travel destinations or hobbies. The generation AI in the property suggestion unit also analyzes a user's social media photos and suggests properties based on the places and objects that appear in the photos. For example, it suggests properties surrounded by nature to a user who takes many nature photos. The generation AI in the property suggestion unit also analyzes a user's social media friendships and suggests properties based on the areas where friends live and their preferences. For example, it suggests properties in areas where many friends live. In this way, the generation AI utilizes the user's social media posts to suggest properties that match their preferences.

[0037] The property suggestion unit can suggest properties based on the user's family composition and future plans. For example, the generation AI collects information about the user's family composition and suggests properties based on that information. For example, for a family with children, it suggests properties that are close to schools and parks. The property suggestion unit also analyzes data about the user's future plans and suggests properties based on that. For example, for a user who plans to keep a pet in the future, it suggests properties that allow pets. The property suggestion unit also performs an integrated analysis of the user's family composition and future plans and suggests the most suitable property. For example, it suggests a spacious property taking into account future family growth. In this way, it makes property suggestions that match the user's family composition and future plans.

[0038] The market price information provider can provide market price information that includes not only past transaction data but also future market forecasts. In the market price information provider, for example, the generation AI analyzes past transaction data and makes future market forecasts. For example, it predicts price trends for the next five years based on transaction data from the past 10 years. In addition, the market price information provider provides future market price information by taking market trends and economic indicators into account. For example, it predicts future price fluctuations based on economic growth rates and interest rate trends. In addition, the market price information provider provides future market price information by analyzing regional development plans and infrastructure development information. For example, it predicts future price increases taking into account construction plans for new railways and roads. In this way, by providing market price information that includes future market forecasts, it supports users' investment decisions.

[0039] The market price information providing unit can provide market price information that takes into account regional economic indicators and infrastructure development plans. For example, the generation AI collects regional economic indicators and provides market price information based on them. For example, the market price information providing unit calculates the fair price of a property taking into account the regional unemployment rate and average income. The generation AI also analyzes regional infrastructure development plans and provides market price information based on the analysis. For example, it predicts future price increases taking into account new railway and road construction plans. The generation AI also analyzes regional economic growth rates and demographics and provides market price information based on the analysis. For example, it predicts property prices in areas where population growth is expected. In this way, market price information that takes into account regional economic indicators and infrastructure development plans is provided to support users' investment decisions.

[0040] The market price information provider can compare market price information from different countries and regions and provide information from a global perspective. For example, the generation AI collects market price information from different countries and regions, compares it, and provides information from a global perspective. For example, it compares property prices in major cities. The market price information provider also analyzes economic indicators from different countries and regions and provides market price information based on that. For example, it predicts property prices taking into account each country's economic growth rate and interest rate trends. The market price information provider also analyzes market trends from different countries and regions and provides market price information based on that. For example, it compares real estate market trends from each country and suggests investment destinations. This provides market price information from a global perspective to support users' investment decisions.

[0041] The market price information providing unit can visualize market price information and enable intuitive understanding with graphs and charts. For example, the generation AI in the market price information providing unit visualizes market price information and enables intuitive understanding with graphs and charts. For example, price trends are displayed in a line graph. The generation AI in the market price information providing unit also displays market price information by region in a heat map to enable intuitive understanding. For example, areas with high prices are displayed in red. The generation AI in the market price information providing unit also provides market price information in interactive graphs and charts to enable users to access detailed information. For example, clicking on a point on a graph displays detailed information. In this way, market price information is visualized to enable users to intuitively understand it.

[0042] The interior proposal unit can make interior proposals that take into account not only the floor plan of the property but also the amount of light entering and ventilation. For example, the generation AI of the interior proposal unit analyzes the floor plan and amount of light entering the property and makes interior proposals based on that. For example, it may suggest placing the living room in a sunny location. The generation AI of the interior proposal unit also analyzes the ventilation of the property and makes interior proposals based on that. For example, it may suggest placing the bedroom in a well-ventilated location. The generation AI of the interior proposal unit also comprehensively analyzes the floor plan, amount of light entering, and amount of ventilation of the property and makes proposals for the optimal interior layout. For example, it may make proposals for interiors that make the most of natural light. In this way, by making interior proposals that take into account the amount of light entering and ventilation, it supports the user's comfortable lifestyle.

[0043] The interior suggestion unit can suggest interior designs based on the user's health condition and lifestyle habits. For example, the generation AI in the interior suggestion unit collects information about the user's health condition and makes interior suggestions based on that information. For example, it suggests installing an air purifier to a user with allergies. The generation AI in the interior suggestion unit also analyzes data about the user's lifestyle habits and makes interior suggestions based on that data. For example, it suggests a comfortable home office layout to a user who works remotely. The generation AI in the interior suggestion unit also comprehensively analyzes the user's health condition and lifestyle habits and makes optimal interior suggestions. For example, it suggests furniture layouts that support a healthy lifestyle. This allows it to make interior suggestions that are tailored to the user's health condition and lifestyle habits.

[0044] The interior suggestion unit can make interior suggestions according to the season or event. In the interior suggestion unit, for example, the generation AI makes interior suggestions for each season. For example, in winter, it suggests interiors using warm colors and materials. In addition, the generation AI makes interior suggestions according to specific events. For example, it suggests Christmas trees and decorations for Christmas. In addition, the generation AI makes interior suggestions according to the season or event, allowing the user to enjoy the seasonal feel or event. For example, it suggests interiors using flowers in spring. In this way, interior suggestions according to the season or event enrich the user's life.

[0045] The interior suggestion unit can make interior suggestions that incorporate different cultures and design styles. For example, the generation AI in the interior suggestion unit makes interior suggestions that incorporate design styles from different cultures. For example, it proposes styles such as Japanese, Nordic, and Mediterranean. The generation AI in the interior suggestion unit also makes interior suggestions that combine design styles from different cultures based on the user's preferences. For example, it proposes a style that combines Japanese and Nordic styles. The generation AI in the interior suggestion unit also analyzes the characteristics of different cultures and design styles and makes interior suggestions based on that. For example, it proposes unique interiors that incorporate the characteristics of each culture. This allows the generation AI to make interior suggestions that incorporate different cultures and design styles, thereby meeting the diverse needs of users.

[0046] The Investment Decision Support Department can support investment decisions by analyzing not only the profitability of a property but also its risk factors in detail. For example, the Investment Decision Support Department uses the generation AI to analyze the profitability of a property, and also to analyze its risk factors in detail. For example, even if a property is highly profitable, the generation AI can warn users if it has many risk factors. The Investment Decision Support Department also supports investment decisions by having the generation AI comprehensively evaluate the profitability and risk factors of a property. For example, it can provide an evaluation that takes into account the balance between profitability and risk. The Investment Decision Support Department also uses the generation AI to analyze the profitability and risk factors of a property in detail, and provide the user with specific investment decision advice. For example, it can suggest measures to mitigate risk. This supports the user's investment decisions by providing a detailed analysis of the property's profitability and risk factors.

[0047] The investment decision support unit can predict future investment value based on past investment performance data. In the investment decision support unit, for example, the generation AI analyzes past investment performance data and predicts future investment value based on that. For example, it predicts future profitability by taking past rates of return and price fluctuations into consideration. The investment decision support unit also predicts future market trends based on past investment performance data. For example, it analyzes past market trends and predicts future price increases or decreases. The investment decision support unit also predicts future risk factors based on past investment performance data. For example, it analyzes past risk factors and predicts future risks. In this way, the investment decision support unit supports the user's investment decisions by predicting future investment value based on past investment performance data.

[0048] The investment decision support unit can simulate different investment portfolios and propose optimal investment strategies. For example, the investment decision support unit uses a generation AI to simulate different investment portfolios and evaluate the profitability and risk of each. For example, it predicts the profitability of a portfolio that combines multiple properties. The investment decision support unit also uses a generation AI to simulate different investment portfolios and propose optimal investment strategies. For example, it proposes a portfolio that takes risk diversification into consideration. The investment decision support unit also uses a generation AI to simulate different investment portfolios and propose the portfolio that is most suitable for the user's investment goals. For example, it proposes a portfolio that emphasizes short-term profits or a portfolio that emphasizes long-term stability. In this way, the investment decision support unit supports the user's investment decisions by simulating different investment portfolios and proposing optimal investment strategies.

[0049] The investment decision support unit can support comprehensive investment decisions that include investment options other than real estate (stocks, bonds, etc.). For example, the investment decision support unit uses the generation AI to analyze investment options other than real estate (stocks, bonds, etc.) and support comprehensive investment decisions that include these. For example, it proposes a portfolio that combines stocks and real estate. The investment decision support unit also uses the generation AI to evaluate the risk and profitability of different investment options and propose a comprehensive investment strategy. For example, it proposes an investment portfolio that takes risk diversification into consideration. The investment decision support unit also uses the generation AI to propose an optimal investment strategy that includes investment options other than real estate based on the user's investment goals. For example, it proposes an investment strategy that emphasizes short-term profits or an investment strategy that emphasizes long-term stability. This provides comprehensive investment decision support that includes investment options other than real estate, thereby providing multifaceted support for the user's investment decisions.

[0050] The contract creation unit can automatically check the legal requirements of a contract and generate a legally problem-free contract. For example, the generation AI in the contract creation unit automatically checks the legal requirements of a contract and generates a legally problem-free contract. For example, it automatically adds necessary clauses and conditions. The generation AI in the contract creation unit also analyzes the legal requirements of a contract and automatically corrects any legally problematic parts. For example, it adds missing clauses or corrects incorrect conditions. The generation AI in the contract creation unit also generates a legally problem-free contract for the user based on the legal requirements of the contract. For example, it adds clauses to avoid legal risks. In this way, the generation AI automatically checks the legal requirements of a contract and generates a legally problem-free contract, increasing the user's sense of security.

[0051] The contract creation unit can provide a function that concisely explains the contents of a contract according to the user's level of understanding. For example, the contract creation unit provides a function in which the generation AI analyzes the contents of a contract and provides a concise explanation according to the user's level of understanding. For example, technical terms are replaced with easier-to-understand language. The contract creation unit also provides a function in which the generation AI extracts the important points of a contract and provides a concise explanation to the user. For example, the main conditions and risks of the contract are explained in an easy-to-understand manner. The contract creation unit also provides a function in which the generation AI customizes the contents of a contract according to the user's level of understanding and provides a concise explanation. For example, the level of detail of the explanation is adjusted to match the user's knowledge level. This deepens the user's understanding by providing a concise explanation of the contents of the contract according to the user's level of understanding.

[0052] The contract creation unit can automatically generate contracts in different languages, making it possible to handle international transactions. For example, the generation AI in the contract creation unit can automatically generate contracts in different languages ​​to handle international transactions. For example, contracts can be provided in multiple languages, such as English, French, and Chinese. The generation AI in the contract creation unit also posts the contents of the contract on a multilingual platform to collect feedback from international users. For example, user opinions can be reflected based on the contracts in each language. The generation AI in the contract creation unit can also customize contracts to suit different cultures and regional characteristics and provide them in each language. For example, it can generate contracts that take cultural backgrounds and regional characteristics into consideration. This allows for the automatic generation of contracts in different languages, making it possible to handle international transactions.

[0053] The contract creation unit can automatically record the change history of the contract to ensure transparency. In the contract creation unit, for example, the generation AI automatically records the change history of the contract to ensure transparency. For example, it automatically records the parts that were changed and the date and time. In addition, the contract creation unit has the generation AI analyze the change history of the contract and provide transparent information to the user. For example, it explains the reasons for and impact of the changes. In addition, the contract creation unit has the generation AI provide the user with a transparent contract based on the change history of the contract. For example, it visually displays the change history. In this way, the change history of the contract is automatically recorded and transparency is ensured, thereby gaining the trust of the user.

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

[0055] The property description creation unit can create descriptions that take into account the property's energy efficiency and environmental impact. For example, the generation AI collects energy consumption data for the property and generates a description such as, "This property is equipped with the latest energy-saving equipment, allowing for significant reductions in annual energy costs." The property description creation unit also evaluates the property's environmental impact and reflects this in the description. For example, it provides information such as, "This property uses renewable energy and offers environmentally friendly living." Furthermore, the property description creation unit highlights the property's eco-friendly features and suggests environmentally conscious properties to users. For example, it generates a description such as, "This property uses recyclable building materials and supports sustainable living." This allows the property's energy efficiency and environmental friendliness to be emphasized, thereby appealing to environmentally conscious users.

[0056] The property suggestion unit can suggest properties based on the user's health condition and fitness goals. For example, the generation AI collects the user's health data and makes a suggestion such as, "This property has a gym or running course nearby, which will support a healthy lifestyle." The property suggestion unit also analyzes the user's fitness goals and suggests properties based on that. For example, it makes a suggestion such as, "This property has a large garden, which is ideal for practicing yoga or Pilates." Furthermore, the property suggestion unit performs an integrated analysis of the user's health condition and fitness goals and suggests the most suitable property. For example, it makes a suggestion such as, "This property has a health food store and organic cafe nearby, which will support a healthy diet." This allows the system to improve user satisfaction by suggesting properties that are tailored to the user's health condition and fitness goals.

[0057] The market price information provider can provide market price information that takes into account a property's energy efficiency and environmental impact. For example, the generation AI analyzes a property's energy consumption data and provides information such as, "Energy-efficient properties in this area are traded at an average price 10% higher." The market price information provider also evaluates the property's environmental impact and reflects that information in market price information. For example, it provides information such as, "Properties in this area that use renewable energy are likely to increase in value in the future." Furthermore, the market price information provider can highlight a property's eco-friendly features and provide users with market price information for environmentally conscious properties. For example, it provides information such as, "Properties in this area that use recyclable building materials are rated higher than other properties." This supports users' investment decisions by providing market price information that takes into account a property's energy efficiency and environmental friendliness.

[0058] The investment decision support department can support investment decisions that take into account a property's energy efficiency and environmental impact. For example, the generation AI analyzes a property's energy consumption data and provides information such as, "This property is highly energy efficient and will reduce energy costs in the future, making it a high investment value." The investment decision support department also evaluates the property's environmental impact and reflects this information in investment decisions. For example, it provides information such as, "This property uses renewable energy and is environmentally friendly, so its value may increase in the future." Furthermore, the investment decision support department highlights the property's eco-friendly features and evaluates the investment value of environmentally conscious properties to users. For example, it provides information such as, "This property uses recyclable building materials and is rated as a sustainable investment." This provides multifaceted support for users' investment decisions by taking into account a property's energy efficiency and environmental friendliness.

[0059] The property description creation unit can create descriptions that emphasize the property's security features and crime prevention measures. For example, the generation AI collects security data about the property and generates a description such as, "This property is equipped with the latest security system, so you can live in peace of mind." The property description creation unit also evaluates the property's crime prevention measures and reflects the evaluation in the description. For example, it provides information such as, "This property has 24-hour surveillance cameras and security guards, so it is highly safe." Furthermore, the property description creation unit uses the generation AI to emphasize the property's security features and recommend highly safe properties to users. For example, it generates a description such as, "This property is equipped with security glass and strong door locks, so you can live in peace of mind." By emphasizing the property's security features and crime prevention measures, it can appeal to users who place importance on safety.

[0060] The property suggestion unit can suggest pet-friendly properties based on information about the user's pet. For example, the generation AI collects data about the user's pet and makes a suggestion such as, "This property allows pets and has a dog park nearby." The property suggestion unit also suggests properties based on the type and size of the user's pet. For example, it makes a suggestion such as, "This property is ideal for homes with large dogs and has a large yard." Furthermore, the property suggestion unit comprehensively analyzes information about the user's pet and suggests the most suitable property. For example, it makes a suggestion such as, "This property is ideal for homes with cats and has space for installing a cat tower." This makes it possible to improve user satisfaction by suggesting pet-friendly properties based on information about the user's pet.

[0061] The market price information provider can provide market price information that takes into account a property's security features and crime prevention measures. For example, the generation AI analyzes the property's security data and provides information such as, "Properties in this area with robust security features are traded at prices 15% higher on average." The market price information provider also evaluates the property's security measures and reflects the evaluation in market price information. For example, it provides information such as, "Properties in this area with 24-hour surveillance cameras are likely to increase in value in the future." Furthermore, the market price information provider highlights the property's security features and provides users with market price information for highly secure properties. For example, it provides information such as, "Properties in this area with security glass and strong door locks are rated higher than other properties." This allows the system to provide market price information that takes into account a property's security features and crime prevention measures, thereby supporting users' investment decisions.

[0062] The processing flow of the first embodiment will be briefly explained below.

[0063] Step 1: The property description creation unit receives property information as input and automatically creates a property description. For example, the AI ​​receives information such as the property's location, layout, price, and surrounding environment as input, and generates a description such as, "This property is located in a quiet residential area and features a spacious living room and the latest amenities." Step 2: The property suggestion unit receives the user's preferences and conditions as input and suggests the most suitable property. For example, if the user enters conditions such as "3LDK, near the station, pets allowed," the generation AI will list properties that meet those conditions and suggest them to the user. Step 3: The market price information provider analyzes past transaction data and market trends to provide the appropriate price for the property. For example, when data on a specific area or property type is entered, the generation AI analyzes the average price and price trends for that area and property type and provides them to the user. Step 4: The interior suggestion unit makes interior suggestions based on the property's layout and the user's preferences. For example, if a user inputs "I like modern designs," the AI ​​generator will suggest furniture and interior layouts that match those preferences. Step 5: The Investment Decision Support Department evaluates the investment value of the property and supports investment decisions. For example, it analyzes the profitability of the property and the possibility of future value increases, and the generation AI provides an assessment such as "This property is expected to be highly profitable." Step 6: The contract creation unit automatically creates the contract required for the transaction. For example, based on a property purchase and sale contract or rental contract template, the generation AI inputs the necessary information to generate the contract.

[0064] (Example 2) The AI ​​system according to an embodiment of the present invention is a system that resolves the problems of insufficient information and human resources, and the difficulty of determining the appropriate price in real estate transactions. This system uses generative AI to create property descriptions, propose properties that suit the user's preferences, provide market price information, propose interior design, support investment decisions, and draft contracts. As a result, the AI ​​system resolves the problems of insufficient information and human resources, and the difficulty of determining the appropriate price in real estate transactions, enabling efficient and effective transactions.

[0065] The AI ​​system according to the embodiment includes a property description creation unit, a property proposal unit, a market price information provision unit, an interior proposal unit, an investment decision support unit, and a contract creation unit. The property description creation unit receives property information as input and automatically creates a property description. For example, the property description creation unit receives information such as the property's location, layout, price, and surrounding environment as input, and the generation AI generates a description such as, "This property is located in a quiet residential area, has a spacious living room, and the latest amenities." The property proposal unit receives a user's preferences and conditions as input and proposes optimal properties. For example, when a user inputs conditions such as "3LDK, near the station, pets allowed," the property proposal unit generates a list of properties that meet the conditions and proposes them to the user. The market price information provision unit analyzes past transaction data and market trends to provide the appropriate price for a property. For example, when the market price information provision unit inputs data related to a specific area or property type, the generation AI analyzes the average price and price trends for that area and property type and provides them to the user. The interior proposal unit proposes interior designs based on the property's layout and the user's preferences. For example, if the user inputs, "I like modern designs," the interior proposal unit proposes furniture and interior layouts that match that preference. The investment decision support unit evaluates the investment value of a property and supports investment decisions. For example, the investment decision support unit analyzes the property's profitability and the possibility of future value increases, and the generation AI provides an evaluation such as, "This property is expected to be highly profitable." The contract creation unit automatically creates contracts required for transactions. For example, the contract creation unit generates contracts by inputting the necessary information based on templates for property sales and rental contracts. As a result, the AI ​​system according to the embodiment can resolve issues such as a lack of information and personnel, and the difficulty of determining appropriate prices in real estate transactions, thereby realizing efficient and effective transactions.

[0066] The property description creation unit can create detailed descriptions that include information about the property's history and past owners. For example, the AI ​​generation unit collects information about the property's past owners and creates a description based on that information. For example, it generates a detailed description such as, "This property was designed by a famous architect and was once home to a famous artist." The AI ​​generation unit also researches the property's construction year and renovation history and incorporates that information into the description. For example, it provides information such as, "This property was built in the 1920s and was most recently fully renovated in 2015." The AI ​​generation unit also researches the property's historical background and the history of the area and incorporates that information into the description. For example, it generates a description such as, "This property is located in an area with a preserved historic streetscape and is surrounded by many cultural assets." This provides detailed information about the property, deepening the user's understanding.

[0067] The property description creation unit can incorporate information about the culture and events in the area surrounding the property into the description. For example, the generation AI in the property description creation unit collects information about cultural facilities and events in the area surrounding the property and reflects that information in the description. For example, the unit generates a description such as, "This property is close to an art museum and a theater, and many cultural events are held there throughout the year." The generation AI in the property description creation unit also researches seasonal event information in the area surrounding the property and incorporates that information into the description. For example, the unit provides information such as, "This property is located in an area where cherry blossom festivals are held in the spring and fireworks displays are held in the summer." The generation AI in the property description creation unit also researches the history and traditional events in the area surrounding the property and reflects that information in the description. For example, the unit generates a description such as, "This property is located in an area where long-standing traditional festivals are held, allowing you to feel the history of the area." This makes it easier to attract users' interest by providing information about the area surrounding the property.

[0068] The property description creation unit can customize the description in a tone that matches the user's emotions using the emotion estimation function. For example, the generation AI in the property description creation unit analyzes the user's emotional state in real time and generates a description in a tone that matches that emotion. For example, if the user is relaxed, the description is created in a calm tone. The property description creation unit also uses the emotion estimation function to customize the description in an energetic tone if the user is excited. For example, the property description creation unit generates a description such as, "This property is located in the center of a vibrant city, so you can enjoy an exciting life." The generation AI in the property description creation unit also creates a description that is in tune with the user's emotions based on the user's emotional data. For example, if the user is feeling anxious, the description is generated in a tone that conveys a sense of security. This improves user satisfaction by providing a description that is in tune with the user's emotions.

[0069] The property description creation unit automatically generates property descriptions in multiple languages, making it possible to accommodate international users. For example, the generation AI in the property description creation unit automatically generates property descriptions in multiple languages, such as English, French, and Chinese. For example, it provides "This property is located in a quiet residential area and features a spacious living room and the latest amenities" in each language. The generation AI in the property description creation unit also posts property descriptions on a multilingual platform and collects feedback from international users. For example, it reflects user opinions based on the descriptions in each language. The generation AI in the property description creation unit also customizes property descriptions to suit different cultures and regional characteristics and provides them in each language. For example, it generates descriptions that take cultural backgrounds and regional characteristics into account. This expands the sales channels of properties by catering to international users.

[0070] The property description creation unit can incorporate 3D tour and virtual reality links into the property description. For example, the generation AI in the property description creation unit incorporates a 3D tour link into the property description, allowing users to virtually tour the property. For example, the generation AI generates a description such as, "For more information about this property, please take a look at this 3D tour." The property description creation unit also adds a virtual reality link to the property description, allowing users to experience the property using a VR device. For example, the generation AI generates a description such as, "To experience this property in virtual reality, please use this link." The property description creation unit also incorporates a link to an interactive 3D model into the property description, allowing users to freely explore the property details. For example, the generation AI generates a description such as, "You can view a 3D model of this property here." This allows users to virtually tour the property.

[0071] The property description creation unit can use the emotion estimation function to generate a description that emphasizes the property features that interest the user most. For example, if the user is interested in a large living room, the property description creation unit generates a description that emphasizes the property features that interest the user most. For example, if the user is interested in a large living room, the unit generates a description such as, "This property, with its spacious living room, is perfect for family gatherings." The property description creation unit also generates a description that emphasizes specific features based on the user's emotion data using the generation AI. For example, if the user is interested in the latest amenities, the unit generates a description such as, "This property, equipped with the latest amenities, offers a comfortable lifestyle." The property description creation unit also uses the emotion estimation function to identify the property features that the user is most likely to be interested in and generates a description that emphasizes them. For example, if the user is looking for a quiet environment, the unit generates a description such as, "This property, located in a quiet residential area, offers a relaxed lifestyle." This provides a description that is likely to attract the user's interest.

[0072] The property suggestion unit analyzes a user's past search history and browsing history to make more accurate property suggestions. For example, the generation AI in the property suggestion unit analyzes a user's past search history and suggests properties based on similar search conditions. For example, it lists the most suitable properties based on the conditions the user has searched for in the past. The property suggestion unit also analyzes a user's browsing history and suggests new properties based on the characteristics of the properties they have viewed. For example, it analyzes the characteristics of properties the user has viewed in the past and suggests properties with similar characteristics. The property suggestion unit also integrates the user's search history and browsing history to make more accurate property suggestions. For example, it combines search conditions and browsing history to suggest properties that best match the user's preferences. This makes use of the user's past behavioral data to make more accurate property suggestions.

[0073] The property suggestion unit can suggest properties based on the user's lifestyle and hobbies. For example, the generation AI collects information about the user's lifestyle and suggests properties based on that information. For example, for a user who likes the outdoors, the generation AI suggests properties with parks or nature nearby. The property suggestion unit also analyzes data about the user's hobbies and suggests properties based on that. For example, for a user whose hobby is cooking, the generation AI suggests properties with spacious kitchens. The property suggestion unit also performs an integrated analysis of the user's lifestyle and hobbies and suggests the most suitable property. For example, for a user who has a pet, the generation AI suggests properties that allow pets. In this way, property suggestions are made that match the user's lifestyle and hobbies.

[0074] The property suggestion unit can use the emotion estimation function to make property suggestions based on the user's emotional state. For example, the property suggestion unit uses the emotion estimation function to analyze the user's emotional state in real time and suggest properties that correspond to that emotion. For example, if the user is relaxed, the property suggestion unit suggests properties in quiet environments. The property suggestion unit also makes property suggestions that match the user's emotional state based on the generation AI's emotional data. For example, if the user is excited, the property suggestion unit suggests properties in lively areas. The property suggestion unit also uses the emotion estimation function to make property suggestions that take the user's emotional state into consideration. For example, if the user is feeling anxious, the property suggestion unit suggests properties that give a sense of security. In this way, by making property suggestions that correspond to the user's emotional state, user satisfaction is improved.

[0075] The property suggestion unit can analyze a user's social media posts and suggest properties that match their preferences. For example, the generation AI in the property suggestion unit analyzes a user's social media posts and identifies the user's preferences from the content of the posts. For example, it suggests optimal properties based on posts about travel destinations or hobbies. The generation AI in the property suggestion unit also analyzes a user's social media photos and suggests properties based on the places and objects that appear in the photos. For example, it suggests properties surrounded by nature to a user who takes many nature photos. The generation AI in the property suggestion unit also analyzes a user's social media friendships and suggests properties based on the areas where friends live and their preferences. For example, it suggests properties in areas where many friends live. In this way, the generation AI utilizes the user's social media posts to suggest properties that match their preferences.

[0076] The property suggestion unit can suggest properties based on the user's family composition and future plans. For example, the generation AI collects information about the user's family composition and suggests properties based on that information. For example, for a family with children, it suggests properties that are close to schools and parks. The property suggestion unit also analyzes data about the user's future plans and suggests properties based on that. For example, for a user who plans to keep a pet in the future, it suggests properties that allow pets. The property suggestion unit also performs an integrated analysis of the user's family composition and future plans and suggests the most suitable property. For example, it suggests a spacious property taking into account future family growth. In this way, it makes property suggestions that match the user's family composition and future plans.

[0077] The property suggestion unit can use the emotion estimation function to suggest properties that evoke the most positive emotions in the user. For example, the property suggestion unit uses the emotion estimation function to identify properties that evoke the most positive emotions in the user and prioritizes suggesting those properties. For example, properties with high emotion scores are displayed at the top of the list. The property suggestion unit also uses the generation AI to suggest properties that elicit positive emotions based on the user's emotion data. For example, it prioritizes suggesting properties that have features that excite the user. The property suggestion unit also uses the emotion estimation function to build a system that prioritizes displaying properties that evoke the most positive emotions in the user. For example, it highlights properties with high emotion scores. This improves user satisfaction by preferentially suggesting properties that evoke the most positive emotions in the user.

[0078] The market price information provider can provide market price information that includes not only past transaction data but also future market forecasts. In the market price information provider, for example, the generation AI analyzes past transaction data and makes future market forecasts. For example, it predicts price trends for the next five years based on transaction data from the past 10 years. In addition, the market price information provider provides future market price information by taking market trends and economic indicators into account. For example, it predicts future price fluctuations based on economic growth rates and interest rate trends. In addition, the market price information provider provides future market price information by analyzing regional development plans and infrastructure development information. For example, it predicts future price increases taking into account construction plans for new railways and roads. In this way, by providing market price information that includes future market forecasts, it supports users' investment decisions.

[0079] The market price information providing unit can provide market price information that takes into account regional economic indicators and infrastructure development plans. For example, the generation AI collects regional economic indicators and provides market price information based on them. For example, the market price information providing unit calculates the fair price of a property taking into account the regional unemployment rate and average income. The generation AI also analyzes regional infrastructure development plans and provides market price information based on the analysis. For example, it predicts future price increases taking into account new railway and road construction plans. The generation AI also analyzes regional economic growth rates and demographics and provides market price information based on the analysis. For example, it predicts property prices in areas where population growth is expected. In this way, market price information that takes into account regional economic indicators and infrastructure development plans is provided to support users' investment decisions.

[0080] The market price information providing unit can use the emotion estimation function to devise a method of presenting market price information that gives the user a sense of security. The market price information providing unit, for example, uses the emotion estimation function to devise a method of presenting market price information that gives the user a sense of security. For example, it uses wording and graphs that elicit positive emotions. The market price information providing unit also provides market price information that gives a sense of security based on the user's emotion data generated by the generation AI. For example, it emphasizes price stability and future outlook. Furthermore, if the user is feeling anxious using the emotion estimation function, the market price information providing unit prioritizes providing information that gives a sense of security. For example, it emphasizes past price stability and positive future predictions. In this way, by devising a method of presenting market price information that gives the user a sense of security, the unit gains the user's trust.

[0081] The market price information provider can compare market price information from different countries and regions and provide information from a global perspective. For example, the generation AI collects market price information from different countries and regions, compares it, and provides information from a global perspective. For example, it compares property prices in major cities. The market price information provider also analyzes economic indicators from different countries and regions and provides market price information based on that. For example, it predicts property prices taking into account each country's economic growth rate and interest rate trends. The market price information provider also analyzes market trends from different countries and regions and provides market price information based on that. For example, it compares real estate market trends from each country and suggests investment destinations. This provides market price information from a global perspective to support users' investment decisions.

[0082] The market price information providing unit can visualize market price information and enable intuitive understanding with graphs and charts. For example, the generation AI in the market price information providing unit visualizes market price information and enables intuitive understanding with graphs and charts. For example, price trends are displayed in a line graph. The generation AI in the market price information providing unit also displays market price information by region in a heat map to enable intuitive understanding. For example, areas with high prices are displayed in red. The generation AI in the market price information providing unit also provides market price information in interactive graphs and charts to enable users to access detailed information. For example, clicking on a point on a graph displays detailed information. In this way, market price information is visualized to enable users to intuitively understand it.

[0083] The market price information providing unit can use the emotion estimation function to prioritize displaying market price information that the user is most interested in. For example, the market price information providing unit uses the emotion estimation function to identify market price information that the user is most interested in and displays it preferentially. For example, information with a high emotion score is displayed at the top of the list. Furthermore, the market price information providing unit provides market price information that is of high interest to the user based on the user's emotion data using the generation AI. For example, price trends in areas that the user is interested in are displayed preferentially. Furthermore, the market price information providing unit uses the emotion estimation function to build a system that highlights market price information that the user is most interested in. For example, information with a high emotion score is displayed prominently. This improves user satisfaction by preferentially displaying market price information that the user is most interested in.

[0084] The interior proposal unit can make interior proposals that take into account not only the floor plan of the property but also the amount of light entering and ventilation. For example, the generation AI of the interior proposal unit analyzes the floor plan and amount of light entering the property and makes interior proposals based on that. For example, it may suggest placing the living room in a sunny location. The generation AI of the interior proposal unit also analyzes the ventilation of the property and makes interior proposals based on that. For example, it may suggest placing the bedroom in a well-ventilated location. The generation AI of the interior proposal unit also comprehensively analyzes the floor plan, amount of light entering, and amount of ventilation of the property and makes proposals for the optimal interior layout. For example, it may make proposals for interiors that make the most of natural light. In this way, by making interior proposals that take into account the amount of light entering and ventilation, it supports the user's comfortable lifestyle.

[0085] The interior suggestion unit can suggest interior designs based on the user's health condition and lifestyle habits. For example, the generation AI in the interior suggestion unit collects information about the user's health condition and makes interior suggestions based on that information. For example, it suggests installing an air purifier to a user with allergies. The generation AI in the interior suggestion unit also analyzes data about the user's lifestyle habits and makes interior suggestions based on that data. For example, it suggests a comfortable home office layout to a user who works remotely. The generation AI in the interior suggestion unit also comprehensively analyzes the user's health condition and lifestyle habits and makes optimal interior suggestions. For example, it suggests furniture layouts that support a healthy lifestyle. This allows it to make interior suggestions that are tailored to the user's health condition and lifestyle habits.

[0086] The interior suggestion unit can use the emotion estimation function to suggest interior layouts that will help the user relax. For example, the interior suggestion unit uses the emotion estimation function to identify interior layouts that will help the user relax and suggest them. For example, it prioritizes suggesting layouts with a high emotion score. The interior suggestion unit also suggests interior layouts with a high relaxing effect based on the user's emotion data using the generation AI. For example, it suggests layouts that use colors and materials that will relax the user. The interior suggestion unit also uses the emotion estimation function to build a system that highlights interior layouts that will help the user relax. For example, it prominently displays layouts with a high emotion score. This suggests interior layouts that will help the user relax, thereby improving user satisfaction.

[0087] The interior suggestion unit can make interior suggestions according to the season or event. In the interior suggestion unit, for example, the generation AI makes interior suggestions for each season. For example, in winter, it suggests interiors using warm colors and materials. In addition, the generation AI makes interior suggestions according to specific events. For example, it suggests Christmas trees and decorations for Christmas. In addition, the generation AI makes interior suggestions according to the season or event, allowing the user to enjoy the seasonal feel or event. For example, it suggests interiors using flowers in spring. In this way, interior suggestions according to the season or event enrich the user's life.

[0088] The interior suggestion unit can make interior suggestions that incorporate different cultures and design styles. For example, the generation AI in the interior suggestion unit makes interior suggestions that incorporate design styles from different cultures. For example, it proposes styles such as Japanese, Nordic, and Mediterranean. The generation AI in the interior suggestion unit also makes interior suggestions that combine design styles from different cultures based on the user's preferences. For example, it proposes a style that combines Japanese and Nordic styles. The generation AI in the interior suggestion unit also analyzes the characteristics of different cultures and design styles and makes interior suggestions based on that. For example, it proposes unique interiors that incorporate the characteristics of each culture. This allows the generation AI to make interior suggestions that incorporate different cultures and design styles, thereby meeting the diverse needs of users.

[0089] The interior suggestion unit can use the emotion estimation function to suggest an interior style that evokes the most positive emotions in the user. For example, the interior suggestion unit uses the emotion estimation function to identify the interior style that evokes the most positive emotions in the user and suggests it. For example, it prioritizes suggesting styles with a high emotion score. The interior suggestion unit also suggests interior styles that elicit positive emotions based on the user's emotion data using a generation AI. For example, it suggests styles that use colors and materials that relax the user. The interior suggestion unit also uses the emotion estimation function to build a system that highlights the interior style that evokes the most positive emotions in the user. For example, it prominently displays styles with a high emotion score. This improves user satisfaction by suggesting interior styles that evoke the most positive emotions in the user.

[0090] The Investment Decision Support Department can support investment decisions by analyzing not only the profitability of a property but also its risk factors in detail. For example, the Investment Decision Support Department uses the generation AI to analyze the profitability of a property, and also to analyze its risk factors in detail. For example, even if a property is highly profitable, the generation AI can warn users if it has many risk factors. The Investment Decision Support Department also supports investment decisions by having the generation AI comprehensively evaluate the profitability and risk factors of a property. For example, it can provide an evaluation that takes into account the balance between profitability and risk. The Investment Decision Support Department also uses the generation AI to analyze the profitability and risk factors of a property in detail, and provide the user with specific investment decision advice. For example, it can suggest measures to mitigate risk. This supports the user's investment decisions by providing a detailed analysis of the property's profitability and risk factors.

[0091] The investment decision support unit can predict future investment value based on past investment performance data. In the investment decision support unit, for example, the generation AI analyzes past investment performance data and predicts future investment value based on that. For example, it predicts future profitability by taking past rates of return and price fluctuations into consideration. The investment decision support unit also predicts future market trends based on past investment performance data. For example, it analyzes past market trends and predicts future price increases or decreases. The investment decision support unit also predicts future risk factors based on past investment performance data. For example, it analyzes past risk factors and predicts future risks. In this way, the investment decision support unit supports the user's investment decisions by predicting future investment value based on past investment performance data.

[0092] The investment decision support unit can use the emotion estimation function to provide information that allows users to make investment decisions with confidence. The investment decision support unit, for example, uses the emotion estimation function to provide information that allows users to make investment decisions with confidence. For example, it uses wording and graphs that elicit positive emotions. The investment decision support unit also provides investment information that gives a sense of security based on the user's emotion data using the generation AI. For example, it emphasizes the stability of profitability and future prospects. Furthermore, if the investment decision support unit uses the emotion estimation function to indicate that the user is feeling anxious, it prioritizes providing information that gives a sense of security. For example, it emphasizes past profit stability and positive future predictions. In this way, the investment decision support unit gains the user's trust by providing information that allows users to make investment decisions with confidence.

[0093] The investment decision support unit can simulate different investment portfolios and propose optimal investment strategies. For example, the investment decision support unit uses a generation AI to simulate different investment portfolios and evaluate the profitability and risk of each. For example, it predicts the profitability of a portfolio that combines multiple properties. The investment decision support unit also uses a generation AI to simulate different investment portfolios and propose optimal investment strategies. For example, it proposes a portfolio that takes risk diversification into consideration. The investment decision support unit also uses a generation AI to simulate different investment portfolios and propose the portfolio that is most suitable for the user's investment goals. For example, it proposes a portfolio that emphasizes short-term profits or a portfolio that emphasizes long-term stability. In this way, the investment decision support unit supports the user's investment decisions by simulating different investment portfolios and proposing optimal investment strategies.

[0094] The investment decision support unit can support comprehensive investment decisions that include investment options other than real estate (stocks, bonds, etc.). For example, the investment decision support unit uses the generation AI to analyze investment options other than real estate (stocks, bonds, etc.) and support comprehensive investment decisions that include these. For example, it proposes a portfolio that combines stocks and real estate. The investment decision support unit also uses the generation AI to evaluate the risk and profitability of different investment options and propose a comprehensive investment strategy. For example, it proposes an investment portfolio that takes risk diversification into consideration. The investment decision support unit also uses the generation AI to propose an optimal investment strategy that includes investment options other than real estate based on the user's investment goals. For example, it proposes an investment strategy that emphasizes short-term profits or an investment strategy that emphasizes long-term stability. This provides comprehensive investment decision support that includes investment options other than real estate, thereby providing multifaceted support for the user's investment decisions.

[0095] The investment decision support unit can use the emotion estimation function to suggest investment options that evoke the most positive emotions in the user. For example, the investment decision support unit uses the emotion estimation function to identify investment options that evoke the most positive emotions in the user and suggest them. For example, it prioritizes suggesting investment options with high emotion scores. The investment decision support unit also uses the generation AI to suggest investment options that elicit positive emotions based on the user's emotion data. For example, it suggests investment options that give the user a sense of security. The investment decision support unit also uses the emotion estimation function to build a system that highlights investment options that evoke the most positive emotions in the user. For example, it prominently displays investment options with high emotion scores. This improves user satisfaction by suggesting investment options that evoke the most positive emotions in the user.

[0096] The contract creation unit can automatically check the legal requirements of a contract and generate a legally problem-free contract. For example, the generation AI in the contract creation unit automatically checks the legal requirements of a contract and generates a legally problem-free contract. For example, it automatically adds necessary clauses and conditions. The generation AI in the contract creation unit also analyzes the legal requirements of a contract and automatically corrects any legally problematic parts. For example, it adds missing clauses or corrects incorrect conditions. The generation AI in the contract creation unit also generates a legally problem-free contract for the user based on the legal requirements of the contract. For example, it adds clauses to avoid legal risks. In this way, the generation AI automatically checks the legal requirements of a contract and generates a legally problem-free contract, increasing the user's sense of security.

[0097] The contract creation unit can provide a function that concisely explains the contents of a contract according to the user's level of understanding. For example, the contract creation unit provides a function in which the generation AI analyzes the contents of a contract and provides a concise explanation according to the user's level of understanding. For example, technical terms are replaced with easier-to-understand language. The contract creation unit also provides a function in which the generation AI extracts the important points of a contract and provides a concise explanation to the user. For example, the main conditions and risks of the contract are explained in an easy-to-understand manner. The contract creation unit also provides a function in which the generation AI customizes the contents of a contract according to the user's level of understanding and provides a concise explanation. For example, the level of detail of the explanation is adjusted to match the user's knowledge level. This deepens the user's understanding by providing a concise explanation of the contents of the contract according to the user's level of understanding.

[0098] The contract creation unit can use the emotion estimation function to provide an explanation that allows the user to sign the contract with confidence. The contract creation unit, for example, uses the emotion estimation function to provide an explanation that allows the user to sign the contract with confidence. For example, it uses wording and graphs that elicit positive emotions. The contract creation unit also uses the generation AI to provide an explanation of the contract that gives a sense of security based on the user's emotion data. For example, it may emphasize low-risk aspects in the explanation. Furthermore, if the contract creation unit uses the emotion estimation function to indicate that the user is feeling anxious, it prioritizes an explanation that gives a sense of security. For example, it may introduce past success stories and positive feedback. In this way, the explanation that allows the user to sign the contract with confidence gains the user's trust.

[0099] The contract creation unit can automatically generate contracts in different languages, making it possible to handle international transactions. For example, the generation AI in the contract creation unit can automatically generate contracts in different languages ​​to handle international transactions. For example, contracts can be provided in multiple languages, such as English, French, and Chinese. The generation AI in the contract creation unit also posts the contents of the contract on a multilingual platform to collect feedback from international users. For example, user opinions can be reflected based on the contracts in each language. The generation AI in the contract creation unit can also customize contracts to suit different cultures and regional characteristics and provide them in each language. For example, it can generate contracts that take cultural backgrounds and regional characteristics into consideration. This allows for the automatic generation of contracts in different languages, making it possible to handle international transactions.

[0100] The contract creation unit can automatically record the change history of the contract to ensure transparency. In the contract creation unit, for example, the generation AI automatically records the change history of the contract to ensure transparency. For example, it automatically records the parts that were changed and the date and time. In addition, the contract creation unit has the generation AI analyze the change history of the contract and provide transparent information to the user. For example, it explains the reasons for and impact of the changes. In addition, the contract creation unit has the generation AI provide the user with a transparent contract based on the change history of the contract. For example, it visually displays the change history. In this way, the change history of the contract is automatically recorded and transparency is ensured, thereby gaining the trust of the user.

[0101] The contract creation unit can use the emotion estimation function to propose a contract format that gives the user the most sense of security. For example, the contract creation unit uses the emotion estimation function to identify and propose a contract format that gives the user the most sense of security. For example, it prioritizes proposing formats with a high emotion score. The contract creation unit also uses the generation AI to propose a contract format that gives the user a sense of security based on the user's emotion data. For example, it proposes a layout or design that gives the user a sense of security. The contract creation unit also uses the emotion estimation function to build a system that highlights the contract format that gives the user the most sense of security. For example, it prominently displays formats with a high emotion score. In this way, the contract creation unit gains the user's trust by proposing a contract format that gives the user the most sense of security.

[0102] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0103] The property description creation unit can create descriptions that take into account the property's energy efficiency and environmental impact. For example, the generation AI collects energy consumption data for the property and generates a description such as, "This property is equipped with the latest energy-saving equipment, allowing for significant reductions in annual energy costs." The property description creation unit also evaluates the property's environmental impact and reflects this in the description. For example, it provides information such as, "This property uses renewable energy and offers environmentally friendly living." Furthermore, the property description creation unit highlights the property's eco-friendly features and suggests environmentally conscious properties to users. For example, it generates a description such as, "This property uses recyclable building materials and supports sustainable living." This allows the property's energy efficiency and environmental friendliness to be emphasized, thereby appealing to environmentally conscious users.

[0104] The property suggestion unit can suggest properties based on the user's health condition and fitness goals. For example, the generation AI collects the user's health data and makes a suggestion such as, "This property has a gym or running course nearby, which will support a healthy lifestyle." The property suggestion unit also analyzes the user's fitness goals and suggests properties based on that. For example, it makes a suggestion such as, "This property has a large garden, which is ideal for practicing yoga or Pilates." Furthermore, the property suggestion unit performs an integrated analysis of the user's health condition and fitness goals and suggests the most suitable property. For example, it makes a suggestion such as, "This property has a health food store and organic cafe nearby, which will support a healthy diet." This allows the system to improve user satisfaction by suggesting properties that are tailored to the user's health condition and fitness goals.

[0105] The market price information provider can provide market price information that takes into account a property's energy efficiency and environmental impact. For example, the generation AI analyzes a property's energy consumption data and provides information such as, "Energy-efficient properties in this area are traded at an average price 10% higher." The market price information provider also evaluates the property's environmental impact and reflects that information in market price information. For example, it provides information such as, "Properties in this area that use renewable energy are likely to increase in value in the future." Furthermore, the market price information provider can highlight a property's eco-friendly features and provide users with market price information for environmentally conscious properties. For example, it provides information such as, "Properties in this area that use recyclable building materials are rated higher than other properties." This supports users' investment decisions by providing market price information that takes into account a property's energy efficiency and environmental friendliness.

[0106] The interior suggestion unit can suggest interior layouts with a high relaxing effect based on the user's emotional state. For example, the generation AI analyzes the user's emotional data and suggests interior layouts using colors and materials with a high relaxing effect. For example, it may suggest, "This living room has blue curtains with a relaxing effect and a soft sofa." The interior suggestion unit also uses its emotion estimation function to identify and suggest the interior layout that will most relax the user. For example, it may suggest, "This bedroom has relaxing lighting and an aroma diffuser." Furthermore, the interior suggestion unit builds a system in which the generation AI highlights interior layouts with a high relaxing effect based on the user's emotional data. For example, it may suggest, "This dining room has plants with a relaxing effect." This allows the system to suggest interior layouts that will help the user relax, thereby improving user satisfaction.

[0107] The investment decision support department can support investment decisions that take into account a property's energy efficiency and environmental impact. For example, the generation AI analyzes a property's energy consumption data and provides information such as, "This property is highly energy efficient and will reduce energy costs in the future, making it a high investment value." The investment decision support department also evaluates the property's environmental impact and reflects this information in investment decisions. For example, it provides information such as, "This property uses renewable energy and is environmentally friendly, so its value may increase in the future." Furthermore, the investment decision support department highlights the property's eco-friendly features and evaluates the investment value of environmentally conscious properties to users. For example, it provides information such as, "This property uses recyclable building materials and is rated as a sustainable investment." This provides multifaceted support for users' investment decisions by taking into account a property's energy efficiency and environmental friendliness.

[0108] The contract creation unit can customize the contents of the contract based on the user's emotional state and provide explanations that give a sense of security. For example, the generation AI analyzes the user's emotional data and explains the contents of the contract using reassuring language and graphs. For example, it may explain, "This contract was created based on past success stories and has been confirmed to pose little risk." Furthermore, if the contract creation unit uses its emotion estimation function to prioritize providing information that gives a sense of security when the user is feeling anxious. For example, it may explain, "This contract has been confirmed to be legally sound, so you can sign it with confidence." Furthermore, the generation AI in the contract creation unit can suggest a contract format that gives a sense of security based on the user's emotional data. For example, it may suggest, "This contract features an easy-to-understand layout and concise explanations." This allows the user to sign the contract with confidence, thereby gaining their trust.

[0109] The property description creation unit can create descriptions that emphasize the property's security features and crime prevention measures. For example, the generation AI collects security data about the property and generates a description such as, "This property is equipped with the latest security system, so you can live in peace of mind." The property description creation unit also evaluates the property's crime prevention measures and reflects the evaluation in the description. For example, it provides information such as, "This property has 24-hour surveillance cameras and security guards, so it is highly safe." Furthermore, the property description creation unit uses the generation AI to emphasize the property's security features and recommend highly safe properties to users. For example, it generates a description such as, "This property is equipped with security glass and strong door locks, so you can live in peace of mind." By emphasizing the property's security features and crime prevention measures, it can appeal to users who place importance on safety.

[0110] The property suggestion unit can suggest pet-friendly properties based on information about the user's pet. For example, the generation AI collects data about the user's pet and makes a suggestion such as, "This property allows pets and has a dog park nearby." The property suggestion unit also suggests properties based on the type and size of the user's pet. For example, it makes a suggestion such as, "This property is ideal for homes with large dogs and has a large yard." Furthermore, the property suggestion unit comprehensively analyzes information about the user's pet and suggests the most suitable property. For example, it makes a suggestion such as, "This property is ideal for homes with cats and has space for installing a cat tower." This makes it possible to improve user satisfaction by suggesting pet-friendly properties based on information about the user's pet.

[0111] The market price information provider can provide market price information that takes into account a property's security features and crime prevention measures. For example, the generation AI analyzes the property's security data and provides information such as, "Properties in this area with robust security features are traded at prices 15% higher on average." The market price information provider also evaluates the property's security measures and reflects the evaluation in market price information. For example, it provides information such as, "Properties in this area with 24-hour surveillance cameras are likely to increase in value in the future." Furthermore, the market price information provider highlights the property's security features and provides users with market price information for highly secure properties. For example, it provides information such as, "Properties in this area with security glass and strong door locks are rated higher than other properties." This allows the system to provide market price information that takes into account a property's security features and crime prevention measures, thereby supporting users' investment decisions.

[0112] The interior suggestion unit can suggest interior layouts with a high relaxing effect based on the user's emotional state. For example, the generation AI analyzes the user's emotional data and suggests interior layouts using colors and materials with a high relaxing effect. For example, it may suggest, "This living room has blue curtains with a relaxing effect and a soft sofa." The interior suggestion unit also uses its emotion estimation function to identify and suggest the interior layout that will most relax the user. For example, it may suggest, "This bedroom has relaxing lighting and an aroma diffuser." Furthermore, the interior suggestion unit builds a system in which the generation AI highlights interior layouts with a high relaxing effect based on the user's emotional data. For example, it may suggest, "This dining room has plants with a relaxing effect." This allows the system to suggest interior layouts that will help the user relax, thereby improving user satisfaction.

[0113] The processing flow of the second embodiment will be briefly explained below.

[0114] Step 1: The property description creation unit receives property information as input and automatically creates a property description. For example, the AI ​​receives information such as the property's location, layout, price, and surrounding environment as input, and generates a description such as, "This property is located in a quiet residential area and features a spacious living room and the latest amenities." Step 2: The property suggestion unit receives the user's preferences and conditions as input and suggests the most suitable property. For example, if the user enters conditions such as "3LDK, near the station, pets allowed," the generation AI will list properties that meet those conditions and suggest them to the user. Step 3: The market price information provider analyzes past transaction data and market trends to provide the appropriate price for the property. For example, when data on a specific area or property type is entered, the generation AI analyzes the average price and price trends for that area and property type and provides them to the user. Step 4: The interior suggestion unit makes interior suggestions based on the property's layout and the user's preferences. For example, if a user inputs "I like modern designs," the AI ​​generator will suggest furniture and interior layouts that match those preferences. Step 5: The Investment Decision Support Department evaluates the investment value of the property and supports investment decisions. For example, it analyzes the profitability of the property and the possibility of future value increases, and the generation AI provides an assessment such as "This property is expected to be highly profitable." Step 6: The contract creation unit automatically creates the contract required for the transaction. For example, based on a property purchase and sale contract or rental contract template, the generation AI inputs the necessary information to generate the contract.

[0115] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0116] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0117] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0118] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0119] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0120] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0121] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0122] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0123] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0124] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0125] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0126] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0127] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0128] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0129] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0130] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0131] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0132] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0133] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0134] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0135] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0136] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0137] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0138] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0139] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0140] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0141] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0142] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0143] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0144] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0145] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

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

[0147] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0148] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0149] 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.

[0150] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0151] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0152] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0153] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0154] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0155] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0156] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0157] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0158] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0159] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0160] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0161] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0162] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0163] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0164] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0165] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0166] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0167] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0168] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0169] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0170] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0171] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0172] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0173] 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.

[0174] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0175] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0176] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0177] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0178] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0179] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0180] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0181] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0182] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a property introduction creation unit that receives property information as input and automatically creates a property introduction; a property suggestion unit that receives user preferences and conditions as input and suggests optimal properties; The Market Information Department analyzes past transaction data and market trends to provide fair property prices, and An interior design proposal department that proposes interior designs based on the layout of the property and the user's preferences; The Investment Decision Support Department evaluates the investment value of properties and supports investment decisions. A contract creation unit that automatically creates a contract required for a transaction. A system characterized by:

2. The property introduction sentence creation unit Create a detailed description of the property, including its history and past owners 2. The system of claim 1.

3. The property introduction sentence creation unit Incorporate local culture and events into your listing description 2. The system of claim 1.

4. The property introduction sentence creation unit Customize your testimonials to match the user's emotional tone 2. The system of claim 1.

5. The property introduction sentence creation unit Automatically generate property descriptions in multiple languages ​​to accommodate international users 2. The system of claim 1.

6. The property introduction sentence creation unit Include links to 3D tours and virtual reality in your listing descriptions 2. The system of claim 1.

7. The property introduction sentence creation unit Generate a description that highlights the features of the property that interest the user most 2. The system of claim 1.

8. The property proposal unit Analyze the user's past search history and browsing history to make more accurate property suggestions 2. The system of claim 1.

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