system

Abstract

We provide the system. [Solution] Means of receiving real estate information, A method for analyzing received real estate information, comparing it with a market database, and calculating property prices, A means of generating contract templates and customizing contracts based on user requirements, A means to automatically schedule property viewings and notify relevant parties, A system that includes means for monitoring the progress of transactions and notifying users of necessary actions.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In real estate transactions, conventionally, real estate agents have been involved, resulting in high fees, which have been an economic burden on both sellers and buyers. Also, since much of the process is administrative work, it takes time and effort, hindering the smooth progress of transactions. In such a situation, there is a need for a new means to enable efficient real estate transactions while reducing brokerage fees.

Means for Solving the Problems

[0005] This invention provides a system that receives and analyzes real estate information and calculates property prices by comparing it with a market database. Furthermore, it has the function to generate contract templates and customize them based on the user's conditions. It also enables the automatic scheduling of property viewings and notification to relevant parties. By monitoring the progress of transactions and notifying the user of necessary actions, it realizes fast and low-cost real estate transactions. By combining these elements, it significantly reduces the traditional human intermediary work and provides efficient and economical real estate transactions.

[0006] "Real estate information" refers to basic data about a property, such as its address, size, year built, and structure.

[0007] A "market database" is a database that stores data on past and present real estate transactions and is used for property valuation and price calculation.

[0008] "Property price" refers to the amount considered fair when buying or selling a property in the real estate market.

[0009] A "contract template" refers to a standard document that covers all the basic items necessary for a real estate transaction.

[0010] "Customization" refers to the act of modifying a standard contract template to meet the specific conditions and requirements of a particular user.

[0011] "Property viewing" refers to the process by which a prospective buyer directly visits a property to check its condition and surroundings.

[0012] "Transaction progress" is an indicator that shows which stage each process of buying and selling real estate is in.

[0013] "Action" refers to the specific actions or procedures required to facilitate a transaction. [Brief explanation of the drawing]

[0014] [Figure 1] It is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] It shows an emotion map to which a plurality of emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined.

Embodiments for Carrying Out the Invention

[0015] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0016] First, the terms used in the following description will be explained.

[0017] In the following embodiments, a numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units 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), and the like.

[0018] In the following embodiments, a numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0019] In the following embodiments, a numbered storage is one or more non-volatile storage devices that store various programs, various parameters, and the like. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[0020] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0021] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0022] [First Embodiment]

[0023] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0024] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0025] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0026] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0027] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0028] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0029] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0030] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0031] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0032] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0033] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0034] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0035] The real estate brokerage system of this invention is a system for efficiently conducting real estate transactions using generative AI. This system operates through the cooperation of three entities: a server, a terminal, and a user.

[0036] The user first enters property information. This information includes basic data such as the property's address, size, year built, and structure.

[0037] The server receives and analyzes this information. It collects data on similar properties using a market database and calculates the fair market value of a property using an AI model. This allows users to quickly understand the market value of a property.

[0038] Next, the terminal generates a contract template necessary for real estate transactions. The template includes all the basic items and provides a base for the user to create their own contract.

[0039] The server customizes the contract based on user input. This customization ensures that specific conditions and requirements are reflected in the contract and that legal requirements are met.

[0040] When a user enters their preferred date and time for a property viewing, the server checks the schedules of the seller and property management company and sets the most suitable viewing date and time. The viewing date and time are notified to the relevant parties by the terminal automatically sending a confirmation email.

[0041] As the transaction progresses, the server continuously monitors the status. If necessary action is required, the terminal notifies the user and prompts them to take immediate action.

[0042] As a concrete example, let's consider a case where a user wants to sell an apartment located in an urban area. The user can input property information into the system and enter into a contract based on the market price suggested by the system. Since contract generation and scheduling are automated, the user can proceed with the real estate transaction quickly and efficiently.

[0043] As described above, the present invention significantly simplifies conventional intermediation procedures, resulting in reduced fees, shorter processing times, and improved transaction transparency.

[0044] The following describes the processing flow.

[0045] Step 1:

[0046] Users enter property information such as the property's address, size, year built, and structure using a dedicated input form.

[0047] Step 2:

[0048] The terminal receives the input and performs an initial check to ensure the property information is accurate. If necessary, it prompts the user for corrections.

[0049] Step 3:

[0050] The server analyzes the received real estate information and sends queries to the market database. It then collects price data for similar properties from the market database.

[0051] Step 4:

[0052] The server uses an AI model to calculate the property's appraised value based on the collected data. This appraisal result is then provided to the user.

[0053] Step 5:

[0054] The terminal generates a standard sales contract template based on the user's request.

[0055] Step 6:

[0056] The server customizes the contract template based on the user's input conditions (price, delivery date, etc.). This includes checking legal requirements.

[0057] Step 7:

[0058] The user enters their preferred date and time for viewing the property into the terminal.

[0059] Step 8:

[0060] The server checks the schedules of the seller and the property management company and assigns the most suitable viewing date and time.

[0061] Step 9:

[0062] The terminal automatically sends confirmation emails regarding the visit date and time to users and related parties.

[0063] Step 10:

[0064] The server continuously monitors the progress of the transaction and sends notifications to the user as needed to prompt action.

[0065] Step 11:

[0066] The terminal confirms the completion of the transaction and displays a screen requesting the user to provide feedback.

[0067] (Example 1)

[0068] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0069] Real estate transactions typically involve a wide range of tasks, including information gathering, property valuation, contract drafting, and viewing arrangements, making them time-consuming and laborious. This often results in inefficient and insufficient transparency in transactions. This service provides methods to address these challenges and conduct transactions quickly and efficiently.

[0070] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0071] In this invention, the server includes means for receiving real estate information, means for analyzing the received information and comparing it with market information to calculate the price of the property, and means for generating a contract template and customizing the contract based on the user's conditions. This makes it possible to proceed with transactions quickly and efficiently, and to ensure transparency and legal consistency.

[0072] "Real estate information" refers to data that includes basic attributes of properties related to real estate transactions, such as address, size, year built, and structure.

[0073] "Market information" refers to sources of information that include data on similar properties in the past and present real estate market.

[0074] "Property price" refers to the fair value of a property, calculated based on market information and other relevant data.

[0075] A "contract template" is an initial contract format that covers the basic items necessary for real estate transactions, and is customizable by the user.

[0076] An "AI model" refers to a group of computational models that use artificial intelligence technology to perform data analysis and prediction.

[0077] A "prompt statement" is an input statement that provides initial conditions to an AI model and generates a specific output.

[0078] A "server" refers to a computer system that processes, stores, and distributes data over a network.

[0079] A "terminal" is a computer device used by a user to interact with a server, and is used for inputting and outputting information.

[0080] This real estate brokerage system is designed to efficiently conduct real estate transactions using a generative AI model. Specific embodiments are shown below.

[0081] The user uses a terminal to input property information such as the property's address, size, year built, and structure. The terminal receives this information and sends it to the server.

[0082] The server analyzes market information based on the received real estate information. Market information is obtained from a database containing data on similar properties from the past and present. The server uses a generative AI model (e.g., a standard computational model built with TENSORFLOW® or PyTorch) to calculate the fair market value of the property. In this process, user input serves as prompts.

[0083] The terminal generates a contract template using calculation results received from the server. This contract template includes the basic items necessary for real estate transactions and can later be customized based on the user's requirements. Furthermore, an additional module is incorporated to verify the legal requirements of the contract, and modifications can be made as needed.

[0084] As a concrete example, consider a case where a user wants to sell an apartment in an urban area. The user inputs property information from their terminal, and the server analyzes this information and calculates a fair market price using an AI model. The user can then use this price as a reference to proceed with the transaction efficiently.

[0085] Examples of prompt messages include, "Calculate a fair market price based on the property information for an apartment in Shinjuku Ward," and "Generate a contract template based on this property information."

[0086] In this way, the system automates each step of a real estate transaction, enabling rapid and efficient execution. This invention aims to significantly improve transparency and efficiency by allowing users to proceed with real estate transactions with minimal effort.

[0087] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0088] Step 1:

[0089] The user enters property information using a terminal. This information includes the property's address, size, year built, and structure. The terminal collects this data and sends it to the server. The entered information is in digital format, and its integrity is checked before it is sent to the server.

[0090] Step 2:

[0091] The server analyzes the real estate information received from the terminal. It accesses a market information database and retrieves information on similar properties. The server searches the database using SQL queries and extracts relevant data. Next, it performs analysis using a generated AI model based on this data to calculate the fair price of the property. The output is the property's appraised value and is sent to the terminal.

[0092] Step 3:

[0093] The terminal receives the appraisal price sent from the server and generates a contract template for real estate transactions. A contract template generation module is used for this process. The contract format is customized according to the user's conditions and requirements. Inputs are the appraisal price from the server and the user's conditions, and output is the customized contract template.

[0094] Step 4:

[0095] The server verifies the legal requirements of the contract. It analyzes the contract's content and verifies that it includes all legally required items. It makes revisions to the contract as needed. This process involves automated determination using a legal knowledge base to detect any necessary modifications. The output is a legally compliant contract, which is then presented to the user.

[0096] Step 5:

[0097] The user enters their desired date and time for viewing the property into the terminal. The terminal sends this information to the server. The server checks the schedules of the seller and property management company and sets the most suitable viewing date and time. The terminal automatically sends a confirmation email to the relevant parties once the date and time have been set. The input is the user's desired viewing date and time, and the output is a notification of the set viewing date and time.

[0098] Step 6:

[0099] The server continuously monitors the progress of the transaction. With each step of the transaction, the progress is evaluated, and if specific action is required, the user is notified via the terminal. The server tracks the progress and triggers a notification protocol when necessary action is required.

[0100] (Application Example 1)

[0101] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0102] Modern real estate transactions often involve significant time and effort spent on physical property viewings, contract procedures, and price appraisals. Furthermore, selling properties outside urban areas is often hindered by the difficulty potential buyers face in actually visiting the property, thus impeding efficient transactions. A system is needed to address these issues and enable swift and efficient real estate transactions.

[0103] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0104] In this invention, the server includes means for receiving real estate information, means for analyzing the received real estate information and comparing it with a market dataset to calculate property prices, means for generating contract templates and customizing contracts based on user conditions, means for automatically scheduling property viewings and notifying relevant parties, means for users to view virtual models of real estate properties, and means for setting and providing dates and times for virtual viewing tours. This overcomes physical constraints and makes it possible to complete the real estate transaction process online.

[0105] A "means for receiving real estate information" refers to an interface for electronically receiving and storing property data entered by users.

[0106] A "market dataset" is a collection of information including real estate market transaction data and price trends, and is a database referenced to calculate the value of a property.

[0107] "Methods for calculating property prices" refers to algorithms that use AI models to calculate the appropriate price of a property based on input real estate information and market data.

[0108] "Methods for generating contract templates" refer to the process of creating templates that cover basic contract terms and preparing documents that can be customized according to the user's transaction conditions.

[0109] "Means of notifying stakeholders" refers to the function of using email or messaging services to inform stakeholders of necessary information, such as property viewings or transaction progress.

[0110] "Means for viewing virtual models" refers to a function that allows users to visually view a 3D model of a property through a computer, head-mounted display, or similar device.

[0111] "The means of setting and providing virtual tour dates and times" refers to a function that allows users to book virtual tours based on their preferences and view real estate properties online at the scheduled time.

[0112] The system for implementing this invention mainly consists of server, terminal, and user interaction. To streamline real estate transactions, each element works together, utilizing information processing and digital technologies.

[0113] First, the terminal receives real estate information from the user. This information includes the property's address, size, price, and structure. The data entered by the user is then compared against a market dataset within the system. Subsequently, the server uses an AI model to calculate the fair market value of the property. This process utilizes the Python programming language and machine learning libraries such as Scikit-learn and TensorFlow.

[0114] Next, the server generates a contract template and customizes it based on the user's requirements. This process utilizes the Google® Docs API to electronically create the contract. The user can then review it on their device and make any necessary modifications.

[0115] Furthermore, the server generates a virtual model of the property and provides it to the user via their terminal. The 3D model of the property is visually represented using Unity or Unreal Engine. Users can view this 3D model and enjoy a virtual tour. The server also handles scheduling and providing the virtual tour, and appropriate information is notified to the relevant parties.

[0116] Finally, the server monitors the progress of the transaction, and users are notified via their devices when necessary actions are required. Cloud services such as the Google Calendar API and Gmail API are used to confirm the viewing schedule and to notify users of important progress updates.

[0117] As a concrete example, consider a scenario where a user puts a large house in a rural village up for sale. In this case, the user uses a terminal to input property information, and as a result, is provided with an environment where the property can be appealed to distant customers through market prices and virtual tours. This system enables online real estate transactions, overcoming physical limitations, and is highly efficient through the use of generative AI models.

[0118] As an example of a prompt message, entering the command "After inputting property information, analyze the market value using the AI ​​model, generate a 3D model, and provide the user with a virtual real estate tour" into the system will automatically start the entire process.

[0119] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0120] Step 1:

[0121] Users enter basic property information using a terminal. This data includes the property's address, size, year built, and structure. This information is immediately transmitted to the cloud and stored as data on a server.

[0122] Step 2:

[0123] The server compares the received real estate information with a market dataset. At this stage, it extracts information on similar properties from the dataset, and uses this information for an AI model to calculate the market value of the property. Machine learning libraries such as Scikit-learn and TensorFlow are used for this calculation. The input is the property information provided by the user and the market dataset, and the output is the calculated market value.

[0124] Step 3:

[0125] The server generates a contract template based on the market value calculation results. This process utilizes the Google Docs API, creating a contract that meets the user's requirements based on a pre-prepared template. The input is the calculated property value data, and the output is a customized contract.

[0126] Step 4:

[0127] Users can review the generated contract through their device and make modifications as needed. Simple editing functions are provided on the device for this purpose.

[0128] Step 5:

[0129] The server generates a 3D virtual model of the property and provides it to the user. Unity or Unreal Engine is used to construct a visually realistic model. The input is detailed structural data of the property, and the output is a 3D model for user reference.

[0130] Step 6:

[0131] Users book virtual tours, and the server sets the date and time. The Google Calendar API is used for this scheduling, and the information is automatically notified to the relevant parties. The input is the user's preferred date and time, and the output is a confirmation and notification of the booked tour date and time.

[0132] Step 7:

[0133] The server monitors the progress of the transaction and notifies the user via the terminal of necessary actions. This allows the user to quickly respond to urgent steps in the transaction. The input is transaction progress data, and the output is notification messages.

[0134] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0135] The real estate brokerage system of this invention achieves user-friendly and efficient real estate transactions by utilizing a combination of generative AI and an emotion engine. This system consists of a server, terminals, and users.

[0136] The user first enters property information into the terminal. This includes the property's address, size, year built, and structure. The entered information is then verified by the terminal.

[0137] The server analyzes property information and accesses market databases to collect data on similar properties. The collected data is analyzed by an AI model to calculate the property's appraised value. This appraisal result is then provided to the user.

[0138] Simultaneously, the terminal uses a generation AI to generate a standard sales contract template. Based on user input, the server customizes this template to reflect the user's terms and legal requirements.

[0139] The emotion engine analyzes user responses in real time and recognizes their emotional state. Based on this data, the server provides transaction support tailored to the user's emotions and suggests appropriate actions. For example, if a user is feeling stressed, the server can take actions such as accelerating the transaction or providing additional information.

[0140] When a user enters their preferred date and time for a property viewing, the server checks the seller's and property management company's schedules and sets the most suitable viewing time. This information is automatically sent as a confirmation email from the user's device.

[0141] Throughout the transaction, the server constantly monitors the progress and notifies the user as needed, prompting them to take action. The emotion engine also supports communication based on the user's emotions throughout this process, facilitating smooth transactions.

[0142] As a concrete example, consider a case where a user purchases a house in a specific area. Using this system, the user can handle everything from entering property information to signing the contract and arranging viewings. Furthermore, the user's emotions are fed back into the system, ensuring that the transaction proceeds in the most comfortable way for the user.

[0143] Based on the above, the present invention improves the user experience, reduces fees, and shortens transaction times by providing more appropriate emotional feedback compared to conventional procedures.

[0144] The following describes the processing flow.

[0145] Step 1:

[0146] The user enters property information into a dedicated input form. This includes the property's address, size, year built, and structure.

[0147] Step 2:

[0148] The terminal verifies the accuracy of the entered information and provides feedback to the user, prompting them to correct the input if necessary.

[0149] Step 3:

[0150] The server uses verified real estate information to access the market database, querying and retrieving price data for similar properties.

[0151] Step 4:

[0152] The server analyzes the acquired data using an AI model and calculates the property's appraised value. The results are then presented to the user.

[0153] Step 5:

[0154] The terminal uses a generation AI to generate a basic sales contract template and displays it to the user.

[0155] Step 6:

[0156] The server incorporates user requirements (price, delivery date, etc.) into a contract template, customizes it, and verifies legal requirements.

[0157] Step 7:

[0158] The emotion engine analyzes user input and responses during operation and reports the emotional state. Based on this, the server adjusts and suggests the necessary communication style and information provision.

[0159] Step 8:

[0160] The user enters their preferred date and time for viewing the property into the terminal.

[0161] Step 9:

[0162] The server checks the schedules of the seller and the property management company and automatically selects the most suitable viewing date and time.

[0163] Step 10:

[0164] The terminal automatically sends a confirmation email to the user and relevant parties regarding the selected visit date and time.

[0165] Step 11:

[0166] The server monitors the progress of the transaction and notifies the user of any necessary actions. If the emotion engine detects user stress during the process, it uses that data to suggest further support.

[0167] Step 12:

[0168] Once the device confirms that the transaction is complete, it displays a screen asking the user for feedback.

[0169] These steps enable real estate transactions to provide an efficient and user-centric experience.

[0170] (Example 2)

[0171] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0172] Traditional real estate transaction processes require enormous time and effort for information gathering and analysis, and it has been difficult to provide services that are tailored to the user's emotions and circumstances. Therefore, there is a need for improved user experience, faster transactions, and more flexible responses.

[0173] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0174] This invention includes a server that analyzes input real estate information, compares it with an information database to collect data on similar properties, and calculates the property's appraised value; a server that generates a contract template using a generation AI and customizes the template based on the user's conditions; an emotion recognition tool that analyzes the user's reactions; and a tool that supports the transaction based on the analysis results and makes appropriate suggestions to the user. This enables efficient and user-friendly real estate transactions.

[0175] "Real estate information" refers to data that includes physical and legal attributes of a property, such as its address, size, year built, and structure.

[0176] "Input method" refers to an interface or device that allows users to provide detailed property information to the system.

[0177] "Means of analysis" refers to a computer program or process that compares input data with an information database to extract and analyze useful data.

[0178] "Generative AI" refers to artificial intelligence systems that automatically create document and contract templates using natural language processing and machine learning techniques.

[0179] "Emotion recognition means" refers to technologies or algorithms that analyze user responses to understand their emotional state and optimize system operation.

[0180] "Property valuation" refers to the estimated price of a property, calculated based on market data for similar properties and the current state of the real estate market.

[0181] A "database" is a collection of data that systematically stores property information and market data, and is a storage system configured to allow for efficient retrieval of necessary information.

[0182] "Means of customization" refers to a process or function for adjusting a generated template to suit the user's specific conditions or legal requirements.

[0183] "Means of supporting transactions" refers to programs or procedures that propose optimal transaction policies and procedures to users based on sentiment recognition results, thereby facilitating the smooth progress of the transaction process.

[0184] This invention aims to make traditional real estate transactions more user-friendly and efficient by combining a generative AI model and an emotion engine in a real estate brokerage system. This system consists of three components: a server, a terminal, and a user.

[0185] The user first enters detailed property information into the terminal. Specifically, this includes information such as the property's address, size, year built, and structure. This information is then verified by the terminal and sent to the server.

[0186] Next, the server analyzes the received property information. This involves a process of collecting data on similar properties using an information database. At this stage, the server uses an AI model to analyze the collected data and calculate the property's estimated value.

[0187] The terminal automatically generates a standard sales contract template using generation AI. This template is later customized by the server based on user input. The customized contract can incorporate the user's terms and region-specific legal requirements.

[0188] Furthermore, an emotion engine is built into the device, which analyzes user reactions in real time. For example, if a user is experiencing stress, emotion data is sent to the server, which then provides support such as speeding up transactions or providing additional information.

[0189] Furthermore, when a user enters their preferred date and time for a property viewing, the server consults the seller's or property management company's schedule and sets the most suitable viewing time. This information is automatically sent to the user as a confirmation email from their device.

[0190] As a concrete example, consider a case where a user purchases a 3LDK apartment less than 5 years old in a specific area of ​​a city. Through this system, the user can efficiently handle everything from inputting property information to generating contracts and scheduling viewings. As an example of a prompt, a specific request could be, "I'm looking for a 3LDK apartment less than 5 years old in Shinagawa Ward, Tokyo."

[0191] Thus, the present invention utilizes a generative AI model and an emotion engine to realize flexible and efficient real estate transactions that respond to the user's emotions and needs.

[0192] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0193] Step 1:

[0194] The user accesses the terminal and enters detailed property information. This information includes the property's address, size, year built, and structure. The entered data is verified on the terminal, and after its accuracy is guaranteed, it is sent to the server.

[0195] Step 2:

[0196] The server analyzes the property information received from the user. It accesses an information database to collect data on similar properties. This process references past sales history and market trend data. Based on the analysis, the server calculates the property's value and provides this data to the user.

[0197] Step 3:

[0198] The terminal uses a generation AI to create a standard sales contract template. This generation process takes into account the user's conditions and general legal requirements. The generated template serves as a prompt for customization and is then sent to the server.

[0199] Step 4:

[0200] The server customizes the contract template based on user input. It incorporates conditions and region-specific legal requirements extracted from the input data into the template, generating the final contract. This contract is saved in a format that allows user review.

[0201] Step 5:

[0202] The device utilizes an emotion engine to analyze the user's emotional state in real time. It detects stress and feelings of security from the user's tone of voice, facial expressions, and other factors. Emotional data is sent to a server, and transaction support is provided as needed.

[0203] Step 6:

[0204] The user enters their preferred date and time for viewing the property. This date and time information is sent to the server, which then compares it with the schedules of the property management company and the seller to determine the most suitable viewing date.

[0205] Step 7:

[0206] The server sends the confirmed tour date and time to the terminal, which then automatically generates a confirmation email and sends it to the user. The email includes tour details and contact information, which the user uses to confirm their schedule.

[0207] Step 8:

[0208] The server monitors the entire transaction process and notifies the user of necessary actions as it progresses. At each stage of the transaction, the server tracks the progress and guides the user through the necessary actions and next steps.

[0209] This system will allow users to experience efficient and stress-free real estate transactions.

[0210] (Application Example 2)

[0211] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0212] In online shopping, there is a need to accurately reflect user needs while improving the purchasing experience. In particular, when selecting products, it is necessary to respond with consideration for the user's emotions, and a system that allows for intuitive operation with minimal stress is required.

[0213] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0214] In this invention, the server includes means for receiving information, means for analyzing the received information, comparing it with a database to generate recommendations, and means for analyzing the user's emotional state and providing information that corresponds to that emotional state. This enables personalized information provision and emotionally sensitive support for the user.

[0215] "Information" refers to the data and conditions that the system receives from the user, and it forms the basis for product selection and recommendations.

[0216] A "database" is a source of information that stores market and historical transaction data, and is used by systems for analysis.

[0217] "Recommendation" refers to the suggestion of the most suitable products and services provided to the user based on information obtained from a database.

[0218] A "template" is a standardized form of documents required for contracts and transactions, which can then be customized according to the user's specific requirements.

[0219] "Conditions" refer to specific requirements or criteria desired by the user, and are the criteria used to generate templates and recommendations.

[0220] "Emotional state" refers to the user's psychological feedback, which the emotion engine analyzes in real time, and which influences the system's output.

[0221] A "generative AI model" is a form of artificial intelligence that automatically analyzes vast amounts of data and creates documents, and is used for recommendations and template generation within a system.

[0222] The system used to implement this application primarily consists of a server, user terminals, and associated databases. The server receives information entered by users through their terminals. This input information, consisting of conditions related to products and services, is then compared with data in the market database. The server is built using a programming language such as Python and utilizes the Django framework. It generates the necessary queries for interaction with the database and retrieves data in real time.

[0223] The server utilizes a generative AI model to automatically generate optimal recommendations based on this data. The generative AI model employs a model excelling in natural language processing, specifically OpenAI's GPT-based model. This provides users with information to support their selection of the most suitable products and services. Furthermore, a template generation function automatically constructs contract templates tailored to the user's specific requirements.

[0224] Furthermore, an emotion engine is used to analyze the user's emotional state. As the user interacts with the system through their device, their psychological feedback is evaluated in real time, and the server provides appropriate product recommendations and additional information based on the results. For example, if the user is undecided, it will present comparisons of similar products and promotional information.

[0225] For example, if a user enters the prompt, "Please show me the best laptop that meets the following conditions and its price: 16GB RAM, 512GB SSD, under 100,000 yen," the server will collect product information that matches the conditions from its database based on this prompt, and then analyze and present it using a generating AI model. This allows the user to find the product they are looking for without any stress.

[0226] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0227] Step 1:

[0228] The user enters prompt text through the terminal regarding the product and criteria they are searching for. This input information includes conditions such as specific specifications and price range. The terminal sends this input information to the server.

[0229] Step 2:

[0230] The server parses the received prompt and generates a query against the relevant database. This query extracts products from the database that meet the conditions specified in the prompt. The server executes the query and retrieves the corresponding product data.

[0231] Step 3:

[0232] The server inputs the acquired product data into a generating AI model. The generating AI model uses natural language generation technology to create optimal recommendation texts. This process ensures that product information is presented in a way that is easy for the user to understand.

[0233] Step 4:

[0234] The generated recommendation text and related product information are sent back to the terminal. The terminal displays this information on its user interface, allowing the user to visually confirm the content.

[0235] Step 5:

[0236] Users make purchase decisions based on the displayed product information and recommendations. If necessary, they can send further feedback to the server via their device to receive additional information or alternative product recommendations.

[0237] Step 6:

[0238] The emotion engine analyzes the user's facial expressions and voice through the device to evaluate the user's emotional state. This data is sent to a server, which then provides information tailored to the user's emotional state. The server generates recommendations and information that take the user's emotions into consideration and sends them back to the device.

[0239] In this way, the program achieves product recommendations that meet the user's needs and provides information tailored to their emotions.

[0240] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0241] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0242] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0243] [Second Embodiment]

[0244] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0245] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0246] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0247] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0248] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0249] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0250] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0251] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0252] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0253] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0254] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0255] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0256] The real estate brokerage system of this invention is a system for efficiently conducting real estate transactions using generative AI. This system operates through the cooperation of three entities: a server, a terminal, and a user.

[0257] The user first enters property information. This information includes basic data such as the property's address, size, year built, and structure.

[0258] The server receives and analyzes this information. It collects data on similar properties using a market database and calculates the fair market value of a property using an AI model. This allows users to quickly understand the market value of a property.

[0259] Next, the terminal generates a contract template necessary for real estate transactions. The template includes all the basic items and provides a base for the user to create their own contract.

[0260] The server customizes the contract based on user input. This customization ensures that specific conditions and requirements are reflected in the contract and that legal requirements are met.

[0261] When a user enters their preferred date and time for a property viewing, the server checks the schedules of the seller and property management company and sets the most suitable viewing date and time. The viewing date and time are notified to the relevant parties by the terminal automatically sending a confirmation email.

[0262] As the transaction progresses, the server continuously monitors the status. If necessary action is required, the terminal notifies the user and prompts them to take immediate action.

[0263] As a concrete example, let's consider a case where a user wants to sell an apartment located in an urban area. The user can input property information into the system and enter into a contract based on the market price suggested by the system. Since contract generation and scheduling are automated, the user can proceed with the real estate transaction quickly and efficiently.

[0264] As described above, the present invention significantly simplifies conventional intermediation procedures, resulting in reduced fees, shorter processing times, and improved transaction transparency.

[0265] The following describes the processing flow.

[0266] Step 1:

[0267] Users enter property information such as the property's address, size, year built, and structure using a dedicated input form.

[0268] Step 2:

[0269] The terminal receives the input and performs an initial check to ensure the property information is accurate. If necessary, it prompts the user for corrections.

[0270] Step 3:

[0271] The server analyzes the received real estate information and sends queries to the market database. It then collects price data for similar properties from the market database.

[0272] Step 4:

[0273] The server uses an AI model to calculate the property's appraised value based on the collected data. This appraisal result is then provided to the user.

[0274] Step 5:

[0275] The terminal generates a standard sales contract template based on the user's request.

[0276] Step 6:

[0277] The server customizes the contract template based on the user's input conditions (price, delivery date, etc.). This includes checking legal requirements.

[0278] Step 7:

[0279] The user enters their preferred date and time for viewing the property into the terminal.

[0280] Step 8:

[0281] The server checks the schedules of the seller and the property management company and assigns the most suitable viewing date and time.

[0282] Step 9:

[0283] The terminal automatically sends a confirmation email of the visit date and time to the user and relevant parties.

[0284] Step 10:

[0285] The server continuously monitors the progress of the transaction, sends notifications to the user as necessary, and prompts corresponding actions.

[0286] Step 11:

[0287] The terminal checks the completion of the transaction and displays a screen asking the user for evaluation feedback.

[0288] (Example 1)

[0289] Next, Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0290] Real estate transactions usually involve a variety of tasks such as information collection, price evaluation, contract preparation, and visit arrangement, which are time-consuming and laborious. As a result, transactions are often inefficient and lack transparency. This invention provides a method to solve these problems and conduct transactions quickly and efficiently.

[0291] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0292] In this invention, the server includes means for receiving real estate information, means for analyzing the received information, comparing it with market information, and calculating the price of the property, and means for generating a contract template and customizing the contract based on the user's conditions. This enables transactions to proceed quickly and efficiently, improving transparency and ensuring legal compliance.

[0293] "Real estate information" refers to data that includes basic attributes of properties related to real estate transactions, such as address, size, year built, and structure.

[0294] "Market information" refers to sources of information that include data on similar properties in the past and present real estate market.

[0295] "Property price" refers to the fair value of a property, calculated based on market information and other relevant data.

[0296] A "contract template" is an initial contract format that covers the basic items necessary for real estate transactions, and is customizable by the user.

[0297] An "AI model" refers to a group of computational models that use artificial intelligence technology to perform data analysis and prediction.

[0298] A "prompt statement" is an input statement that provides initial conditions to an AI model and generates a specific output.

[0299] A "server" refers to a computer system that processes, stores, and distributes data over a network.

[0300] A "terminal" is a computer device used by a user to interact with a server, and is used for inputting and outputting information.

[0301] This real estate brokerage system is designed to efficiently conduct real estate transactions using a generative AI model. Specific embodiments are shown below.

[0302] The user uses a terminal to input property information such as the property's address, size, year built, and structure. The terminal receives this information and sends it to the server.

[0303] The server analyzes market information based on the received real estate information. The market information is obtained from a database containing data on past and current similar properties. The server uses a generative AI model (e.g., a normal computational model built with TensorFlow or PyTorch) to calculate the appropriate price of the property. In this process, the input information received from the user functions as a prompt sentence.

[0304] The terminal generates a contract template using the calculation result received from the server. This contract template contains the basic items necessary for real estate transactions and will be customized later based on the user's conditions. Furthermore, an additional module for checking the legal requirements of the contract is incorporated, and modifications can be made as needed.

[0305] As a specific example, consider the case where a user wishes to sell an apartment in the city center. The user inputs the property information from the terminal, and the server analyzes the information and calculates the appropriate market price using an AI model. Referring to this price, the user can proceed with the transaction efficiently.

[0306] Examples of prompt sentences include "Please calculate the appropriate market price based on the property information of an apartment in Shinjuku Ward" and "Please generate a contract template based on this property information."

[0307] In this way, the system automates each step of the real estate transaction, enabling quick and efficient execution. The purpose of this invention is to greatly improve transparency and efficiency by allowing users to proceed with real estate transactions with minimal effort.

[0308] The flow of the specific process in Example 1 will be described using FIG. 11.

[0309] Step 1:

[0310] The user enters property information using a terminal. This information includes the property's address, size, year built, and structure. The terminal collects this data and sends it to the server. The entered information is in digital format, and its integrity is checked before it is sent to the server.

[0311] Step 2:

[0312] The server analyzes the real estate information received from the terminal. It accesses a market information database and retrieves information on similar properties. The server searches the database using SQL queries and extracts relevant data. Next, it performs analysis using a generated AI model based on this data to calculate the fair price of the property. The output is the property's appraised value and is sent to the terminal.

[0313] Step 3:

[0314] The terminal receives the appraisal price sent from the server and generates a contract template for real estate transactions. A contract template generation module is used for this process. The contract format is customized according to the user's conditions and requirements. Inputs are the appraisal price from the server and the user's conditions, and output is the customized contract template.

[0315] Step 4:

[0316] The server verifies the legal requirements of the contract. It analyzes the contract's content and verifies that it includes all legally required items. It makes revisions to the contract as needed. This process involves automated determination using a legal knowledge base to detect any necessary modifications. The output is a legally compliant contract, which is then presented to the user.

[0317] Step 5:

[0318] The user enters their desired date and time for viewing the property into the terminal. The terminal sends this information to the server. The server checks the schedules of the seller and property management company and sets the most suitable viewing date and time. The terminal automatically sends a confirmation email to the relevant parties once the date and time have been set. The input is the user's desired viewing date and time, and the output is a notification of the set viewing date and time.

[0319] Step 6:

[0320] The server continuously monitors the progress of the transaction. With each step of the transaction, the progress is evaluated, and if specific action is required, the user is notified via the terminal. The server tracks the progress and triggers a notification protocol when necessary action is required.

[0321] (Application Example 1)

[0322] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0323] Modern real estate transactions often involve significant time and effort spent on physical property viewings, contract procedures, and price appraisals. Furthermore, selling properties outside urban areas is often hindered by the difficulty potential buyers face in actually visiting the property, thus impeding efficient transactions. A system is needed to address these issues and enable swift and efficient real estate transactions.

[0324] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0325] In this invention, the server includes means for receiving real estate information, means for analyzing the received real estate information and comparing it with a market dataset to calculate property prices, means for generating contract templates and customizing contracts based on user conditions, means for automatically scheduling property viewings and notifying relevant parties, means for users to view virtual models of real estate properties, and means for setting and providing dates and times for virtual viewing tours. This overcomes physical constraints and makes it possible to complete the real estate transaction process online.

[0326] A "means for receiving real estate information" refers to an interface for electronically receiving and storing property data entered by users.

[0327] A "market dataset" is a collection of information including real estate market transaction data and price trends, and is a database referenced to calculate the value of a property.

[0328] "Methods for calculating property prices" refers to algorithms that use AI models to calculate the appropriate price of a property based on input real estate information and market data.

[0329] "Methods for generating contract templates" refer to the process of creating templates that cover basic contract terms and preparing documents that can be customized according to the user's transaction conditions.

[0330] "Means of notifying stakeholders" refers to the function of using email or messaging services to inform stakeholders of necessary information, such as property viewings or transaction progress.

[0331] "Means for viewing virtual models" refers to a function that allows users to visually view a 3D model of a property through a computer, head-mounted display, or similar device.

[0332] "The means of setting and providing virtual tour dates and times" refers to a function that allows users to book virtual tours based on their preferences and view real estate properties online at the scheduled time.

[0333] The system for implementing this invention mainly consists of server, terminal, and user interaction. To streamline real estate transactions, each element works together, utilizing information processing and digital technologies.

[0334] First, the terminal receives real estate information from the user. This information includes the property's address, size, price, and structure. The data entered by the user is then compared against a market dataset within the system. Subsequently, the server uses an AI model to calculate the fair market value of the property. This process utilizes the Python programming language and machine learning libraries such as Scikit-learn and TensorFlow.

[0335] Next, the server generates a contract template and customizes it based on the user's requirements. This process utilizes the Google Docs API to electronically create the contract. The user can then review it on their device and make any necessary modifications.

[0336] Furthermore, the server generates a virtual model of the property and provides it to the user via their terminal. The 3D model of the property is visually represented using Unity or Unreal Engine. Users can view this 3D model and enjoy a virtual tour. The server also handles scheduling and providing the virtual tour, and appropriate information is notified to the relevant parties.

[0337] Finally, the server monitors the progress of the transaction, and users are notified via their devices when necessary actions are required. Cloud services such as the Google Calendar API and Gmail API are used to confirm the viewing schedule and to notify users of important progress updates.

[0338] As a concrete example, consider a scenario where a user puts a large house in a rural village up for sale. In this case, the user uses a terminal to input property information, and as a result, is provided with an environment where the property can be appealed to distant customers through market prices and virtual tours. This system enables online real estate transactions, overcoming physical limitations, and is highly efficient through the use of generative AI models.

[0339] As an example of a prompt message, entering the command "After inputting property information, analyze the market value using the AI ​​model, generate a 3D model, and provide the user with a virtual real estate tour" into the system will automatically start the entire process.

[0340] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0341] Step 1:

[0342] Users enter basic property information using a terminal. This data includes the property's address, size, year built, and structure. This information is immediately transmitted to the cloud and stored as data on a server.

[0343] Step 2:

[0344] The server compares the received real estate information with a market dataset. At this stage, it extracts information on similar properties from the dataset, and uses this information for an AI model to calculate the market value of the property. Machine learning libraries such as Scikit-learn and TensorFlow are used for this calculation. The input is the property information provided by the user and the market dataset, and the output is the calculated market value.

[0345] Step 3:

[0346] The server generates a contract template based on the market value calculation results. This process utilizes the Google Docs API, creating a contract that meets the user's requirements based on a pre-prepared template. The input is the calculated property value data, and the output is a customized contract.

[0347] Step 4:

[0348] Users can review the generated contract through their device and make modifications as needed. Simple editing functions are provided on the device for this purpose.

[0349] Step 5:

[0350] The server generates a 3D virtual model of the property and provides it to the user. Unity or Unreal Engine is used to construct a visually realistic model. The input is detailed structural data of the property, and the output is a 3D model for user reference.

[0351] Step 6:

[0352] Users book virtual tours, and the server sets the date and time. The Google Calendar API is used for this scheduling, and the information is automatically notified to the relevant parties. The input is the user's preferred date and time, and the output is a confirmation and notification of the booked tour date and time.

[0353] Step 7:

[0354] The server monitors the progress of the transaction and notifies the user via the terminal of necessary actions. This allows the user to quickly respond to urgent steps in the transaction. The input is transaction progress data, and the output is notification messages.

[0355] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0356] The real estate brokerage system of this invention achieves user-friendly and efficient real estate transactions by utilizing a combination of generative AI and an emotion engine. This system consists of a server, terminals, and users.

[0357] The user first enters property information into the terminal. This includes the property's address, size, year built, and structure. The entered information is then verified by the terminal.

[0358] The server analyzes property information and accesses market databases to collect data on similar properties. The collected data is analyzed by an AI model to calculate the property's appraised value. This appraisal result is then provided to the user.

[0359] Simultaneously, the terminal uses a generation AI to generate a standard sales contract template. Based on user input, the server customizes this template to reflect the user's terms and legal requirements.

[0360] The emotion engine analyzes user responses in real time and recognizes their emotional state. Based on this data, the server provides transaction support tailored to the user's emotions and suggests appropriate actions. For example, if a user is feeling stressed, the server can take actions such as accelerating the transaction or providing additional information.

[0361] When a user enters their preferred date and time for a property viewing, the server checks the seller's and property management company's schedules and sets the most suitable viewing time. This information is automatically sent as a confirmation email from the user's device.

[0362] Throughout the transaction, the server constantly monitors the progress and notifies the user as needed, prompting them to take action. The emotion engine also supports communication based on the user's emotions throughout this process, facilitating smooth transactions.

[0363] As a concrete example, consider a case where a user purchases a house in a specific area. Using this system, the user can handle everything from entering property information to signing the contract and arranging viewings. Furthermore, the user's emotions are fed back into the system, ensuring that the transaction proceeds in the most comfortable way for the user.

[0364] Based on the above, the present invention improves the user experience, reduces fees, and shortens transaction times by providing more appropriate emotional feedback compared to conventional procedures.

[0365] The following describes the processing flow.

[0366] Step 1:

[0367] The user enters property information into a dedicated input form. This includes the property's address, size, year built, and structure.

[0368] Step 2:

[0369] The terminal verifies the accuracy of the entered information and provides feedback to the user, prompting them to correct the input if necessary.

[0370] Step 3:

[0371] The server uses verified real estate information to access the market database, querying and retrieving price data for similar properties.

[0372] Step 4:

[0373] The server analyzes the acquired data using an AI model and calculates the property's appraised value. The results are then presented to the user.

[0374] Step 5:

[0375] The terminal uses a generation AI to generate a basic sales contract template and displays it to the user.

[0376] Step 6:

[0377] The server incorporates user requirements (price, delivery date, etc.) into a contract template, customizes it, and verifies legal requirements.

[0378] Step 7:

[0379] The emotion engine analyzes user input and responses during operation and reports the emotional state. Based on this, the server adjusts and suggests the necessary communication style and information provision.

[0380] Step 8:

[0381] The user enters their preferred date and time for viewing the property into the terminal.

[0382] Step 9:

[0383] The server checks the schedules of the seller and the property management company and automatically selects the most suitable viewing date and time.

[0384] Step 10:

[0385] The terminal automatically sends a confirmation email to the user and relevant parties regarding the selected visit date and time.

[0386] Step 11:

[0387] The server monitors the progress of the transaction and notifies the user of any necessary actions. If the emotion engine detects user stress during the process, it uses that data to suggest further support.

[0388] Step 12:

[0389] Once the device confirms that the transaction is complete, it displays a screen asking the user for feedback.

[0390] These steps enable real estate transactions to provide an efficient and user-centric experience.

[0391] (Example 2)

[0392] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0393] Traditional real estate transaction processes require enormous time and effort for information gathering and analysis, and it has been difficult to provide services that are tailored to the user's emotions and circumstances. Therefore, there is a need for improved user experience, faster transactions, and more flexible responses.

[0394] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0395] This invention includes a server that analyzes input real estate information, compares it with an information database to collect data on similar properties, and calculates the property's appraised value; a server that generates a contract template using a generation AI and customizes the template based on the user's conditions; an emotion recognition tool that analyzes the user's reactions; and a tool that supports the transaction based on the analysis results and makes appropriate suggestions to the user. This enables efficient and user-friendly real estate transactions.

[0396] "Real estate information" refers to data that includes physical and legal attributes of a property, such as its address, size, year built, and structure.

[0397] "Input method" refers to an interface or device that allows users to provide detailed property information to the system.

[0398] "Means of analysis" refers to a computer program or process that compares input data with an information database to extract and analyze useful data.

[0399] "Generative AI" refers to artificial intelligence systems that automatically create document and contract templates using natural language processing and machine learning techniques.

[0400] "Emotion recognition means" refers to technologies or algorithms that analyze user responses to understand their emotional state and optimize system operation.

[0401] "Property valuation" refers to the estimated price of a property, calculated based on market data for similar properties and the current state of the real estate market.

[0402] A "database" is a collection of data that systematically stores property information and market data, and is a storage system configured to allow for efficient retrieval of necessary information.

[0403] "Means of customization" refers to a process or function for adjusting a generated template to suit the user's specific conditions or legal requirements.

[0404] "Means of supporting transactions" refers to programs or procedures that propose optimal transaction policies and procedures to users based on sentiment recognition results, thereby facilitating the smooth progress of the transaction process.

[0405] This invention aims to make traditional real estate transactions more user-friendly and efficient by combining a generative AI model and an emotion engine in a real estate brokerage system. This system consists of three components: a server, a terminal, and a user.

[0406] The user first enters detailed property information into the terminal. Specifically, this includes information such as the property's address, size, year built, and structure. This information is then verified by the terminal and sent to the server.

[0407] Next, the server analyzes the received property information. This involves a process of collecting data on similar properties using an information database. At this stage, the server uses an AI model to analyze the collected data and calculate the property's estimated value.

[0408] The terminal automatically generates a standard sales contract template using generation AI. This template is later customized by the server based on user input. The customized contract can incorporate the user's terms and region-specific legal requirements.

[0409] Furthermore, an emotion engine is built into the device, which analyzes user reactions in real time. For example, if a user is experiencing stress, emotion data is sent to the server, which then provides support such as speeding up transactions or providing additional information.

[0410] Furthermore, when a user enters their preferred date and time for a property viewing, the server consults the seller's or property management company's schedule and sets the most suitable viewing time. This information is automatically sent to the user as a confirmation email from their device.

[0411] As a concrete example, consider a case where a user purchases a 3LDK apartment less than 5 years old in a specific area of ​​a city. Through this system, the user can efficiently handle everything from inputting property information to generating contracts and scheduling viewings. As an example of a prompt, a specific request could be, "I'm looking for a 3LDK apartment less than 5 years old in Shinagawa Ward, Tokyo."

[0412] Thus, the present invention utilizes a generative AI model and an emotion engine to realize flexible and efficient real estate transactions that respond to the user's emotions and needs.

[0413] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0414] Step 1:

[0415] The user accesses the terminal and enters detailed property information. This information includes the property's address, size, year built, and structure. The entered data is verified on the terminal, and after its accuracy is guaranteed, it is sent to the server.

[0416] Step 2:

[0417] The server analyzes the property information received from the user. It accesses an information database to collect data on similar properties. This process references past sales history and market trend data. Based on the analysis, the server calculates the property's value and provides this data to the user.

[0418] Step 3:

[0419] The terminal uses a generation AI to create a standard sales contract template. This generation process takes into account the user's conditions and general legal requirements. The generated template serves as a prompt for customization and is then sent to the server.

[0420] Step 4:

[0421] The server customizes the contract template based on user input. It incorporates conditions and region-specific legal requirements extracted from the input data into the template, generating the final contract. This contract is saved in a format that allows user review.

[0422] Step 5:

[0423] The device utilizes an emotion engine to analyze the user's emotional state in real time. It detects stress and feelings of security from the user's tone of voice, facial expressions, and other factors. Emotional data is sent to a server, and transaction support is provided as needed.

[0424] Step 6:

[0425] The user enters their preferred date and time for viewing the property. This date and time information is sent to the server, which then compares it with the schedules of the property management company and the seller to determine the most suitable viewing date.

[0426] Step 7:

[0427] The server sends the confirmed tour date and time to the terminal, which then automatically generates a confirmation email and sends it to the user. The email includes tour details and contact information, which the user uses to confirm their schedule.

[0428] Step 8:

[0429] The server monitors the entire transaction process and notifies the user of necessary actions as it progresses. At each stage of the transaction, the server tracks the progress and guides the user through the necessary actions and next steps.

[0430] This system will allow users to experience efficient and stress-free real estate transactions.

[0431] (Application Example 2)

[0432] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0433] In online shopping, there is a need to accurately reflect user needs while improving the purchasing experience. In particular, when selecting products, it is necessary to respond with consideration for the user's emotions, and a system that allows for intuitive operation with minimal stress is required.

[0434] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0435] In this invention, the server includes means for receiving information, means for analyzing the received information, comparing it with a database to generate recommendations, and means for analyzing the user's emotional state and providing information that corresponds to that emotional state. This enables personalized information provision and emotionally sensitive support for the user.

[0436] "Information" refers to the data and conditions that the system receives from the user, and it forms the basis for product selection and recommendations.

[0437] A "database" is a source of information that stores market and historical transaction data, and is used by systems for analysis.

[0438] "Recommendation" refers to the suggestion of the most suitable products and services provided to the user based on information obtained from a database.

[0439] A "template" is a standardized form of documents required for contracts and transactions, which can then be customized according to the user's specific requirements.

[0440] "Conditions" refer to specific requirements or criteria desired by the user, and are the criteria used to generate templates and recommendations.

[0441] "Emotional state" refers to the user's psychological feedback, which the emotion engine analyzes in real time, and which influences the system's output.

[0442] A "generative AI model" is a form of artificial intelligence that automatically analyzes vast amounts of data and creates documents, and is used for recommendations and template generation within a system.

[0443] The system used to implement this application primarily consists of a server, user terminals, and associated databases. The server receives information entered by users through their terminals. This input information, consisting of conditions related to products and services, is then compared with data in the market database. The server is built using a programming language such as Python and utilizes the Django framework. It generates the necessary queries for interaction with the database and retrieves data in real time.

[0444] The server utilizes a generative AI model to automatically generate optimal recommendations based on this data. The generative AI model employs a model excelling in natural language processing, specifically OpenAI's GPT-based model. This provides users with information to support their selection of the most suitable products and services. Furthermore, a template generation function automatically constructs contract templates tailored to the user's specific requirements.

[0445] Furthermore, an emotion engine is used to analyze the user's emotional state. As the user interacts with the system through their device, their psychological feedback is evaluated in real time, and the server provides appropriate product recommendations and additional information based on the results. For example, if the user is undecided, it will present comparisons of similar products and promotional information.

[0446] For example, if a user enters the prompt, "Please show me the best laptop that meets the following conditions and its price: 16GB RAM, 512GB SSD, under 100,000 yen," the server will collect product information that matches the conditions from its database based on this prompt, and then analyze and present it using a generating AI model. This allows the user to find the product they are looking for without any stress.

[0447] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0448] Step 1:

[0449] The user enters prompt text through the terminal regarding the product and criteria they are searching for. This input information includes conditions such as specific specifications and price range. The terminal sends this input information to the server.

[0450] Step 2:

[0451] The server parses the received prompt and generates a query against the relevant database. This query extracts products from the database that meet the conditions specified in the prompt. The server executes the query and retrieves the corresponding product data.

[0452] Step 3:

[0453] The server inputs the acquired product data into a generating AI model. The generating AI model uses natural language generation technology to create optimal recommendation texts. This process ensures that product information is presented in a way that is easy for the user to understand.

[0454] Step 4:

[0455] The generated recommendation text and related product information are sent back to the terminal. The terminal displays this information on its user interface, allowing the user to visually confirm the content.

[0456] Step 5:

[0457] Users make purchase decisions based on the displayed product information and recommendations. If necessary, they can send further feedback to the server via their device to receive additional information or alternative product recommendations.

[0458] Step 6:

[0459] The emotion engine analyzes the user's facial expressions and voice through the device to evaluate the user's emotional state. This data is sent to a server, which then provides information tailored to the user's emotional state. The server generates recommendations and information that take the user's emotions into consideration and sends them back to the device.

[0460] In this way, the program achieves product recommendations that meet the user's needs and provides information tailored to their emotions.

[0461] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0462] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0463] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0464] [Third Embodiment]

[0465] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0466] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0467] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0468] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0469] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0470] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0471] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0472] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0473] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0474] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0475] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0476] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0477] The real estate brokerage system of this invention is a system for efficiently conducting real estate transactions using generative AI. This system operates through the cooperation of three entities: a server, a terminal, and a user.

[0478] The user first enters property information. This information includes basic data such as the property's address, size, year built, and structure.

[0479] The server receives and analyzes this information. It collects data on similar properties using a market database and calculates the fair market value of a property using an AI model. This allows users to quickly understand the market value of a property.

[0480] Next, the terminal generates a contract template necessary for real estate transactions. The template includes all the basic items and provides a base for the user to create their own contract.

[0481] The server customizes the contract based on user input. This customization ensures that specific conditions and requirements are reflected in the contract and that legal requirements are met.

[0482] When a user enters their preferred date and time for a property viewing, the server checks the schedules of the seller and property management company and sets the most suitable viewing date and time. The viewing date and time are notified to the relevant parties by the terminal automatically sending a confirmation email.

[0483] As the transaction progresses, the server continuously monitors the status. If necessary action is required, the terminal notifies the user and prompts them to take immediate action.

[0484] As a concrete example, let's consider a case where a user wants to sell an apartment located in an urban area. The user can input property information into the system and enter into a contract based on the market price suggested by the system. Since contract generation and scheduling are automated, the user can proceed with the real estate transaction quickly and efficiently.

[0485] As described above, the present invention significantly simplifies conventional intermediation procedures, resulting in reduced fees, shorter processing times, and improved transaction transparency.

[0486] The following describes the processing flow.

[0487] Step 1:

[0488] Users enter property information such as the property's address, size, year built, and structure using a dedicated input form.

[0489] Step 2:

[0490] The terminal receives the input and performs an initial check to ensure the property information is accurate. If necessary, it prompts the user for corrections.

[0491] Step 3:

[0492] The server analyzes the received real estate information and sends queries to the market database. It then collects price data for similar properties from the market database.

[0493] Step 4:

[0494] The server uses an AI model to calculate the property's appraised value based on the collected data. This appraisal result is then provided to the user.

[0495] Step 5:

[0496] The terminal generates a standard sales contract template based on the user's request.

[0497] Step 6:

[0498] The server customizes the contract template based on the user's input conditions (price, delivery date, etc.). This includes checking legal requirements.

[0499] Step 7:

[0500] The user enters their preferred date and time for viewing the property into the terminal.

[0501] Step 8:

[0502] The server checks the schedules of the seller and the property management company and assigns the most suitable viewing date and time.

[0503] Step 9:

[0504] The terminal automatically sends confirmation emails regarding the visit date and time to users and related parties.

[0505] Step 10:

[0506] The server continuously monitors the progress of the transaction and sends notifications to the user as needed to prompt action.

[0507] Step 11:

[0508] The terminal confirms the completion of the transaction and displays a screen requesting the user to provide feedback.

[0509] (Example 1)

[0510] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0511] Real estate transactions typically involve a wide range of tasks, including information gathering, property valuation, contract drafting, and viewing arrangements, making them time-consuming and laborious. This often results in inefficient and insufficient transparency in transactions. This service provides methods to address these challenges and conduct transactions quickly and efficiently.

[0512] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0513] In this invention, the server includes means for receiving real estate information, means for analyzing the received information and comparing it with market information to calculate the price of the property, and means for generating a contract template and customizing the contract based on the user's conditions. This makes it possible to proceed with transactions quickly and efficiently, and to ensure transparency and legal consistency.

[0514] "Real estate information" refers to data that includes basic attributes of properties related to real estate transactions, such as address, size, year built, and structure.

[0515] "Market information" refers to sources of information that include data on similar properties in the past and present real estate market.

[0516] "Property price" refers to the fair value of a property, calculated based on market information and other relevant data.

[0517] A "contract template" is an initial contract format that covers the basic items necessary for real estate transactions, and is customizable by the user.

[0518] An "AI model" refers to a group of computational models that use artificial intelligence technology to perform data analysis and prediction.

[0519] A "prompt statement" is an input statement that provides initial conditions to an AI model and generates a specific output.

[0520] A "server" refers to a computer system that processes, stores, and distributes data over a network.

[0521] A "terminal" is a computer device used by a user to interact with a server, and is used for inputting and outputting information.

[0522] This real estate brokerage system is designed to efficiently conduct real estate transactions using a generative AI model. Specific embodiments are shown below.

[0523] The user uses a terminal to input property information such as the property's address, size, year built, and structure. The terminal receives this information and sends it to the server.

[0524] The server analyzes market information based on the received real estate information. This market information is obtained from a database containing data on similar properties from the past and present. The server uses a generative AI model (e.g., a standard computation model built with TensorFlow or PyTorch) to calculate the fair market value of the property. In this process, user input serves as prompts.

[0525] The terminal generates a contract template using calculation results received from the server. This contract template includes the basic items necessary for real estate transactions and can later be customized based on the user's requirements. Furthermore, an additional module is incorporated to verify the legal requirements of the contract, and modifications can be made as needed.

[0526] As a concrete example, consider a case where a user wants to sell an apartment in an urban area. The user inputs property information from their terminal, and the server analyzes this information and calculates a fair market price using an AI model. The user can then use this price as a reference to proceed with the transaction efficiently.

[0527] Examples of prompt messages include, "Calculate a fair market price based on the property information for an apartment in Shinjuku Ward," and "Generate a contract template based on this property information."

[0528] In this way, the system automates each step of a real estate transaction, enabling rapid and efficient execution. This invention aims to significantly improve transparency and efficiency by allowing users to proceed with real estate transactions with minimal effort.

[0529] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0530] Step 1:

[0531] The user enters property information using a terminal. This information includes the property's address, size, year built, and structure. The terminal collects this data and sends it to the server. The entered information is in digital format, and its integrity is checked before it is sent to the server.

[0532] Step 2:

[0533] The server analyzes the real estate information received from the terminal. It accesses a market information database and retrieves information on similar properties. The server searches the database using SQL queries and extracts relevant data. Next, it performs analysis using a generated AI model based on this data to calculate the fair price of the property. The output is the property's appraised value and is sent to the terminal.

[0534] Step 3:

[0535] The terminal receives the appraisal price sent from the server and generates a contract template for real estate transactions. A contract template generation module is used for this process. The contract format is customized according to the user's conditions and requirements. Inputs are the appraisal price from the server and the user's conditions, and output is the customized contract template.

[0536] Step 4:

[0537] The server verifies the legal requirements of the contract. It analyzes the contract's content and verifies that it includes all legally required items. It makes revisions to the contract as needed. This process involves automated determination using a legal knowledge base to detect any necessary modifications. The output is a legally compliant contract, which is then presented to the user.

[0538] Step 5:

[0539] The user enters their desired date and time for viewing the property into the terminal. The terminal sends this information to the server. The server checks the schedules of the seller and property management company and sets the most suitable viewing date and time. The terminal automatically sends a confirmation email to the relevant parties once the date and time have been set. The input is the user's desired viewing date and time, and the output is a notification of the set viewing date and time.

[0540] Step 6:

[0541] The server continuously monitors the progress of the transaction. With each step of the transaction, the progress is evaluated, and if specific action is required, the user is notified via the terminal. The server tracks the progress and triggers a notification protocol when necessary action is required.

[0542] (Application Example 1)

[0543] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0544] Modern real estate transactions often involve significant time and effort spent on physical property viewings, contract procedures, and price appraisals. Furthermore, selling properties outside urban areas is often hindered by the difficulty potential buyers face in actually visiting the property, thus impeding efficient transactions. A system is needed to address these issues and enable swift and efficient real estate transactions.

[0545] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0546] In this invention, the server includes means for receiving real estate information, means for analyzing the received real estate information and comparing it with a market dataset to calculate property prices, means for generating contract templates and customizing contracts based on user conditions, means for automatically scheduling property viewings and notifying relevant parties, means for users to view virtual models of real estate properties, and means for setting and providing dates and times for virtual viewing tours. This overcomes physical constraints and makes it possible to complete the real estate transaction process online.

[0547] A "means for receiving real estate information" refers to an interface for electronically receiving and storing property data entered by users.

[0548] A "market dataset" is a collection of information including real estate market transaction data and price trends, and is a database referenced to calculate the value of a property.

[0549] "Methods for calculating property prices" refers to algorithms that use AI models to calculate the appropriate price of a property based on input real estate information and market data.

[0550] "Methods for generating contract templates" refer to the process of creating templates that cover basic contract terms and preparing documents that can be customized according to the user's transaction conditions.

[0551] "Means of notifying stakeholders" refers to the function of using email or messaging services to inform stakeholders of necessary information, such as property viewings or transaction progress.

[0552] "Means for viewing virtual models" refers to a function that allows users to visually view a 3D model of a property through a computer, head-mounted display, or similar device.

[0553] "The means of setting and providing virtual tour dates and times" refers to a function that allows users to book virtual tours based on their preferences and view real estate properties online at the scheduled time.

[0554] The system for implementing this invention mainly consists of server, terminal, and user interaction. To streamline real estate transactions, each element works together, utilizing information processing and digital technologies.

[0555] First, the terminal receives real estate information from the user. This information includes the property's address, size, price, and structure. The data entered by the user is then compared against a market dataset within the system. Subsequently, the server uses an AI model to calculate the fair market value of the property. This process utilizes the Python programming language and machine learning libraries such as Scikit-learn and TensorFlow.

[0556] Next, the server generates a contract template and customizes it based on the user's requirements. This process utilizes the Google Docs API to electronically create the contract. The user can then review it on their device and make any necessary modifications.

[0557] Furthermore, the server generates a virtual model of the property and provides it to the user via their terminal. The 3D model of the property is visually represented using Unity or Unreal Engine. Users can view this 3D model and enjoy a virtual tour. The server also handles scheduling and providing the virtual tour, and appropriate information is notified to the relevant parties.

[0558] Finally, the server monitors the progress of the transaction, and users are notified via their devices when necessary actions are required. Cloud services such as the Google Calendar API and Gmail API are used to confirm the viewing schedule and to notify users of important progress updates.

[0559] As a concrete example, consider a scenario where a user puts a large house in a rural village up for sale. In this case, the user uses a terminal to input property information, and as a result, is provided with an environment where the property can be appealed to distant customers through market prices and virtual tours. This system enables online real estate transactions, overcoming physical limitations, and is highly efficient through the use of generative AI models.

[0560] As an example of a prompt message, entering the command "After inputting property information, analyze the market value using the AI ​​model, generate a 3D model, and provide the user with a virtual real estate tour" into the system will automatically start the entire process.

[0561] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0562] Step 1:

[0563] Users enter basic property information using a terminal. This data includes the property's address, size, year built, and structure. This information is immediately transmitted to the cloud and stored as data on a server.

[0564] Step 2:

[0565] The server compares the received real estate information with a market dataset. At this stage, it extracts information on similar properties from the dataset, and uses this information for an AI model to calculate the market value of the property. Machine learning libraries such as Scikit-learn and TensorFlow are used for this calculation. The input is the property information provided by the user and the market dataset, and the output is the calculated market value.

[0566] Step 3:

[0567] The server generates a contract template based on the market value calculation results. This process utilizes the Google Docs API, creating a contract that meets the user's requirements based on a pre-prepared template. The input is the calculated property value data, and the output is a customized contract.

[0568] Step 4:

[0569] Users can review the generated contract through their device and make modifications as needed. Simple editing functions are provided on the device for this purpose.

[0570] Step 5:

[0571] The server generates a 3D virtual model of the property and provides it to the user. Unity or Unreal Engine is used to construct a visually realistic model. The input is detailed structural data of the property, and the output is a 3D model for user reference.

[0572] Step 6:

[0573] Users book virtual tours, and the server sets the date and time. The Google Calendar API is used for this scheduling, and the information is automatically notified to the relevant parties. The input is the user's preferred date and time, and the output is a confirmation and notification of the booked tour date and time.

[0574] Step 7:

[0575] The server monitors the progress of the transaction and notifies the user via the terminal of necessary actions. This allows the user to quickly respond to urgent steps in the transaction. The input is transaction progress data, and the output is notification messages.

[0576] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0577] The real estate brokerage system of this invention achieves user-friendly and efficient real estate transactions by utilizing a combination of generative AI and an emotion engine. This system consists of a server, terminals, and users.

[0578] The user first enters property information into the terminal. This includes the property's address, size, year built, and structure. The entered information is then verified by the terminal.

[0579] The server analyzes property information and accesses market databases to collect data on similar properties. The collected data is analyzed by an AI model to calculate the property's appraised value. This appraisal result is then provided to the user.

[0580] Simultaneously, the terminal uses a generation AI to generate a standard sales contract template. Based on user input, the server customizes this template to reflect the user's terms and legal requirements.

[0581] The emotion engine analyzes user responses in real time and recognizes their emotional state. Based on this data, the server provides transaction support tailored to the user's emotions and suggests appropriate actions. For example, if a user is feeling stressed, the server can take actions such as accelerating the transaction or providing additional information.

[0582] When a user enters their preferred date and time for a property viewing, the server checks the seller's and property management company's schedules and sets the most suitable viewing time. This information is automatically sent as a confirmation email from the user's device.

[0583] Throughout the transaction, the server constantly monitors the progress and notifies the user as needed, prompting them to take action. The emotion engine also supports communication based on the user's emotions throughout this process, facilitating smooth transactions.

[0584] As a concrete example, consider a case where a user purchases a house in a specific area. Using this system, the user can handle everything from entering property information to signing the contract and arranging viewings. Furthermore, the user's emotions are fed back into the system, ensuring that the transaction proceeds in the most comfortable way for the user.

[0585] Based on the above, the present invention improves the user experience, reduces fees, and shortens transaction times by providing more appropriate emotional feedback compared to conventional procedures.

[0586] The following describes the processing flow.

[0587] Step 1:

[0588] The user enters property information into a dedicated input form. This includes the property's address, size, year built, and structure.

[0589] Step 2:

[0590] The terminal verifies the accuracy of the entered information and provides feedback to the user, prompting them to correct the input if necessary.

[0591] Step 3:

[0592] The server uses verified real estate information to access the market database, querying and retrieving price data for similar properties.

[0593] Step 4:

[0594] The server analyzes the acquired data using an AI model and calculates the property's appraised value. The results are then presented to the user.

[0595] Step 5:

[0596] The terminal uses a generation AI to generate a basic sales contract template and displays it to the user.

[0597] Step 6:

[0598] The server incorporates user requirements (price, delivery date, etc.) into a contract template, customizes it, and verifies legal requirements.

[0599] Step 7:

[0600] The emotion engine analyzes user input and responses during operation and reports the emotional state. Based on this, the server adjusts and suggests the necessary communication style and information provision.

[0601] Step 8:

[0602] The user enters their preferred date and time for viewing the property into the terminal.

[0603] Step 9:

[0604] The server checks the schedules of the seller and the property management company and automatically selects the most suitable viewing date and time.

[0605] Step 10:

[0606] The terminal automatically sends a confirmation email to the user and relevant parties regarding the selected visit date and time.

[0607] Step 11:

[0608] The server monitors the progress of the transaction and notifies the user of any necessary actions. If the emotion engine detects user stress during the process, it uses that data to suggest further support.

[0609] Step 12:

[0610] Once the device confirms that the transaction is complete, it displays a screen asking the user for feedback.

[0611] These steps enable real estate transactions to provide an efficient and user-centric experience.

[0612] (Example 2)

[0613] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0614] Traditional real estate transaction processes require enormous time and effort for information gathering and analysis, and it has been difficult to provide services that are tailored to the user's emotions and circumstances. Therefore, there is a need for improved user experience, faster transactions, and more flexible responses.

[0615] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0616] This invention includes a server that analyzes input real estate information, compares it with an information database to collect data on similar properties, and calculates the property's appraised value; a server that generates a contract template using a generation AI and customizes the template based on the user's conditions; an emotion recognition tool that analyzes the user's reactions; and a tool that supports the transaction based on the analysis results and makes appropriate suggestions to the user. This enables efficient and user-friendly real estate transactions.

[0617] "Real estate information" refers to data that includes physical and legal attributes of a property, such as its address, size, year built, and structure.

[0618] "Input method" refers to an interface or device that allows users to provide detailed property information to the system.

[0619] "Means of analysis" refers to a computer program or process that compares input data with an information database to extract and analyze useful data.

[0620] "Generative AI" refers to artificial intelligence systems that automatically create document and contract templates using natural language processing and machine learning techniques.

[0621] "Emotion recognition means" refers to technologies or algorithms that analyze user responses to understand their emotional state and optimize system operation.

[0622] "Property valuation" refers to the estimated price of a property, calculated based on market data for similar properties and the current state of the real estate market.

[0623] A "database" is a collection of data that systematically stores property information and market data, and is a storage system configured to allow for efficient retrieval of necessary information.

[0624] "Means of customization" refers to a process or function for adjusting a generated template to suit the user's specific conditions or legal requirements.

[0625] "Means of supporting transactions" refers to programs or procedures that propose optimal transaction policies and procedures to users based on sentiment recognition results, thereby facilitating the smooth progress of the transaction process.

[0626] This invention aims to make traditional real estate transactions more user-friendly and efficient by combining a generative AI model and an emotion engine in a real estate brokerage system. This system consists of three components: a server, a terminal, and a user.

[0627] The user first enters detailed property information into the terminal. Specifically, this includes information such as the property's address, size, year built, and structure. This information is then verified by the terminal and sent to the server.

[0628] Next, the server analyzes the received property information. This involves a process of collecting data on similar properties using an information database. At this stage, the server uses an AI model to analyze the collected data and calculate the property's estimated value.

[0629] The terminal automatically generates a standard sales contract template using generation AI. This template is later customized by the server based on user input. The customized contract can incorporate the user's terms and region-specific legal requirements.

[0630] Furthermore, an emotion engine is built into the device, which analyzes user reactions in real time. For example, if a user is experiencing stress, emotion data is sent to the server, which then provides support such as speeding up transactions or providing additional information.

[0631] Furthermore, when a user enters their preferred date and time for a property viewing, the server consults the seller's or property management company's schedule and sets the most suitable viewing time. This information is automatically sent to the user as a confirmation email from their device.

[0632] As a concrete example, consider a case where a user purchases a 3LDK apartment less than 5 years old in a specific area of ​​a city. Through this system, the user can efficiently handle everything from inputting property information to generating contracts and scheduling viewings. As an example of a prompt, a specific request could be, "I'm looking for a 3LDK apartment less than 5 years old in Shinagawa Ward, Tokyo."

[0633] Thus, the present invention utilizes a generative AI model and an emotion engine to realize flexible and efficient real estate transactions that respond to the user's emotions and needs.

[0634] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0635] Step 1:

[0636] The user accesses the terminal and enters detailed property information. This information includes the property's address, size, year built, and structure. The entered data is verified on the terminal, and after its accuracy is guaranteed, it is sent to the server.

[0637] Step 2:

[0638] The server analyzes the property information received from the user. It accesses an information database to collect data on similar properties. This process references past sales history and market trend data. Based on the analysis, the server calculates the property's value and provides this data to the user.

[0639] Step 3:

[0640] The terminal uses a generation AI to create a standard sales contract template. This generation process takes into account the user's conditions and general legal requirements. The generated template serves as a prompt for customization and is then sent to the server.

[0641] Step 4:

[0642] The server customizes the contract template based on user input. It incorporates conditions and region-specific legal requirements extracted from the input data into the template, generating the final contract. This contract is saved in a format that allows user review.

[0643] Step 5:

[0644] The device utilizes an emotion engine to analyze the user's emotional state in real time. It detects stress and feelings of security from the user's tone of voice, facial expressions, and other factors. Emotional data is sent to a server, and transaction support is provided as needed.

[0645] Step 6:

[0646] The user enters their preferred date and time for viewing the property. This date and time information is sent to the server, which then compares it with the schedules of the property management company and the seller to determine the most suitable viewing date.

[0647] Step 7:

[0648] The server sends the confirmed tour date and time to the terminal, which then automatically generates a confirmation email and sends it to the user. The email includes tour details and contact information, which the user uses to confirm their schedule.

[0649] Step 8:

[0650] The server monitors the entire transaction process and notifies the user of necessary actions as it progresses. At each stage of the transaction, the server tracks the progress and guides the user through the necessary actions and next steps.

[0651] This system will allow users to experience efficient and stress-free real estate transactions.

[0652] (Application Example 2)

[0653] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0654] In online shopping, there is a need to accurately reflect user needs while improving the purchasing experience. In particular, when selecting products, it is necessary to respond with consideration for the user's emotions, and a system that allows for intuitive operation with minimal stress is required.

[0655] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0656] In this invention, the server includes means for receiving information, means for analyzing the received information, comparing it with a database to generate recommendations, and means for analyzing the user's emotional state and providing information that corresponds to that emotional state. This enables personalized information provision and emotionally sensitive support for the user.

[0657] "Information" refers to the data and conditions that the system receives from the user, and it forms the basis for product selection and recommendations.

[0658] A "database" is a source of information that stores market and historical transaction data, and is used by systems for analysis.

[0659] "Recommendation" refers to the suggestion of the most suitable products and services provided to the user based on information obtained from a database.

[0660] A "template" is a standardized form of documents required for contracts and transactions, which can then be customized according to the user's specific requirements.

[0661] "Conditions" refer to specific requirements or criteria desired by the user, and are the criteria used to generate templates and recommendations.

[0662] "Emotional state" refers to the user's psychological feedback, which the emotion engine analyzes in real time, and which influences the system's output.

[0663] A "generative AI model" is a form of artificial intelligence that automatically analyzes vast amounts of data and creates documents, and is used for recommendations and template generation within a system.

[0664] The system used to implement this application primarily consists of a server, user terminals, and associated databases. The server receives information entered by users through their terminals. This input information, consisting of conditions related to products and services, is then compared with data in the market database. The server is built using a programming language such as Python and utilizes the Django framework. It generates the necessary queries for interaction with the database and retrieves data in real time.

[0665] The server utilizes a generative AI model to automatically generate optimal recommendations based on this data. The generative AI model employs a model excelling in natural language processing, specifically OpenAI's GPT-based model. This provides users with information to support their selection of the most suitable products and services. Furthermore, a template generation function automatically constructs contract templates tailored to the user's specific requirements.

[0666] Furthermore, an emotion engine is used to analyze the user's emotional state. As the user interacts with the system through their device, their psychological feedback is evaluated in real time, and the server provides appropriate product recommendations and additional information based on the results. For example, if the user is undecided, it will present comparisons of similar products and promotional information.

[0667] For example, if a user enters the prompt, "Please show me the best laptop that meets the following conditions and its price: 16GB RAM, 512GB SSD, under 100,000 yen," the server will collect product information that matches the conditions from its database based on this prompt, and then analyze and present it using a generating AI model. This allows the user to find the product they are looking for without any stress.

[0668] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0669] Step 1:

[0670] The user enters prompt text through the terminal regarding the product and criteria they are searching for. This input information includes conditions such as specific specifications and price range. The terminal sends this input information to the server.

[0671] Step 2:

[0672] The server parses the received prompt and generates a query against the relevant database. This query extracts products from the database that meet the conditions specified in the prompt. The server executes the query and retrieves the corresponding product data.

[0673] Step 3:

[0674] The server inputs the acquired product data into a generating AI model. The generating AI model uses natural language generation technology to create optimal recommendation texts. This process ensures that product information is presented in a way that is easy for the user to understand.

[0675] Step 4:

[0676] The generated recommendation text and related product information are sent back to the terminal. The terminal displays this information on its user interface, allowing the user to visually confirm the content.

[0677] Step 5:

[0678] Users make purchase decisions based on the displayed product information and recommendations. If necessary, they can send further feedback to the server via their device to receive additional information or alternative product recommendations.

[0679] Step 6:

[0680] The emotion engine analyzes the user's facial expressions and voice through the device to evaluate the user's emotional state. This data is sent to a server, which then provides information tailored to the user's emotional state. The server generates recommendations and information that take the user's emotions into consideration and sends them back to the device.

[0681] In this way, the program achieves product recommendations that meet the user's needs and provides information tailored to their emotions.

[0682] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0683] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0684] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0685] [Fourth Embodiment]

[0686] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0687] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0688] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0689] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0690] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0691] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0692] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0693] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0694] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0695] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0696] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0697] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0698] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0699] The real estate brokerage system of this invention is a system for efficiently conducting real estate transactions using generative AI. This system operates through the cooperation of three entities: a server, a terminal, and a user.

[0700] The user first enters property information. This information includes basic data such as the property's address, size, year built, and structure.

[0701] The server receives and analyzes this information. It collects data on similar properties using a market database and calculates the fair market value of a property using an AI model. This allows users to quickly understand the market value of a property.

[0702] Next, the terminal generates a contract template necessary for real estate transactions. The template includes all the basic items and provides a base for the user to create their own contract.

[0703] The server customizes the contract based on user input. This customization ensures that specific conditions and requirements are reflected in the contract and that legal requirements are met.

[0704] When a user enters their preferred date and time for a property viewing, the server checks the schedules of the seller and property management company and sets the most suitable viewing date and time. The viewing date and time are notified to the relevant parties by the terminal automatically sending a confirmation email.

[0705] As the transaction progresses, the server continuously monitors the status. If necessary action is required, the terminal notifies the user and prompts them to take immediate action.

[0706] As a concrete example, let's consider a case where a user wants to sell an apartment located in an urban area. The user can input property information into the system and enter into a contract based on the market price suggested by the system. Since contract generation and scheduling are automated, the user can proceed with the real estate transaction quickly and efficiently.

[0707] As described above, the present invention significantly simplifies conventional intermediation procedures, resulting in reduced fees, shorter processing times, and improved transaction transparency.

[0708] The following describes the processing flow.

[0709] Step 1:

[0710] Users enter property information such as the property's address, size, year built, and structure using a dedicated input form.

[0711] Step 2:

[0712] The terminal receives the input and performs an initial check to ensure the property information is accurate. If necessary, it prompts the user for corrections.

[0713] Step 3:

[0714] The server analyzes the received real estate information and sends queries to the market database. It then collects price data for similar properties from the market database.

[0715] Step 4:

[0716] The server uses an AI model to calculate the property's appraised value based on the collected data. This appraisal result is then provided to the user.

[0717] Step 5:

[0718] The terminal generates a standard sales contract template based on the user's request.

[0719] Step 6:

[0720] The server customizes the contract template based on the user's input conditions (price, delivery date, etc.). This includes checking legal requirements.

[0721] Step 7:

[0722] The user enters their preferred date and time for viewing the property into the terminal.

[0723] Step 8:

[0724] The server checks the schedules of the seller and the property management company and assigns the most suitable viewing date and time.

[0725] Step 9:

[0726] The terminal automatically sends confirmation emails regarding the visit date and time to users and related parties.

[0727] Step 10:

[0728] The server continuously monitors the progress of the transaction and sends notifications to the user as needed to prompt action.

[0729] Step 11:

[0730] The terminal confirms the completion of the transaction and displays a screen requesting the user to provide feedback.

[0731] (Example 1)

[0732] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0733] Real estate transactions typically involve a wide range of tasks, including information gathering, property valuation, contract drafting, and viewing arrangements, making them time-consuming and laborious. This often results in inefficient and insufficient transparency in transactions. This service provides methods to address these challenges and conduct transactions quickly and efficiently.

[0734] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0735] In this invention, the server includes means for receiving real estate information, means for analyzing the received information and comparing it with market information to calculate the price of the property, and means for generating a contract template and customizing the contract based on the user's conditions. This makes it possible to proceed with transactions quickly and efficiently, and to ensure transparency and legal consistency.

[0736] "Real estate information" refers to data that includes basic attributes of properties related to real estate transactions, such as address, size, year built, and structure.

[0737] "Market information" refers to sources of information that include data on similar properties in the past and present real estate market.

[0738] "Property price" refers to the fair value of a property, calculated based on market information and other relevant data.

[0739] A "contract template" is an initial contract format that covers the basic items necessary for real estate transactions, and is customizable by the user.

[0740] An "AI model" refers to a group of computational models that use artificial intelligence technology to perform data analysis and prediction.

[0741] A "prompt statement" is an input statement that provides initial conditions to an AI model and generates a specific output.

[0742] A "server" refers to a computer system that processes, stores, and distributes data over a network.

[0743] A "terminal" is a computer device used by a user to interact with a server, and is used for inputting and outputting information.

[0744] This real estate brokerage system is designed to efficiently conduct real estate transactions using a generative AI model. Specific embodiments are shown below.

[0745] The user uses a terminal to input property information such as the property's address, size, year built, and structure. The terminal receives this information and sends it to the server.

[0746] The server analyzes market information based on the received real estate information. This market information is obtained from a database containing data on similar properties from the past and present. The server uses a generative AI model (e.g., a standard computation model built with TensorFlow or PyTorch) to calculate the fair market value of the property. In this process, user input serves as prompts.

[0747] The terminal generates a contract template using calculation results received from the server. This contract template includes the basic items necessary for real estate transactions and can later be customized based on the user's requirements. Furthermore, an additional module is incorporated to verify the legal requirements of the contract, and modifications can be made as needed.

[0748] As a concrete example, consider a case where a user wants to sell an apartment in an urban area. The user inputs property information from their terminal, and the server analyzes this information and calculates a fair market price using an AI model. The user can then use this price as a reference to proceed with the transaction efficiently.

[0749] Examples of prompt messages include, "Calculate a fair market price based on the property information for an apartment in Shinjuku Ward," and "Generate a contract template based on this property information."

[0750] In this way, the system automates each step of a real estate transaction, enabling rapid and efficient execution. This invention aims to significantly improve transparency and efficiency by allowing users to proceed with real estate transactions with minimal effort.

[0751] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0752] Step 1:

[0753] The user enters property information using a terminal. This information includes the property's address, size, year built, and structure. The terminal collects this data and sends it to the server. The entered information is in digital format, and its integrity is checked before it is sent to the server.

[0754] Step 2:

[0755] The server analyzes the real estate information received from the terminal. It accesses a market information database and retrieves information on similar properties. The server searches the database using SQL queries and extracts relevant data. Next, it performs analysis using a generated AI model based on this data to calculate the fair price of the property. The output is the property's appraised value and is sent to the terminal.

[0756] Step 3:

[0757] The terminal receives the appraisal price sent from the server and generates a contract template for real estate transactions. A contract template generation module is used for this process. The contract format is customized according to the user's conditions and requirements. Inputs are the appraisal price from the server and the user's conditions, and output is the customized contract template.

[0758] Step 4:

[0759] The server verifies the legal requirements of the contract. It analyzes the contract's content and verifies that it includes all legally required items. It makes revisions to the contract as needed. This process involves automated determination using a legal knowledge base to detect any necessary modifications. The output is a legally compliant contract, which is then presented to the user.

[0760] Step 5:

[0761] The user enters their desired date and time for viewing the property into the terminal. The terminal sends this information to the server. The server checks the schedules of the seller and property management company and sets the most suitable viewing date and time. The terminal automatically sends a confirmation email to the relevant parties once the date and time have been set. The input is the user's desired viewing date and time, and the output is a notification of the set viewing date and time.

[0762] Step 6:

[0763] The server continuously monitors the progress of the transaction. With each step of the transaction, the progress is evaluated, and if specific action is required, the user is notified via the terminal. The server tracks the progress and triggers a notification protocol when necessary action is required.

[0764] (Application Example 1)

[0765] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0766] Modern real estate transactions often involve significant time and effort spent on physical property viewings, contract procedures, and price appraisals. Furthermore, selling properties outside urban areas is often hindered by the difficulty potential buyers face in actually visiting the property, thus impeding efficient transactions. A system is needed to address these issues and enable swift and efficient real estate transactions.

[0767] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0768] In this invention, the server includes means for receiving real estate information, means for analyzing the received real estate information and comparing it with a market dataset to calculate property prices, means for generating contract templates and customizing contracts based on user conditions, means for automatically scheduling property viewings and notifying relevant parties, means for users to view virtual models of real estate properties, and means for setting and providing dates and times for virtual viewing tours. This overcomes physical constraints and makes it possible to complete the real estate transaction process online.

[0769] A "means for receiving real estate information" refers to an interface for electronically receiving and storing property data entered by users.

[0770] A "market dataset" is a collection of information including real estate market transaction data and price trends, and is a database referenced to calculate the value of a property.

[0771] "Methods for calculating property prices" refers to algorithms that use AI models to calculate the appropriate price of a property based on input real estate information and market data.

[0772] "Methods for generating contract templates" refer to the process of creating templates that cover basic contract terms and preparing documents that can be customized according to the user's transaction conditions.

[0773] "Means of notifying stakeholders" refers to the function of using email or messaging services to inform stakeholders of necessary information, such as property viewings or transaction progress.

[0774] "Means for viewing virtual models" refers to a function that allows users to visually view a 3D model of a property through a computer, head-mounted display, or similar device.

[0775] "The means of setting and providing virtual tour dates and times" refers to a function that allows users to book virtual tours based on their preferences and view real estate properties online at the scheduled time.

[0776] The system for implementing this invention mainly consists of server, terminal, and user interaction. To streamline real estate transactions, each element works together, utilizing information processing and digital technologies.

[0777] First, the terminal receives real estate information from the user. This information includes the property's address, size, price, and structure. The data entered by the user is then compared against a market dataset within the system. Subsequently, the server uses an AI model to calculate the fair market value of the property. This process utilizes the Python programming language and machine learning libraries such as Scikit-learn and TensorFlow.

[0778] Next, the server generates a contract template and customizes it based on the user's requirements. This process utilizes the Google Docs API to electronically create the contract. The user can then review it on their device and make any necessary modifications.

[0779] Furthermore, the server generates a virtual model of the property and provides it to the user via their terminal. The 3D model of the property is visually represented using Unity or Unreal Engine. Users can view this 3D model and enjoy a virtual tour. The server also handles scheduling and providing the virtual tour, and appropriate information is notified to the relevant parties.

[0780] Finally, the server monitors the progress of the transaction, and users are notified via their devices when necessary actions are required. Cloud services such as the Google Calendar API and Gmail API are used to confirm the viewing schedule and to notify users of important progress updates.

[0781] As a concrete example, consider a scenario where a user puts a large house in a rural village up for sale. In this case, the user uses a terminal to input property information, and as a result, is provided with an environment where the property can be appealed to distant customers through market prices and virtual tours. This system enables online real estate transactions, overcoming physical limitations, and is highly efficient through the use of generative AI models.

[0782] As an example of a prompt message, entering the command "After inputting property information, analyze the market value using the AI ​​model, generate a 3D model, and provide the user with a virtual real estate tour" into the system will automatically start the entire process.

[0783] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0784] Step 1:

[0785] Users enter basic property information using a terminal. This data includes the property's address, size, year built, and structure. This information is immediately transmitted to the cloud and stored as data on a server.

[0786] Step 2:

[0787] The server compares the received real estate information with a market dataset. At this stage, it extracts information on similar properties from the dataset, and uses this information for an AI model to calculate the market value of the property. Machine learning libraries such as Scikit-learn and TensorFlow are used for this calculation. The input is the property information provided by the user and the market dataset, and the output is the calculated market value.

[0788] Step 3:

[0789] The server generates a contract template based on the market value calculation results. This process utilizes the Google Docs API, creating a contract that meets the user's requirements based on a pre-prepared template. The input is the calculated property value data, and the output is a customized contract.

[0790] Step 4:

[0791] Users can review the generated contract through their device and make modifications as needed. Simple editing functions are provided on the device for this purpose.

[0792] Step 5:

[0793] The server generates a 3D virtual model of the property and provides it to the user. Unity or Unreal Engine is used to construct a visually realistic model. The input is detailed structural data of the property, and the output is a 3D model for user reference.

[0794] Step 6:

[0795] Users book virtual tours, and the server sets the date and time. The Google Calendar API is used for this scheduling, and the information is automatically notified to the relevant parties. The input is the user's preferred date and time, and the output is a confirmation and notification of the booked tour date and time.

[0796] Step 7:

[0797] The server monitors the progress of the transaction and notifies the user via the terminal of necessary actions. This allows the user to quickly respond to urgent steps in the transaction. The input is transaction progress data, and the output is notification messages.

[0798] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0799] The real estate brokerage system of this invention achieves user-friendly and efficient real estate transactions by utilizing a combination of generative AI and an emotion engine. This system consists of a server, terminals, and users.

[0800] The user first enters property information into the terminal. This includes the property's address, size, year built, and structure. The entered information is then verified by the terminal.

[0801] The server analyzes property information and accesses market databases to collect data on similar properties. The collected data is analyzed by an AI model to calculate the property's appraised value. This appraisal result is then provided to the user.

[0802] Simultaneously, the terminal uses a generation AI to generate a standard sales contract template. Based on user input, the server customizes this template to reflect the user's terms and legal requirements.

[0803] The emotion engine analyzes user responses in real time and recognizes their emotional state. Based on this data, the server provides transaction support tailored to the user's emotions and suggests appropriate actions. For example, if a user is feeling stressed, the server can take actions such as accelerating the transaction or providing additional information.

[0804] When a user enters their preferred date and time for a property viewing, the server checks the seller's and property management company's schedules and sets the most suitable viewing time. This information is automatically sent as a confirmation email from the user's device.

[0805] Throughout the transaction, the server constantly monitors the progress and notifies the user as needed, prompting them to take action. The emotion engine also supports communication based on the user's emotions throughout this process, facilitating smooth transactions.

[0806] As a concrete example, consider a case where a user purchases a house in a specific area. Using this system, the user can handle everything from entering property information to signing the contract and arranging viewings. Furthermore, the user's emotions are fed back into the system, ensuring that the transaction proceeds in the most comfortable way for the user.

[0807] Based on the above, the present invention improves the user experience, reduces fees, and shortens transaction times by providing more appropriate emotional feedback compared to conventional procedures.

[0808] The following describes the processing flow.

[0809] Step 1:

[0810] The user enters property information into a dedicated input form. This includes the property's address, size, year built, and structure.

[0811] Step 2:

[0812] The terminal verifies the accuracy of the entered information and provides feedback to the user, prompting them to correct the input if necessary.

[0813] Step 3:

[0814] The server uses verified real estate information to access the market database, querying and retrieving price data for similar properties.

[0815] Step 4:

[0816] The server analyzes the acquired data using an AI model and calculates the property's appraised value. The results are then presented to the user.

[0817] Step 5:

[0818] The terminal uses a generation AI to generate a basic sales contract template and displays it to the user.

[0819] Step 6:

[0820] The server incorporates user requirements (price, delivery date, etc.) into a contract template, customizes it, and verifies legal requirements.

[0821] Step 7:

[0822] The emotion engine analyzes user input and responses during operation and reports the emotional state. Based on this, the server adjusts and suggests the necessary communication style and information provision.

[0823] Step 8:

[0824] The user enters their preferred date and time for viewing the property into the terminal.

[0825] Step 9:

[0826] The server checks the schedules of the seller and the property management company and automatically selects the most suitable viewing date and time.

[0827] Step 10:

[0828] The terminal automatically sends a confirmation email to the user and relevant parties regarding the selected visit date and time.

[0829] Step 11:

[0830] The server monitors the progress of the transaction and notifies the user of any necessary actions. If the emotion engine detects user stress during the process, it uses that data to suggest further support.

[0831] Step 12:

[0832] Once the device confirms that the transaction is complete, it displays a screen asking the user for feedback.

[0833] These steps enable real estate transactions to provide an efficient and user-centric experience.

[0834] (Example 2)

[0835] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0836] Traditional real estate transaction processes require enormous time and effort for information gathering and analysis, and it has been difficult to provide services that are tailored to the user's emotions and circumstances. Therefore, there is a need for improved user experience, faster transactions, and more flexible responses.

[0837] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0838] This invention includes a server that analyzes input real estate information, compares it with an information database to collect data on similar properties, and calculates the property's appraised value; a server that generates a contract template using a generation AI and customizes the template based on the user's conditions; an emotion recognition tool that analyzes the user's reactions; and a tool that supports the transaction based on the analysis results and makes appropriate suggestions to the user. This enables efficient and user-friendly real estate transactions.

[0839] "Real estate information" refers to data that includes physical and legal attributes of a property, such as its address, size, year built, and structure.

[0840] "Input method" refers to an interface or device that allows users to provide detailed property information to the system.

[0841] "Means of analysis" refers to a computer program or process that compares input data with an information database to extract and analyze useful data.

[0842] "Generative AI" refers to artificial intelligence systems that automatically create document and contract templates using natural language processing and machine learning techniques.

[0843] "Emotion recognition means" refers to technologies or algorithms that analyze user responses to understand their emotional state and optimize system operation.

[0844] "Property valuation" refers to the estimated price of a property, calculated based on market data for similar properties and the current state of the real estate market.

[0845] A "database" is a collection of data that systematically stores property information and market data, and is a storage system configured to allow for efficient retrieval of necessary information.

[0846] "Means of customization" refers to a process or function for adjusting a generated template to suit the user's specific conditions or legal requirements.

[0847] "Means of supporting transactions" refers to programs or procedures that propose optimal transaction policies and procedures to users based on sentiment recognition results, thereby facilitating the smooth progress of the transaction process.

[0848] This invention aims to make traditional real estate transactions more user-friendly and efficient by combining a generative AI model and an emotion engine in a real estate brokerage system. This system consists of three components: a server, a terminal, and a user.

[0849] The user first enters detailed property information into the terminal. Specifically, this includes information such as the property's address, size, year built, and structure. This information is then verified by the terminal and sent to the server.

[0850] Next, the server analyzes the received property information. This involves a process of collecting data on similar properties using an information database. At this stage, the server uses an AI model to analyze the collected data and calculate the property's estimated value.

[0851] The terminal automatically generates a standard sales contract template using generation AI. This template is later customized by the server based on user input. The customized contract can incorporate the user's terms and region-specific legal requirements.

[0852] Furthermore, an emotion engine is built into the device, which analyzes user reactions in real time. For example, if a user is experiencing stress, emotion data is sent to the server, which then provides support such as speeding up transactions or providing additional information.

[0853] Furthermore, when a user enters their preferred date and time for a property viewing, the server consults the seller's or property management company's schedule and sets the most suitable viewing time. This information is automatically sent to the user as a confirmation email from their device.

[0854] As a concrete example, consider a case where a user purchases a 3LDK apartment less than 5 years old in a specific area of ​​a city. Through this system, the user can efficiently handle everything from inputting property information to generating contracts and scheduling viewings. As an example of a prompt, a specific request could be, "I'm looking for a 3LDK apartment less than 5 years old in Shinagawa Ward, Tokyo."

[0855] Thus, the present invention utilizes a generative AI model and an emotion engine to realize flexible and efficient real estate transactions that respond to the user's emotions and needs.

[0856] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0857] Step 1:

[0858] The user accesses the terminal and enters detailed property information. This information includes the property's address, size, year built, and structure. The entered data is verified on the terminal, and after its accuracy is guaranteed, it is sent to the server.

[0859] Step 2:

[0860] The server analyzes the property information received from the user. It accesses an information database to collect data on similar properties. This process references past sales history and market trend data. Based on the analysis, the server calculates the property's value and provides this data to the user.

[0861] Step 3:

[0862] The terminal uses a generation AI to create a standard sales contract template. This generation process takes into account the user's conditions and general legal requirements. The generated template serves as a prompt for customization and is then sent to the server.

[0863] Step 4:

[0864] The server customizes the contract template based on user input. It incorporates conditions and region-specific legal requirements extracted from the input data into the template, generating the final contract. This contract is saved in a format that allows user review.

[0865] Step 5:

[0866] The device utilizes an emotion engine to analyze the user's emotional state in real time. It detects stress and feelings of security from the user's tone of voice, facial expressions, and other factors. Emotional data is sent to a server, and transaction support is provided as needed.

[0867] Step 6:

[0868] The user enters their preferred date and time for viewing the property. This date and time information is sent to the server, which then compares it with the schedules of the property management company and the seller to determine the most suitable viewing date.

[0869] Step 7:

[0870] The server sends the confirmed tour date and time to the terminal, which then automatically generates a confirmation email and sends it to the user. The email includes tour details and contact information, which the user uses to confirm their schedule.

[0871] Step 8:

[0872] The server monitors the entire transaction process and notifies the user of necessary actions as it progresses. At each stage of the transaction, the server tracks the progress and guides the user through the necessary actions and next steps.

[0873] This system will allow users to experience efficient and stress-free real estate transactions.

[0874] (Application Example 2)

[0875] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0876] In online shopping, there is a need to accurately reflect user needs while improving the purchasing experience. In particular, when selecting products, it is necessary to respond with consideration for the user's emotions, and a system that allows for intuitive operation with minimal stress is required.

[0877] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0878] In this invention, the server includes means for receiving information, means for analyzing the received information, comparing it with a database to generate recommendations, and means for analyzing the user's emotional state and providing information that corresponds to that emotional state. This enables personalized information provision and emotionally sensitive support for the user.

[0879] "Information" refers to the data and conditions that the system receives from the user, and it forms the basis for product selection and recommendations.

[0880] A "database" is a source of information that stores market and historical transaction data, and is used by systems for analysis.

[0881] "Recommendation" refers to the suggestion of the most suitable products and services provided to the user based on information obtained from a database.

[0882] A "template" is a standardized form of documents required for contracts and transactions, which can then be customized according to the user's specific requirements.

[0883] "Conditions" refer to specific requirements or criteria desired by the user, and are the criteria used to generate templates and recommendations.

[0884] "Emotional state" refers to the user's psychological feedback, which the emotion engine analyzes in real time, and which influences the system's output.

[0885] A "generative AI model" is a form of artificial intelligence that automatically analyzes vast amounts of data and creates documents, and is used for recommendations and template generation within a system.

[0886] The system used to implement this application primarily consists of a server, user terminals, and associated databases. The server receives information entered by users through their terminals. This input information, consisting of conditions related to products and services, is then compared with data in the market database. The server is built using a programming language such as Python and utilizes the Django framework. It generates the necessary queries for interaction with the database and retrieves data in real time.

[0887] The server utilizes a generative AI model to automatically generate optimal recommendations based on this data. The generative AI model employs a model excelling in natural language processing, specifically OpenAI's GPT-based model. This provides users with information to support their selection of the most suitable products and services. Furthermore, a template generation function automatically constructs contract templates tailored to the user's specific requirements.

[0888] Furthermore, an emotion engine is used to analyze the user's emotional state. As the user interacts with the system through their device, their psychological feedback is evaluated in real time, and the server provides appropriate product recommendations and additional information based on the results. For example, if the user is undecided, it will present comparisons of similar products and promotional information.

[0889] For example, if a user enters the prompt, "Please show me the best laptop that meets the following conditions and its price: 16GB RAM, 512GB SSD, under 100,000 yen," the server will collect product information that matches the conditions from its database based on this prompt, and then analyze and present it using a generating AI model. This allows the user to find the product they are looking for without any stress.

[0890] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0891] Step 1:

[0892] The user enters prompt text through the terminal regarding the product and criteria they are searching for. This input information includes conditions such as specific specifications and price range. The terminal sends this input information to the server.

[0893] Step 2:

[0894] The server parses the received prompt and generates a query against the relevant database. This query extracts products from the database that meet the conditions specified in the prompt. The server executes the query and retrieves the corresponding product data.

[0895] Step 3:

[0896] The server inputs the acquired product data into a generating AI model. The generating AI model uses natural language generation technology to create optimal recommendation texts. This process ensures that product information is presented in a way that is easy for the user to understand.

[0897] Step 4:

[0898] The generated recommendation text and related product information are sent back to the terminal. The terminal displays this information on its user interface, allowing the user to visually confirm the content.

[0899] Step 5:

[0900] Users make purchase decisions based on the displayed product information and recommendations. If necessary, they can send further feedback to the server via their device to receive additional information or alternative product recommendations.

[0901] Step 6:

[0902] The emotion engine analyzes the user's facial expressions and voice through the device to evaluate the user's emotional state. This data is sent to a server, which then provides information tailored to the user's emotional state. The server generates recommendations and information that take the user's emotions into consideration and sends them back to the device.

[0903] In this way, the program achieves product recommendations that meet the user's needs and provides information tailored to their emotions.

[0904] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0905] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0906] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0907] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0908] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0909] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0910] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0911] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0912] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0913] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0914] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0915] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0916] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0917] 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.

[0918] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0919] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0920] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0921] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0922] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0923] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0924] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0925] The following is further disclosed regarding the embodiments described above.

[0926] (Claim 1)

[0927] Means of receiving real estate information,

[0928] A method for analyzing received real estate information, comparing it with a market database, and calculating property prices,

[0929] A means of generating contract templates and customizing contracts based on user requirements,

[0930] A means to automatically schedule property viewings and notify relevant parties,

[0931] A system that includes means for monitoring the progress of transactions and notifying users of necessary actions.

[0932] (Claim 2)

[0933] The system according to claim 1, further comprising means for verifying the legal requirements of the generated contract and making modifications as necessary.

[0934] (Claim 3)

[0935] The system according to claim 1, further comprising means for calculating property prices using an AI model based on data obtained from a market database.

[0936] "Example 1"

[0937] (Claim 1)

[0938] Means of receiving real estate information,

[0939] A method for analyzing received real estate information, cross-referencing it with market information, and calculating the price of a property,

[0940] A means of generating a contract template and customizing the contract based on the user's conditions,

[0941] A means to automatically schedule property viewings and notify relevant parties,

[0942] A means of monitoring the progress of a transaction and notifying the user of necessary actions,

[0943] A method for using the analyzed data as prompts when calculating property prices using an AI model,

[0944] A system that includes means to review the legal requirements of generated contracts and make modifications as necessary.

[0945] (Claim 2)

[0946] The system according to claim 1, further comprising means for verifying the legal requirements of the generated contract and making modifications as necessary.

[0947] (Claim 3)

[0948] The system according to claim 1, further comprising means for calculating property prices using an AI model generated based on data obtained from market information.

[0949] "Application Example 1"

[0950] (Claim 1)

[0951] Means of receiving real estate information,

[0952] A method for analyzing received real estate information, comparing it with market datasets, and calculating property prices,

[0953] A means of generating a contract template and customizing the contract based on the user's conditions,

[0954] A means to automatically schedule property viewings and notify relevant parties,

[0955] A means of monitoring the progress of a transaction and notifying the user of necessary actions,

[0956] A means for users to view virtual models of real estate properties,

[0957] A system that includes a means of setting and providing users with the date and time for a virtual guided tour.

[0958] (Claim 2)

[0959] The system according to claim 1, further comprising means for verifying the legal requirements of the generated contract and making modifications as necessary.

[0960] (Claim 3)

[0961] The system according to claim 1, further comprising means for calculating property prices using an AI model based on data obtained from a market dataset.

[0962] "Example 2 of combining an emotion engine"

[0963] (Claim 1)

[0964] Means of entering real estate information,

[0965] A means of analyzing input real estate information, comparing it with an information database to collect data on similar properties, and calculating the property's appraised value.

[0966] A method for generating contract templates using generation AI and customizing the templates based on the user's requirements,

[0967] A means of sentiment recognition that analyzes user reactions, and a means of supporting transactions and making appropriate suggestions to users based on the analysis results.

[0968] A method for automatically scheduling property viewings and notifying relevant parties,

[0969] A system that includes means to monitor the progress of the entire transaction and notify the user of any necessary actions.

[0970] (Claim 2)

[0971] The system according to claim 1, further comprising means for verifying the legal terms of a generated contract and making modifications if necessary.

[0972] (Claim 3)

[0973] The system according to claim 1, further comprising means for calculating the appraised value of a property using an AI model generated based on data collected from an information database.

[0974] "Application example 2 when combining with an emotional engine"

[0975] (Claim 1)

[0976] Means of receiving information,

[0977] A means of analyzing the received information, comparing it with a database, and generating recommendations,

[0978] A means of generating contract templates and customizing them based on conditions,

[0979] A means of automatically setting schedules and notifying relevant parties,

[0980] A means of monitoring progress and notifying necessary actions,

[0981] A means of analyzing the user's emotional state and providing information corresponding to that emotion,

[0982] A means of presenting information using a generative AI model,

[0983] A system that includes this.

[0984] (Claim 2)

[0985] The system according to claim 1, further comprising means for reviewing the requirements of the generated template and making modifications as necessary.

[0986] (Claim 3)

[0987] The system according to claim 1, further comprising means for generating recommendations using an AI model based on data obtained from a database. [Explanation of Symbols]

[0988] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. Means of receiving real estate information, A method for analyzing received real estate information, comparing it with a market database, and calculating property prices, A means of generating contract templates and customizing contracts based on user requirements, A means to automatically schedule property viewings and notify relevant parties, A system that includes means for monitoring the progress of transactions and notifying users of necessary actions.

2. The system according to claim 1, further comprising means for verifying the legal requirements of a generated contract and making modifications as necessary.

3. The system according to claim 1, further comprising means for calculating property prices using an AI model based on data obtained from a market database.

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