system

Abstract

We provide the system. [Solution] A means of entering the rental conditions for the property, A means of inputting the tenant's desired conditions, Means for storing the rental conditions of the aforementioned property and the desired conditions of the aforementioned lessee in a database, A means for analyzing the stored loan conditions and desired conditions and calculating combinations of high suitability, A means of proposing a share house plan based on the calculated optimal matching results, Means for generating a contract based on the aforementioned proposed conditions, A system that includes this.

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 character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance that responds 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 recent years, the demand for shared houses among young people and single individuals has been increasing. However, it is difficult for property owners to formulate appropriate contract conditions for converting properties into shared houses. Also, it is difficult for lessees to find a shared house or co-inhabitants that meet their desired conditions. It is necessary to overcome such complexities in real estate operation and difficulties in choosing a living environment, and to realize the provision of a comfortable living environment and the maximization of the interests of real estate owners.

Means for Solving the Problems

[0005] This invention provides a system for efficiently matching property rental conditions with tenant preferences. The system includes means for inputting property rental conditions and tenant preferences and storing them in a database. Furthermore, it analyzes the stored conditions and calculates the combination with the optimal degree of fit. It then proposes a shared housing plan based on the calculated matching results and generates a contract based on the proposed conditions. This makes it possible to realize a contract with conditions that satisfy both the owner and the tenant.

[0006] "Property" refers to a building or part thereof that a real estate owner provides for rental or residential use.

[0007] "Lending conditions" refer to the specific standards and rules that a property owner sets for renting out a property.

[0008] "Desired conditions" refer to the elements and requirements that the tenant seeks in their living space.

[0009] A "database" is an information system that systematically stores information and makes it possible to efficiently search and use that information.

[0010] "Analysis" is the act of breaking down information into smaller parts, understanding their meaning for a specific purpose, and processing them effectively.

[0011] "Fit" is a measure that indicates the degree to which the loan conditions and desired conditions match or harmonize.

[0012] "Matching results" refer to the optimal combination of tenant and property based on the degree of suitability obtained through analysis.

[0013] A "share house plan" is a plan and set of conditions for communal living proposed based on the optimal matching results.

[0014] A "contract" is an agreement between a landlord and a tenant that includes legally established obligations and rights.

Brief Description of the Drawings

[0015] [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

[0016] 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.

[0017] First, the terms used in the following description will be explained.

[0018] In the following embodiments, a labeled 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.

[0019] In the following embodiments, a labeled RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0020] In the following embodiments, a labeled storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[0021] 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).

[0022] 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."

[0023] [First Embodiment]

[0024] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0025] 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.

[0026] 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).

[0027] 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.

[0028] 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.

[0029] 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.

[0030] 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.

[0031] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0032] 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.

[0033] 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.

[0034] 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.

[0035] 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".

[0036] The system for implementing this invention efficiently matches the requirements of both property owners who wish to rent out their properties and tenants who wish to live in a shared house, and generates and manages optimal contracts based on these matching conditions. The details are shown below.

[0037] First, the user (landlord) enters detailed property information and rental conditions using a terminal. This information includes the property's location, floor plan, desired rent, number of occupants allowed, and whether pets are permitted.

[0038] Next, the user (tenant) enters their desired conditions for the share house via the terminal. This includes their preferred location, maximum rent, and desired conditions for roommates (gender, age, etc.).

[0039] Here, all entered information is sent from the terminal to the server. The server stores this data in a database, preparing it for the subsequent matching process. Based on the aforementioned conditions, the server (AI) uses advanced algorithms to analyze the conditions of both the landlord and the tenant and find the most suitable combination. Because this process is automated, it significantly reduces time and produces more effective matching results.

[0040] This system also optimizes the conditions by proposing a shared housing plan based on the best matching results calculated by AI. This plan includes contract terms (rent, move-in date, contract period, etc.). The server sends this proposal to the terminal, and the process proceeds for the user (landlord and tenant) to review the proposal.

[0041] As a concrete example, consider a landlord with a 2LDK apartment in an urban area looking for a tenant who wants to live in a shared apartment. In this case, the landlord specifies the detailed conditions of the property, and multiple potential tenants enter their desired conditions. A server (AI) processes this information, finds the best combination, and delivers proposals in both directions. Once a user accepts a proposal, the server automatically creates a formal contract, and the contract is finalized through electronic signatures, etc. This system enables efficient and smooth conclusion of rental agreements.

[0042] The following describes the processing flow.

[0043] Step 1:

[0044] Users (landlords) enter detailed property information and rental conditions via a terminal. This includes the property's location, floor plan, desired rent, whether pets are allowed, and the number of people who can share the property. The terminal provides guidance to ensure that the information is entered accurately in real time.

[0045] Step 2:

[0046] The user (tenant) enters their desired conditions for a shared house into the terminal. This information includes preferred area, maximum rent, desired attributes of roommates (gender, age range), and lifestyle preferences. After the entered data is reviewed, it is sent to the next processing step.

[0047] Step 3:

[0048] The terminal transmits information obtained from the landlord and tenant to the server. This is done through a secure communication protocol, ensuring the safety of the information.

[0049] Step 4:

[0050] The server stores the received data in a database. This database serves as a source of information for subsequent analysis and matching. The information is accurately categorized and managed to ensure that all conditions are taken into consideration.

[0051] Step 5:

[0052] The server (AI) analyzes the stored data and calculates the degree of fit based on the conditions. Here, it uses an advanced matching algorithm to evaluate in detail the intersection of property conditions and tenant preferences and find the most suitable combination.

[0053] Step 6:

[0054] The server identifies the optimal match based on the analysis results and proposes a shared housing plan accordingly. The proposal includes contract terms (rent, move-in date, contract period, etc.).

[0055] Step 7:

[0056] The server sends the generated shared house plan to the terminal, notifying the landlord and tenant of the proposal. Both users review the proposal on their terminals and either approve the terms or submit revised proposals.

[0057] Step 8:

[0058] If the users (landlord and tenant) approve the terms, the server will generate a formal contract. This contract will be provided in electronic format and will include all necessary legal requirements and agreements.

[0059] Step 9:

[0060] The user reviews the generated contract and completes the contract procedures using digital signatures, etc. This formally concludes the contract, and the system's matching process is completed.

[0061] (Example 1)

[0062] 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."

[0063] In the current real estate rental market, efficiently matching the conditions of property owners and tenants and automatically generating optimal contracts quickly is a challenging task. Manually adjusting conditions and drafting contract documents is time-consuming and labor-intensive, and the process of finding a suitable partner is inherently uncertain. To solve these problems, a system is needed that utilizes effective matching algorithms to quickly aggregate information and make decisions.

[0064] 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.

[0065] In this invention, the server includes means for inputting property information, means for inputting tenant conditions, and means for storing the property information and tenant conditions on a recording medium. This enables instantaneous matching of the conditions of property owners and tenants, facilitating rapid and effective contract formation.

[0066] "Property information" refers to a collection of data that shows the location, floor plan, rent, number of occupants allowed, and other rental conditions of a property.

[0067] "Tenant conditions" refer to a set of requirements specifications that include the desired location of the property, the maximum rent, and the conditions for cohabitants.

[0068] A "recording medium" is a physical or virtual device or service used to store data and make it accessible later.

[0069] "Analysis" is the act of processing input information and evaluating and classifying it based on specific criteria.

[0070] "Suitability" is a criterion for selecting the optimal combination by quantifying the degree to which property information and tenant requirements match.

[0071] A "rental plan" is a plan or proposal for real estate rental, constructed based on the proposed contract terms.

[0072] A "contract document" is an official document that records the details of a lease agreement and is created in a legally valid manner.

[0073] "Communication" is the process of sending and receiving data or information within a system, and it takes place over a network.

[0074] The system for implementing this invention efficiently matches the conditions of both property owners who want to rent out their properties and tenants who want to live in a shared house, and generates and manages the optimal contract based on that matching. The system mainly consists of three components: a server, a terminal, and a user, and specifically operates as follows.

[0075] The user (landlord) enters property details using a terminal. This terminal refers to a computing device such as a PC or smartphone, and the entered data includes items such as the property's location, floor plan, desired rent, number of occupants allowed, and whether pets are allowed. The landlord logs in to the interface on the terminal and enters the data according to the guide.

[0076] Similarly, users (tenants) use a terminal to input their desired conditions for the share house. This includes their preferred location, maximum rent, and preferences regarding roommates (gender, age, etc.). Tenants also proceed with inputting information on the terminal based on prompts.

[0077] All entered information is securely transmitted from the terminal to the server. The server then stores the data using a robust database management system (e.g., SQL Server). This stored data is then used in subsequent analysis and matching processes.

[0078] The server (AI) performs sophisticated algorithmic analysis based on the landlord and tenant conditions stored in the database. This algorithm incorporates a generative AI model and identifies the optimal combination by scoring based on the conditions. The algorithm uses past successful matching data as a reference, resulting in highly accurate suggestions.

[0079] Furthermore, the server generates a shared housing plan, including rental conditions, based on the optimal results. This plan is then presented to the user as a potential rental agreement.

[0080] For example, a prompt might say, "I'm looking for a 2LDK apartment in an urban area. My maximum rent is 80,000 yen, and I'd prefer a female roommate in her late 20s." Based on this, the server can combine the conditions of multiple landlords and tenants to generate proposals.

[0081] The proposal is notified to the terminal, and users (landlord and tenant) review the content and approve or modify it. The server then automatically creates the contract, and the contract is concluded through electronic signatures. This entire process ensures that rental agreements are concluded efficiently and quickly.

[0082] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0083] Step 1:

[0084] The user (landlord) enters property information into the terminal. This information includes the property's location, floor plan, desired rent, number of occupants allowed, and whether pets are permitted. The terminal temporarily stores this information in memory and verifies that all required fields are present by checking the format of the input data. Once the input data is complete, the terminal prepares to send the information to the server.

[0085] Step 2:

[0086] The user (tenant) enters their desired conditions for a shared house into the terminal. This includes preferred location, maximum rent, and conditions regarding roommates (gender, age). The terminal, like the landlord, stores this information in memory and verifies the necessary format and fields. The terminal then prepares to securely send the completed dataset to the server.

[0087] Step 3:

[0088] The terminal encrypts the input data from both the landlord and tenant and sends it to the server using a secure protocol (such as HTTPS). The input consists of two datasets: property information and tenant conditions. The server receives the transmitted data and verifies its integrity and completeness. This data serves as foundational information for the next process.

[0089] Step 4:

[0090] The server stores the received data in a database. During storage, a database management system (e.g., SQL Server) is used, and the data is properly arranged according to a specified format. This enables rapid data access in subsequent matching processes.

[0091] Step 5:

[0092] The server initiates a matching process based on the information stored in the database. Specifically, the server uses a generative AI model to compare property information with tenant requirements, applies a scoring algorithm, and calculates the degree of suitability. The input is the information in the database, and the output is the best candidate pair and its score.

[0093] Step 6:

[0094] Based on the calculated suitability, the server generates a shared housing plan for the most promising landlord-tenant combination. The generated plan includes the available contract terms (rent, move-in date, contract period). Based on this, a plan proposal is created and prepared to be sent to the terminal.

[0095] Step 7:

[0096] The server sends the generated plan to the terminal, making it available for review by the users (landlord and tenant). Users view the proposed rental terms on their terminal and are given the option to approve or modify them. This input is important in the next processing step.

[0097] Step 8:

[0098] Once the user approves the proposal, the device sends that information to the server. Based on the received approval, the server automatically generates a formal contract document. This contract incorporates AI-optimized data, and approval is granted via electronic signature on the server. This completes the contract process.

[0099] (Application Example 1)

[0100] 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."

[0101] In today's real estate market, there is a need for efficient and effective matching of property owners who want to rent out their properties with tenants seeking shared housing, and for smooth contract procedures. Furthermore, there is a lack of means for tenants to verify details without actually visiting the property. There is also a need for visual means to easily verify property details, even from remote locations.

[0102] 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.

[0103] In this invention, the server includes means for inputting the rental conditions of the property, means for inputting the desired conditions of the tenant, means for visually displaying the property in a virtual reality environment, and means for enabling virtual property viewings using a smart device. This makes the matching of conditions between property owners and tenants more efficient and allows for detailed property verification without actually visiting the property.

[0104] "Property" refers to a building or land that is subject to rental or ownership.

[0105] "Lending conditions" refer to the terms of use and contractual clauses set by the property owner when providing the property.

[0106] "Desired conditions" refer to the conditions such as location and price range that the tenant desires when using the property.

[0107] An "information storage device" refers to a system or medium for accumulating and holding data.

[0108] "High degree of suitability" refers to a measure that evaluates the extent to which the conditions presented between the property owner and the tenant are mutually compatible.

[0109] A "shared living plan" refers to a proposal outlining the terms of the contract and lifestyle plan for a shared house.

[0110] A "virtual reality environment" refers to a technology that provides a realistic experience within a computer-generated three-dimensional space.

[0111] A "smart device" refers to a portable electronic device that possesses advanced functions through internet connectivity and applications.

[0112] This invention is a system for efficiently providing property matching and detailed verification. The server stores data from terminals used to input property rental conditions and tenant preferences in an information storage device. Based on the stored data, the server uses a generated AI model to calculate the degree of suitability and proposes an optimal co-living plan. Furthermore, the server provides a function to visually display properties in a virtual reality environment, allowing users to virtually tour them through smart devices.

[0113] Specifically, we will build 3D property models using Unity and implement AI algorithms by combining Python and TensorFlow®. In particular, when analyzing the user's desired conditions, we will utilize TensorFlow to calculate the degree of fit using deep learning. This will allow users to visually check properties while receiving AI-generated suggestions for the most suitable properties.

[0114] For example, if user A enters a prompt such as, "I'm looking for a pet-friendly property in the city center with a rent of under 100,000 yen," the system will present properties that match those criteria through virtual reality. This implementation streamlines the property selection process and improves user satisfaction.

[0115] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0116] Step 1:

[0117] The terminal receives input from both the property owner and the tenant regarding their desired conditions and rental conditions. The entered data is immediately transmitted to the information storage device. Specifically, this step involves the user entering conditions using the terminal's interface, which are then stored in the database.

[0118] Step 2:

[0119] The server retrieves loan conditions and desired conditions stored in the information storage device and analyzes the data using a generative AI model. The data processing performed here is to calculate the degree of fit, and the output is a list of the combinations that best match the conditions of the property owner and the tenant. Specifically, the AI ​​algorithm processes the data using TensorFlow and calculates the degree of fit.

[0120] Step 3:

[0121] The server generates an optimal cohabitation plan based on the results of its high-fitness calculation. This plan takes all relevant conditions into consideration and is automatically proposed by the system. In this step, the server automatically generates a shared house plan and includes the necessary contract details.

[0122] Step 4:

[0123] The server sends the generated cohabitation plan to the terminal, and the user reviews the proposal. The terminal presents the information to the user in visual or text format. Specifically, the user can review the received data and review the AI's proposal.

[0124] Step 5:

[0125] Users can visually tour properties in a virtual reality environment using smart devices. This step utilizes 3D modeling technology using Unity. Based on user input, a virtual tour can be conducted by visually confirming the interior of the property.

[0126] Step 6:

[0127] The server receives feedback from the user, modifies the cohabitation plan as needed, and generates the final contract. The final contract is created electronically and provided to the user. Specifically, the server receives modification requests from the user and makes adjustments within the system.

[0128] 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.

[0129] This invention efficiently matches the desired conditions of landlords who rent out properties with those of tenants seeking shared housing, and generates optimal contracts based on these matching conditions. Furthermore, by incorporating an emotion engine, it recognizes and analyzes the user's emotions, enabling the proposal and optimization of plans based on these emotions. The following describes a specific embodiment of this invention.

[0130] First, the user (landlord) enters detailed property information and rental conditions via a terminal. Specifically, this includes the property's location, floor plan, desired rent, and whether pets are allowed. Similarly, the user (tenant) enters their desired conditions for the share house. This includes their preferred area, maximum rent, and desired roommates.

[0131] This information is sent from the terminal to the server and stored in a database. Based on the stored data, the server (AI) analyzes the conditions and calculates matching results with a high degree of fit. The matching at this stage is based on objective conditions.

[0132] Furthermore, a key feature of this invention is that an emotion engine embedded in the server operates based on user interaction. The emotion engine analyzes user input, responses, and dialogue data to infer the user's emotional state. This emotional data is reflected in the matching results and the evaluation of proposed share house plans, enabling suggestions that take into account the user's emotional satisfaction.

[0133] For example, if a user (tenant) expresses dissatisfaction with a proposed plan, the emotion engine can recognize that emotion, and the server can automatically modify the plan. This modification may include adjusting the rent or re-evaluating the location. This improves the accuracy of the proposals and enables personalized service for each user.

[0134] Ultimately, for proposals approved by the user, the server generates a formal contract, which is then finalized through electronic signature. This system ensures that housing is provided with the utmost consideration for the user's feelings and wishes, thereby improving satisfaction for both parties.

[0135] The following describes the processing flow.

[0136] Step 1:

[0137] The user (landlord) enters detailed property information and desired rental conditions via a terminal. This includes the property's location, floor plan, desired rent, and whether or not shared facilities are available. They also enter detailed conditions such as whether pets are allowed and the contract period.

[0138] Step 2:

[0139] Users (tenants) enter their desired conditions for a shared house via a terminal. These include preferred location, maximum rent, desired attributes of roommates (gender, age, etc.), and lifestyle preferences.

[0140] Step 3:

[0141] The terminal sends the entered information to the server. This information is securely transmitted and received via a communication protocol.

[0142] Step 4:

[0143] The server stores the received landlord and tenant information in a database. This organizes the conditions and prepares them for subsequent processing.

[0144] Step 5:

[0145] The server (AI) performs analysis to calculate the suitability between landlords and tenants based on the conditions stored in the database. Here, it evaluates how well the conditions of both parties match and executes an algorithm to find the optimal combination.

[0146] Step 6:

[0147] The server activates an emotion engine and collects emotional data through user interaction. This includes analyzing user reactions and comments to estimate satisfaction and favorability towards the suggestions.

[0148] Step 7:

[0149] The server takes sentiment data into account and automatically adjusts the plan. If the user expresses dissatisfaction with the current proposal, it adjusts specific elements such as rent, conditions, and roommate profiles and makes a revised proposal.

[0150] Step 8:

[0151] The revised shared housing plan is sent from the server to the terminal and reviewed by the users (landlord and tenant). The users review the plan details and either give final approval or request further revisions.

[0152] Step 9:

[0153] If the user approves the proposal, the server generates a contract and sends it to the device for further online consent procedures. This formally concludes the contract and the shared house arrangement begins.

[0154] (Example 2)

[0155] 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".

[0156] Current property matching systems lack the ability to provide housing plans that reflect not only the user's physical requirements but also their emotional satisfaction. Therefore, it is difficult to achieve matching results that are emotionally satisfying for both landlords and tenants, and there is a need for flexible proposals that meet the user's needs.

[0157] 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.

[0158] In this invention, the server includes means for inputting attribute information, means for inputting user preference information, and means for analyzing the user's emotional information regarding the proposed plan. This enables optimal property matching and proposals that take into account the user's emotional state.

[0159] "Attribute information" refers to basic data related to a property, including details such as location, floor plan, and rental conditions.

[0160] "User preference information" refers to the conditions and requests desired by tenants or residents, including information such as preferred area, rent limit, and cohabitation conditions.

[0161] A "recording medium" refers to a device or technology for storing information in data form, and includes all storage devices, including database systems.

[0162] A "highly suitable combination" refers to the pairing result where the conditions best match between the landlord's attribute information and the tenant's desired information.

[0163] A "housing plan" is a proposed plan of the living environment, including the property conditions and services offered by the landlord.

[0164] "Emotional information" refers to data that analyzes the emotional responses that users show to suggestions, including states such as satisfaction, dissatisfaction, and joy.

[0165] A "letter of agreement" is a document that records the terms of the agreement between the landlord and the tenant regarding the conditions for renting out the property and living in it.

[0166] This invention is a system that effectively matches the requirements of property providers and housing seekers, proposes an optimal housing plan based on emotions, and generates a contract. At the heart of the system are a terminal operated by the user and a server that processes data, which are interconnected via a network.

[0167] The user (provider) inputs attribute information such as the property's location, floor plan, and rental conditions via a terminal, while the user (client) similarly inputs desired information such as their preferred area and maximum rent. The terminal quickly transmits this information to the server, which then stores it in a database.

[0168] The server analyzes the stored data and matches the provider's attribute information with the user's preferences. It uses a generative AI model to analyze the conditions and derive combinations with a high degree of fit. Furthermore, the server uses an emotion engine to analyze user responses in real time and collect them as emotion information. This analysis is performed using natural language processing.

[0169] Emotional information is reflected in the proposed housing plan, and the server adjusts the plan if necessary based on the analysis results. This process aims to increase the user's emotional satisfaction. Once the user agrees to the proposal, the server automatically generates a contract and concludes it with an electronic signature.

[0170] As a concrete example, the user enters the following prompt:

[0171] Example prompt:

[0172] "I have a cat and am looking for pet-friendly housing in an urban area. My monthly rent is limited to 80,000 yen, and ideally, I'd like to live near a train station and in a shopping area."

[0173] In response to this prompt, the system considers the user's preferences and proposes the most suitable properties. Ultimately, it can provide comprehensive recommendations based on attribute information, preference information, and emotional information.

[0174] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0175] Step 1:

[0176] The user (provider) enters property information via a terminal. This information includes location, floor plan, and rental conditions. The terminal formats this information and sends it to the server. The data is received by the server and stored on a recording medium.

[0177] Step 2:

[0178] The user enters their desired conditions into a terminal. Specifically, these include the desired area, maximum rent, and required facilities. The terminal sends the entered information to the server, which receives the data and stores it in a database. The server manages this data as user preference information.

[0179] Step 3:

[0180] The server uses a generative AI model to analyze the provider's attribute information and the user's preferences. This analysis matches the conditions of both to find the combination with the highest degree of fit. The input data consists of attribute information and preferences, and the output is the data of the combination with the highest degree of fit.

[0181] Step 4:

[0182] The server uses an emotion engine to analyze user feedback and responses. It extracts emotional information using natural language processing techniques. Input is text data based on user responses, and output is data indicating the user's emotional state.

[0183] Step 5:

[0184] The server adjusts the optimal housing plan based on the extracted emotional information. Specifically, it readjusts the rent or relaxes the conditions. The input for this process is emotional information and matching data, and the output is the revised housing plan.

[0185] Step 6:

[0186] Finally, the server generates a contract for the housing plan agreed upon by the user. The contract includes all necessary terms and conditions, and the user concludes the agreement via electronic signature. The input is the agreed-upon housing plan, and the output is the formal contract.

[0187] (Application Example 2)

[0188] 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".

[0189] Traditional property matching systems rely on a single matching method based on the landlord's and tenant's conditions, making it difficult to adequately consider the emotional satisfaction of users. Similarly, in e-commerce, suggestions often fail to take consumer emotions into account, resulting in a lack of purchasing intent. This leads to challenges such as inadequate suitability for shared housing and product selection, and a lack of a balanced user experience.

[0190] 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.

[0191] In this invention, the server includes means for inputting the rental conditions of the property, means for inputting the tenant's desired conditions, and means for analyzing the user's emotional data and optimizing the plan based on the emotional state. This enables personalized proposals by presenting products and adjusting property plans based on the user's reactions.

[0192] "Method for entering property rental conditions" refers to the process by which the landlord enters detailed conditions regarding the property into the system.

[0193] "Means for inputting tenant's desired conditions" refers to the process by which tenants input their preferences and requests regarding the property into the system.

[0194] "Method of saving to a database" refers to the process of recording the entered loan conditions and desired conditions and saving them in a way that allows for later access.

[0195] "Means for analyzing and calculating combinations of high degree of fit" refers to the process of analyzing saved conditions and calculating the combination that matches the conditions at the highest level.

[0196] "Methods for proposing plans" refers to the process of presenting the optimal plan to the user based on the calculated matching results.

[0197] "Means of generating a contract" refers to the process of creating a formal contract once the user agrees to the proposed terms and conditions.

[0198] "A means of analyzing user emotional data and optimizing plans based on emotional states" refers to the process of analyzing user emotions and adjusting the proposed content to the most satisfying form based on the results.

[0199] "A means of adjusting product suggestions in real time according to emotions" refers to a process that instantly optimizes product suggestions in response to changes in the user's emotions.

[0200] The system that realizes this invention consists of multiple steps, including user input of conditions, storage in a database, condition analysis, recognition and analysis of sentiment data, and real-time suggestion of results. The details are described below.

[0201] First, users (landlords and tenants) enter the rental conditions and desired conditions for the property via a terminal. This includes specific conditions such as the property's location, floor plan, and rent. This data is stored in a cloud-based database and remains accessible at all times.

[0202] The server analyzes the stored conditions and calculates the combination with the highest degree of fit. In addition to the normal matching process, it utilizes user input and historical data to provide more accurate matching results.

[0203] Furthermore, an emotion recognition engine installed on the server acquires and analyzes emotional data from the user's facial expressions and voice. The analysis results are then used to optimize the plan based on the user's emotional state. This process utilizes existing emotion recognition technologies, such as Microsoft® Azure® Face API and Google® Cloud Vision API.

[0204] The product selections and property plans suggested by the platform are adjusted in real time based on this emotional data. For example, if a user makes a surprised face while browsing products on their smartphone, the platform can instantly present new products that might interest them based on that data.

[0205] As a concrete example, suppose a user is browsing outdoor equipment while online shopping. If the system detects that the user is satisfied with the tent being displayed, it can recommend similar camping equipment, thereby increasing their purchase intent.

[0206] An example of a prompt message would be: "Explain how to acquire emotional data from a user's facial expressions and voice while they are browsing products, and how to adjust relevant product suggestions in real time based on that emotional data." This improves the user experience and enables more effective matching and product suggestions.

[0207] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0208] Step 1:

[0209] Users input rental conditions and desired conditions for properties via a terminal. This input data includes the property's location, floor plan, and rent. The terminal receives this data, formats it, and sends it to a database in the cloud.

[0210] Step 2:

[0211] The server retrieves the loan conditions and desired conditions stored in the database. Next, it analyzes the conditions to calculate the optimal combination. Specifically, it calculates the degree of agreement between each condition and generates a matching score based on that. The output is the data for the combination with the highest degree of fit.

[0212] Step 3:

[0213] The server uses an emotion recognition engine to acquire emotion data from the user's facial expressions and voice. It processes real-time facial images and audio signals as input, analyzing the type of emotion (e.g., joy, surprise, sadness). The output is estimated emotion data.

[0214] Step 4:

[0215] The server integrates sentiment data and matching scores to propose the most satisfying plan to the user. The proposal is adjusted in real time, and the plan changes according to the user's emotional state. The output provides details of the adjusted plan.

[0216] Step 5:

[0217] The user reviews the details of the proposed plan on the device and approves or modifies it. The device receives the adjusted plan information as input and considers the options. The device records the user's selection and sends the final decision to the server.

[0218] 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.

[0219] 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.

[0220] 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.

[0221] [Second Embodiment]

[0222] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0223] 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.

[0224] 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).

[0225] 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.

[0226] 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.

[0227] 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).

[0228] 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.

[0229] 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.

[0230] 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.

[0231] 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.

[0232] 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.

[0233] 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".

[0234] The system for implementing this invention efficiently matches the requirements of both property owners who wish to rent out their properties and tenants who wish to live in a shared house, and generates and manages optimal contracts based on these matching conditions. The details are shown below.

[0235] First, the user (landlord) enters detailed property information and rental conditions using a terminal. This information includes the property's location, floor plan, desired rent, number of occupants allowed, and whether pets are permitted.

[0236] Next, the user (tenant) enters their desired conditions for the share house via the terminal. This includes their preferred location, maximum rent, and desired conditions for roommates (gender, age, etc.).

[0237] Here, all entered information is sent from the terminal to the server. The server stores this data in a database, preparing it for the subsequent matching process. Based on the aforementioned conditions, the server (AI) uses advanced algorithms to analyze the conditions of both the landlord and the tenant and find the most suitable combination. Because this process is automated, it significantly reduces time and produces more effective matching results.

[0238] This system also optimizes the conditions by proposing a shared housing plan based on the best matching results calculated by AI. This plan includes contract terms (rent, move-in date, contract period, etc.). The server sends this proposal to the terminal, and the process proceeds for the user (landlord and tenant) to review the proposal.

[0239] As a concrete example, consider a landlord with a 2LDK apartment in an urban area looking for a tenant who wants to live in a shared apartment. In this case, the landlord specifies the detailed conditions of the property, and multiple potential tenants enter their desired conditions. A server (AI) processes this information, finds the best combination, and delivers proposals in both directions. Once a user accepts a proposal, the server automatically creates a formal contract, and the contract is finalized through electronic signatures, etc. This system enables efficient and smooth conclusion of rental agreements.

[0240] The following describes the processing flow.

[0241] Step 1:

[0242] Users (landlords) enter detailed property information and rental conditions via a terminal. This includes the property's location, floor plan, desired rent, whether pets are allowed, and the number of people who can share the property. The terminal provides guidance to ensure that the information is entered accurately in real time.

[0243] Step 2:

[0244] The user (tenant) enters their desired conditions for a shared house into the terminal. This information includes preferred area, maximum rent, desired attributes of roommates (gender, age range), and lifestyle preferences. After the entered data is reviewed, it is sent to the next processing step.

[0245] Step 3:

[0246] The terminal transmits information obtained from the landlord and tenant to the server. This is done through a secure communication protocol, ensuring the safety of the information.

[0247] Step 4:

[0248] The server stores the received data in a database. This database serves as a source of information for subsequent analysis and matching. The information is accurately categorized and managed to ensure that all conditions are taken into consideration.

[0249] Step 5:

[0250] The server (AI) analyzes the stored data and calculates the degree of fit based on the conditions. Here, it uses an advanced matching algorithm to evaluate in detail the intersection of property conditions and tenant preferences and find the most suitable combination.

[0251] Step 6:

[0252] The server identifies the optimal match based on the analysis results and proposes a shared housing plan accordingly. The proposal includes contract terms (rent, move-in date, contract period, etc.).

[0253] Step 7:

[0254] The server sends the generated shared house plan to the terminal, notifying the landlord and tenant of the proposal. Both users review the proposal on their terminals and either approve the terms or submit revised proposals.

[0255] Step 8:

[0256] If the users (landlord and tenant) approve the terms, the server will generate a formal contract. This contract will be provided in electronic format and will include all necessary legal requirements and agreements.

[0257] Step 9:

[0258] The user reviews the generated contract and completes the contract procedures using digital signatures, etc. This formally concludes the contract, and the system's matching process is completed.

[0259] (Example 1)

[0260] 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 glasses 214 will be referred to as the "terminal."

[0261] In the current real estate rental market, efficiently matching the conditions of property owners and tenants and automatically generating optimal contracts quickly is a challenging task. Manually adjusting conditions and drafting contract documents is time-consuming and labor-intensive, and the process of finding a suitable partner is inherently uncertain. To solve these problems, a system is needed that utilizes effective matching algorithms to quickly aggregate information and make decisions.

[0262] 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.

[0263] In this invention, the server includes means for inputting property information, means for inputting tenant conditions, and means for storing the property information and tenant conditions on a recording medium. This enables instantaneous matching of the conditions of property owners and tenants, facilitating rapid and effective contract formation.

[0264] "Property information" refers to a collection of data that shows the location, floor plan, rent, number of occupants allowed, and other rental conditions of a property.

[0265] "Tenant conditions" refer to a set of requirements specifications that include the desired location of the property, the maximum rent, and the conditions for cohabitants.

[0266] A "recording medium" is a physical or virtual device or service used to store data and make it accessible later.

[0267] "Analysis" is the act of processing input information and evaluating and classifying it based on specific criteria.

[0268] "Suitability" is a criterion for selecting the optimal combination by quantifying the degree to which property information and tenant requirements match.

[0269] A "rental plan" is a plan or proposal for real estate rental, constructed based on the proposed contract terms.

[0270] A "contract document" is an official document that records the details of a lease agreement and is created in a legally valid manner.

[0271] "Communication" is the process of sending and receiving data or information within a system, and it takes place over a network.

[0272] The system for implementing this invention efficiently matches the conditions of both property owners who want to rent out their properties and tenants who want to live in a shared house, and generates and manages the optimal contract based on that matching. The system mainly consists of three components: a server, a terminal, and a user, and specifically operates as follows.

[0273] The user (landlord) enters property details using a terminal. This terminal refers to a computing device such as a PC or smartphone, and the entered data includes items such as the property's location, floor plan, desired rent, number of occupants allowed, and whether pets are allowed. The landlord logs in to the interface on the terminal and enters the data according to the guide.

[0274] Similarly, users (tenants) use a terminal to input their desired conditions for the share house. This includes their preferred location, maximum rent, and preferences regarding roommates (gender, age, etc.). Tenants also proceed with inputting information on the terminal based on prompts.

[0275] All entered information is securely transmitted from the terminal to the server. The server then stores the data using a robust database management system (e.g., SQL Server). This stored data is then used in subsequent analysis and matching processes.

[0276] The server (AI) performs sophisticated algorithmic analysis based on the landlord and tenant conditions stored in the database. This algorithm incorporates a generative AI model and identifies the optimal combination by scoring based on the conditions. The algorithm uses past successful matching data as a reference, resulting in highly accurate suggestions.

[0277] Furthermore, the server generates a shared housing plan, including rental conditions, based on the optimal results. This plan is then presented to the user as a potential rental agreement.

[0278] For example, a prompt might say, "I'm looking for a 2LDK apartment in an urban area. My maximum rent is 80,000 yen, and I'd prefer a female roommate in her late 20s." Based on this, the server can combine the conditions of multiple landlords and tenants to generate proposals.

[0279] The proposal is notified to the terminal, and the users (landlord and tenant) check the content and approve or modify it. Then, the server automatically creates a contract and concludes the contract through electronic signature. Through this series of processes, the lease contract is realized efficiently and quickly.

[0280] The flow of the specific process in Embodiment 1 will be described with reference to FIG. 11.

[0281] Step 1:

[0282] The user (landlord) inputs property information into the terminal. The input includes the location of the property, floor plan, expected rent, number of people who can move in, and whether pets are allowed. The terminal temporarily stores this information in the memory, checks the format of the input data, and verifies that all required items are present. After the input data is complete, the terminal prepares to send the information to the server. [[ID=1,3]]

[0283] Step 2:

[0284] The user (tenant) inputs the desired conditions for the shared house into the terminal. The input includes the desired location, upper limit of rent, conditions regarding co-tenants (gender, age). The terminal stores this information in the memory in the same way as the landlord, checks the necessary format and items. The terminal prepares to securely send the completed data set to the server.

[0285] Step 3: '

[0286] The terminal encrypts the input data of the landlord and the tenant and then sends it to the server using a secure protocol (such as HTTPS). The input consists of two data sets: property information and tenant conditions. The server receives the transmitted data and checks its integrity and completeness. This data serves as the basic information for the next process.

[0287] Step 4:

[0288] The server stores the received data in a database. During storage, a database management system (e.g., SQL Server) is used, and the data is properly arranged according to a specified format. This enables rapid data access in subsequent matching processes.

[0289] Step 5:

[0290] The server initiates a matching process based on the information stored in the database. Specifically, the server uses a generative AI model to compare property information with tenant requirements, applies a scoring algorithm, and calculates the degree of suitability. The input is the information in the database, and the output is the best candidate pair and its score.

[0291] Step 6:

[0292] Based on the calculated suitability, the server generates a shared housing plan for the most promising landlord-tenant combination. The generated plan includes the available contract terms (rent, move-in date, contract period). Based on this, a plan proposal is created and prepared to be sent to the terminal.

[0293] Step 7:

[0294] The server sends the generated plan to the terminal, making it available for review by the users (landlord and tenant). Users view the proposed rental terms on their terminal and are given the option to approve or modify them. This input is important in the next processing step.

[0295] Step 8:

[0296] Once the user approves the proposal, the device sends that information to the server. Based on the received approval, the server automatically generates a formal contract document. This contract incorporates AI-optimized data, and approval is granted via electronic signature on the server. This completes the contract process.

[0297] (Application Example 1)

[0298] 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."

[0299] In today's real estate market, there is a need for efficient and effective matching of property owners who want to rent out their properties with tenants seeking shared housing, and for smooth contract procedures. Furthermore, there is a lack of means for tenants to verify details without actually visiting the property. There is also a need for visual means to easily verify property details, even from remote locations.

[0300] 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.

[0301] In this invention, the server includes means for inputting the rental conditions of the property, means for inputting the desired conditions of the tenant, means for visually displaying the property in a virtual reality environment, and means for enabling virtual property viewings using a smart device. This makes the matching of conditions between property owners and tenants more efficient and allows for detailed property verification without actually visiting the property.

[0302] "Property" refers to a building or land that is subject to rental or ownership.

[0303] "Lending conditions" refer to the terms of use and contractual clauses set by the property owner when providing the property.

[0304] "Desired conditions" refer to the conditions such as location and price range that the tenant desires when using the property.

[0305] An "information storage device" refers to a system or medium for accumulating and holding data.

[0306] "High degree of suitability" refers to a measure that evaluates the extent to which the conditions presented between the property owner and the tenant are mutually compatible.

[0307] The "co - living plan" means the proposal of contract content and life design in a shared house.

[0308] The "virtual reality environment" refers to a technology that provides an experience close to reality within a three - dimensional space created by computer generation.

[0309] The "smart device" means a portable electronic device with advanced functions by using an Internet connection and applications.

[0310] This invention is a system for efficiently providing property matching and detailed confirmation. The server stores data from a terminal for inputting property rental conditions and the lessee's desired conditions in an information storage device. Based on the stored data, the server calculates a high degree of fitness using a generative AI model and proposes an optimal co - living plan. Furthermore, the server visually displays the property in a virtual reality environment and provides a function that allows users to virtually visit through smart devices.

[0311] Specifically, a 3D property model is constructed using Unity, and an AI algorithm is implemented by combining Python and TensorFlow. In particular, when analyzing the user's desired conditions, TensorFlow is utilized to calculate the fitness by deep learning. As a result, users can visually confirm the property and receive an optimal property proposal by AI.

[0312] For example, when user A inputs a prompt such as "I hope for a pet - friendly property and seek a rent of less than 100,000 yen in the city center", the system presents properties that meet the conditions through virtual reality. By this implementation, the property selection process is streamlined and user satisfaction is improved.

[0313] The flow of the specific process in Application Example 1 will be described using Figure 12.

[0314] Step 1:

[0315] The terminal receives input from both the property owner and the tenant regarding their desired conditions and rental conditions. The entered data is immediately transmitted to the information storage device. Specifically, this step involves the user entering conditions using the terminal's interface, which are then stored in the database.

[0316] Step 2:

[0317] The server retrieves loan conditions and desired conditions stored in the information storage device and analyzes the data using a generative AI model. The data processing performed here is to calculate the degree of fit, and the output is a list of the combinations that best match the conditions of the property owner and the tenant. Specifically, the AI ​​algorithm processes the data using TensorFlow and calculates the degree of fit.

[0318] Step 3:

[0319] The server generates an optimal cohabitation plan based on the results of its high-fitness calculation. This plan takes all relevant conditions into consideration and is automatically proposed by the system. In this step, the server automatically generates a shared house plan and includes the necessary contract details.

[0320] Step 4:

[0321] The server sends the generated cohabitation plan to the terminal, and the user reviews the proposal. The terminal presents the information to the user in visual or text format. Specifically, the user can review the received data and review the AI's proposal.

[0322] Step 5:

[0323] Users can visually tour properties in a virtual reality environment using smart devices. This step utilizes 3D modeling technology using Unity. Based on user input, a virtual tour can be conducted by visually confirming the interior of the property.

[0324] Step 6:

[0325] The server receives feedback from the user, modifies the cohabitation plan as needed, and generates the final contract. The final contract is created electronically and provided to the user. Specifically, the server receives modification requests from the user and makes adjustments within the system.

[0326] 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.

[0327] This invention efficiently matches the desired conditions of landlords who rent out properties with those of tenants seeking shared housing, and generates optimal contracts based on these matching conditions. Furthermore, by incorporating an emotion engine, it recognizes and analyzes the user's emotions, enabling the proposal and optimization of plans based on these emotions. The following describes a specific embodiment of this invention.

[0328] First, the user (landlord) enters detailed property information and rental conditions via a terminal. Specifically, this includes the property's location, floor plan, desired rent, and whether pets are allowed. Similarly, the user (tenant) enters their desired conditions for the share house. This includes their preferred area, maximum rent, and desired roommates.

[0329] This information is sent from the terminal to the server and stored in a database. Based on the stored data, the server (AI) analyzes the conditions and calculates matching results with a high degree of fit. The matching at this stage is based on objective conditions.

[0330] Furthermore, a key feature of this invention is that an emotion engine embedded in the server operates based on user interaction. The emotion engine analyzes user input, responses, and dialogue data to infer the user's emotional state. This emotional data is reflected in the matching results and the evaluation of proposed share house plans, enabling suggestions that take into account the user's emotional satisfaction.

[0331] For example, if a user (tenant) expresses dissatisfaction with a proposed plan, the emotion engine can recognize that emotion, and the server can automatically modify the plan. This modification may include adjusting the rent or re-evaluating the location. This improves the accuracy of the proposals and enables personalized service for each user.

[0332] Ultimately, for proposals approved by the user, the server generates a formal contract, which is then finalized through electronic signature. This system ensures that housing is provided with the utmost consideration for the user's feelings and wishes, thereby improving satisfaction for both parties.

[0333] The following describes the processing flow.

[0334] Step 1:

[0335] The user (landlord) enters detailed property information and desired rental conditions via a terminal. This includes the property's location, floor plan, desired rent, and whether or not shared facilities are available. They also enter detailed conditions such as whether pets are allowed and the contract period.

[0336] Step 2:

[0337] Users (tenants) enter their desired conditions for a shared house via a terminal. These include preferred location, maximum rent, desired attributes of roommates (gender, age, etc.), and lifestyle preferences.

[0338] Step 3:

[0339] The terminal sends the entered information to the server. This information is securely transmitted and received via a communication protocol.

[0340] Step 4:

[0341] The server stores the received landlord and tenant information in a database. This organizes the conditions and prepares them for subsequent processing.

[0342] Step 5:

[0343] The server (AI) performs analysis to calculate the suitability between landlords and tenants based on the conditions stored in the database. Here, it evaluates how well the conditions of both parties match and executes an algorithm to find the optimal combination.

[0344] Step 6:

[0345] The server activates an emotion engine and collects emotional data through user interaction. This includes analyzing user reactions and comments to estimate satisfaction and favorability towards the suggestions.

[0346] Step 7:

[0347] The server takes sentiment data into account and automatically adjusts the plan. If the user expresses dissatisfaction with the current proposal, it adjusts specific elements such as rent, conditions, and roommate profiles and makes a revised proposal.

[0348] Step 8:

[0349] The revised shared housing plan is sent from the server to the terminal and reviewed by the users (landlord and tenant). The users review the plan details and either give final approval or request further revisions.

[0350] Step 9:

[0351] If the user approves the proposal, the server generates a contract and sends it to the device for further online consent procedures. This formally concludes the contract and the shared house arrangement begins.

[0352] (Example 2)

[0353] 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".

[0354] Current property matching systems lack the ability to provide housing plans that reflect not only the user's physical requirements but also their emotional satisfaction. Therefore, it is difficult to achieve matching results that are emotionally satisfying for both landlords and tenants, and there is a need for flexible proposals that meet the user's needs.

[0355] 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.

[0356] In this invention, the server includes means for inputting attribute information, means for inputting user preference information, and means for analyzing the user's emotional information regarding the proposed plan. This enables optimal property matching and proposals that take into account the user's emotional state.

[0357] "Attribute information" refers to basic data related to a property, including details such as location, floor plan, and rental conditions.

[0358] "User preference information" refers to the conditions and requests desired by tenants or residents, including information such as preferred area, rent limit, and cohabitation conditions.

[0359] A "recording medium" refers to a device or technology for storing information in data form, and includes all storage devices, including database systems.

[0360] A "highly suitable combination" refers to the pairing result where the conditions best match between the landlord's attribute information and the tenant's desired information.

[0361] A "housing plan" is a proposed plan of the living environment, including the property conditions and services offered by the landlord.

[0362] "Emotional information" refers to data that analyzes the emotional responses that users show to suggestions, including states such as satisfaction, dissatisfaction, and joy.

[0363] A "letter of agreement" is a document that records the terms of the agreement between the landlord and the tenant regarding the conditions for renting out the property and living in it.

[0364] This invention is a system that effectively matches the requirements of property providers and housing seekers, proposes an optimal housing plan based on emotions, and generates a contract. At the heart of the system are a terminal operated by the user and a server that processes data, which are interconnected via a network.

[0365] The user (provider) inputs attribute information such as the property's location, floor plan, and rental conditions via a terminal, while the user (client) similarly inputs desired information such as their preferred area and maximum rent. The terminal quickly transmits this information to the server, which then stores it in a database.

[0366] The server analyzes the stored data and matches the provider's attribute information with the user's preferences. It uses a generative AI model to analyze the conditions and derive combinations with a high degree of fit. Furthermore, the server uses an emotion engine to analyze user responses in real time and collect them as emotion information. This analysis is performed using natural language processing.

[0367] Emotional information is reflected in the proposed housing plan, and the server adjusts the plan if necessary based on the analysis results. This process aims to increase the user's emotional satisfaction. Once the user agrees to the proposal, the server automatically generates a contract and concludes it with an electronic signature.

[0368] As a concrete example, the user enters the following prompt:

[0369] Example prompt:

[0370] "I have a cat and am looking for pet-friendly housing in an urban area. My monthly rent is limited to 80,000 yen, and ideally, I'd like to live near a train station and in a shopping area."

[0371] In response to this prompt, the system considers the user's preferences and proposes the most suitable properties. Ultimately, it can provide comprehensive recommendations based on attribute information, preference information, and emotional information.

[0372] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0373] Step 1:

[0374] The user (provider) enters property information via a terminal. This information includes location, floor plan, and rental conditions. The terminal formats this information and sends it to the server. The data is received by the server and stored on a recording medium.

[0375] Step 2:

[0376] The user enters their desired conditions into a terminal. Specifically, these include the desired area, maximum rent, and required facilities. The terminal sends the entered information to the server, which receives the data and stores it in a database. The server manages this data as user preference information.

[0377] Step 3:

[0378] The server uses a generative AI model to analyze the provider's attribute information and the user's preferences. This analysis matches the conditions of both to find the combination with the highest degree of fit. The input data consists of attribute information and preferences, and the output is the data of the combination with the highest degree of fit.

[0379] Step 4:

[0380] The server uses an emotion engine to analyze user feedback and responses. It extracts emotional information using natural language processing techniques. Input is text data based on user responses, and output is data indicating the user's emotional state.

[0381] Step 5:

[0382] The server adjusts the optimal housing plan based on the extracted emotional information. Specifically, it readjusts the rent or relaxes the conditions. The input for this process is emotional information and matching data, and the output is the revised housing plan.

[0383] Step 6:

[0384] Finally, the server generates a contract for the housing plan agreed upon by the user. The contract includes all necessary terms and conditions, and the user concludes the agreement via electronic signature. The input is the agreed-upon housing plan, and the output is the formal contract.

[0385] (Application Example 2)

[0386] 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."

[0387] Traditional property matching systems rely on a single matching method based on the landlord's and tenant's conditions, making it difficult to adequately consider the emotional satisfaction of users. Similarly, in e-commerce, suggestions often fail to take consumer emotions into account, resulting in a lack of purchasing intent. This leads to challenges such as inadequate suitability for shared housing and product selection, and a lack of a balanced user experience.

[0388] 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.

[0389] In this invention, the server includes means for inputting the rental conditions of the property, means for inputting the tenant's desired conditions, and means for analyzing the user's emotional data and optimizing the plan based on the emotional state. This enables personalized proposals by presenting products and adjusting property plans based on the user's reactions.

[0390] "Method for entering property rental conditions" refers to the process by which the landlord enters detailed conditions regarding the property into the system.

[0391] "Means for inputting tenant's desired conditions" refers to the process by which tenants input their preferences and requests regarding the property into the system.

[0392] "Method of saving to a database" refers to the process of recording the entered loan conditions and desired conditions and saving them in a way that allows for later access.

[0393] "Means for analyzing and calculating combinations of high degree of fit" refers to the process of analyzing saved conditions and calculating the combination that matches the conditions at the highest level.

[0394] "Methods for proposing plans" refers to the process of presenting the optimal plan to the user based on the calculated matching results.

[0395] "Means of generating a contract" refers to the process of creating a formal contract once the user agrees to the proposed terms and conditions.

[0396] "A means of analyzing user emotional data and optimizing plans based on emotional states" refers to the process of analyzing user emotions and adjusting the proposed content to the most satisfying form based on the results.

[0397] "A means of adjusting product suggestions in real time according to emotions" refers to a process that instantly optimizes product suggestions in response to changes in the user's emotions.

[0398] The system that realizes this invention consists of multiple steps, including user input of conditions, storage in a database, condition analysis, recognition and analysis of sentiment data, and real-time suggestion of results. The details are described below.

[0399] First, users (landlords and tenants) enter the rental conditions and desired conditions for the property via a terminal. This includes specific conditions such as the property's location, floor plan, and rent. This data is stored in a cloud-based database and remains accessible at all times.

[0400] The server analyzes the stored conditions and calculates the combination with the highest degree of fit. In addition to the normal matching process, it utilizes user input and historical data to provide more accurate matching results.

[0401] Furthermore, an emotion recognition engine installed on the server acquires and analyzes emotional data from the user's facial expressions and voice. The analysis results are then used to optimize the plan based on the user's emotional state. This process utilizes existing emotion recognition technologies, such as the Microsoft Azure Face API and the Google Cloud Vision API.

[0402] The product selections and property plans suggested by the platform are adjusted in real time based on this emotional data. For example, if a user makes a surprised face while browsing products on their smartphone, the platform can instantly present new products that might interest them based on that data.

[0403] As a concrete example, suppose a user is browsing outdoor equipment while online shopping. If the system detects that the user is satisfied with the tent being displayed, it can recommend similar camping equipment, thereby increasing their purchase intent.

[0404] An example of a prompt message would be: "Explain how to acquire emotional data from a user's facial expressions and voice while they are browsing products, and how to adjust relevant product suggestions in real time based on that emotional data." This improves the user experience and enables more effective matching and product suggestions.

[0405] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0406] Step 1:

[0407] Users input rental conditions and desired conditions for properties via a terminal. This input data includes the property's location, floor plan, and rent. The terminal receives this data, formats it, and sends it to a database in the cloud.

[0408] Step 2:

[0409] The server retrieves the loan conditions and desired conditions stored in the database. Next, it analyzes the conditions to calculate the optimal combination. Specifically, it calculates the degree of agreement between each condition and generates a matching score based on that. The output is the data for the combination with the highest degree of fit.

[0410] Step 3:

[0411] The server uses an emotion recognition engine to acquire emotion data from the user's facial expressions and voice. It processes real-time facial images and audio signals as input, analyzing the type of emotion (e.g., joy, surprise, sadness). The output is estimated emotion data.

[0412] Step 4:

[0413] The server integrates sentiment data and matching scores to propose the most satisfying plan to the user. The proposal is adjusted in real time, and the plan changes according to the user's emotional state. The output provides details of the adjusted plan.

[0414] Step 5:

[0415] The user reviews the details of the proposed plan on the device and approves or modifies it. The device receives the adjusted plan information as input and considers the options. The device records the user's selection and sends the final decision to the server.

[0416] 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.

[0417] 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.

[0418] 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.

[0419] [Third Embodiment]

[0420] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0421] 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.

[0422] 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).

[0423] 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.

[0424] 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.

[0425] 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).

[0426] 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.

[0427] 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.

[0428] 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.

[0429] 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.

[0430] 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.

[0431] 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".

[0432] The system for implementing this invention efficiently matches the requirements of both property owners who wish to rent out their properties and tenants who wish to live in a shared house, and generates and manages optimal contracts based on these matching conditions. The details are shown below.

[0433] First, the user (landlord) enters detailed property information and rental conditions using a terminal. This information includes the property's location, floor plan, desired rent, number of occupants allowed, and whether pets are permitted.

[0434] Next, the user (tenant) enters their desired conditions for the share house via the terminal. This includes their preferred location, maximum rent, and desired conditions for roommates (gender, age, etc.).

[0435] Here, all entered information is sent from the terminal to the server. The server stores this data in a database, preparing it for the subsequent matching process. Based on the aforementioned conditions, the server (AI) uses advanced algorithms to analyze the conditions of both the landlord and the tenant and find the most suitable combination. Because this process is automated, it significantly reduces time and produces more effective matching results.

[0436] This system also optimizes the conditions by proposing a shared housing plan based on the best matching results calculated by AI. This plan includes contract terms (rent, move-in date, contract period, etc.). The server sends this proposal to the terminal, and the process proceeds for the user (landlord and tenant) to review the proposal.

[0437] As a concrete example, consider a landlord with a 2LDK apartment in an urban area looking for a tenant who wants to live in a shared apartment. In this case, the landlord specifies the detailed conditions of the property, and multiple potential tenants enter their desired conditions. A server (AI) processes this information, finds the best combination, and delivers proposals in both directions. Once a user accepts a proposal, the server automatically creates a formal contract, and the contract is finalized through electronic signatures, etc. This system enables efficient and smooth conclusion of rental agreements.

[0438] The following describes the processing flow.

[0439] Step 1:

[0440] Users (landlords) enter detailed property information and rental conditions via a terminal. This includes the property's location, floor plan, desired rent, whether pets are allowed, and the number of people who can share the property. The terminal provides guidance to ensure that the information is entered accurately in real time.

[0441] Step 2:

[0442] The user (tenant) enters their desired conditions for a shared house into the terminal. This information includes preferred area, maximum rent, desired attributes of roommates (gender, age range), and lifestyle preferences. After the entered data is reviewed, it is sent to the next processing step.

[0443] Step 3:

[0444] The terminal transmits information obtained from the landlord and tenant to the server. This is done through a secure communication protocol, ensuring the safety of the information.

[0445] Step 4:

[0446] The server stores the received data in a database. This database serves as a source of information for subsequent analysis and matching. The information is accurately categorized and managed to ensure that all conditions are taken into consideration.

[0447] Step 5:

[0448] The server (AI) analyzes the stored data and calculates the degree of fit based on the conditions. Here, it uses an advanced matching algorithm to evaluate in detail the intersection of property conditions and tenant preferences and find the most suitable combination.

[0449] Step 6:

[0450] The server identifies the optimal match based on the analysis results and proposes a shared housing plan accordingly. The proposal includes contract terms (rent, move-in date, contract period, etc.).

[0451] Step 7:

[0452] The server sends the generated shared house plan to the terminal, notifying the landlord and tenant of the proposal. Both users review the proposal on their terminals and either approve the terms or submit revised proposals.

[0453] Step 8:

[0454] If the users (landlord and tenant) approve the terms, the server will generate a formal contract. This contract will be provided in electronic format and will include all necessary legal requirements and agreements.

[0455] Step 9:

[0456] The user reviews the generated contract and completes the contract procedures using digital signatures, etc. This formally concludes the contract, and the system's matching process is completed.

[0457] (Example 1)

[0458] 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."

[0459] In the current real estate rental market, efficiently matching the conditions of property owners and tenants and automatically generating optimal contracts quickly is a challenging task. Manually adjusting conditions and drafting contract documents is time-consuming and labor-intensive, and the process of finding a suitable partner is inherently uncertain. To solve these problems, a system is needed that utilizes effective matching algorithms to quickly aggregate information and make decisions.

[0460] 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.

[0461] In this invention, the server includes means for inputting property information, means for inputting tenant conditions, and means for storing the property information and tenant conditions on a recording medium. This enables instantaneous matching of the conditions of property owners and tenants, facilitating rapid and effective contract formation.

[0462] "Property information" refers to a collection of data that shows the location, floor plan, rent, number of occupants allowed, and other rental conditions of a property.

[0463] "Tenant conditions" refer to a set of requirements specifications that include the desired location of the property, the maximum rent, and the conditions for cohabitants.

[0464] A "recording medium" is a physical or virtual device or service used to store data and make it accessible later.

[0465] "Analysis" is the act of processing input information and evaluating and classifying it based on specific criteria.

[0466] "Suitability" is a criterion for selecting the optimal combination by quantifying the degree to which property information and tenant requirements match.

[0467] A "rental plan" is a plan or proposal for real estate rental, constructed based on the proposed contract terms.

[0468] A "contract document" is an official document that records the details of a lease agreement and is created in a legally valid manner.

[0469] "Communication" is the process of sending and receiving data or information within a system, and it takes place over a network.

[0470] The system for implementing this invention efficiently matches the conditions of both property owners who want to rent out their properties and tenants who want to live in a shared house, and generates and manages the optimal contract based on that matching. The system mainly consists of three components: a server, a terminal, and a user, and specifically operates as follows.

[0471] The user (landlord) enters property details using a terminal. This terminal refers to a computing device such as a PC or smartphone, and the entered data includes items such as the property's location, floor plan, desired rent, number of occupants allowed, and whether pets are allowed. The landlord logs in to the interface on the terminal and enters the data according to the guide.

[0472] Similarly, users (tenants) use a terminal to input their desired conditions for the share house. This includes their preferred location, maximum rent, and preferences regarding roommates (gender, age, etc.). Tenants also proceed with inputting information on the terminal based on prompts.

[0473] All entered information is securely transmitted from the terminal to the server. The server then stores the data using a robust database management system (e.g., SQL Server). This stored data is then used in subsequent analysis and matching processes.

[0474] The server (AI) performs sophisticated algorithmic analysis based on the landlord and tenant conditions stored in the database. This algorithm incorporates a generative AI model and identifies the optimal combination by scoring based on the conditions. The algorithm uses past successful matching data as a reference, resulting in highly accurate suggestions.

[0475] Furthermore, the server generates a shared housing plan, including rental conditions, based on the optimal results. This plan is then presented to the user as a potential rental agreement.

[0476] For example, a prompt might say, "I'm looking for a 2LDK apartment in an urban area. My maximum rent is 80,000 yen, and I'd prefer a female roommate in her late 20s." Based on this, the server can combine the conditions of multiple landlords and tenants to generate proposals.

[0477] The proposal is notified to the terminal, and users (landlord and tenant) review the content and approve or modify it. The server then automatically creates the contract, and the contract is concluded through electronic signatures. This entire process ensures that rental agreements are concluded efficiently and quickly.

[0478] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0479] Step 1:

[0480] The user (landlord) enters property information into the terminal. This information includes the property's location, floor plan, desired rent, number of occupants allowed, and whether pets are permitted. The terminal temporarily stores this information in memory and verifies that all required fields are present by checking the format of the input data. Once the input data is complete, the terminal prepares to send the information to the server.

[0481] Step 2:

[0482] The user (tenant) enters their desired conditions for a shared house into the terminal. This includes preferred location, maximum rent, and conditions regarding roommates (gender, age). The terminal, like the landlord, stores this information in memory and verifies the necessary format and fields. The terminal then prepares to securely send the completed dataset to the server.

[0483] Step 3:

[0484] The terminal encrypts the input data from both the landlord and tenant and sends it to the server using a secure protocol (such as HTTPS). The input consists of two datasets: property information and tenant conditions. The server receives the transmitted data and verifies its integrity and completeness. This data serves as foundational information for the next process.

[0485] Step 4:

[0486] The server stores the received data in a database. During storage, a database management system (e.g., SQL Server) is used, and the data is properly arranged according to a specified format. This enables rapid data access in subsequent matching processes.

[0487] Step 5:

[0488] The server initiates a matching process based on the information stored in the database. Specifically, the server uses a generative AI model to compare property information with tenant requirements, applies a scoring algorithm, and calculates the degree of suitability. The input is the information in the database, and the output is the best candidate pair and its score.

[0489] Step 6:

[0490] Based on the calculated suitability, the server generates a shared housing plan for the most promising landlord-tenant combination. The generated plan includes the available contract terms (rent, move-in date, contract period). Based on this, a plan proposal is created and prepared to be sent to the terminal.

[0491] Step 7:

[0492] The server sends the generated plan to the terminal, making it available for review by the users (landlord and tenant). Users view the proposed rental terms on their terminal and are given the option to approve or modify them. This input is important in the next processing step.

[0493] Step 8:

[0494] Once the user approves the proposal, the device sends that information to the server. Based on the received approval, the server automatically generates a formal contract document. This contract incorporates AI-optimized data, and approval is granted via electronic signature on the server. This completes the contract process.

[0495] (Application Example 1)

[0496] 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."

[0497] In today's real estate market, there is a need for efficient and effective matching of property owners who want to rent out their properties with tenants seeking shared housing, and for smooth contract procedures. Furthermore, there is a lack of means for tenants to verify details without actually visiting the property. There is also a need for visual means to easily verify property details, even from remote locations.

[0498] 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.

[0499] In this invention, the server includes means for inputting the rental conditions of the property, means for inputting the desired conditions of the tenant, means for visually displaying the property in a virtual reality environment, and means for enabling virtual property viewings using a smart device. This makes the matching of conditions between property owners and tenants more efficient and allows for detailed property verification without actually visiting the property.

[0500] "Property" refers to a building or land that is subject to rental or ownership.

[0501] "Lending conditions" refer to the terms of use and contractual clauses set by the property owner when providing the property.

[0502] "Desired conditions" refer to the conditions such as location and price range that the tenant desires when using the property.

[0503] An "information storage device" refers to a system or medium for accumulating and holding data.

[0504] "High degree of suitability" refers to a measure that evaluates the extent to which the conditions presented between the property owner and the tenant are mutually compatible.

[0505] A "shared living plan" refers to a proposal outlining the terms of the contract and lifestyle plan for a shared house.

[0506] A "virtual reality environment" refers to a technology that provides a realistic experience within a computer-generated three-dimensional space.

[0507] A "smart device" refers to a portable electronic device that possesses advanced functions through internet connectivity and applications.

[0508] This invention is a system for efficiently providing property matching and detailed verification. The server stores data from terminals used to input property rental conditions and tenant preferences in an information storage device. Based on the stored data, the server uses a generated AI model to calculate the degree of suitability and proposes an optimal co-living plan. Furthermore, the server provides a function to visually display properties in a virtual reality environment, allowing users to virtually tour them through smart devices.

[0509] Specifically, we will build 3D property models using Unity and implement AI algorithms by combining Python and TensorFlow. In particular, we will utilize TensorFlow to calculate the degree of fit using deep learning when analyzing the user's desired conditions. This will allow users to visually check properties while receiving AI-generated suggestions for the most suitable properties.

[0510] For example, if user A enters a prompt such as, "I'm looking for a pet-friendly property in the city center with a rent of under 100,000 yen," the system will present properties that match those criteria through virtual reality. This implementation streamlines the property selection process and improves user satisfaction.

[0511] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0512] Step 1:

[0513] The terminal receives input from both the property owner and the tenant regarding their desired conditions and rental conditions. The entered data is immediately transmitted to the information storage device. Specifically, this step involves the user entering conditions using the terminal's interface, which are then stored in the database.

[0514] Step 2:

[0515] The server retrieves loan conditions and desired conditions stored in the information storage device and analyzes the data using a generative AI model. The data processing performed here is to calculate the degree of fit, and the output is a list of the combinations that best match the conditions of the property owner and the tenant. Specifically, the AI ​​algorithm processes the data using TensorFlow and calculates the degree of fit.

[0516] Step 3:

[0517] The server generates an optimal cohabitation plan based on the results of its high-fitness calculation. This plan takes all relevant conditions into consideration and is automatically proposed by the system. In this step, the server automatically generates a shared house plan and includes the necessary contract details.

[0518] Step 4:

[0519] The server sends the generated cohabitation plan to the terminal, and the user reviews the proposal. The terminal presents the information to the user in visual or text format. Specifically, the user can review the received data and review the AI's proposal.

[0520] Step 5:

[0521] Users can visually tour properties in a virtual reality environment using smart devices. This step utilizes 3D modeling technology using Unity. Based on user input, a virtual tour can be conducted by visually confirming the interior of the property.

[0522] Step 6:

[0523] The server receives feedback from the user, modifies the cohabitation plan as needed, and generates the final contract. The final contract is created electronically and provided to the user. Specifically, the server receives modification requests from the user and makes adjustments within the system.

[0524] 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.

[0525] This invention efficiently matches the desired conditions of landlords who rent out properties with those of tenants seeking shared housing, and generates optimal contracts based on these matching conditions. Furthermore, by incorporating an emotion engine, it recognizes and analyzes the user's emotions, enabling the proposal and optimization of plans based on these emotions. The following describes a specific embodiment of this invention.

[0526] First, the user (landlord) enters detailed property information and rental conditions via a terminal. Specifically, this includes the property's location, floor plan, desired rent, and whether pets are allowed. Similarly, the user (tenant) enters their desired conditions for the share house. This includes their preferred area, maximum rent, and desired roommates.

[0527] This information is sent from the terminal to the server and stored in a database. Based on the stored data, the server (AI) analyzes the conditions and calculates matching results with a high degree of fit. The matching at this stage is based on objective conditions.

[0528] Furthermore, a key feature of this invention is that an emotion engine embedded in the server operates based on user interaction. The emotion engine analyzes user input, responses, and dialogue data to infer the user's emotional state. This emotional data is reflected in the matching results and the evaluation of proposed share house plans, enabling suggestions that take into account the user's emotional satisfaction.

[0529] For example, if a user (tenant) expresses dissatisfaction with a proposed plan, the emotion engine can recognize that emotion, and the server can automatically modify the plan. This modification may include adjusting the rent or re-evaluating the location. This improves the accuracy of the proposals and enables personalized service for each user.

[0530] Ultimately, for proposals approved by the user, the server generates a formal contract, which is then finalized through electronic signature. This system ensures that housing is provided with the utmost consideration for the user's feelings and wishes, thereby improving satisfaction for both parties.

[0531] The following describes the processing flow.

[0532] Step 1:

[0533] The user (landlord) enters detailed property information and desired rental conditions via a terminal. This includes the property's location, floor plan, desired rent, and whether or not shared facilities are available. They also enter detailed conditions such as whether pets are allowed and the contract period.

[0534] Step 2:

[0535] Users (tenants) enter their desired conditions for a shared house via a terminal. These include preferred location, maximum rent, desired attributes of roommates (gender, age, etc.), and lifestyle preferences.

[0536] Step 3:

[0537] The terminal sends the entered information to the server. This information is securely transmitted and received via a communication protocol.

[0538] Step 4:

[0539] The server stores the received landlord and tenant information in a database. This organizes the conditions and prepares them for subsequent processing.

[0540] Step 5:

[0541] The server (AI) performs analysis to calculate the suitability between landlords and tenants based on the conditions stored in the database. Here, it evaluates how well the conditions of both parties match and executes an algorithm to find the optimal combination.

[0542] Step 6:

[0543] The server activates an emotion engine and collects emotional data through user interaction. This includes analyzing user reactions and comments to estimate satisfaction and favorability towards the suggestions.

[0544] Step 7:

[0545] The server takes sentiment data into account and automatically adjusts the plan. If the user expresses dissatisfaction with the current proposal, it adjusts specific elements such as rent, conditions, and roommate profiles and makes a revised proposal.

[0546] Step 8:

[0547] The revised shared housing plan is sent from the server to the terminal and reviewed by the users (landlord and tenant). The users review the plan details and either give final approval or request further revisions.

[0548] Step 9:

[0549] If the user approves the proposal, the server generates a contract and sends it to the device for further online consent procedures. This formally concludes the contract and the shared house arrangement begins.

[0550] (Example 2)

[0551] 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."

[0552] Current property matching systems lack the ability to provide housing plans that reflect not only the user's physical requirements but also their emotional satisfaction. Therefore, it is difficult to achieve matching results that are emotionally satisfying for both landlords and tenants, and there is a need for flexible proposals that meet the user's needs.

[0553] 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.

[0554] In this invention, the server includes means for inputting attribute information, means for inputting user preference information, and means for analyzing the user's emotional information regarding the proposed plan. This enables optimal property matching and proposals that take into account the user's emotional state.

[0555] "Attribute information" refers to basic data related to a property, including details such as location, floor plan, and rental conditions.

[0556] "User preference information" refers to the conditions and requests desired by tenants or residents, including information such as preferred area, rent limit, and cohabitation conditions.

[0557] A "recording medium" refers to a device or technology for storing information in data form, and includes all storage devices, including database systems.

[0558] A "highly suitable combination" refers to the pairing result where the conditions best match between the landlord's attribute information and the tenant's desired information.

[0559] A "housing plan" is a proposed plan of the living environment, including the property conditions and services offered by the landlord.

[0560] "Emotional information" refers to data that analyzes the emotional responses that users show to suggestions, including states such as satisfaction, dissatisfaction, and joy.

[0561] A "letter of agreement" is a document that records the terms of the agreement between the landlord and the tenant regarding the conditions for renting out the property and living in it.

[0562] This invention is a system that effectively matches the requirements of property providers and housing seekers, proposes an optimal housing plan based on emotions, and generates a contract. At the heart of the system are a terminal operated by the user and a server that processes data, which are interconnected via a network.

[0563] The user (provider) inputs attribute information such as the property's location, floor plan, and rental conditions via a terminal, while the user (client) similarly inputs desired information such as their preferred area and maximum rent. The terminal quickly transmits this information to the server, which then stores it in a database.

[0564] The server analyzes the stored data and matches the provider's attribute information with the user's preferences. It uses a generative AI model to analyze the conditions and derive combinations with a high degree of fit. Furthermore, the server uses an emotion engine to analyze user responses in real time and collect them as emotion information. This analysis is performed using natural language processing.

[0565] Emotional information is reflected in the proposed housing plan, and the server adjusts the plan if necessary based on the analysis results. This process aims to increase the user's emotional satisfaction. Once the user agrees to the proposal, the server automatically generates a contract and concludes it with an electronic signature.

[0566] As a concrete example, the user enters the following prompt:

[0567] Example prompt:

[0568] "I have a cat and am looking for pet-friendly housing in an urban area. My monthly rent is limited to 80,000 yen, and ideally, I'd like to live near a train station and in a shopping area."

[0569] In response to this prompt, the system considers the user's preferences and proposes the most suitable properties. Ultimately, it can provide comprehensive recommendations based on attribute information, preference information, and emotional information.

[0570] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0571] Step 1:

[0572] The user (provider) enters property information via a terminal. This information includes location, floor plan, and rental conditions. The terminal formats this information and sends it to the server. The data is received by the server and stored on a recording medium.

[0573] Step 2:

[0574] The user enters their desired conditions into a terminal. Specifically, these include the desired area, maximum rent, and required facilities. The terminal sends the entered information to the server, which receives the data and stores it in a database. The server manages this data as user preference information.

[0575] Step 3:

[0576] The server uses a generative AI model to analyze the provider's attribute information and the user's preferences. This analysis matches the conditions of both to find the combination with the highest degree of fit. The input data consists of attribute information and preferences, and the output is the data of the combination with the highest degree of fit.

[0577] Step 4:

[0578] The server uses an emotion engine to analyze user feedback and responses. It extracts emotional information using natural language processing techniques. Input is text data based on user responses, and output is data indicating the user's emotional state.

[0579] Step 5:

[0580] The server adjusts the optimal housing plan based on the extracted emotional information. Specifically, it readjusts the rent or relaxes the conditions. The input for this process is emotional information and matching data, and the output is the revised housing plan.

[0581] Step 6:

[0582] Finally, the server generates a contract for the housing plan agreed upon by the user. The contract includes all necessary terms and conditions, and the user concludes the agreement via electronic signature. The input is the agreed-upon housing plan, and the output is the formal contract.

[0583] (Application Example 2)

[0584] 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."

[0585] Traditional property matching systems rely on a single matching method based on the landlord's and tenant's conditions, making it difficult to adequately consider the emotional satisfaction of users. Similarly, in e-commerce, suggestions often fail to take consumer emotions into account, resulting in a lack of purchasing intent. This leads to challenges such as inadequate suitability for shared housing and product selection, and a lack of a balanced user experience.

[0586] 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.

[0587] In this invention, the server includes means for inputting the rental conditions of the property, means for inputting the tenant's desired conditions, and means for analyzing the user's emotional data and optimizing the plan based on the emotional state. This enables personalized proposals by presenting products and adjusting property plans based on the user's reactions.

[0588] "Method for entering property rental conditions" refers to the process by which the landlord enters detailed conditions regarding the property into the system.

[0589] "Means for inputting tenant's desired conditions" refers to the process by which tenants input their preferences and requests regarding the property into the system.

[0590] "Method of saving to a database" refers to the process of recording the entered loan conditions and desired conditions and saving them in a way that allows for later access.

[0591] "Means for analyzing and calculating combinations of high degree of fit" refers to the process of analyzing saved conditions and calculating the combination that matches the conditions at the highest level.

[0592] "Methods for proposing plans" refers to the process of presenting the optimal plan to the user based on the calculated matching results.

[0593] "Means of generating a contract" refers to the process of creating a formal contract once the user agrees to the proposed terms and conditions.

[0594] "A means of analyzing user emotional data and optimizing plans based on emotional states" refers to the process of analyzing user emotions and adjusting the proposed content to the most satisfying form based on the results.

[0595] "A means of adjusting product suggestions in real time according to emotions" refers to a process that instantly optimizes product suggestions in response to changes in the user's emotions.

[0596] The system that realizes this invention consists of multiple steps, including user input of conditions, storage in a database, condition analysis, recognition and analysis of sentiment data, and real-time suggestion of results. The details are described below.

[0597] First, users (landlords and tenants) enter the rental conditions and desired conditions for the property via a terminal. This includes specific conditions such as the property's location, floor plan, and rent. This data is stored in a cloud-based database and remains accessible at all times.

[0598] The server analyzes the stored conditions and calculates the combination with the highest degree of fit. In addition to the normal matching process, it utilizes user input and historical data to provide more accurate matching results.

[0599] Furthermore, an emotion recognition engine installed on the server acquires and analyzes emotional data from the user's facial expressions and voice. The analysis results are then used to optimize the plan based on the user's emotional state. This process utilizes existing emotion recognition technologies, such as the Microsoft Azure Face API and the Google Cloud Vision API.

[0600] The product selections and property plans suggested by the platform are adjusted in real time based on this emotional data. For example, if a user makes a surprised face while browsing products on their smartphone, the platform can instantly present new products that might interest them based on that data.

[0601] As a concrete example, suppose a user is browsing outdoor equipment while online shopping. If the system detects that the user is satisfied with the tent being displayed, it can recommend similar camping equipment, thereby increasing their purchase intent.

[0602] An example of a prompt message would be: "Explain how to acquire emotional data from a user's facial expressions and voice while they are browsing products, and how to adjust relevant product suggestions in real time based on that emotional data." This improves the user experience and enables more effective matching and product suggestions.

[0603] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0604] Step 1:

[0605] Users input rental conditions and desired conditions for properties via a terminal. This input data includes the property's location, floor plan, and rent. The terminal receives this data, formats it, and sends it to a database in the cloud.

[0606] Step 2:

[0607] The server retrieves the loan conditions and desired conditions stored in the database. Next, it analyzes the conditions to calculate the optimal combination. Specifically, it calculates the degree of agreement between each condition and generates a matching score based on that. The output is the data for the combination with the highest degree of fit.

[0608] Step 3:

[0609] The server uses an emotion recognition engine to acquire emotion data from the user's facial expressions and voice. It processes real-time facial images and audio signals as input, analyzing the type of emotion (e.g., joy, surprise, sadness). The output is estimated emotion data.

[0610] Step 4:

[0611] The server integrates sentiment data and matching scores to propose the most satisfying plan to the user. The proposal is adjusted in real time, and the plan changes according to the user's emotional state. The output provides details of the adjusted plan.

[0612] Step 5:

[0613] The user reviews the details of the proposed plan on the device and approves or modifies it. The device receives the adjusted plan information as input and considers the options. The device records the user's selection and sends the final decision to the server.

[0614] 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.

[0615] 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.

[0616] 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.

[0617] [Fourth Embodiment]

[0618] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0619] 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.

[0620] 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).

[0621] 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.

[0622] 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.

[0623] 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).

[0624] 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.

[0625] 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.

[0626] 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.

[0627] 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.

[0628] 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.

[0629] 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.

[0630] 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".

[0631] The system for implementing this invention efficiently matches the requirements of both property owners who wish to rent out their properties and tenants who wish to live in a shared house, and generates and manages optimal contracts based on these matching conditions. The details are shown below.

[0632] First, the user (landlord) enters detailed property information and rental conditions using a terminal. This information includes the property's location, floor plan, desired rent, number of occupants allowed, and whether pets are permitted.

[0633] Next, the user (tenant) enters their desired conditions for the share house via the terminal. This includes their preferred location, maximum rent, and desired conditions for roommates (gender, age, etc.).

[0634] Here, all entered information is sent from the terminal to the server. The server stores this data in a database, preparing it for the subsequent matching process. Based on the aforementioned conditions, the server (AI) uses advanced algorithms to analyze the conditions of both the landlord and the tenant and find the most suitable combination. Because this process is automated, it significantly reduces time and produces more effective matching results.

[0635] This system also optimizes the conditions by proposing a shared housing plan based on the best matching results calculated by AI. This plan includes contract terms (rent, move-in date, contract period, etc.). The server sends this proposal to the terminal, and the process proceeds for the user (landlord and tenant) to review the proposal.

[0636] As a concrete example, consider a landlord with a 2LDK apartment in an urban area looking for a tenant who wants to live in a shared apartment. In this case, the landlord specifies the detailed conditions of the property, and multiple potential tenants enter their desired conditions. A server (AI) processes this information, finds the best combination, and delivers proposals in both directions. Once a user accepts a proposal, the server automatically creates a formal contract, and the contract is finalized through electronic signatures, etc. This system enables efficient and smooth conclusion of rental agreements.

[0637] The following describes the processing flow.

[0638] Step 1:

[0639] Users (landlords) enter detailed property information and rental conditions via a terminal. This includes the property's location, floor plan, desired rent, whether pets are allowed, and the number of people who can share the property. The terminal provides guidance to ensure that the information is entered accurately in real time.

[0640] Step 2:

[0641] The user (tenant) enters their desired conditions for a shared house into the terminal. This information includes preferred area, maximum rent, desired attributes of roommates (gender, age range), and lifestyle preferences. After the entered data is reviewed, it is sent to the next processing step.

[0642] Step 3:

[0643] The terminal transmits information obtained from the landlord and tenant to the server. This is done through a secure communication protocol, ensuring the safety of the information.

[0644] Step 4:

[0645] The server stores the received data in a database. This database serves as a source of information for subsequent analysis and matching. The information is accurately categorized and managed to ensure that all conditions are taken into consideration.

[0646] Step 5:

[0647] The server (AI) analyzes the stored data and calculates the degree of fit based on the conditions. Here, it uses an advanced matching algorithm to evaluate in detail the intersection of property conditions and tenant preferences and find the most suitable combination.

[0648] Step 6:

[0649] The server identifies the optimal match based on the analysis results and proposes a shared housing plan accordingly. The proposal includes contract terms (rent, move-in date, contract period, etc.).

[0650] Step 7:

[0651] The server sends the generated shared house plan to the terminal, notifying the landlord and tenant of the proposal. Both users review the proposal on their terminals and either approve the terms or submit revised proposals.

[0652] Step 8:

[0653] If the users (landlord and tenant) approve the terms, the server will generate a formal contract. This contract will be provided in electronic format and will include all necessary legal requirements and agreements.

[0654] Step 9:

[0655] The user reviews the generated contract and completes the contract procedures using digital signatures, etc. This formally concludes the contract, and the system's matching process is completed.

[0656] (Example 1)

[0657] 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".

[0658] In the current real estate rental market, efficiently matching the conditions of property owners and tenants and automatically generating optimal contracts quickly is a challenging task. Manually adjusting conditions and drafting contract documents is time-consuming and labor-intensive, and the process of finding a suitable partner is inherently uncertain. To solve these problems, a system is needed that utilizes effective matching algorithms to quickly aggregate information and make decisions.

[0659] 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.

[0660] In this invention, the server includes means for inputting property information, means for inputting tenant conditions, and means for storing the property information and tenant conditions on a recording medium. This enables instantaneous matching of the conditions of property owners and tenants, facilitating rapid and effective contract formation.

[0661] "Property information" refers to a collection of data that shows the location, floor plan, rent, number of occupants allowed, and other rental conditions of a property.

[0662] "Tenant conditions" refer to a set of requirements specifications that include the desired location of the property, the maximum rent, and the conditions for cohabitants.

[0663] A "recording medium" is a physical or virtual device or service used to store data and make it accessible later.

[0664] "Analysis" is the act of processing input information and evaluating and classifying it based on specific criteria.

[0665] "Suitability" is a criterion for selecting the optimal combination by quantifying the degree to which property information and tenant requirements match.

[0666] A "rental plan" is a plan or proposal for real estate rental, constructed based on the proposed contract terms.

[0667] A "contract document" is an official document that records the details of a lease agreement and is created in a legally valid manner.

[0668] "Communication" is the process of sending and receiving data or information within a system, and it takes place over a network.

[0669] The system for implementing this invention efficiently matches the conditions of both property owners who want to rent out their properties and tenants who want to live in a shared house, and generates and manages the optimal contract based on that matching. The system mainly consists of three components: a server, a terminal, and a user, and specifically operates as follows.

[0670] The user (landlord) enters property details using a terminal. This terminal refers to a computing device such as a PC or smartphone, and the entered data includes items such as the property's location, floor plan, desired rent, number of occupants allowed, and whether pets are allowed. The landlord logs in to the interface on the terminal and enters the data according to the guide.

[0671] Similarly, users (tenants) use a terminal to input their desired conditions for the share house. This includes their preferred location, maximum rent, and preferences regarding roommates (gender, age, etc.). Tenants also proceed with inputting information on the terminal based on prompts.

[0672] All entered information is securely transmitted from the terminal to the server. The server then stores the data using a robust database management system (e.g., SQL Server). This stored data is then used in subsequent analysis and matching processes.

[0673] The server (AI) performs sophisticated algorithmic analysis based on the landlord and tenant conditions stored in the database. This algorithm incorporates a generative AI model and identifies the optimal combination by scoring based on the conditions. The algorithm uses past successful matching data as a reference, resulting in highly accurate suggestions.

[0674] Furthermore, the server generates a shared housing plan, including rental conditions, based on the optimal results. This plan is then presented to the user as a potential rental agreement.

[0675] For example, a prompt might say, "I'm looking for a 2LDK apartment in an urban area. My maximum rent is 80,000 yen, and I'd prefer a female roommate in her late 20s." Based on this, the server can combine the conditions of multiple landlords and tenants to generate proposals.

[0676] The proposal is notified to the terminal, and users (landlord and tenant) review the content and approve or modify it. The server then automatically creates the contract, and the contract is concluded through electronic signatures. This entire process ensures that rental agreements are concluded efficiently and quickly.

[0677] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0678] Step 1:

[0679] The user (landlord) enters property information into the terminal. This information includes the property's location, floor plan, desired rent, number of occupants allowed, and whether pets are permitted. The terminal temporarily stores this information in memory and verifies that all required fields are present by checking the format of the input data. Once the input data is complete, the terminal prepares to send the information to the server.

[0680] Step 2:

[0681] The user (tenant) enters their desired conditions for a shared house into the terminal. This includes preferred location, maximum rent, and conditions regarding roommates (gender, age). The terminal, like the landlord, stores this information in memory and verifies the necessary format and fields. The terminal then prepares to securely send the completed dataset to the server.

[0682] Step 3:

[0683] The terminal encrypts the input data from both the landlord and tenant and sends it to the server using a secure protocol (such as HTTPS). The input consists of two datasets: property information and tenant conditions. The server receives the transmitted data and verifies its integrity and completeness. This data serves as foundational information for the next process.

[0684] Step 4:

[0685] The server stores the received data in a database. During storage, a database management system (e.g., SQL Server) is used, and the data is properly arranged according to a specified format. This enables rapid data access in subsequent matching processes.

[0686] Step 5:

[0687] The server initiates a matching process based on the information stored in the database. Specifically, the server uses a generative AI model to compare property information with tenant requirements, applies a scoring algorithm, and calculates the degree of suitability. The input is the information in the database, and the output is the best candidate pair and its score.

[0688] Step 6:

[0689] Based on the calculated suitability, the server generates a shared housing plan for the most promising landlord-tenant combination. The generated plan includes the available contract terms (rent, move-in date, contract period). Based on this, a plan proposal is created and prepared to be sent to the terminal.

[0690] Step 7:

[0691] The server sends the generated plan to the terminal, making it available for review by the users (landlord and tenant). Users view the proposed rental terms on their terminal and are given the option to approve or modify them. This input is important in the next processing step.

[0692] Step 8:

[0693] Once the user approves the proposal, the device sends that information to the server. Based on the received approval, the server automatically generates a formal contract document. This contract incorporates AI-optimized data, and approval is granted via electronic signature on the server. This completes the contract process.

[0694] (Application Example 1)

[0695] 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".

[0696] In today's real estate market, there is a need for efficient and effective matching of property owners who want to rent out their properties with tenants seeking shared housing, and for smooth contract procedures. Furthermore, there is a lack of means for tenants to verify details without actually visiting the property. There is also a need for visual means to easily verify property details, even from remote locations.

[0697] 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.

[0698] In this invention, the server includes means for inputting the rental conditions of the property, means for inputting the desired conditions of the tenant, means for visually displaying the property in a virtual reality environment, and means for enabling virtual property viewings using a smart device. This makes the matching of conditions between property owners and tenants more efficient and allows for detailed property verification without actually visiting the property.

[0699] "Property" refers to a building or land that is subject to rental or ownership.

[0700] "Lending conditions" refer to the terms of use and contractual clauses set by the property owner when providing the property.

[0701] "Desired conditions" refer to the conditions such as location and price range that the tenant desires when using the property.

[0702] An "information storage device" refers to a system or medium for accumulating and holding data.

[0703] "High degree of suitability" refers to a measure that evaluates the extent to which the conditions presented between the property owner and the tenant are mutually compatible.

[0704] A "shared living plan" refers to a proposal outlining the terms of the contract and lifestyle plan for a shared house.

[0705] A "virtual reality environment" refers to a technology that provides a realistic experience within a computer-generated three-dimensional space.

[0706] A "smart device" refers to a portable electronic device that possesses advanced functions through internet connectivity and applications.

[0707] This invention is a system for efficiently providing property matching and detailed verification. The server stores data from terminals used to input property rental conditions and tenant preferences in an information storage device. Based on the stored data, the server uses a generated AI model to calculate the degree of suitability and proposes an optimal co-living plan. Furthermore, the server provides a function to visually display properties in a virtual reality environment, allowing users to virtually tour them through smart devices.

[0708] Specifically, we will build 3D property models using Unity and implement AI algorithms by combining Python and TensorFlow. In particular, we will utilize TensorFlow to calculate the degree of fit using deep learning when analyzing the user's desired conditions. This will allow users to visually check properties while receiving AI-generated suggestions for the most suitable properties.

[0709] For example, if user A enters a prompt such as, "I'm looking for a pet-friendly property in the city center with a rent of under 100,000 yen," the system will present properties that match those criteria through virtual reality. This implementation streamlines the property selection process and improves user satisfaction.

[0710] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0711] Step 1:

[0712] The terminal receives input from both the property owner and the tenant regarding their desired conditions and rental conditions. The entered data is immediately transmitted to the information storage device. Specifically, this step involves the user entering conditions using the terminal's interface, which are then stored in the database.

[0713] Step 2:

[0714] The server retrieves loan conditions and desired conditions stored in the information storage device and analyzes the data using a generative AI model. The data processing performed here is to calculate the degree of fit, and the output is a list of the combinations that best match the conditions of the property owner and the tenant. Specifically, the AI ​​algorithm processes the data using TensorFlow and calculates the degree of fit.

[0715] Step 3:

[0716] The server generates an optimal cohabitation plan based on the results of its high-fitness calculation. This plan takes all relevant conditions into consideration and is automatically proposed by the system. In this step, the server automatically generates a shared house plan and includes the necessary contract details.

[0717] Step 4:

[0718] The server sends the generated cohabitation plan to the terminal, and the user reviews the proposal. The terminal presents the information to the user in visual or text format. Specifically, the user can review the received data and review the AI's proposal.

[0719] Step 5:

[0720] Users can visually tour properties in a virtual reality environment using smart devices. This step utilizes 3D modeling technology using Unity. Based on user input, a virtual tour can be conducted by visually confirming the interior of the property.

[0721] Step 6:

[0722] The server receives feedback from the user, modifies the cohabitation plan as needed, and generates the final contract. The final contract is created electronically and provided to the user. Specifically, the server receives modification requests from the user and makes adjustments within the system.

[0723] 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.

[0724] This invention efficiently matches the desired conditions of landlords who rent out properties with those of tenants seeking shared housing, and generates optimal contracts based on these matching conditions. Furthermore, by incorporating an emotion engine, it recognizes and analyzes the user's emotions, enabling the proposal and optimization of plans based on these emotions. The following describes a specific embodiment of this invention.

[0725] First, the user (landlord) enters detailed property information and rental conditions via a terminal. Specifically, this includes the property's location, floor plan, desired rent, and whether pets are allowed. Similarly, the user (tenant) enters their desired conditions for the share house. This includes their preferred area, maximum rent, and desired roommates.

[0726] This information is sent from the terminal to the server and stored in a database. Based on the stored data, the server (AI) analyzes the conditions and calculates matching results with a high degree of fit. The matching at this stage is based on objective conditions.

[0727] Furthermore, a key feature of this invention is that an emotion engine embedded in the server operates based on user interaction. The emotion engine analyzes user input, responses, and dialogue data to infer the user's emotional state. This emotional data is reflected in the matching results and the evaluation of proposed share house plans, enabling suggestions that take into account the user's emotional satisfaction.

[0728] For example, if a user (tenant) expresses dissatisfaction with a proposed plan, the emotion engine can recognize that emotion, and the server can automatically modify the plan. This modification may include adjusting the rent or re-evaluating the location. This improves the accuracy of the proposals and enables personalized service for each user.

[0729] Ultimately, for proposals approved by the user, the server generates a formal contract, which is then finalized through electronic signature. This system ensures that housing is provided with the utmost consideration for the user's feelings and wishes, thereby improving satisfaction for both parties.

[0730] The following describes the processing flow.

[0731] Step 1:

[0732] The user (landlord) enters detailed property information and desired rental conditions via a terminal. This includes the property's location, floor plan, desired rent, and whether or not shared facilities are available. They also enter detailed conditions such as whether pets are allowed and the contract period.

[0733] Step 2:

[0734] Users (tenants) enter their desired conditions for a shared house via a terminal. These include preferred location, maximum rent, desired attributes of roommates (gender, age, etc.), and lifestyle preferences.

[0735] Step 3:

[0736] The terminal sends the entered information to the server. This information is securely transmitted and received via a communication protocol.

[0737] Step 4:

[0738] The server stores the received landlord and tenant information in a database. This organizes the conditions and prepares them for subsequent processing.

[0739] Step 5:

[0740] The server (AI) performs analysis to calculate the suitability between landlords and tenants based on the conditions stored in the database. Here, it evaluates how well the conditions of both parties match and executes an algorithm to find the optimal combination.

[0741] Step 6:

[0742] The server activates an emotion engine and collects emotional data through user interaction. This includes analyzing user reactions and comments to estimate satisfaction and favorability towards the suggestions.

[0743] Step 7:

[0744] The server takes sentiment data into account and automatically adjusts the plan. If the user expresses dissatisfaction with the current proposal, it adjusts specific elements such as rent, conditions, and roommate profiles and makes a revised proposal.

[0745] Step 8:

[0746] The revised shared housing plan is sent from the server to the terminal and reviewed by the users (landlord and tenant). The users review the plan details and either give final approval or request further revisions.

[0747] Step 9:

[0748] If the user approves the proposal, the server generates a contract and sends it to the device for further online consent procedures. This formally concludes the contract and the shared house arrangement begins.

[0749] (Example 2)

[0750] 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".

[0751] Current property matching systems lack the ability to provide housing plans that reflect not only the user's physical requirements but also their emotional satisfaction. Therefore, it is difficult to achieve matching results that are emotionally satisfying for both landlords and tenants, and there is a need for flexible proposals that meet the user's needs.

[0752] 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.

[0753] In this invention, the server includes means for inputting attribute information, means for inputting user preference information, and means for analyzing the user's emotional information regarding the proposed plan. This enables optimal property matching and proposals that take into account the user's emotional state.

[0754] "Attribute information" refers to basic data related to a property, including details such as location, floor plan, and rental conditions.

[0755] "User preference information" refers to the conditions and requests desired by tenants or residents, including information such as preferred area, rent limit, and cohabitation conditions.

[0756] A "recording medium" refers to a device or technology for storing information in data form, and includes all storage devices, including database systems.

[0757] A "highly suitable combination" refers to the pairing result where the conditions best match between the landlord's attribute information and the tenant's desired information.

[0758] A "housing plan" is a proposed plan of the living environment, including the property conditions and services offered by the landlord.

[0759] "Emotional information" refers to data that analyzes the emotional responses that users show to suggestions, including states such as satisfaction, dissatisfaction, and joy.

[0760] A "letter of agreement" is a document that records the terms of the agreement between the landlord and the tenant regarding the conditions for renting out the property and living in it.

[0761] This invention is a system that effectively matches the requirements of property providers and housing seekers, proposes an optimal housing plan based on emotions, and generates a contract. At the heart of the system are a terminal operated by the user and a server that processes data, which are interconnected via a network.

[0762] The user (provider) inputs attribute information such as the property's location, floor plan, and rental conditions via a terminal, while the user (client) similarly inputs desired information such as their preferred area and maximum rent. The terminal quickly transmits this information to the server, which then stores it in a database.

[0763] The server analyzes the stored data and matches the provider's attribute information with the user's preferences. It uses a generative AI model to analyze the conditions and derive combinations with a high degree of fit. Furthermore, the server uses an emotion engine to analyze user responses in real time and collect them as emotion information. This analysis is performed using natural language processing.

[0764] Emotional information is reflected in the proposed housing plan, and the server adjusts the plan if necessary based on the analysis results. This process aims to increase the user's emotional satisfaction. Once the user agrees to the proposal, the server automatically generates a contract and concludes it with an electronic signature.

[0765] As a concrete example, the user enters the following prompt:

[0766] Example prompt:

[0767] "I have a cat and am looking for pet-friendly housing in an urban area. My monthly rent is limited to 80,000 yen, and ideally, I'd like to live near a train station and in a shopping area."

[0768] In response to this prompt, the system considers the user's preferences and proposes the most suitable properties. Ultimately, it can provide comprehensive recommendations based on attribute information, preference information, and emotional information.

[0769] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0770] Step 1:

[0771] The user (provider) enters property information via a terminal. This information includes location, floor plan, and rental conditions. The terminal formats this information and sends it to the server. The data is received by the server and stored on a recording medium.

[0772] Step 2:

[0773] The user enters their desired conditions into a terminal. Specifically, these include the desired area, maximum rent, and required facilities. The terminal sends the entered information to the server, which receives the data and stores it in a database. The server manages this data as user preference information.

[0774] Step 3:

[0775] The server uses a generative AI model to analyze the provider's attribute information and the user's preferences. This analysis matches the conditions of both to find the combination with the highest degree of fit. The input data consists of attribute information and preferences, and the output is the data of the combination with the highest degree of fit.

[0776] Step 4:

[0777] The server uses an emotion engine to analyze user feedback and responses. It extracts emotional information using natural language processing techniques. Input is text data based on user responses, and output is data indicating the user's emotional state.

[0778] Step 5:

[0779] The server adjusts the optimal housing plan based on the extracted emotional information. Specifically, it readjusts the rent or relaxes the conditions. The input for this process is emotional information and matching data, and the output is the revised housing plan.

[0780] Step 6:

[0781] Finally, the server generates a contract for the housing plan agreed upon by the user. The contract includes all necessary terms and conditions, and the user concludes the agreement via electronic signature. The input is the agreed-upon housing plan, and the output is the formal contract.

[0782] (Application Example 2)

[0783] 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".

[0784] Traditional property matching systems rely on a single matching method based on the landlord's and tenant's conditions, making it difficult to adequately consider the emotional satisfaction of users. Similarly, in e-commerce, suggestions often fail to take consumer emotions into account, resulting in a lack of purchasing intent. This leads to challenges such as inadequate suitability for shared housing and product selection, and a lack of a balanced user experience.

[0785] 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.

[0786] In this invention, the server includes means for inputting the rental conditions of the property, means for inputting the tenant's desired conditions, and means for analyzing the user's emotional data and optimizing the plan based on the emotional state. This enables personalized proposals by presenting products and adjusting property plans based on the user's reactions.

[0787] "Method for entering property rental conditions" refers to the process by which the landlord enters detailed conditions regarding the property into the system.

[0788] "Means for inputting tenant's desired conditions" refers to the process by which tenants input their preferences and requests regarding the property into the system.

[0789] "Method of saving to a database" refers to the process of recording the entered loan conditions and desired conditions and saving them in a way that allows for later access.

[0790] "Means for analyzing and calculating combinations of high degree of fit" refers to the process of analyzing saved conditions and calculating the combination that matches the conditions at the highest level.

[0791] "Methods for proposing plans" refers to the process of presenting the optimal plan to the user based on the calculated matching results.

[0792] "Means of generating a contract" refers to the process of creating a formal contract once the user agrees to the proposed terms and conditions.

[0793] "A means of analyzing user emotional data and optimizing plans based on emotional states" refers to the process of analyzing user emotions and adjusting the proposed content to the most satisfying form based on the results.

[0794] "A means of adjusting product suggestions in real time according to emotions" refers to a process that instantly optimizes product suggestions in response to changes in the user's emotions.

[0795] The system that realizes this invention consists of multiple steps, including user input of conditions, storage in a database, condition analysis, recognition and analysis of sentiment data, and real-time suggestion of results. The details are described below.

[0796] First, users (landlords and tenants) enter the rental conditions and desired conditions for the property via a terminal. This includes specific conditions such as the property's location, floor plan, and rent. This data is stored in a cloud-based database and remains accessible at all times.

[0797] The server analyzes the stored conditions and calculates the combination with the highest degree of fit. In addition to the normal matching process, it utilizes user input and historical data to provide more accurate matching results.

[0798] Furthermore, an emotion recognition engine installed on the server acquires and analyzes emotional data from the user's facial expressions and voice. The analysis results are then used to optimize the plan based on the user's emotional state. This process utilizes existing emotion recognition technologies, such as the Microsoft Azure Face API and the Google Cloud Vision API.

[0799] The product selections and property plans suggested by the platform are adjusted in real time based on this emotional data. For example, if a user makes a surprised face while browsing products on their smartphone, the platform can instantly present new products that might interest them based on that data.

[0800] As a concrete example, suppose a user is browsing outdoor equipment while online shopping. If the system detects that the user is satisfied with the tent being displayed, it can recommend similar camping equipment, thereby increasing their purchase intent.

[0801] An example of a prompt message would be: "Explain how to acquire emotional data from a user's facial expressions and voice while they are browsing products, and how to adjust relevant product suggestions in real time based on that emotional data." This improves the user experience and enables more effective matching and product suggestions.

[0802] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0803] Step 1:

[0804] Users input rental conditions and desired conditions for properties via a terminal. This input data includes the property's location, floor plan, and rent. The terminal receives this data, formats it, and sends it to a database in the cloud.

[0805] Step 2:

[0806] The server retrieves the loan conditions and desired conditions stored in the database. Next, it analyzes the conditions to calculate the optimal combination. Specifically, it calculates the degree of agreement between each condition and generates a matching score based on that. The output is the data for the combination with the highest degree of fit.

[0807] Step 3:

[0808] The server uses an emotion recognition engine to acquire emotion data from the user's facial expressions and voice. It processes real-time facial images and audio signals as input, analyzing the type of emotion (e.g., joy, surprise, sadness). The output is estimated emotion data.

[0809] Step 4:

[0810] The server integrates sentiment data and matching scores to propose the most satisfying plan to the user. The proposal is adjusted in real time, and the plan changes according to the user's emotional state. The output provides details of the adjusted plan.

[0811] Step 5:

[0812] The user reviews the details of the proposed plan on the device and approves or modifies it. The device receives the adjusted plan information as input and considers the options. The device records the user's selection and sends the final decision to the server.

[0813] 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.

[0814] 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.

[0815] 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.

[0816] 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.

[0817] 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.

[0818] 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.

[0819] 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.

[0820] 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.

[0821] 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."

[0822] 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.

[0823] 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.

[0824] 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.

[0825] 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.

[0826] 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.

[0827] 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.

[0828] 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.

[0829] 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.

[0830] 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.

[0831] 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.

[0832] 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.

[0833] 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.

[0834] The following is further disclosed regarding the embodiments described above.

[0835] (Claim 1)

[0836] A means of entering the rental conditions for the property,

[0837] A means of inputting the tenant's desired conditions,

[0838] Means for storing the rental conditions of the aforementioned property and the desired conditions of the aforementioned lessee in a database,

[0839] A means for analyzing the stored loan conditions and desired conditions and calculating combinations of high suitability,

[0840] A means of proposing a share house plan based on the calculated optimal matching results,

[0841] Means for generating a contract based on the aforementioned proposed conditions,

[0842] A system that includes this.

[0843] (Claim 2)

[0844] The system according to claim 1, further comprising means for notifying the user of the matching result.

[0845] (Claim 3)

[0846] The system according to claim 1, further comprising means for a user to approve or modify the proposed shared housing plan.

[0847] "Example 1"

[0848] (Claim 1)

[0849] Methods for entering property information,

[0850] A means of entering borrower conditions,

[0851] Means for storing the aforementioned property information and the aforementioned tenant conditions on a recording medium,

[0852] A means for analyzing the stored property information and tenant conditions and calculating the degree of suitability,

[0853] A means of proposing a rental plan based on the calculated optimal result,

[0854] Means for creating a contract document based on the aforementioned proposed conditions,

[0855] A means of communication for sending and receiving information,

[0856] A system that includes this.

[0857] (Claim 2)

[0858] The system according to claim 1, further comprising means for communicating the results to a user.

[0859] (Claim 3)

[0860] The system according to claim 1, further comprising means for a user to review or modify the proposed rental plan.

[0861] "Application Example 1"

[0862] (Claim 1)

[0863] A means of entering the rental conditions for the property,

[0864] A means of inputting the tenant's desired conditions,

[0865] Means for storing the rental conditions of the property and the desired conditions of the lessee in an information storage device,

[0866] A means for analyzing the stored loan conditions and desired conditions and calculating combinations of high suitability,

[0867] A means of proposing a cohabitation plan based on the calculated optimal matching results,

[0868] Means for generating a contract based on the aforementioned proposed conditions,

[0869] A means of visually displaying a property in a virtual reality environment,

[0870] A means to enable virtual property tours using smart devices,

[0871] A system that includes this.

[0872] (Claim 2)

[0873] The system according to claim 1, further comprising means for notifying the user of the matching results.

[0874] (Claim 3)

[0875] The system according to claim 1, further comprising means for a user to approve or modify the proposed cohabitation plan.

[0876] "Example 2 of combining an emotion engine"

[0877] (Claim 1)

[0878] A means of inputting attribute information,

[0879] A means for users to input their desired information,

[0880] Means for storing the attribute information and the desired information on a recording medium,

[0881] A means for analyzing the stored attribute information and desired information and deriving a combination with a high degree of fit,

[0882] A means of proposing a housing plan based on the derived suitable combination,

[0883] A means for analyzing user sentiment information regarding the proposed plan,

[0884] A means of adjusting the plan based on the analyzed emotional information,

[0885] Means for creating an agreement based on the aforementioned proposed conditions,

[0886] A system that includes this.

[0887] (Claim 2)

[0888] The system according to claim 1, further comprising means for notifying the user of the aforementioned compatible combination.

[0889] (Claim 3)

[0890] The system according to claim 1, further comprising means for a user to approve or modify the proposed housing plan.

[0891] "Application example 2 when combining with an emotional engine"

[0892] (Claim 1)

[0893] A means of entering the rental conditions for the property,

[0894] A means of inputting the tenant's desired conditions,

[0895] Means for storing the rental conditions of the aforementioned property and the desired conditions of the aforementioned lessee in a database,

[0896] A means for analyzing the stored loan conditions and desired conditions and calculating combinations of high suitability,

[0897] A means of proposing a plan based on the calculated optimal matching result,

[0898] Means for generating a contract based on the aforementioned proposed conditions,

[0899] A means of analyzing user emotional data and optimizing plans based on emotional states,

[0900] A means of adjusting product suggestions in real time according to emotions,

[0901] A system that includes this.

[0902] (Claim 2)

[0903] The system according to claim 1, further comprising means for notifying the user of the matching result.

[0904] (Claim 3)

[0905] The system according to claim 1, further comprising means for a user to approve or modify the proposed plan. [Explanation of Symbols]

[0906] 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. A means of entering the rental conditions for the property, A means of inputting the tenant's desired conditions, Means for storing the rental conditions of the aforementioned property and the desired conditions of the aforementioned lessee in a database, A means for analyzing the stored loan conditions and desired conditions and calculating combinations of high suitability, A means of proposing a share house plan based on the calculated optimal matching results, Means for generating a contract based on the aforementioned proposed conditions, A system that includes this.

2. The system according to claim 1, further comprising means for notifying the user of the matching result.

3. The system according to claim 1, further comprising means for a user to approve or modify the proposed shared housing plan.

Images