System

Abstract

A system is provided.SOLUTION: A system comprising: means for receiving information on a family structure, a place of work, a school, a daily activity range, a hobby, an important condition required for a property to be purchased, and a budget; means for analyzing the information and storing an analysis result in a database; means for recommending an optimum property and area by using a AI algorithm; and means for displaying a recommendation result.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] ---

[0005] It is a time-consuming and labor-intensive task for users to find a property that suits their circumstances and preferences. Conventional real estate search services require users to directly research property information and select a property that suits their preferences, making them inefficient and difficult to find the right property. Furthermore, when a corporation considers opening a new store, it must consider a wide range of factors, such as the ideal target population, trade area, and sales forecast, making the selection process difficult. For this reason, there is a demand for a service that can quickly and accurately respond to individual user needs and corporations' trade area analyses. [Means for solving the problem]

[0006] The present invention solves these problems with a system that includes: a means for receiving information entered by users regarding family composition, place of work, school, daily activities, hobbies, important requirements for a property to purchase, and budget; a means for analyzing the information and saving the analysis results in a database; a means for acquiring area and property data from the database; a means for recommending optimal properties and areas based on the acquired data using an AI algorithm; and a means for displaying the recommendation results on the user's device. This system allows users to quickly find the optimal property based on their needs, and also enables corporations to effectively select locations for their stores. Another feature is that the system continuously improves the quality of its service based on input data and past user feedback.

[0007] ---

[0008] "User information" refers to information regarding family composition, place of employment, school attended, daily range of activities, hobbies, important conditions required for a property to be purchased, and budget.

[0009] A "database" is an information management system that systematically organizes and stores information and allows it to be searched and extracted as needed.

[0010] "Regional data" refers to information about facilities and infrastructure in a particular area, such as schools, public transportation, medical facilities, parks, and shopping malls.

[0011] "Property data" refers to detailed information about the type of real estate property, floor plan, price, location, age, facilities, surrounding environment, etc.

[0012] An "AI algorithm" is a calculation procedure or method that uses artificial intelligence technology to analyze data and derive optimal recommendation results.

[0013] "Recommendation results" are a list of the most suitable properties and areas selected by AI based on the conditions entered by the user.

[0014] "User terminal" refers to the device used by a user to enter information and check the results, such as a PC, smartphone, or tablet.

[0015] "Web interface" refers to the operation screen on a web browser that allows users to use services via the Internet.

[0016] A "mobile application" is software that runs on a mobile device such as a smartphone or tablet, and allows users to enter information and check the results.

[0017] "Analysis" refers to processing received user information, converting and breaking it down into an understandable form, and is a preprocessing step for storing it in a database. [Brief explanation of the drawings]

[0018] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0019] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0020] First, the terms used in the following description will be explained.

[0021] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0022] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0023] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0024] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0025] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0026] [First embodiment]

[0027] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0028] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0029] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0030] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0031] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0032] 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 of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0033] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0034] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0035] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0036] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0037] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0038] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0039] ---

[0040] The real estate search system of the present invention uses an AI algorithm to recommend optimal properties and areas based on information entered by the user. The system aims to quickly and accurately meet the user's needs and resolve the problems associated with conventional real estate search services. Specific embodiments of the present invention are described below.

[0041] Entering user information

[0042] Users: Through a web interface or mobile application, users enter information about their family structure, place of employment, school attendance, daily activities, hobbies, important requirements for a property, and budget. This information accurately reflects the user's desires and lifestyle patterns, and is necessary to improve the system's recommendations.

[0043] Sending and Receiving Data

[0044] Terminal: After the user has finished entering the information, they click the "Search" button, which sends the entered data to the server. The data is encrypted using SSL / TLS protocol for security.

[0045] Server: Receives data sent from the device and begins analysis. The analysis process parses the received data in JSON or XML format and stores each item as a key in a database.

[0046] Obtaining area and property data

[0047] Server: Based on the user's criteria, the server accesses real estate and local information databases to retrieve local and property data that matches the user's input. This data includes property details (such as floor plan, price, and location) and information about local public facilities and infrastructure (such as schools, parks, and public transportation).

[0048] AI recommendation algorithm

[0049] Server: The server uses a sophisticated AI algorithm to analyze the acquired property and area data. The AI ​​algorithm works as follows:

[0050] 1. Score the degree of match between the user's conditions and the property.

[0051] 2. Evaluate the area's amenities, considering commute times, school districts, and access to public facilities.

[0052] 3. Look at past user feedback data and apply a model to predict satisfaction.

[0053] 4. Calculate an overall score and rank the best properties and neighborhoods.

[0054] Generating and displaying recommendations

[0055] Server: Based on the results of the AI ​​algorithm, the server generates a list of properties and areas to recommend to the user. The list of recommended properties is sorted by ranking and includes detailed information about each property (price, location, photos, etc.).

[0056] Terminal: Receives the recommendation results sent from the server and displays them in a user-friendly format, allowing users to intuitively identify the most suitable property.

[0057] Specific examples

[0058] Entering user information

[0059] User: Family composition: 4 people (2 adults, 2 children), Workplace: Shinjuku, School: Ueno Ward Daiichi Elementary School, Daily range of activities: Within Tokyo's 23 wards, Hobbies: Jogging in the park, Important conditions for a property to purchase: 4LDK, parking, pets allowed, Budget: 70 million yen.

[0060] Sending and Receiving Data

[0061] Terminal: Input data is sent to the server using the https protocol.

[0062] Server: Receives the data and parses information such as family size: 4 people, workplace: Shinjuku, school attended: Ueno Ward Daiichi Elementary School, and saves it in a database.

[0063] Obtaining area and property data

[0064] Server: Accesses the real estate database to retrieve property listings that match the criteria and information about the surrounding area (nearby parks, schools, public transportation, etc.).

[0065] AI recommendation algorithm

[0066] Server: Uses AI to score every property, evaluating factors like commute convenience, access to schools, proximity to parks, etc., and calculates an overall score that also takes into account past feedback data.

[0067] Generating and displaying recommendations

[0068] Server: Generates a list of properties A-E, including detailed information about each property (e.g. price, location, photos, etc.).

[0069] Device: Receives the recommendation results and displays them to the user, such as Property A: within budget, convenient commute, 10-minute walk to the park, Property B: within school district, pet-friendly, 15-minute walk to Shinjuku.

[0070] In this way, the real estate search system of the present invention can quickly and accurately recommend the most suitable properties and areas for users and corporations. This system meets the diverse needs of users and provides a stress-free property search.

[0071] The processing flow will be explained below.

[0072] ---

[0073] Step 1: Enter your user information

[0074] Users: Enter information about their family, place of employment, school attendance, daily activities, hobbies, key requirements for the property they are looking for, and budget via a web interface or mobile application.

[0075] Step 2: Submit input data

[0076] On the device: When the user clicks the "Search" button, all entered information is sent to the server, where the data is encrypted using the SSL / TLS protocol.

[0077] Step 3: Receiving and analyzing data

[0078] Server: Receives user input data sent from the device. The data is received in JSON or XML format and parsed. Then, the parsed data is saved in the database.

[0079] Step 4: Obtaining location and property data

[0080] Server: Based on the user's criteria, the server accesses real estate and local information databases to search for suitable areas and properties. From the databases, the server obtains detailed information about the properties (floor plan, price, location, etc.) and information about local public facilities and infrastructure (schools, parks, public transportation, etc.).

[0081] Step 5: Run the AI ​​recommendation algorithm

[0082] Server: Using high-performance AI algorithms, the acquired property and area data is analyzed. Specifically, the following processes are performed:

[0083] 1. Score the degree of match between the user's conditions and the property.

[0084] 2. Evaluate the area's amenities (commute time, school district, access to public facilities).

[0085] 3. Look at past user feedback data and apply a model to predict satisfaction.

[0086] 4. Calculate an overall score and rank the best properties and neighborhoods.

[0087] Step 6: Generate recommendations

[0088] Server: Based on the results of the AI ​​algorithm, the server generates a list of properties and areas to recommend to users, including detailed information about each property (price, location, photos, etc.).

[0089] Step 7: Submit and view your nominations

[0090] Server: The recommendation results are sent to the user's device in JSON or XML format, and the data is encrypted using SSL / TLS protocol.

[0091] Device: Receives the recommendations sent from the server and displays them in a user-friendly format, presented as a list with property images and details.

[0092] ---

[0093] Above, we have explained the specific steps of the program's processing flow. This system allows users to quickly and accurately find properties that meet their needs.

[0094] Example 1

[0095] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0096] Conventional real estate search systems have the problem of making it difficult to quickly and accurately find properties that meet users' requirements. Furthermore, they are unable to properly evaluate commute times, school districts, and access to public facilities, and are therefore unable to adequately meet the diverse needs of users. In particular, they lack a means to increase user satisfaction by utilizing AI algorithms.

[0097] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0098] In this invention, the server includes means for receiving information input by a user, means for analyzing the information and saving it in a database, means for encrypting the input information using the SSL / TLS protocol and sending it to the server, means for parsing the information in JSON or XML format, means for acquiring area and property data from the database, means for analyzing the acquired property data and area data using an AI algorithm based on the user's conditions and recommending optimal properties and areas, and means for displaying the recommendation results on the user's terminal, thereby making it possible to quickly and accurately recommend optimal properties and areas that meet the user's conditions.

[0099] "Information entered by the user" refers to information regarding family composition, place of employment, school attended, range of daily activities, hobbies, important conditions required for a property to be purchased, and budget.

[0100] The "means for analyzing information and storing it in a database" refers to the means for converting information received from a user into a structured data format and executing the process of storing it in a database.

[0101] "Means of encrypting using the SSL / TLS protocol and sending it to the server" is a mechanism for encrypting data entered by the user using the SSL / TLS protocol and transmitting it securely to the server.

[0102] The "means for parsing in JSON or XML format" is a process for converting received data into JSON or XML format and parsing it to obtain each item.

[0103] "Means for retrieving locality and property data" means a mechanism for querying and retrieving relevant data from real estate and locality databases based on stored user criteria.

[0104] "Means for analyzing property data and area data obtained using AI algorithms and recommending optimal properties and areas" refers to a means for using AI to evaluate data collected based on the user's conditions and carry out a process to select the optimal property and area.

[0105] "Means for displaying recommendation results on the user's device" refers to a mechanism for displaying recommendation results selected by AI on the user's device in an easy-to-understand format.

[0106] This invention is a real estate search system that uses an AI algorithm to recommend optimal properties and areas based on information entered by the user. The system aims to quickly and accurately meet user needs and resolve the problems of conventional real estate search services.

[0107] System Overview

[0108] The system includes the following elements:

[0109] 1. A means of receiving information entered by the user

[0110] 2. Means for analyzing the information and storing it in a database

[0111] 3. Encrypting data using the SSL / TLS protocol and sending it to the server

[0112] 4. Means for parsing said information in JSON or XML format

[0113] 5. How to obtain area and property data

[0114] 6. A method to use AI algorithms to analyze acquired property and area data and recommend the most suitable property and area

[0115] 7. Means for displaying the recommendation results on the user terminal

[0116] Hardware and Software Configuration

[0117] This embodiment uses a dedicated server and user terminals, which can be PCs, smartphones, tablets, etc. Users input information through a web interface or mobile application, which provides basic UI elements including a data entry form and a submit button.

[0118] The server is composed of a high-performance computer and is equipped with a communication module compatible with the SSL / TLS protocol, as well as a data analysis module, an AI algorithm module, and a database management system.

[0119] Specific processing of the program

[0120] Using a web interface or mobile application, users enter details such as family size, place of employment, school attendance, daily activities, hobbies, key requirements for a property, and budget. This information is entered in a form format, with required and optional fields clearly indicated. For example, a family size might be "4 people (2 adults, 2 children)," a place of employment might be "Shinjuku," and a school attendance might be "Ueno Ward First Elementary School."

[0121] When the user clicks the "Search" button, the device encrypts the entered data using the SSL / TLS protocol and sends it to the server. The server receives this data, parses it in JSON or XML format, and stores each item in a database. For example, data such as family size "4 people," workplace "Shinjuku," and school attended "Ueno Ward First Elementary School" are stored in the respective fields.

[0122] The server then accesses a real estate database and a local area information database to retrieve property and local area data that matches the user's criteria, including property details (such as floor plan, price, and location) and information about local public facilities and infrastructure (such as schools, parks, and public transportation).

[0123] The AI ​​algorithm scores properties based on user criteria, taking into account commute time, school districts, and access to public facilities, and then calculates an overall score by looking at past user feedback data and applying a model to predict satisfaction, then ranks the best properties and neighborhoods.

[0124] Finally, based on the results of the AI ​​algorithm, a list of properties and areas recommended to the user is generated and sent to the device along with detailed information (price, location, photos, etc.). The device receives these recommendations and displays them to the user in an intuitive format. For example, it might say, "Property A is within your budget, convenient for commuting, and a 10-minute walk to the park," or "Property B is within the school district, pet-friendly, and 15 minutes to Shinjuku."

[0125] Specific examples

[0126] As an example of user information input, let's assume a family of four (two adults, two children), workplace in Shinjuku, school attended by Ueno Ward Daiichi Elementary School, daily activities within Tokyo's 23 wards, hobby jogging in parks, requirements for a property to be purchased: 4LDK, with parking, pets allowed, budget of 70 million yen.

[0127] An example of a prompt sentence is, "Please recommend a property with a 4LDK, parking, pet-friendly, and a budget of 70 million yen for a family of four, who commute to Shinjuku and have a child attending Ueno Ward Daiichi Elementary School."

[0128] As described above, the real estate search system of the present invention can meet the diverse needs of users, quickly and accurately recommend the most suitable properties and areas, and provide a stress-free real estate search.

[0129] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0130] Step 1:

[0131] Using a web interface or mobile application, users enter details such as family composition, place of employment, school attendance, daily activities, hobbies, key requirements for a property to purchase, budget, etc. This information is entered in a form format, with required and optional fields clearly marked.

[0132] Input: User information such as family composition, place of employment, school attended, etc.

[0133] Output: A set of form data

[0134] Step 2:

[0135] When the user clicks the "Search" button, the terminal encrypts the data entered in the form using SSL / TLS protocol and sends it to the server, ensuring secure communication during this process.

[0136] Input: User details entered

[0137] Output: Encrypted data packet

[0138] Step 3:

[0139] The server receives the data sent from the device and decrypts it using SSL / TLS. It then parses the data in JSON or XML format, extracts each item, and stores it in a database. For example, information such as family size ("4 people"), workplace ("Shinjuku"), and school attended ("Ueno Ward First Elementary School") are stored in each field of the database.

[0140] Input: Encrypted data

[0141] Output: Structured data stored in a database

[0142] Step 4:

[0143] The server accesses the database and queries relevant neighborhood and property data based on the user's criteria. In this process, the data retrieved includes property details and local infrastructure information (schools, parks, public transportation, etc.).

[0144] Input: A query based on the user's criteria

[0145] Output: Property and area information as query results

[0146] Step 5:

[0147] The server uses AI algorithms to analyze the acquired property and area data, including the following specific operations:

[0148] 1. Scoring: Scoring the property's suitability to the user's criteria.

[0149] 2. Convenience evaluation: Evaluate by taking into account commute time, school districts, and access to public facilities.

[0150] 3. Feedback reference: Apply a model to predict satisfaction based on past user feedback data.

[0151] 4. Overall score calculation: Each evaluation item is combined to calculate an overall score and rank the property.

[0152] Input: Property data and area data

[0153] Output: Ranked property list

[0154] Step 6:

[0155] Based on the results of the AI ​​algorithm, the server generates a list of properties and areas recommended to the user, and adds detailed information (price, location, photos, etc.).

[0156] Input: A list of properties with an overall score

[0157] Output: Recommendation result list

[0158] Step 7:

[0159] The device receives the recommendation results sent from the server and displays them in a format that is easy for the user to understand. Specifically, information such as property price, location, and photos is displayed in a dashboard format, and the properties are ranked.

[0160] Input: Recommendation results received from the server

[0161] Output: The displayed property list and details

[0162] In this way, each step works in tandem, allowing users to quickly and accurately find the perfect property and area.

[0163] (Application example 1)

[0164] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0165] With conventional real estate search systems, users must search on websites or mobile applications to obtain property information, which requires processing large amounts of information at once. Furthermore, when viewing properties on-site, users must carry a separate device (such as a smartphone or tablet) to refer to information they have previously researched. Furthermore, there are limited ways to view detailed information about properties and surrounding facilities at a glance. There is a need for a system that can solve these problems and enable users to more intuitively obtain and view real estate information in real time.

[0166] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0167] In this invention, the server includes means for receiving information entered by the user regarding family composition, place of work, school, daily range of activities, hobbies, important conditions for a property to be purchased, and budget, means for analyzing the information and saving the analysis results in a database, means for acquiring area and property data from the database, means for recommending optimal properties and areas based on the acquired data using an AI algorithm, and means for displaying the recommendation results on the user's terminal and displaying area and property information using augmented reality via a smart device.This allows users to visually acquire and check property information and surrounding facility information on site in real time, enabling them to use real estate information more intuitively and efficiently.

[0168] "Information entered by the user" refers to information entered by the user regarding family composition, place of work, school attended, range of daily activities, hobbies, important conditions required for a property to be purchased, and budget.

[0169] "Analysis" refers to the process of analyzing the information received from users and using that information to identify the best properties and areas.

[0170] A "database" is a system for storing user information and area / property data, and retrieving it as needed.

[0171] "Retrieval" refers to pulling the required data from the database.

[0172] An "AI algorithm" is an algorithm that uses artificial intelligence to solve complex problems and recommend the best properties and areas.

[0173] "Recommendation" means presenting the best options based on the information entered by the user.

[0174] "Display" means providing information visually to a user terminal or smart device.

[0175] "Smart devices" refers to high-function devices such as smart glasses and head-mounted displays.

[0176] "Augmented reality" is a technology that displays digital information overlaid on real-world scenery.

[0177] The system of the present invention recommends optimal real estate properties based on information entered by the user, and allows the user to obtain and confirm that information in real time. The system includes a server, a user terminal, and a smart device.

[0178] 1. Input and Receipt of User Information

[0179] Server: Using a web interface, mobile application, or smart device, the user enters information about their family, place of employment, school attendance, daily activities, hobbies, key property requirements, and budget. This information is provided via voice or text input.

[0180] 2. Data submission and analysis

[0181] On the device: The information entered by the user is sent to the server in JSON format, encrypted using the HTTPS protocol.

[0182] Server: Analyzes the received data and stores it in a database, which contains real estate property data and local information data (e.g., MySQL).

[0183] 3. Obtaining area and property data

[0184] Server: Accesses real estate databases and local information databases based on the user's requirements and retrieves the required data.

[0185] 4. AI-based data analysis and recommendations

[0186] Server: Runs AI algorithms using TensorFlow and Scikit-learn to analyze the acquired property and neighborhood data, taking into account commute times, school districts, access to public facilities, and past user feedback.

[0187] 5. Generating and Displaying Recommendations

[0188] Server: Based on the analysis results of the AI ​​algorithm, the server generates a ranking of the most suitable properties and areas, and sends the recommendation results to the user's terminal or smart device.

[0189] 6. Display and operation using smart devices

[0190] Terminal: Smart devices (e.g., smart glasses or head-mounted displays) use Unity or ARKit to display property information using augmented reality (AR). Users can use the device on-site to visually check property information and surrounding facilities in real time.

[0191] Examples of concrete examples and prompts

[0192] Example: While a user is walking around Shinjuku Station, the smart glasses display information such as "Shinjuku Building 5, 4LDK, 68 million yen, 5-minute walk from the station, 3-minute walk to the supermarket." If the user uses the device's microphone to ask, "Where is the nearest park from here?", the location of the park will be displayed in the user's field of vision.

[0193] Example prompt sentence:

[0194] User input prompt: "Find properties within 15 minutes of Shinjuku Station, suitable for families, and budgets under 70 million yen."

[0195] Prompt for generative AI model: "Based on the user's input, please have the algorithm recommend the best family-friendly properties in the Shinjuku area."

[0196] This makes it possible to specifically implement the form of the invention, and allows users to intuitively use real estate information on-site.

[0197] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0198] Step 1:

[0199] Enter and submit user information

[0200] Users: Through a web interface, mobile application, or smart device, they enter information about their family composition, place of employment, school attendance, daily activities, hobbies, key requirements for a property, and budget.

[0201] Input: Family composition, place of work, school, daily activities, hobbies, important conditions, budget

[0202] Output: User information in JSON format

[0203] Specific operation: The user provides information by voice or touch input. The device converts the input data into JSON format and sends it to the server via HTTPS protocol.

[0204] Step 2:

[0205] Data reception and analysis

[0206] Server: Receives JSON formatted data sent by the user and parses it using a JSON parser.

[0207] Input: User information in JSON format

[0208] Output: Structured user information (e.g., information as variables and fields)

[0209] What happens: The server receives the HTTPS request, decodes the JSON data, converts it into a structured format, and saves it in the database.

[0210] Step 3:

[0211] Obtaining area and property data

[0212] Server: Based on the user information, accesses the real estate database and area information database to retrieve the appropriate area and property data.

[0213] Input: Structured user information

[0214] Output: Area and property data

[0215] What it does: Executes a database query to retrieve properties and locality information that match the user's criteria. For example, it uses a MySQL query to pull the required information from the database.

[0216] Step 4:

[0217] AI-based data analysis and recommendations

[0218] Server: Using TensorFlow and Scikit-learn, the acquired property data and area data are analyzed using AI algorithms.

[0219] Input: Area and property data

[0220] Output: Recommended property list (with scores)

[0221] How it works: Data is fed into an AI model, which scores properties based on factors such as commute time, school district, access to public facilities, and past user feedback, generating a ranked list of recommended properties.

[0222] Step 5:

[0223] Generating and sending recommendations

[0224] Server: Generates a list of recommended properties and sends it to the user's terminal or smart device.

[0225] Input: Recommended property list

[0226] Output: Recommendation results in JSON format

[0227] Specific operation: Convert the recommended property list into JSON format and send it to the user terminal or smart device using the HTTPS protocol.

[0228] Step 6:

[0229] Display and operation using a smart device

[0230] Device: Uses the AR function of your smart device to display recommended property information and local information.

[0231] Input: Recommendation results in JSON format

[0232] Output: Property information displayed in AR

[0233] How it works: Using Unity and ARKit, property information is overlaid on the smart device's camera view. Users can view the information in real time through their device, and additional information is displayed in response to input prompts.

[0234] This will enable users to more intuitively obtain and check real estate property information using their smart devices, enabling them to make real-time decisions.

[0235] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0236] ---

[0237] The real estate search system of the present invention uses an AI algorithm to recommend optimal properties and areas based on information entered by a user, and also combines an emotion engine that recognizes the user's emotions to provide more personalized recommendations. Specific embodiments of the present invention are described below.

[0238] Entering user information

[0239] Users: Through a web interface or mobile application, users enter information about their family structure, place of employment, school attendance, daily activities, hobbies, important requirements for a property, and budget. This information is important for improving the system's recommendation accuracy, as it accurately reflects the user's desires and lifestyle patterns.

[0240] Emotion recognition by emotion engine

[0241] Emotion engine: When a user inputs information, it uses a facial recognition camera and a voice analysis microphone to recognize the user's emotions in real time. It analyzes the input data, facial expressions during operation, and tone of voice to determine the user's emotional state (e.g., satisfaction, dissatisfaction, expectation, excitement, etc.).

[0242] Sending and Receiving Data

[0243] On the device: When the user clicks the "Search" button, the entered data and the emotion data from the emotion engine are sent to the server, encrypted using the SSL / TLS protocol.

[0244] Server: Receives data sent from the device and begins analysis. The analysis process parses the received data in JSON or XML format and stores each item as a key in a database.

[0245] Obtaining area and property data

[0246] Server: Based on the user's criteria and emotion data, the server accesses the real estate database and local information database to search for suitable areas and properties. From the database, the server obtains detailed information about the property (floor plan, price, location, etc.) and information about local public facilities and infrastructure (schools, parks, public transportation, etc.).

[0247] AI recommendation algorithm

[0248] Server: The server uses sophisticated AI algorithms to analyze the acquired property and area data. The AI ​​algorithms work as follows:

[0249] 1. Score the degree of match between the user's conditions and the property.

[0250] 2. Evaluate the area's amenities, considering commute times, school districts, and access to public facilities.

[0251] 3. Emotional data obtained from the emotion engine is added to adjust the recommendation results based on the user's emotional state.

[0252] 4. Look at past user feedback data and apply a model to predict satisfaction.

[0253] 5. Calculate an overall score and rank the best properties and neighborhoods.

[0254] Generating and displaying recommendations

[0255] Server: Based on the results of the AI ​​algorithm, the server generates a list of properties and areas to recommend to users. The recommended properties are sorted by ranking and include detailed information about each property (price, location, photos, etc.).

[0256] Terminal: Receives the recommendation results sent from the server and displays them in a user-friendly format, allowing users to intuitively identify the most suitable property.

[0257] Specific examples

[0258] Entering user information

[0259] User: Family composition: 4 people (2 adults, 2 children), Workplace: Shinjuku, School: Ueno Ward Daiichi Elementary School, Daily range of activities: Within Tokyo's 23 wards, Hobbies: Jogging in the park, Important conditions for a property to purchase: 4LDK, parking, pets allowed, Budget: 70 million yen.

[0260] Emotion recognition by emotion engine

[0261] Emotion engine: Analyzes the user's facial expressions and tone of voice when inputting information to identify the user's current emotional state. For example, if the user looks anxious, the system will capture that information as emotion data.

[0262] Sending and Receiving Data

[0263] Device: When the user presses the "Search" button, the input data and emotion data are sent to the server using the https protocol.

[0264] Server: Receives the data, parses the user's family size: 4 people, workplace: Shinjuku, school attended: Ueno Ward Daiichi Elementary School, and emotional state (e.g., anxiety) and stores it in a database.

[0265] Obtaining area and property data

[0266] Server: Accesses the real estate database to retrieve property listings that match the criteria and information about the surrounding area (e.g., nearby parks, schools, public transportation, etc.).

[0267] AI recommendation algorithm

[0268] Server: Using AI, the server scores all properties, evaluating factors such as commute convenience, access to schools, and proximity to parks suitable for jogging. It also takes into account sentiment data, prioritizing safer neighborhoods to alleviate anxiety. It also takes into account past feedback data to calculate an overall score.

[0269] Generating and displaying recommendations

[0270] Server: Generates a list of properties A-E, including detailed information about each property (e.g. price, location, photos, etc.).

[0271] Device: Receives the recommendation results and displays them to the user, such as Property A: within budget, convenient commute, 10-minute walk to the park, Property B: within school district, pet-friendly, 15-minute walk to Shinjuku.

[0272] In this way, the real estate search system of the present invention can provide more personalized property recommendations that reflect the user's needs and emotional state, allowing users to find the property that best suits them without stress and improving the quality of service.

[0273] The processing flow will be explained below.

[0274] ---

[0275] Step 1:

[0276] User: Using the web interface or mobile application, user enters information about family size, workplace, school commute, daily activities, hobbies, important requirements for a property, and budget. For example, user enters: Family size: 4 people, Workplace: Shinjuku, School commute: Ueno Ward Daiichi Elementary School, Hobbies: Jogging, Requirements for property purchase: 4LDK, with parking, pets allowed, Budget: 70 million yen.

[0277] Step 2:

[0278] Emotion engine: When a user enters information, the camera and microphone are used to analyze the user's facial expressions and tone of voice in real time. It determines whether the user is expressing emotions such as satisfaction, dissatisfaction, or expectation, and captures this as emotional data. For example, if the user shows a smile or a relieved expression while entering information, that emotional data is collected.

[0279] Step 3:

[0280] On the device: When the user clicks the "Search" button, the entered information and the emotion data generated by the emotion engine are sent to the server. This transmission is encrypted using the SSL / TLS protocol.

[0281] Step 4:

[0282] Server: Receives data sent from the device. The received data is parsed in JSON or XML format, and the data such as family structure, place of employment, school attended, hobbies, purchasing conditions, budget, and emotional data is analyzed and stored in a database.

[0283] Step 5:

[0284] Server: Accesses real estate and local information databases to retrieve relevant local and property data that meet the user's criteria. For example, searches for property listings with 4 bedrooms, kitchens, parking spaces, and pet-friendly rooms, as well as information on parks, schools, and public transportation.

[0285] Step 6:

[0286] Server: Uses sophisticated AI algorithms to analyze acquired property and area data. Specific processes include:

[0287] 1. Score the degree of match between the user's conditions and the property.

[0288] 2. Evaluate the area's amenities, taking into account commute times and access to public facilities.

[0289] 3. Taking into account emotional data, adjustments are made, such as prioritizing properties that users feel more comfortable with.

[0290] 4. Look at past user feedback data and apply a model to predict satisfaction.

[0291] 5. Calculate an overall score and rank the best properties and neighborhoods.

[0292] Step 7:

[0293] Server: Based on the results of the AI ​​algorithm, the server generates a list of properties and areas to recommend to the user, including detailed information about each property (price, location, photos, etc.).

[0294] Step 8:

[0295] Server: The recommendation results are sent to the user's device in JSON or XML format, and the data is encrypted using the SSL / TLS protocol.

[0296] Step 9:

[0297] Device: Receives the recommendation results sent from the server and displays them in an easy-to-understand manner to the user. For example, Property A: Within budget, easy commute, 10 minutes walk to the park; Property B: Within school district, pets allowed, 15 minutes to Shinjuku.

[0298] ---

[0299] We have explained in detail the processing steps of a real estate search system that combines an emotion engine. This system allows users to receive more personalized property recommendations based on their emotional state.

[0300] Example 2

[0301] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0302] Conventional real estate search systems recommend properties based only on the user's simple criteria (such as family composition and budget), making it difficult to provide personalized recommendations that reflect the user's emotions and lifestyle. Furthermore, general AI algorithms do not take the user's emotional state into account, making it difficult to find properties that will satisfy them. This makes it difficult for users to quickly and efficiently find the perfect property, often resulting in frustration.

[0303] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes: means for receiving information input by the user regarding family composition, place of work, educational institutions, daily range of activities, hobbies, important conditions for the purchase request, and budget; means for analyzing the information and saving the analysis results in a database; means for acquiring area and property data from the database; means for acquiring user emotion data through facial expression recognition and voice analysis; means for using the emotion data to recommend optimal properties and areas using an AI algorithm; and means for displaying the recommendation results on the user terminal. This enables more personalized real estate property recommendations that reflect the user's emotional state and individual lifestyle.

[0304] "User Information" means information entered by a User through the web interface or mobile application, such as family composition, place of employment, educational institution, daily activities, hobbies, important purchase requirements, and budget.

[0305] "Analysis" refers to storing user information in a database, extracting the necessary items, and processing them.

[0306] A "database" is a collection of data used to store and manage real estate property information, local information, user information, etc.

[0307] "Local data" refers to information about public facilities, transportation, infrastructure, etc. in a particular local area.

[0308] "Property data" refers to detailed information about a real estate property, including, for example, floor plan, price, location, facilities, etc.

[0309] "Facial expression recognition" is a technology that uses a camera to analyze a user's facial expressions in real time and identify their emotional state.

[0310] "Voice analysis" is a technology that analyzes a user's tone of voice and speaking style in real time through a microphone to identify their emotional state.

[0311] "Emotional data" is information about the user's emotional state obtained through facial expression recognition and voice analysis.

[0312] An "AI algorithm" is a mathematical method that uses machine learning and data analysis to process input data and recommend the most suitable properties and areas.

[0313] "Recommendation Results" refers to a list of properties and areas suitable for the user, derived through analysis by an AI algorithm.

[0314] This real estate search system recommends the best properties and areas based on information entered by the user and sentiment data collected in real time. The system is implemented using the following hardware and software:

[0315] Hardware and software used

[0316] 1. Device: A device such as a PC, smartphone, or tablet with a web browser is used. Users enter information using these devices.

[0317] 2. Facial Recognition Camera: Uses a camera to analyze the user's facial expressions in real time.

[0318] 3. Voice Analysis Microphone: Uses a microphone to analyze the user's tone of voice and speaking style.

[0319] 4. Server: A server is used to run high-performance AI algorithms. This server also handles database management and calculations.

[0320] Specific operation of the system

[0321] Entering user information

[0322] Users enter information about their family, place of employment, educational institution, daily activities, hobbies, important purchase requirements, and budget through a web interface or mobile application. This information is important for improving the system's recommendation accuracy. For example, suppose a user enters the following information:

[0323] Family size: 4 people (2 adults, 2 children)

[0324] Location: Shinjuku

[0325] Educational institution: Ueno Ward Daiichi Elementary School

[0326] Daily range of activities: Tokyo's 23 wards

[0327] Hobbies: Jogging in the park

[0328] Important conditions for purchase: 4LDK, parking space, pets allowed

[0329] Budget: 70 million yen

[0330] Emotion recognition by emotion engine

[0331] The emotion engine uses a facial recognition camera and a voice analysis microphone to analyze the user's emotions in real time while they are entering information. For example, if a user looks anxious, the emotional state will be recorded as "anxiety." This emotional data is reflected in the recommendation algorithm to increase user satisfaction.

[0332] Sending and Receiving Data

[0333] When a user clicks the "Search" button, the entered information and emotion data are encrypted using the SSL / TLS protocol and sent to the server, which then analyzes the data and stores it in a database using each item as a key.

[0334] Obtaining area and property data

[0335] The server accesses the database and searches for suitable areas and properties based on the user's criteria and emotional data. Property details (e.g., floor plan, price, location, etc.) are obtained from the real estate database, and information on public facilities and infrastructure is obtained from the local information database.

[0336] AI recommendation algorithm

[0337] The server analyzes the acquired data using a high-performance AI algorithm and performs the following processes:

[0338] 1. Score the degree of match between the input conditions and the property.

[0339] 2. Evaluate the area's amenities, considering factors such as commute time, access to schools, and proximity to parks suitable for jogging.

[0340] 3. Use emotional data to tailor recommendation results based on the user's emotional state.

[0341] 4. Look at past user feedback data and apply a model to predict satisfaction.

[0342] 5. Calculate an overall score and display the best properties and areas in ranked order.

[0343] Generating and displaying recommendations

[0344] Based on the results of the AI ​​algorithm, the server generates a list of properties and areas to recommend to the user. The recommendations are ranked and include detailed information about each property (e.g., price, location, photos, etc.). The device receives this information and displays it in a user-friendly format.

[0345] Examples and prompts

[0346] Specific examples

[0347] Family size: 4 people (2 adults, 2 children)

[0348] Location: Shinjuku

[0349] Educational institution: Ueno Ward Daiichi Elementary School

[0350] Daily range of activities: Tokyo's 23 wards

[0351] Hobbies: Jogging in the park

[0352] Important conditions for purchase: 4LDK, parking space, pets allowed

[0353] Budget: 70 million yen

[0354] Prompt Sentence Examples

[0355] "Enter your family composition, place of employment, school, and other requirements. We will recommend the ideal property for you."

[0356] "We take into account emotional data to recommend properties that users can feel comfortable with."

[0357] "We will design a system that uses user criteria and emotional data to recommend the best real estate properties."

[0358] As a result, this real estate search system is able to provide personalized recommendations that reflect the user's feelings and requirements.

[0359] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0360] Step 1: Enter your user information

[0361] Users open a web interface or mobile application and enter information about their family composition, workplace, educational institution, daily activities, hobbies, key purchase criteria, and budget. Examples of input information include "Family composition: 4 people (2 adults, 2 children)," "Workplace: Shinjuku," "Educational institution: Ueno Ward Daiichi Elementary School," "Daily activities: Within Tokyo's 23 wards," "Hobbies: Jogging in the park," "Key purchase criteria: 4LDK, parking, pets allowed," and "Budget: 70 million yen." This information serves as input data and is important for specifically reflecting the user's needs and criteria.

[0362] Step 2: Emotion recognition by the emotion engine

[0363] The server uses a facial expression recognition camera and a voice analysis microphone to analyze the user's facial expressions and voice as they are input. The facial expression recognition camera captures the subtle movements of the user's face, and the voice analysis microphone analyzes the user's tone of voice and speaking style. For example, if the user has an anxious expression or a low tone of voice, the emotional state of "anxiety" is detected. This emotional data is used as input data for the next processing step.

[0364] Step 3: Sending data

[0365] When the user clicks the "Search" button, the device encrypts the information entered by the user and the emotion data obtained by the emotion engine using the SSL / TLS protocol and sends it to the server. This sent data becomes the input data for the server.

[0366] Step 4: Receiving and storing data

[0367] The server receives the data sent from the device and parses it in JSON or XML format. The received data includes the user's family structure, place of work, educational institution, daily range of activities, hobbies, important conditions for desired purchases, budget, and emotional state. This data is analyzed and each item is stored as a key in the database. The analysis results are stored in the database and used for search processing in the next step.

[0368] Step 5: Obtaining location and property data

[0369] The server accesses the real estate database and local information database based on the user's criteria and emotion data stored in the database. Specifically, it extracts property listings that match the user's filter criteria and obtains information about the surrounding area (nearby parks, schools, public transportation, etc.). This obtained data becomes input data for the AI ​​algorithm in the next step.

[0370] Step 6: AI recommendation algorithm

[0371] The server uses a high-performance AI algorithm to analyze the input data and perform the following processes: First, it scores the degree of match between the user's input criteria and the property. It also evaluates the convenience of the area, taking into account factors such as commute time, access to schools, and proximity to public facilities. It also uses emotional data to adjust the recommendation results based on the user's emotional state. It references past user feedback data and applies a model to predict satisfaction. By processing and calculating these data, it calculates an overall score and ranks the properties.

[0372] Step 7: Generate and display recommendations

[0373] Based on the results of the AI ​​algorithm, the server generates a list of properties and areas to recommend to the user. The generated list also includes detailed property information (price, location, photos, etc.). These recommendation results become the output data from the server. The device receives the recommended results and displays them in a format that is easy for the user to understand. Based on this information, the user can intuitively and efficiently select the most suitable property.

[0374] Detailed processing is carried out at each step, resulting in personalized property recommendations that reflect the user's needs and emotions.

[0375] (Application example 2)

[0376] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0377] While conventional real estate search systems can recommend properties based on user input, they are limited in providing personalized recommendations that take into account the user's emotional state. Furthermore, viewing property details requires a site visit to get a real feel for the property, which is time-consuming and labor-intensive for users. The present invention aims to solve these problems and provide a real estate search system that enables more personalized property recommendations and property viewing using virtual reality technology.

[0378] The specification processing by the specification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving information and emotional state input by the user regarding family composition, place of work, school, daily range of activities, hobbies, important conditions for a property to be purchased, and budget; means for analyzing the information and emotional state and saving the analysis results in a database; means for acquiring area and property data from the database; means for recommending optimal properties and areas based on the acquired data using an AI algorithm; and means for displaying the recommendation results on the user terminal and allowing the user to view properties using virtual reality technology. This allows for more personalized property recommendations based on the user's emotional state, and allows the user to view properties using virtual reality technology through a smartphone application.

[0379] "User information" refers to information regarding family composition, place of employment, school attended, daily range of activities, hobbies, important conditions required for a property to be purchased, and budget.

[0380] "Emotional state" is data that represents the user's emotions, and is information obtained by analyzing the user's facial expressions, tone of voice, etc.

[0381] "Analysis" is the process of processing user information and emotional states as data to understand their content.

[0382] "Database" means a digital repository for organizing and storing information, including user information, emotional states, local area information, and property information.

[0383] "Local and Property Data" means detailed information about the recommended real estate and its surrounding area.

[0384] "AI Algorithm" refers to an artificial intelligence calculation method used to recommend optimal properties and areas based on user information and emotional state.

[0385] "Virtual reality technology" is a technology that provides users with a sense of realism through a computer-generated virtual space.

[0386] "Recommendation results" are a list of the best properties and areas selected by an AI algorithm.

[0387] The real estate search system of the present invention is a system that uses an AI algorithm to recommend optimal properties and areas based on the user's input information and emotional state, and allows the user to view properties using virtual reality technology. Specific embodiments of the system are described below.

[0388] 1. Entering user information and acquiring emotional state

[0389] Users enter information about their family, place of employment, school, daily activities, hobbies, important requirements for a property to purchase, and budget through a smartphone application. The smartphone's front camera and microphone are used to analyze the user's facial expressions and tone of voice. Specifically, OpenCV is used for facial recognition, and the Google Cloud Speech-to-Text API is used to analyze emotions from voice.

[0390] Examples:

[0391] A user enters information into an input field within an application.

[0392] Example prompt: "Please tell us about your family structure, place of work, daily activities, hobbies, property requirements, and budget."

[0393] 2. Data transmission to the server and analysis

[0394] The device sends the entered user information and analyzed emotional state data to the server using SSL / TLS encryption, which then analyzes the data and stores it in a database. This process is performed using Python and Django.

[0395] 3. Obtaining area and property data

[0396] The server accesses a real estate database to retrieve property and area information that matches the user's criteria, using a relational database such as PostgreSQL.

[0397] 4. Recommendation of optimal properties using AI

[0398] The server uses AI algorithms using TensorFlow or PyTorch to score and recommend the best properties and neighborhoods based on user information and emotional state, taking into account commute times, school districts, access to public facilities, past user feedback, and emotional data.

[0399] 5. Recommendation results and virtual reality property viewing

[0400] The server generates recommendations and sends them to a smartphone application that allows users to view properties using virtual reality technology. Within the app, users can view the list of recommended properties and view each property in detail in VR mode.

[0401] Examples:

[0402] Recommended properties will be displayed on your smartphone.

[0403] Example prompt: "The following properties are recommended for you. Property A: Within your budget, easy commute, 10 minutes' walk to the park. Property B: Within the school district, pet-friendly, 15 minutes' walk to Shinjuku."

[0404] This system not only enables personalized property recommendations that reflect the user's emotional state, but also allows users to intuitively experience detailed property information using virtual reality, which is expected to significantly reduce the time and effort required when selecting a property.

[0405] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0406] Step 1:

[0407] Users use a smartphone application to input information about their family structure, place of work, school, daily range of activities, hobbies, important requirements for a property to purchase, and budget. At this time, the smartphone's front camera and microphone are used to analyze the user's facial expressions and tone of voice. Text information is entered as input data, and audio and video data acquired from the camera and microphone are used for emotion analysis. The input data consists of the user's desired conditions (text) and emotional data (video and audio). The output data after analysis is the user's information and emotional state.

[0408] Step 2:

[0409] The terminal transmits the information entered by the user and the analyzed emotion data to the server using the SSL / TLS encryption protocol. The specific process involves packaging and encrypting the user information and emotion data and transmitting them to the server. The input data is the user's desired conditions and emotion data, and the output data is the encrypted transmission data.

[0410] Step 3:

[0411] The server analyzes the received user information and emotion data and stores them in a database. This process uses Django and involves parsing the data and storing it in the database. The input data is encrypted user information and emotion data, and the output data is the user information and emotion data stored in the database.

[0412] Step 4:

[0413] The server retrieves area and property data from a real estate database. PostgreSQL is used as the database. A query is executed for properties that match the criteria, and relevant property information is retrieved. The input data is the user's desired criteria, and the output data is a list of candidate property data.

[0414] Step 5:

[0415] The server uses an AI algorithm using TensorFlow or PyTorch to score the best properties and areas based on the user's desired conditions and emotional state. The algorithm includes commute time, school district, access to public facilities, past user feedback, and emotional data. The input data is user information, emotional data, and property data, and the output data is a list of scored properties.

[0416] Step 6:

[0417] The server generates recommendation results and sends them to the user's device. The recommendation results include a list of scored properties with detailed information. The input data is the property scoring results, and the output data is the list of recommendation results.

[0418] Step 7:

[0419] Users receive recommendation results on their smartphone application and can view properties using virtual reality technology. Users can check the list of recommended properties within the app and view each property in detail in VR mode. The input data is the recommendation results, and the output data is property information displayed in virtual reality.

[0420] Through the above processing steps, users can receive personalized property recommendations that take into account their emotional state and can experience detailed property information using virtual reality technology.

[0421] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0422] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0423] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0424] [Second embodiment]

[0425] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0426] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0427] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0428] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0429] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0430] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0431] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0432] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0433] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific 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.

[0434] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0435] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0436] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. 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."

[0437] ---

[0438] The real estate search system of the present invention uses an AI algorithm to recommend optimal properties and areas based on information entered by the user. The system aims to quickly and accurately meet the user's needs and resolve the problems associated with conventional real estate search services. Specific embodiments of the present invention are described below.

[0439] Entering user information

[0440] Users: Through a web interface or mobile application, users enter information about their family structure, place of employment, school attendance, daily activities, hobbies, important requirements for a property, and budget. This information accurately reflects the user's desires and lifestyle patterns, and is necessary to improve the system's recommendations.

[0441] Sending and Receiving Data

[0442] Terminal: After the user has finished entering the information, they click the "Search" button, which sends the entered data to the server. The data is encrypted using SSL / TLS protocol for security.

[0443] Server: Receives data sent from the device and begins analysis. The analysis process parses the received data in JSON or XML format and stores each item as a key in a database.

[0444] Obtaining area and property data

[0445] Server: Based on the user's criteria, the server accesses real estate and local information databases to retrieve local and property data that matches the user's input. This data includes property details (such as floor plan, price, and location) and information about local public facilities and infrastructure (such as schools, parks, and public transportation).

[0446] AI recommendation algorithm

[0447] Server: The server uses a sophisticated AI algorithm to analyze the acquired property and area data. The AI ​​algorithm works as follows:

[0448] 1. Score the degree of match between the user's conditions and the property.

[0449] 2. Evaluate the area's amenities, considering commute times, school districts, and access to public facilities.

[0450] 3. Look at past user feedback data and apply a model to predict satisfaction.

[0451] 4. Calculate an overall score and rank the best properties and neighborhoods.

[0452] Generating and displaying recommendations

[0453] Server: Based on the results of the AI ​​algorithm, the server generates a list of properties and areas to recommend to the user. The list of recommended properties is sorted by ranking and includes detailed information about each property (price, location, photos, etc.).

[0454] Terminal: Receives the recommendation results sent from the server and displays them in a user-friendly format, allowing users to intuitively identify the most suitable property.

[0455] Specific examples

[0456] Entering user information

[0457] User: Family composition: 4 people (2 adults, 2 children), Workplace: Shinjuku, School: Ueno Ward Daiichi Elementary School, Daily range of activities: Within Tokyo's 23 wards, Hobbies: Jogging in the park, Important conditions for a property to purchase: 4LDK, parking, pets allowed, Budget: 70 million yen.

[0458] Sending and Receiving Data

[0459] Terminal: Input data is sent to the server using the https protocol.

[0460] Server: Receives the data and parses information such as family size: 4 people, workplace: Shinjuku, school attended: Ueno Ward Daiichi Elementary School, and saves it in a database.

[0461] Obtaining area and property data

[0462] Server: Accesses the real estate database to retrieve property listings that match the criteria and information about the surrounding area (nearby parks, schools, public transportation, etc.).

[0463] AI recommendation algorithm

[0464] Server: Uses AI to score every property, evaluating factors like commute convenience, access to schools, proximity to parks, etc., and calculates an overall score that also takes into account past feedback data.

[0465] Generating and displaying recommendations

[0466] Server: Generates a list of properties A-E, including detailed information about each property (e.g. price, location, photos, etc.).

[0467] Device: Receives the recommendation results and displays them to the user, such as Property A: within budget, convenient commute, 10-minute walk to the park, Property B: within school district, pet-friendly, 15-minute walk to Shinjuku.

[0468] In this way, the real estate search system of the present invention can quickly and accurately recommend the most suitable properties and areas for users and corporations. This system meets the diverse needs of users and provides a stress-free property search.

[0469] The processing flow will be explained below.

[0470] ---

[0471] Step 1: Enter your user information

[0472] Users: Enter information about their family, place of employment, school attendance, daily activities, hobbies, key requirements for the property they are looking for, and budget via a web interface or mobile application.

[0473] Step 2: Submit input data

[0474] On the device: When the user clicks the "Search" button, all entered information is sent to the server, where the data is encrypted using the SSL / TLS protocol.

[0475] Step 3: Receiving and analyzing data

[0476] Server: Receives user input data sent from the device. The data is received in JSON or XML format and parsed. Then, the parsed data is saved in the database.

[0477] Step 4: Obtaining location and property data

[0478] Server: Based on the user's criteria, the server accesses real estate and local information databases to search for suitable areas and properties. From the databases, the server obtains detailed information about the properties (floor plan, price, location, etc.) and information about local public facilities and infrastructure (schools, parks, public transportation, etc.).

[0479] Step 5: Run the AI ​​recommendation algorithm

[0480] Server: Using high-performance AI algorithms, the acquired property and area data is analyzed. Specifically, the following processes are performed:

[0481] 1. Score the degree of match between the user's conditions and the property.

[0482] 2. Evaluate the area's amenities (commute time, school district, access to public facilities).

[0483] 3. Look at past user feedback data and apply a model to predict satisfaction.

[0484] 4. Calculate an overall score and rank the best properties and neighborhoods.

[0485] Step 6: Generate recommendations

[0486] Server: Based on the results of the AI ​​algorithm, the server generates a list of properties and areas to recommend to users, including detailed information about each property (price, location, photos, etc.).

[0487] Step 7: Submit and view your nominations

[0488] Server: The recommendation results are sent to the user's device in JSON or XML format, and the data is encrypted using SSL / TLS protocol.

[0489] Device: Receives the recommendations sent from the server and displays them in a user-friendly format, presented as a list with property images and details.

[0490] ---

[0491] Above, we have explained the specific steps of the program's processing flow. This system allows users to quickly and accurately find properties that meet their needs.

[0492] Example 1

[0493] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0494] Conventional real estate search systems have the problem of making it difficult to quickly and accurately find properties that meet users' requirements. Furthermore, they are unable to properly evaluate commute times, school districts, and access to public facilities, and are therefore unable to adequately meet the diverse needs of users. In particular, they lack a means to increase user satisfaction by utilizing AI algorithms.

[0495] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0496] In this invention, the server includes means for receiving information input by a user, means for analyzing the information and saving it in a database, means for encrypting the input information using the SSL / TLS protocol and sending it to the server, means for parsing the information in JSON or XML format, means for acquiring area and property data from the database, means for analyzing the acquired property data and area data using an AI algorithm based on the user's conditions and recommending optimal properties and areas, and means for displaying the recommendation results on the user's terminal, thereby making it possible to quickly and accurately recommend optimal properties and areas that meet the user's conditions.

[0497] "Information entered by the user" refers to information regarding family composition, place of employment, school attended, range of daily activities, hobbies, important conditions required for a property to be purchased, and budget.

[0498] The "means for analyzing information and storing it in a database" refers to the means for converting information received from a user into a structured data format and executing the process of storing it in a database.

[0499] "Means of encrypting using the SSL / TLS protocol and sending it to the server" is a mechanism for encrypting data entered by the user using the SSL / TLS protocol and transmitting it securely to the server.

[0500] The "means for parsing in JSON or XML format" is a process for converting received data into JSON or XML format and parsing it to obtain each item.

[0501] "Means for retrieving locality and property data" means a mechanism for querying and retrieving relevant data from real estate and locality databases based on stored user criteria.

[0502] "Means for analyzing property data and area data obtained using AI algorithms and recommending optimal properties and areas" refers to a means for using AI to evaluate data collected based on the user's conditions and carry out a process to select the optimal property and area.

[0503] "Means for displaying recommendation results on the user's device" refers to a mechanism for displaying recommendation results selected by AI on the user's device in an easy-to-understand format.

[0504] This invention is a real estate search system that uses an AI algorithm to recommend optimal properties and areas based on information entered by the user. The system aims to quickly and accurately meet user needs and resolve the problems of conventional real estate search services.

[0505] System Overview

[0506] The system includes the following elements:

[0507] 1. A means of receiving information entered by the user

[0508] 2. Means for analyzing the information and storing it in a database

[0509] 3. Encrypting data using the SSL / TLS protocol and sending it to the server

[0510] 4. Means for parsing said information in JSON or XML format

[0511] 5. How to obtain area and property data

[0512] 6. A method to use AI algorithms to analyze acquired property and area data and recommend the most suitable property and area

[0513] 7. Means for displaying the recommendation results on the user terminal

[0514] Hardware and Software Configuration

[0515] This embodiment uses a dedicated server and user terminals, which can be PCs, smartphones, tablets, etc. Users input information through a web interface or mobile application, which provides basic UI elements including a data entry form and a submit button.

[0516] The server is composed of a high-performance computer and is equipped with a communication module compatible with the SSL / TLS protocol, as well as a data analysis module, an AI algorithm module, and a database management system.

[0517] Specific processing of the program

[0518] Using a web interface or mobile application, users enter details such as family size, place of employment, school attendance, daily activities, hobbies, key requirements for a property, and budget. This information is entered in a form format, with required and optional fields clearly indicated. For example, a family size might be "4 people (2 adults, 2 children)," a place of employment might be "Shinjuku," and a school attendance might be "Ueno Ward First Elementary School."

[0519] When the user clicks the "Search" button, the device encrypts the entered data using the SSL / TLS protocol and sends it to the server. The server receives this data, parses it in JSON or XML format, and stores each item in a database. For example, data such as family size "4 people," workplace "Shinjuku," and school attended "Ueno Ward First Elementary School" are stored in the respective fields.

[0520] The server then accesses a real estate database and a local area information database to retrieve property and local area data that matches the user's criteria, including property details (such as floor plan, price, and location) and information about local public facilities and infrastructure (such as schools, parks, and public transportation).

[0521] The AI ​​algorithm scores properties based on user criteria, taking into account commute time, school districts, and access to public facilities, and then calculates an overall score by looking at past user feedback data and applying a model to predict satisfaction, then ranks the best properties and neighborhoods.

[0522] Finally, based on the results of the AI ​​algorithm, a list of properties and areas recommended to the user is generated and sent to the device along with detailed information (price, location, photos, etc.). The device receives these recommendations and displays them to the user in an intuitive format. For example, it might say, "Property A is within your budget, convenient for commuting, and a 10-minute walk to the park," or "Property B is within the school district, pet-friendly, and 15 minutes to Shinjuku."

[0523] Specific examples

[0524] As an example of user information input, let's assume a family of four (two adults, two children), workplace in Shinjuku, school attended by Ueno Ward Daiichi Elementary School, daily activities within Tokyo's 23 wards, hobby jogging in parks, requirements for a property to be purchased: 4LDK, with parking, pets allowed, budget of 70 million yen.

[0525] An example of a prompt sentence is, "Please recommend a property with a 4LDK, parking, pet-friendly, and a budget of 70 million yen for a family of four, who commute to Shinjuku and have a child attending Ueno Ward Daiichi Elementary School."

[0526] As described above, the real estate search system of the present invention can meet the diverse needs of users, quickly and accurately recommend the most suitable properties and areas, and provide a stress-free real estate search.

[0527] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0528] Step 1:

[0529] Using a web interface or mobile application, users enter details such as family composition, place of employment, school attendance, daily activities, hobbies, key requirements for a property to purchase, budget, etc. This information is entered in a form format, with required and optional fields clearly marked.

[0530] Input: User information such as family composition, place of employment, school attended, etc.

[0531] Output: A set of form data

[0532] Step 2:

[0533] When the user clicks the "Search" button, the terminal encrypts the data entered in the form using SSL / TLS protocol and sends it to the server, ensuring secure communication during this process.

[0534] Input: User details entered

[0535] Output: Encrypted data packet

[0536] Step 3:

[0537] The server receives the data sent from the device and decrypts it using SSL / TLS. It then parses the data in JSON or XML format, extracts each item, and stores it in a database. For example, information such as family size ("4 people"), workplace ("Shinjuku"), and school attended ("Ueno Ward First Elementary School") are stored in each field of the database.

[0538] Input: Encrypted data

[0539] Output: Structured data stored in a database

[0540] Step 4:

[0541] The server accesses the database and queries relevant neighborhood and property data based on the user's criteria. In this process, the data retrieved includes property details and local infrastructure information (schools, parks, public transportation, etc.).

[0542] Input: A query based on the user's criteria

[0543] Output: Property and area information as query results

[0544] Step 5:

[0545] The server uses AI algorithms to analyze the acquired property and area data, including the following specific operations:

[0546] 1. Scoring: Scoring the property's suitability to the user's criteria.

[0547] 2. Convenience evaluation: Evaluate by taking into account commute time, school districts, and access to public facilities.

[0548] 3. Feedback reference: Apply a model to predict satisfaction based on past user feedback data.

[0549] 4. Overall score calculation: Each evaluation item is combined to calculate an overall score and rank the property.

[0550] Input: Property data and area data

[0551] Output: Ranked property list

[0552] Step 6:

[0553] Based on the results of the AI ​​algorithm, the server generates a list of properties and areas recommended to the user, and adds detailed information (price, location, photos, etc.).

[0554] Input: A list of properties with an overall score

[0555] Output: Recommendation result list

[0556] Step 7:

[0557] The device receives the recommendation results sent from the server and displays them in a format that is easy for the user to understand. Specifically, information such as property price, location, and photos is displayed in a dashboard format, and the properties are ranked.

[0558] Input: Recommendation results received from the server

[0559] Output: The displayed property list and details

[0560] In this way, each step works in tandem, allowing users to quickly and accurately find the perfect property and area.

[0561] (Application example 1)

[0562] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0563] With conventional real estate search systems, users must search on websites or mobile applications to obtain property information, which requires processing large amounts of information at once. Furthermore, when viewing properties on-site, users must carry a separate device (such as a smartphone or tablet) to refer to information they have previously researched. Furthermore, there are limited ways to view detailed information about properties and surrounding facilities at a glance. There is a need for a system that can solve these problems and enable users to more intuitively obtain and view real estate information in real time.

[0564] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0565] In this invention, the server includes means for receiving information entered by the user regarding family composition, place of work, school, daily range of activities, hobbies, important conditions for a property to be purchased, and budget, means for analyzing the information and saving the analysis results in a database, means for acquiring area and property data from the database, means for recommending optimal properties and areas based on the acquired data using an AI algorithm, and means for displaying the recommendation results on the user's terminal and displaying area and property information using augmented reality via a smart device.This allows users to visually acquire and check property information and surrounding facility information on site in real time, enabling them to use real estate information more intuitively and efficiently.

[0566] "Information entered by the user" refers to information entered by the user regarding family composition, place of work, school attended, range of daily activities, hobbies, important conditions required for a property to be purchased, and budget.

[0567] "Analysis" refers to the process of analyzing the information received from users and using that information to identify the best properties and areas.

[0568] A "database" is a system for storing user information and area / property data, and retrieving it as needed.

[0569] "Retrieval" refers to pulling the required data from the database.

[0570] An "AI algorithm" is an algorithm that uses artificial intelligence to solve complex problems and recommend the best properties and areas.

[0571] "Recommendation" means presenting the best options based on the information entered by the user.

[0572] "Display" means providing information visually to a user terminal or smart device.

[0573] "Smart devices" refers to high-function devices such as smart glasses and head-mounted displays.

[0574] "Augmented reality" is a technology that displays digital information overlaid on real-world scenery.

[0575] The system of the present invention recommends optimal real estate properties based on information entered by the user, and allows the user to obtain and confirm that information in real time. The system includes a server, a user terminal, and a smart device.

[0576] 1. Input and Receipt of User Information

[0577] Server: Using a web interface, mobile application, or smart device, the user enters information about their family, place of employment, school attendance, daily activities, hobbies, key property requirements, and budget. This information is provided via voice or text input.

[0578] 2. Data submission and analysis

[0579] On the device: The information entered by the user is sent to the server in JSON format, encrypted using the HTTPS protocol.

[0580] Server: Analyzes the received data and stores it in a database, which contains real estate property data and local information data (e.g., MySQL).

[0581] 3. Obtaining area and property data

[0582] Server: Accesses real estate databases and local information databases based on the user's requirements and retrieves the required data.

[0583] 4. AI-based data analysis and recommendations

[0584] Server: Runs AI algorithms using TensorFlow and Scikit-learn to analyze the acquired property and neighborhood data, taking into account commute times, school districts, access to public facilities, and past user feedback.

[0585] 5. Generating and Displaying Recommendations

[0586] Server: Based on the analysis results of the AI ​​algorithm, the server generates a ranking of the most suitable properties and areas, and sends the recommendation results to the user's terminal or smart device.

[0587] 6. Display and operation using smart devices

[0588] Terminal: Smart devices (e.g., smart glasses or head-mounted displays) use Unity or ARKit to display property information using augmented reality (AR). Users can use the device on-site to visually check property information and surrounding facilities in real time.

[0589] Examples of concrete examples and prompts

[0590] Example: While a user is walking around Shinjuku Station, the smart glasses display information such as "Shinjuku Building 5, 4LDK, 68 million yen, 5-minute walk from the station, 3-minute walk to the supermarket." If the user uses the device's microphone to ask, "Where is the nearest park from here?", the location of the park will be displayed in the user's field of vision.

[0591] Example prompt sentence:

[0592] User input prompt: "Find properties within 15 minutes of Shinjuku Station, suitable for families, and budgets under 70 million yen."

[0593] Prompt for generative AI model: "Based on the user's input, please have the algorithm recommend the best family-friendly properties in the Shinjuku area."

[0594] This makes it possible to specifically implement the form of the invention, and allows users to intuitively use real estate information on-site.

[0595] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0596] Step 1:

[0597] Enter and submit user information

[0598] Users: Through a web interface, mobile application, or smart device, they enter information about their family composition, place of employment, school attendance, daily activities, hobbies, key requirements for a property, and budget.

[0599] Input: Family composition, place of work, school, daily activities, hobbies, important conditions, budget

[0600] Output: User information in JSON format

[0601] Specific operation: The user provides information by voice or touch input. The device converts the input data into JSON format and sends it to the server via HTTPS protocol.

[0602] Step 2:

[0603] Data reception and analysis

[0604] Server: Receives JSON formatted data sent by the user and parses it using a JSON parser.

[0605] Input: User information in JSON format

[0606] Output: Structured user information (e.g., information as variables and fields)

[0607] What happens: The server receives the HTTPS request, decodes the JSON data, converts it into a structured format, and saves it in the database.

[0608] Step 3:

[0609] Obtaining area and property data

[0610] Server: Based on the user information, accesses the real estate database and area information database to retrieve the appropriate area and property data.

[0611] Input: Structured user information

[0612] Output: Area and property data

[0613] What it does: Executes a database query to retrieve properties and locality information that match the user's criteria. For example, it uses a MySQL query to pull the required information from the database.

[0614] Step 4:

[0615] AI-based data analysis and recommendations

[0616] Server: Using TensorFlow and Scikit-learn, the acquired property data and area data are analyzed using AI algorithms.

[0617] Input: Area and property data

[0618] Output: Recommended property list (with scores)

[0619] How it works: Data is fed into an AI model, which scores properties based on factors such as commute time, school district, access to public facilities, and past user feedback, generating a ranked list of recommended properties.

[0620] Step 5:

[0621] Generating and sending recommendations

[0622] Server: Generates a list of recommended properties and sends it to the user's terminal or smart device.

[0623] Input: Recommended property list

[0624] Output: Recommendation results in JSON format

[0625] Specific operation: Convert the recommended property list into JSON format and send it to the user terminal or smart device using the HTTPS protocol.

[0626] Step 6:

[0627] Display and operation using a smart device

[0628] Device: Uses the AR function of your smart device to display recommended property information and local information.

[0629] Input: Recommendation results in JSON format

[0630] Output: Property information displayed in AR

[0631] How it works: Using Unity and ARKit, property information is overlaid on the smart device's camera view. Users can view the information in real time through their device, and additional information is displayed in response to input prompts.

[0632] This will enable users to more intuitively obtain and check real estate property information using their smart devices, enabling them to make real-time decisions.

[0633] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0634] ---

[0635] The real estate search system of the present invention uses an AI algorithm to recommend optimal properties and areas based on information entered by a user, and also combines an emotion engine that recognizes the user's emotions to provide more personalized recommendations. Specific embodiments of the present invention are described below.

[0636] Entering user information

[0637] Users: Through a web interface or mobile application, users enter information about their family structure, place of employment, school attendance, daily activities, hobbies, important requirements for a property, and budget. This information is important for improving the system's recommendation accuracy, as it accurately reflects the user's desires and lifestyle patterns.

[0638] Emotion recognition by emotion engine

[0639] Emotion engine: When a user inputs information, it uses a facial recognition camera and a voice analysis microphone to recognize the user's emotions in real time. It analyzes the input data, facial expressions during operation, and tone of voice to determine the user's emotional state (e.g., satisfaction, dissatisfaction, expectation, excitement, etc.).

[0640] Sending and Receiving Data

[0641] On the device: When the user clicks the "Search" button, the entered data and the emotion data from the emotion engine are sent to the server, encrypted using the SSL / TLS protocol.

[0642] Server: Receives data sent from the device and begins analysis. The analysis process parses the received data in JSON or XML format and stores each item as a key in a database.

[0643] Obtaining area and property data

[0644] Server: Based on the user's criteria and emotion data, the server accesses the real estate database and local information database to search for suitable areas and properties. From the database, the server obtains detailed information about the property (floor plan, price, location, etc.) and information about local public facilities and infrastructure (schools, parks, public transportation, etc.).

[0645] AI recommendation algorithm

[0646] Server: The server uses sophisticated AI algorithms to analyze the acquired property and area data. The AI ​​algorithms work as follows:

[0647] 1. Score the degree of match between the user's conditions and the property.

[0648] 2. Evaluate the area's amenities, considering commute times, school districts, and access to public facilities.

[0649] 3. Emotional data obtained from the emotion engine is added to adjust the recommendation results based on the user's emotional state.

[0650] 4. Look at past user feedback data and apply a model to predict satisfaction.

[0651] 5. Calculate an overall score and rank the best properties and neighborhoods.

[0652] Generating and displaying recommendations

[0653] Server: Based on the results of the AI ​​algorithm, the server generates a list of properties and areas to recommend to users. The recommended properties are sorted by ranking and include detailed information about each property (price, location, photos, etc.).

[0654] Terminal: Receives the recommendation results sent from the server and displays them in a user-friendly format, allowing users to intuitively identify the most suitable property.

[0655] Specific examples

[0656] Entering user information

[0657] User: Family composition: 4 people (2 adults, 2 children), Workplace: Shinjuku, School: Ueno Ward Daiichi Elementary School, Daily range of activities: Within Tokyo's 23 wards, Hobbies: Jogging in the park, Important conditions for a property to purchase: 4LDK, parking, pets allowed, Budget: 70 million yen.

[0658] Emotion recognition by emotion engine

[0659] Emotion engine: Analyzes the user's facial expressions and tone of voice when inputting information to identify the user's current emotional state. For example, if the user looks anxious, the system will capture that information as emotion data.

[0660] Sending and Receiving Data

[0661] Device: When the user presses the "Search" button, the input data and emotion data are sent to the server using the https protocol.

[0662] Server: Receives the data, parses the user's family size: 4 people, workplace: Shinjuku, school attended: Ueno Ward Daiichi Elementary School, and emotional state (e.g., anxiety) and stores it in a database.

[0663] Obtaining area and property data

[0664] Server: Accesses the real estate database to retrieve property listings that match the criteria and information about the surrounding area (e.g., nearby parks, schools, public transportation, etc.).

[0665] AI recommendation algorithm

[0666] Server: Using AI, the server scores all properties, evaluating factors such as commute convenience, access to schools, and proximity to parks suitable for jogging. It also takes into account sentiment data, prioritizing safer neighborhoods to alleviate anxiety. It also takes into account past feedback data to calculate an overall score.

[0667] Generating and displaying recommendations

[0668] Server: Generates a list of properties A-E, including detailed information about each property (e.g. price, location, photos, etc.).

[0669] Device: Receives the recommendation results and displays them to the user, such as Property A: within budget, convenient commute, 10-minute walk to the park, Property B: within school district, pet-friendly, 15-minute walk to Shinjuku.

[0670] In this way, the real estate search system of the present invention can provide more personalized property recommendations that reflect the user's needs and emotional state, allowing users to find the property that best suits them without stress and improving the quality of service.

[0671] The processing flow will be explained below.

[0672] ---

[0673] Step 1:

[0674] User: Using the web interface or mobile application, user enters information about family size, workplace, school commute, daily activities, hobbies, important requirements for a property, and budget. For example, user enters: Family size: 4 people, Workplace: Shinjuku, School commute: Ueno Ward Daiichi Elementary School, Hobbies: Jogging, Requirements for property purchase: 4LDK, with parking, pets allowed, Budget: 70 million yen.

[0675] Step 2:

[0676] Emotion engine: When a user enters information, the camera and microphone are used to analyze the user's facial expressions and tone of voice in real time. It determines whether the user is expressing emotions such as satisfaction, dissatisfaction, or expectation, and captures this as emotional data. For example, if the user shows a smile or a relieved expression while entering information, that emotional data is collected.

[0677] Step 3:

[0678] On the device: When the user clicks the "Search" button, the entered information and the emotion data generated by the emotion engine are sent to the server. This transmission is encrypted using the SSL / TLS protocol.

[0679] Step 4:

[0680] Server: Receives data sent from the device. The received data is parsed in JSON or XML format, and the data such as family structure, place of employment, school attended, hobbies, purchasing conditions, budget, and emotional data is analyzed and stored in a database.

[0681] Step 5:

[0682] Server: Accesses real estate and local information databases to retrieve relevant local and property data that meet the user's criteria. For example, searches for property listings with 4 bedrooms, kitchens, parking spaces, and pet-friendly rooms, as well as information on parks, schools, and public transportation.

[0683] Step 6:

[0684] Server: Uses sophisticated AI algorithms to analyze acquired property and area data. Specific processes include:

[0685] 1. Score the degree of match between the user's conditions and the property.

[0686] 2. Evaluate the area's amenities, taking into account commute times and access to public facilities.

[0687] 3. Taking into account emotional data, adjustments are made, such as prioritizing properties that users feel more comfortable with.

[0688] 4. Look at past user feedback data and apply a model to predict satisfaction.

[0689] 5. Calculate an overall score and rank the best properties and neighborhoods.

[0690] Step 7:

[0691] Server: Based on the results of the AI ​​algorithm, the server generates a list of properties and areas to recommend to the user, including detailed information about each property (price, location, photos, etc.).

[0692] Step 8:

[0693] Server: The recommendation results are sent to the user's device in JSON or XML format, and the data is encrypted using the SSL / TLS protocol.

[0694] Step 9:

[0695] Device: Receives the recommendation results sent from the server and displays them in an easy-to-understand manner to the user. For example, Property A: Within budget, easy commute, 10 minutes walk to the park; Property B: Within school district, pets allowed, 15 minutes to Shinjuku.

[0696] ---

[0697] We have explained in detail the processing steps of a real estate search system that combines an emotion engine. This system allows users to receive more personalized property recommendations based on their emotional state.

[0698] Example 2

[0699] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0700] Conventional real estate search systems recommend properties based only on the user's simple criteria (such as family composition and budget), making it difficult to provide personalized recommendations that reflect the user's emotions and lifestyle. Furthermore, general AI algorithms do not take the user's emotional state into account, making it difficult to find properties that will satisfy them. This makes it difficult for users to quickly and efficiently find the perfect property, often resulting in frustration.

[0701] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes: means for receiving information input by the user regarding family composition, place of work, educational institutions, daily range of activities, hobbies, important conditions for the purchase request, and budget; means for analyzing the information and saving the analysis results in a database; means for acquiring area and property data from the database; means for acquiring user emotion data through facial expression recognition and voice analysis; means for using the emotion data to recommend optimal properties and areas using an AI algorithm; and means for displaying the recommendation results on the user terminal. This enables more personalized real estate property recommendations that reflect the user's emotional state and individual lifestyle.

[0702] "User Information" means information entered by a User through the web interface or mobile application, such as family composition, place of employment, educational institution, daily activities, hobbies, important purchase requirements, and budget.

[0703] "Analysis" refers to storing user information in a database, extracting the necessary items, and processing them.

[0704] A "database" is a collection of data used to store and manage real estate property information, local information, user information, etc.

[0705] "Local data" refers to information about public facilities, transportation, infrastructure, etc. in a particular local area.

[0706] "Property data" refers to detailed information about a real estate property, including, for example, floor plan, price, location, facilities, etc.

[0707] "Facial expression recognition" is a technology that uses a camera to analyze a user's facial expressions in real time and identify their emotional state.

[0708] "Voice analysis" is a technology that analyzes a user's tone of voice and speaking style in real time through a microphone to identify their emotional state.

[0709] "Emotional data" is information about the user's emotional state obtained through facial expression recognition and voice analysis.

[0710] An "AI algorithm" is a mathematical method that uses machine learning and data analysis to process input data and recommend the most suitable properties and areas.

[0711] "Recommendation Results" refers to a list of properties and areas suitable for the user, derived through analysis by an AI algorithm.

[0712] This real estate search system recommends the best properties and areas based on information entered by the user and sentiment data collected in real time. The system is implemented using the following hardware and software:

[0713] Hardware and software used

[0714] 1. Device: A device such as a PC, smartphone, or tablet with a web browser is used. Users enter information using these devices.

[0715] 2. Facial Recognition Camera: Uses a camera to analyze the user's facial expressions in real time.

[0716] 3. Voice Analysis Microphone: Uses a microphone to analyze the user's tone of voice and speaking style.

[0717] 4. Server: A server is used to run high-performance AI algorithms. This server also handles database management and calculations.

[0718] Specific operation of the system

[0719] Entering user information

[0720] Users enter information about their family, place of employment, educational institution, daily activities, hobbies, important purchase requirements, and budget through a web interface or mobile application. This information is important for improving the system's recommendation accuracy. For example, suppose a user enters the following information:

[0721] Family size: 4 people (2 adults, 2 children)

[0722] Location: Shinjuku

[0723] Educational institution: Ueno Ward Daiichi Elementary School

[0724] Daily range of activities: Tokyo's 23 wards

[0725] Hobbies: Jogging in the park

[0726] Important conditions for purchase: 4LDK, parking space, pets allowed

[0727] Budget: 70 million yen

[0728] Emotion recognition by emotion engine

[0729] The emotion engine uses a facial recognition camera and a voice analysis microphone to analyze the user's emotions in real time while they are entering information. For example, if a user looks anxious, the emotional state will be recorded as "anxiety." This emotional data is reflected in the recommendation algorithm to increase user satisfaction.

[0730] Sending and Receiving Data

[0731] When a user clicks the "Search" button, the entered information and emotion data are encrypted using the SSL / TLS protocol and sent to the server, which then analyzes the data and stores it in a database using each item as a key.

[0732] Obtaining area and property data

[0733] The server accesses the database and searches for suitable areas and properties based on the user's criteria and emotional data. Property details (e.g., floor plan, price, location, etc.) are obtained from the real estate database, and information on public facilities and infrastructure is obtained from the local information database.

[0734] AI recommendation algorithm

[0735] The server analyzes the acquired data using a high-performance AI algorithm and performs the following processes:

[0736] 1. Score the degree of match between the input conditions and the property.

[0737] 2. Evaluate the area's amenities, considering factors such as commute time, access to schools, and proximity to parks suitable for jogging.

[0738] 3. Use emotional data to tailor recommendation results based on the user's emotional state.

[0739] 4. Look at past user feedback data and apply a model to predict satisfaction.

[0740] 5. Calculate an overall score and display the best properties and areas in ranked order.

[0741] Generating and displaying recommendations

[0742] Based on the results of the AI ​​algorithm, the server generates a list of properties and areas to recommend to the user. The recommendations are ranked and include detailed information about each property (e.g., price, location, photos, etc.). The device receives this information and displays it in a user-friendly format.

[0743] Examples and prompts

[0744] Specific examples

[0745] Family size: 4 people (2 adults, 2 children)

[0746] Location: Shinjuku

[0747] Educational institution: Ueno Ward Daiichi Elementary School

[0748] Daily range of activities: Tokyo's 23 wards

[0749] Hobbies: Jogging in the park

[0750] Important conditions for purchase: 4LDK, parking space, pets allowed

[0751] Budget: 70 million yen

[0752] Prompt Sentence Examples

[0753] "Enter your family composition, place of employment, school, and other requirements. We will recommend the ideal property for you."

[0754] "We take into account emotional data to recommend properties that users can feel comfortable with."

[0755] "We will design a system that uses user criteria and emotional data to recommend the best real estate properties."

[0756] As a result, this real estate search system is able to provide personalized recommendations that reflect the user's feelings and requirements.

[0757] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0758] Step 1: Enter your user information

[0759] Users open a web interface or mobile application and enter information about their family composition, workplace, educational institution, daily activities, hobbies, key purchase criteria, and budget. Examples of input information include "Family composition: 4 people (2 adults, 2 children)," "Workplace: Shinjuku," "Educational institution: Ueno Ward Daiichi Elementary School," "Daily activities: Within Tokyo's 23 wards," "Hobbies: Jogging in the park," "Key purchase criteria: 4LDK, parking, pets allowed," and "Budget: 70 million yen." This information serves as input data and is important for specifically reflecting the user's needs and criteria.

[0760] Step 2: Emotion recognition by the emotion engine

[0761] The server uses a facial expression recognition camera and a voice analysis microphone to analyze the user's facial expressions and voice as they are input. The facial expression recognition camera captures the subtle movements of the user's face, and the voice analysis microphone analyzes the user's tone of voice and speaking style. For example, if the user has an anxious expression or a low tone of voice, the emotional state of "anxiety" is detected. This emotional data is used as input data for the next processing step.

[0762] Step 3: Sending data

[0763] When the user clicks the "Search" button, the device encrypts the information entered by the user and the emotion data obtained by the emotion engine using the SSL / TLS protocol and sends it to the server. This sent data becomes the input data for the server.

[0764] Step 4: Receiving and storing data

[0765] The server receives the data sent from the device and parses it in JSON or XML format. The received data includes the user's family structure, place of work, educational institution, daily range of activities, hobbies, important conditions for desired purchases, budget, and emotional state. This data is analyzed and each item is stored as a key in the database. The analysis results are stored in the database and used for search processing in the next step.

[0766] Step 5: Obtaining location and property data

[0767] The server accesses the real estate database and local information database based on the user's criteria and emotion data stored in the database. Specifically, it extracts property listings that match the user's filter criteria and obtains information about the surrounding area (nearby parks, schools, public transportation, etc.). This obtained data becomes input data for the AI ​​algorithm in the next step.

[0768] Step 6: AI recommendation algorithm

[0769] The server uses a high-performance AI algorithm to analyze the input data and perform the following processes: First, it scores the degree of match between the user's input criteria and the property. It also evaluates the convenience of the area, taking into account factors such as commute time, access to schools, and proximity to public facilities. It also uses emotional data to adjust the recommendation results based on the user's emotional state. It references past user feedback data and applies a model to predict satisfaction. By processing and calculating these data, it calculates an overall score and ranks the properties.

[0770] Step 7: Generate and display recommendations

[0771] Based on the results of the AI ​​algorithm, the server generates a list of properties and areas to recommend to the user. The generated list also includes detailed property information (price, location, photos, etc.). These recommendation results become the output data from the server. The device receives the recommended results and displays them in a format that is easy for the user to understand. Based on this information, the user can intuitively and efficiently select the most suitable property.

[0772] Detailed processing is carried out at each step, resulting in personalized property recommendations that reflect the user's needs and emotions.

[0773] (Application example 2)

[0774] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0775] While conventional real estate search systems can recommend properties based on user input, they are limited in providing personalized recommendations that take into account the user's emotional state. Furthermore, viewing property details requires a site visit to get a real feel for the property, which is time-consuming and labor-intensive for users. The present invention aims to solve these problems and provide a real estate search system that enables more personalized property recommendations and property viewing using virtual reality technology.

[0776] The specification processing by the specification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving information and emotional state input by the user regarding family composition, place of work, school, daily range of activities, hobbies, important conditions for a property to be purchased, and budget; means for analyzing the information and emotional state and saving the analysis results in a database; means for acquiring area and property data from the database; means for recommending optimal properties and areas based on the acquired data using an AI algorithm; and means for displaying the recommendation results on the user terminal and allowing the user to view properties using virtual reality technology. This allows for more personalized property recommendations based on the user's emotional state, and allows the user to view properties using virtual reality technology through a smartphone application.

[0777] "User information" refers to information regarding family composition, place of employment, school attended, daily range of activities, hobbies, important conditions required for a property to be purchased, and budget.

[0778] "Emotional state" is data that represents the user's emotions, and is information obtained by analyzing the user's facial expressions, tone of voice, etc.

[0779] "Analysis" is the process of processing user information and emotional states as data to understand their content.

[0780] "Database" means a digital repository for organizing and storing information, including user information, emotional states, local area information, and property information.

[0781] "Local and Property Data" means detailed information about the recommended real estate and its surrounding area.

[0782] "AI Algorithm" refers to an artificial intelligence calculation method used to recommend optimal properties and areas based on user information and emotional state.

[0783] "Virtual reality technology" is a technology that provides users with a sense of realism through a computer-generated virtual space.

[0784] "Recommendation results" are a list of the best properties and areas selected by an AI algorithm.

[0785] The real estate search system of the present invention is a system that uses an AI algorithm to recommend optimal properties and areas based on the user's input information and emotional state, and allows the user to view properties using virtual reality technology. Specific embodiments of the system are described below.

[0786] 1. Entering user information and acquiring emotional state

[0787] Users enter information about their family, place of employment, school, daily activities, hobbies, important requirements for a property to purchase, and budget through a smartphone application. The smartphone's front camera and microphone are used to analyze the user's facial expressions and tone of voice. Specifically, OpenCV is used for facial recognition, and the Google Cloud Speech-to-Text API is used to analyze emotions from voice.

[0788] Examples:

[0789] A user enters information into an input field within an application.

[0790] Example prompt: "Please tell us about your family structure, place of work, daily activities, hobbies, property requirements, and budget."

[0791] 2. Data transmission to the server and analysis

[0792] The device sends the entered user information and analyzed emotional state data to the server using SSL / TLS encryption, which then analyzes the data and stores it in a database. This process is performed using Python and Django.

[0793] 3. Obtaining area and property data

[0794] The server accesses a real estate database to retrieve property and area information that matches the user's criteria, using a relational database such as PostgreSQL.

[0795] 4. Recommendation of optimal properties using AI

[0796] The server uses AI algorithms using TensorFlow or PyTorch to score and recommend the best properties and neighborhoods based on user information and emotional state, taking into account commute times, school districts, access to public facilities, past user feedback, and emotional data.

[0797] 5. Recommendation results and virtual reality property viewing

[0798] The server generates recommendations and sends them to a smartphone application that allows users to view properties using virtual reality technology. Within the app, users can view the list of recommended properties and view each property in detail in VR mode.

[0799] Examples:

[0800] Recommended properties will be displayed on your smartphone.

[0801] Example prompt: "The following properties are recommended for you. Property A: Within your budget, easy commute, 10 minutes' walk to the park. Property B: Within the school district, pet-friendly, 15 minutes' walk to Shinjuku."

[0802] This system not only enables personalized property recommendations that reflect the user's emotional state, but also allows users to intuitively experience detailed property information using virtual reality, which is expected to significantly reduce the time and effort required when selecting a property.

[0803] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0804] Step 1:

[0805] Users use a smartphone application to input information about their family structure, place of work, school, daily range of activities, hobbies, important requirements for a property to purchase, and budget. At this time, the smartphone's front camera and microphone are used to analyze the user's facial expressions and tone of voice. Text information is entered as input data, and audio and video data acquired from the camera and microphone are used for emotion analysis. The input data consists of the user's desired conditions (text) and emotional data (video and audio). The output data after analysis is the user's information and emotional state.

[0806] Step 2:

[0807] The terminal transmits the information entered by the user and the analyzed emotion data to the server using the SSL / TLS encryption protocol. The specific process involves packaging and encrypting the user information and emotion data and transmitting them to the server. The input data is the user's desired conditions and emotion data, and the output data is the encrypted transmission data.

[0808] Step 3:

[0809] The server analyzes the received user information and emotion data and stores them in a database. This process uses Django and involves parsing the data and storing it in the database. The input data is encrypted user information and emotion data, and the output data is the user information and emotion data stored in the database.

[0810] Step 4:

[0811] The server retrieves area and property data from a real estate database. PostgreSQL is used as the database. A query is executed for properties that match the criteria, and relevant property information is retrieved. The input data is the user's desired criteria, and the output data is a list of candidate property data.

[0812] Step 5:

[0813] The server uses an AI algorithm using TensorFlow or PyTorch to score the best properties and areas based on the user's desired conditions and emotional state. The algorithm includes commute time, school district, access to public facilities, past user feedback, and emotional data. The input data is user information, emotional data, and property data, and the output data is a list of scored properties.

[0814] Step 6:

[0815] The server generates recommendation results and sends them to the user's device. The recommendation results include a list of scored properties with detailed information. The input data is the property scoring results, and the output data is the list of recommendation results.

[0816] Step 7:

[0817] Users receive recommendation results on their smartphone application and can view properties using virtual reality technology. Users can check the list of recommended properties within the app and view each property in detail in VR mode. The input data is the recommendation results, and the output data is property information displayed in virtual reality.

[0818] Through the above processing steps, users can receive personalized property recommendations that take into account their emotional state and can experience detailed property information using virtual reality technology.

[0819] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0820] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0821] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0822] [Third embodiment]

[0823] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0824] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0825] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0826] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0827] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0828] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0829] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0830] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0831] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific 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.

[0832] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0833] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0834] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. 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."

[0835] ---

[0836] The real estate search system of the present invention uses an AI algorithm to recommend optimal properties and areas based on information entered by the user. The system aims to quickly and accurately meet the user's needs and resolve the problems associated with conventional real estate search services. Specific embodiments of the present invention are described below.

[0837] Entering user information

[0838] Users: Through a web interface or mobile application, users enter information about their family structure, place of employment, school attendance, daily activities, hobbies, important requirements for a property, and budget. This information accurately reflects the user's desires and lifestyle patterns, and is necessary to improve the system's recommendations.

[0839] Sending and Receiving Data

[0840] Terminal: After the user has finished entering the information, they click the "Search" button, which sends the entered data to the server. The data is encrypted using SSL / TLS protocol for security.

[0841] Server: Receives data sent from the device and begins analysis. The analysis process parses the received data in JSON or XML format and stores each item as a key in a database.

[0842] Obtaining area and property data

[0843] Server: Based on the user's criteria, the server accesses real estate and local information databases to retrieve local and property data that matches the user's input. This data includes property details (such as floor plan, price, and location) and information about local public facilities and infrastructure (such as schools, parks, and public transportation).

[0844] AI recommendation algorithm

[0845] Server: The server uses a sophisticated AI algorithm to analyze the acquired property and area data. The AI ​​algorithm works as follows:

[0846] 1. Score the degree of match between the user's conditions and the property.

[0847] 2. Evaluate the area's amenities, considering commute times, school districts, and access to public facilities.

[0848] 3. Look at past user feedback data and apply a model to predict satisfaction.

[0849] 4. Calculate an overall score and rank the best properties and neighborhoods.

[0850] Generating and displaying recommendations

[0851] Server: Based on the results of the AI ​​algorithm, the server generates a list of properties and areas to recommend to the user. The list of recommended properties is sorted by ranking and includes detailed information about each property (price, location, photos, etc.).

[0852] Terminal: Receives the recommendation results sent from the server and displays them in a user-friendly format, allowing users to intuitively identify the most suitable property.

[0853] Specific examples

[0854] Entering user information

[0855] User: Family composition: 4 people (2 adults, 2 children), Workplace: Shinjuku, School: Ueno Ward Daiichi Elementary School, Daily range of activities: Within Tokyo's 23 wards, Hobbies: Jogging in the park, Important conditions for a property to purchase: 4LDK, parking, pets allowed, Budget: 70 million yen.

[0856] Sending and Receiving Data

[0857] Terminal: Input data is sent to the server using the https protocol.

[0858] Server: Receives the data and parses information such as family size: 4 people, workplace: Shinjuku, school attended: Ueno Ward Daiichi Elementary School, and saves it in a database.

[0859] Obtaining area and property data

[0860] Server: Accesses the real estate database to retrieve property listings that match the criteria and information about the surrounding area (nearby parks, schools, public transportation, etc.).

[0861] AI recommendation algorithm

[0862] Server: Uses AI to score every property, evaluating factors like commute convenience, access to schools, proximity to parks, etc., and calculates an overall score that also takes into account past feedback data.

[0863] Generating and displaying recommendations

[0864] Server: Generates a list of properties A-E, including detailed information about each property (e.g. price, location, photos, etc.).

[0865] Device: Receives the recommendation results and displays them to the user, such as Property A: within budget, convenient commute, 10-minute walk to the park, Property B: within school district, pet-friendly, 15-minute walk to Shinjuku.

[0866] In this way, the real estate search system of the present invention can quickly and accurately recommend the most suitable properties and areas for users and corporations. This system meets the diverse needs of users and provides a stress-free property search.

[0867] The processing flow will be explained below.

[0868] ---

[0869] Step 1: Enter your user information

[0870] Users: Enter information about their family, place of employment, school attendance, daily activities, hobbies, key requirements for the property they are looking for, and budget via a web interface or mobile application.

[0871] Step 2: Submit input data

[0872] On the device: When the user clicks the "Search" button, all entered information is sent to the server, where the data is encrypted using the SSL / TLS protocol.

[0873] Step 3: Receiving and analyzing data

[0874] Server: Receives user input data sent from the device. The data is received in JSON or XML format and parsed. Then, the parsed data is saved in the database.

[0875] Step 4: Obtaining location and property data

[0876] Server: Based on the user's criteria, the server accesses real estate and local information databases to search for suitable areas and properties. From the databases, the server obtains detailed information about the properties (floor plan, price, location, etc.) and information about local public facilities and infrastructure (schools, parks, public transportation, etc.).

[0877] Step 5: Run the AI ​​recommendation algorithm

[0878] Server: Using high-performance AI algorithms, the acquired property and area data is analyzed. Specifically, the following processes are performed:

[0879] 1. Score the degree of match between the user's conditions and the property.

[0880] 2. Evaluate the area's amenities (commute time, school district, access to public facilities).

[0881] 3. Look at past user feedback data and apply a model to predict satisfaction.

[0882] 4. Calculate an overall score and rank the best properties and neighborhoods.

[0883] Step 6: Generate recommendations

[0884] Server: Based on the results of the AI ​​algorithm, the server generates a list of properties and areas to recommend to users, including detailed information about each property (price, location, photos, etc.).

[0885] Step 7: Submit and view your nominations

[0886] Server: The recommendation results are sent to the user's device in JSON or XML format, and the data is encrypted using SSL / TLS protocol.

[0887] Device: Receives the recommendations sent from the server and displays them in a user-friendly format, presented as a list with property images and details.

[0888] ---

[0889] Above, we have explained the specific steps of the program's processing flow. This system allows users to quickly and accurately find properties that meet their needs.

[0890] Example 1

[0891] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0892] Conventional real estate search systems have the problem of making it difficult to quickly and accurately find properties that meet users' requirements. Furthermore, they are unable to properly evaluate commute times, school districts, and access to public facilities, and are therefore unable to adequately meet the diverse needs of users. In particular, they lack a means to increase user satisfaction by utilizing AI algorithms.

[0893] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0894] In this invention, the server includes means for receiving information input by a user, means for analyzing the information and saving it in a database, means for encrypting the input information using the SSL / TLS protocol and sending it to the server, means for parsing the information in JSON or XML format, means for acquiring area and property data from the database, means for analyzing the acquired property data and area data using an AI algorithm based on the user's conditions and recommending optimal properties and areas, and means for displaying the recommendation results on the user's terminal, thereby making it possible to quickly and accurately recommend optimal properties and areas that meet the user's conditions.

[0895] "Information entered by the user" refers to information regarding family composition, place of employment, school attended, range of daily activities, hobbies, important conditions required for a property to be purchased, and budget.

[0896] The "means for analyzing information and storing it in a database" refers to the means for converting information received from a user into a structured data format and executing the process of storing it in a database.

[0897] "Means of encrypting using the SSL / TLS protocol and sending it to the server" is a mechanism for encrypting data entered by the user using the SSL / TLS protocol and transmitting it securely to the server.

[0898] The "means for parsing in JSON or XML format" is a process for converting received data into JSON or XML format and parsing it to obtain each item.

[0899] "Means for retrieving locality and property data" means a mechanism for querying and retrieving relevant data from real estate and locality databases based on stored user criteria.

[0900] "Means for analyzing property data and area data obtained using AI algorithms and recommending optimal properties and areas" refers to a means for using AI to evaluate data collected based on the user's conditions and carry out a process to select the optimal property and area.

[0901] "Means for displaying recommendation results on the user's device" refers to a mechanism for displaying recommendation results selected by AI on the user's device in an easy-to-understand format.

[0902] This invention is a real estate search system that uses an AI algorithm to recommend optimal properties and areas based on information entered by the user. The system aims to quickly and accurately meet user needs and resolve the problems of conventional real estate search services.

[0903] System Overview

[0904] The system includes the following elements:

[0905] 1. A means of receiving information entered by the user

[0906] 2. Means for analyzing the information and storing it in a database

[0907] 3. Encrypting data using the SSL / TLS protocol and sending it to the server

[0908] 4. Means for parsing said information in JSON or XML format

[0909] 5. How to obtain area and property data

[0910] 6. A method to use AI algorithms to analyze acquired property and area data and recommend the most suitable property and area

[0911] 7. Means for displaying the recommendation results on the user terminal

[0912] Hardware and Software Configuration

[0913] This embodiment uses a dedicated server and user terminals, which can be PCs, smartphones, tablets, etc. Users input information through a web interface or mobile application, which provides basic UI elements including a data entry form and a submit button.

[0914] The server is composed of a high-performance computer and is equipped with a communication module compatible with the SSL / TLS protocol, as well as a data analysis module, an AI algorithm module, and a database management system.

[0915] Specific processing of the program

[0916] Using a web interface or mobile application, users enter details such as family size, place of employment, school attendance, daily activities, hobbies, key requirements for a property, and budget. This information is entered in a form format, with required and optional fields clearly indicated. For example, a family size might be "4 people (2 adults, 2 children)," a place of employment might be "Shinjuku," and a school attendance might be "Ueno Ward First Elementary School."

[0917] When the user clicks the "Search" button, the device encrypts the entered data using the SSL / TLS protocol and sends it to the server. The server receives this data, parses it in JSON or XML format, and stores each item in a database. For example, data such as family size "4 people," workplace "Shinjuku," and school attended "Ueno Ward First Elementary School" are stored in the respective fields.

[0918] The server then accesses a real estate database and a local area information database to retrieve property and local area data that matches the user's criteria, including property details (such as floor plan, price, and location) and information about local public facilities and infrastructure (such as schools, parks, and public transportation).

[0919] The AI ​​algorithm scores properties based on user criteria, taking into account commute time, school districts, and access to public facilities, and then calculates an overall score by looking at past user feedback data and applying a model to predict satisfaction, then ranks the best properties and neighborhoods.

[0920] Finally, based on the results of the AI ​​algorithm, a list of properties and areas recommended to the user is generated and sent to the device along with detailed information (price, location, photos, etc.). The device receives these recommendations and displays them to the user in an intuitive format. For example, it might say, "Property A is within your budget, convenient for commuting, and a 10-minute walk to the park," or "Property B is within the school district, pet-friendly, and 15 minutes to Shinjuku."

[0921] Specific examples

[0922] As an example of user information input, let's assume a family of four (two adults, two children), workplace in Shinjuku, school attended by Ueno Ward Daiichi Elementary School, daily activities within Tokyo's 23 wards, hobby jogging in parks, requirements for a property to be purchased: 4LDK, with parking, pets allowed, budget of 70 million yen.

[0923] An example of a prompt sentence is, "Please recommend a property with a 4LDK, parking, pet-friendly, and a budget of 70 million yen for a family of four, who commute to Shinjuku and have a child attending Ueno Ward Daiichi Elementary School."

[0924] As described above, the real estate search system of the present invention can meet the diverse needs of users, quickly and accurately recommend the most suitable properties and areas, and provide a stress-free real estate search.

[0925] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0926] Step 1:

[0927] Using a web interface or mobile application, users enter details such as family composition, place of employment, school attendance, daily activities, hobbies, key requirements for a property to purchase, budget, etc. This information is entered in a form format, with required and optional fields clearly marked.

[0928] Input: User information such as family composition, place of employment, school attended, etc.

[0929] Output: A set of form data

[0930] Step 2:

[0931] When the user clicks the "Search" button, the terminal encrypts the data entered in the form using SSL / TLS protocol and sends it to the server, ensuring secure communication during this process.

[0932] Input: User details entered

[0933] Output: Encrypted data packet

[0934] Step 3:

[0935] The server receives the data sent from the device and decrypts it using SSL / TLS. It then parses the data in JSON or XML format, extracts each item, and stores it in a database. For example, information such as family size ("4 people"), workplace ("Shinjuku"), and school attended ("Ueno Ward First Elementary School") are stored in each field of the database.

[0936] Input: Encrypted data

[0937] Output: Structured data stored in a database

[0938] Step 4:

[0939] The server accesses the database and queries relevant neighborhood and property data based on the user's criteria. In this process, the data retrieved includes property details and local infrastructure information (schools, parks, public transportation, etc.).

[0940] Input: A query based on the user's criteria

[0941] Output: Property and area information as query results

[0942] Step 5:

[0943] The server uses AI algorithms to analyze the acquired property and area data, including the following specific operations:

[0944] 1. Scoring: Scoring the property's suitability to the user's criteria.

[0945] 2. Convenience evaluation: Evaluate by taking into account commute time, school districts, and access to public facilities.

[0946] 3. Feedback reference: Apply a model to predict satisfaction based on past user feedback data.

[0947] 4. Overall score calculation: Each evaluation item is combined to calculate an overall score and rank the property.

[0948] Input: Property data and area data

[0949] Output: Ranked property list

[0950] Step 6:

[0951] Based on the results of the AI ​​algorithm, the server generates a list of properties and areas recommended to the user, and adds detailed information (price, location, photos, etc.).

[0952] Input: A list of properties with an overall score

[0953] Output: Recommendation result list

[0954] Step 7:

[0955] The device receives the recommendation results sent from the server and displays them in a format that is easy for the user to understand. Specifically, information such as property price, location, and photos is displayed in a dashboard format, and the properties are ranked.

[0956] Input: Recommendation results received from the server

[0957] Output: The displayed property list and details

[0958] In this way, each step works in tandem, allowing users to quickly and accurately find the perfect property and area.

[0959] (Application example 1)

[0960] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0961] With conventional real estate search systems, users must search on websites or mobile applications to obtain property information, which requires processing large amounts of information at once. Furthermore, when viewing properties on-site, users must carry a separate device (such as a smartphone or tablet) to refer to information they have previously researched. Furthermore, there are limited ways to view detailed information about properties and surrounding facilities at a glance. There is a need for a system that can solve these problems and enable users to more intuitively obtain and view real estate information in real time.

[0962] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0963] In this invention, the server includes means for receiving information entered by the user regarding family composition, place of work, school, daily range of activities, hobbies, important conditions for a property to be purchased, and budget, means for analyzing the information and saving the analysis results in a database, means for acquiring area and property data from the database, means for recommending optimal properties and areas based on the acquired data using an AI algorithm, and means for displaying the recommendation results on the user's terminal and displaying area and property information using augmented reality via a smart device.This allows users to visually acquire and check property information and surrounding facility information on site in real time, enabling them to use real estate information more intuitively and efficiently.

[0964] "Information entered by the user" refers to information entered by the user regarding family composition, place of work, school attended, range of daily activities, hobbies, important conditions required for a property to be purchased, and budget.

[0965] "Analysis" refers to the process of analyzing the information received from users and using that information to identify the best properties and areas.

[0966] A "database" is a system for storing user information and area / property data, and retrieving it as needed.

[0967] "Retrieval" refers to pulling the required data from the database.

[0968] An "AI algorithm" is an algorithm that uses artificial intelligence to solve complex problems and recommend the best properties and areas.

[0969] "Recommendation" means presenting the best options based on the information entered by the user.

[0970] "Display" means providing information visually to a user terminal or smart device.

[0971] "Smart devices" refers to high-function devices such as smart glasses and head-mounted displays.

[0972] "Augmented reality" is a technology that displays digital information overlaid on real-world scenery.

[0973] The system of the present invention recommends optimal real estate properties based on information entered by the user, and allows the user to obtain and confirm that information in real time. The system includes a server, a user terminal, and a smart device.

[0974] 1. Input and Receipt of User Information

[0975] Server: Using a web interface, mobile application, or smart device, the user enters information about their family, place of employment, school attendance, daily activities, hobbies, key property requirements, and budget. This information is provided via voice or text input.

[0976] 2. Data submission and analysis

[0977] On the device: The information entered by the user is sent to the server in JSON format, encrypted using the HTTPS protocol.

[0978] Server: Analyzes the received data and stores it in a database, which contains real estate property data and local information data (e.g., MySQL).

[0979] 3. Obtaining area and property data

[0980] Server: Accesses real estate databases and local information databases based on the user's requirements and retrieves the required data.

[0981] 4. AI-based data analysis and recommendations

[0982] Server: Runs AI algorithms using TensorFlow and Scikit-learn to analyze the acquired property and neighborhood data, taking into account commute times, school districts, access to public facilities, and past user feedback.

[0983] 5. Generating and Displaying Recommendations

[0984] Server: Based on the analysis results of the AI ​​algorithm, the server generates a ranking of the most suitable properties and areas, and sends the recommendation results to the user's terminal or smart device.

[0985] 6. Display and operation using smart devices

[0986] Terminal: Smart devices (e.g., smart glasses or head-mounted displays) use Unity or ARKit to display property information using augmented reality (AR). Users can use the device on-site to visually check property information and surrounding facilities in real time.

[0987] Examples of concrete examples and prompts

[0988] Example: While a user is walking around Shinjuku Station, the smart glasses display information such as "Shinjuku Building 5, 4LDK, 68 million yen, 5-minute walk from the station, 3-minute walk to the supermarket." If the user uses the device's microphone to ask, "Where is the nearest park from here?", the location of the park will be displayed in the user's field of vision.

[0989] Example prompt sentence:

[0990] User input prompt: "Find properties within 15 minutes of Shinjuku Station, suitable for families, and budgets under 70 million yen."

[0991] Prompt for generative AI model: "Based on the user's input, please have the algorithm recommend the best family-friendly properties in the Shinjuku area."

[0992] This makes it possible to specifically implement the form of the invention, and allows users to intuitively use real estate information on-site.

[0993] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0994] Step 1:

[0995] Enter and submit user information

[0996] Users: Through a web interface, mobile application, or smart device, they enter information about their family composition, place of employment, school attendance, daily activities, hobbies, key requirements for a property, and budget.

[0997] Input: Family composition, place of work, school, daily activities, hobbies, important conditions, budget

[0998] Output: User information in JSON format

[0999] Specific operation: The user provides information by voice or touch input. The device converts the input data into JSON format and sends it to the server via HTTPS protocol.

[1000] Step 2:

[1001] Data reception and analysis

[1002] Server: Receives JSON formatted data sent by the user and parses it using a JSON parser.

[1003] Input: User information in JSON format

[1004] Output: Structured user information (e.g., information as variables and fields)

[1005] What happens: The server receives the HTTPS request, decodes the JSON data, converts it into a structured format, and saves it in the database.

[1006] Step 3:

[1007] Obtaining area and property data

[1008] Server: Based on the user information, accesses the real estate database and area information database to retrieve the appropriate area and property data.

[1009] Input: Structured user information

[1010] Output: Area and property data

[1011] What it does: Executes a database query to retrieve properties and locality information that match the user's criteria. For example, it uses a MySQL query to pull the required information from the database.

[1012] Step 4:

[1013] AI-based data analysis and recommendations

[1014] Server: Using TensorFlow and Scikit-learn, the acquired property data and area data are analyzed using AI algorithms.

[1015] Input: Area and property data

[1016] Output: Recommended property list (with scores)

[1017] How it works: Data is fed into an AI model, which scores properties based on factors such as commute time, school district, access to public facilities, and past user feedback, generating a ranked list of recommended properties.

[1018] Step 5:

[1019] Generating and sending recommendations

[1020] Server: Generates a list of recommended properties and sends it to the user's terminal or smart device.

[1021] Input: Recommended property list

[1022] Output: Recommendation results in JSON format

[1023] Specific operation: Convert the recommended property list into JSON format and send it to the user terminal or smart device using the HTTPS protocol.

[1024] Step 6:

[1025] Display and operation using a smart device

[1026] Device: Uses the AR function of your smart device to display recommended property information and local information.

[1027] Input: Recommendation results in JSON format

[1028] Output: Property information displayed in AR

[1029] How it works: Using Unity and ARKit, property information is overlaid on the smart device's camera view. Users can view the information in real time through their device, and additional information is displayed in response to input prompts.

[1030] This will enable users to more intuitively obtain and check real estate property information using their smart devices, enabling them to make real-time decisions.

[1031] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1032] ---

[1033] The real estate search system of the present invention uses an AI algorithm to recommend optimal properties and areas based on information entered by a user, and also combines an emotion engine that recognizes the user's emotions to provide more personalized recommendations. Specific embodiments of the present invention are described below.

[1034] Entering user information

[1035] Users: Through a web interface or mobile application, users enter information about their family structure, place of employment, school attendance, daily activities, hobbies, important requirements for a property, and budget. This information is important for improving the system's recommendation accuracy, as it accurately reflects the user's desires and lifestyle patterns.

[1036] Emotion recognition by emotion engine

[1037] Emotion engine: When a user inputs information, it uses a facial recognition camera and a voice analysis microphone to recognize the user's emotions in real time. It analyzes the input data, facial expressions during operation, and tone of voice to determine the user's emotional state (e.g., satisfaction, dissatisfaction, expectation, excitement, etc.).

[1038] Sending and Receiving Data

[1039] On the device: When the user clicks the "Search" button, the entered data and the emotion data from the emotion engine are sent to the server, encrypted using the SSL / TLS protocol.

[1040] Server: Receives data sent from the device and begins analysis. The analysis process parses the received data in JSON or XML format and stores each item as a key in a database.

[1041] Obtaining area and property data

[1042] Server: Based on the user's criteria and emotion data, the server accesses the real estate database and local information database to search for suitable areas and properties. From the database, the server obtains detailed information about the property (floor plan, price, location, etc.) and information about local public facilities and infrastructure (schools, parks, public transportation, etc.).

[1043] AI recommendation algorithm

[1044] Server: The server uses sophisticated AI algorithms to analyze the acquired property and area data. The AI ​​algorithms work as follows:

[1045] 1. Score the degree of match between the user's conditions and the property.

[1046] 2. Evaluate the area's amenities, considering commute times, school districts, and access to public facilities.

[1047] 3. Emotional data obtained from the emotion engine is added to adjust the recommendation results based on the user's emotional state.

[1048] 4. Look at past user feedback data and apply a model to predict satisfaction.

[1049] 5. Calculate an overall score and rank the best properties and neighborhoods.

[1050] Generating and displaying recommendations

[1051] Server: Based on the results of the AI ​​algorithm, the server generates a list of properties and areas to recommend to users. The recommended properties are sorted by ranking and include detailed information about each property (price, location, photos, etc.).

[1052] Terminal: Receives the recommendation results sent from the server and displays them in a user-friendly format, allowing users to intuitively identify the most suitable property.

[1053] Specific examples

[1054] Entering user information

[1055] User: Family composition: 4 people (2 adults, 2 children), Workplace: Shinjuku, School: Ueno Ward Daiichi Elementary School, Daily range of activities: Within Tokyo's 23 wards, Hobbies: Jogging in the park, Important conditions for a property to purchase: 4LDK, parking, pets allowed, Budget: 70 million yen.

[1056] Emotion recognition by emotion engine

[1057] Emotion engine: Analyzes the user's facial expressions and tone of voice when inputting information to identify the user's current emotional state. For example, if the user looks anxious, the system will capture that information as emotion data.

[1058] Sending and Receiving Data

[1059] Device: When the user presses the "Search" button, the input data and emotion data are sent to the server using the https protocol.

[1060] Server: Receives the data, parses the user's family size: 4 people, workplace: Shinjuku, school attended: Ueno Ward Daiichi Elementary School, and emotional state (e.g., anxiety) and stores it in a database.

[1061] Obtaining area and property data

[1062] Server: Accesses the real estate database to retrieve property listings that match the criteria and information about the surrounding area (e.g., nearby parks, schools, public transportation, etc.).

[1063] AI recommendation algorithm

[1064] Server: Using AI, the server scores all properties, evaluating factors such as commute convenience, access to schools, and proximity to parks suitable for jogging. It also takes into account sentiment data, prioritizing safer neighborhoods to alleviate anxiety. It also takes into account past feedback data to calculate an overall score.

[1065] Generating and displaying recommendations

[1066] Server: Generates a list of properties A-E, including detailed information about each property (e.g. price, location, photos, etc.).

[1067] Device: Receives the recommendation results and displays them to the user, such as Property A: within budget, convenient commute, 10-minute walk to the park, Property B: within school district, pet-friendly, 15-minute walk to Shinjuku.

[1068] In this way, the real estate search system of the present invention can provide more personalized property recommendations that reflect the user's needs and emotional state, allowing users to find the property that best suits them without stress and improving the quality of service.

[1069] The processing flow will be explained below.

[1070] ---

[1071] Step 1:

[1072] User: Using the web interface or mobile application, user enters information about family size, workplace, school commute, daily activities, hobbies, important requirements for a property, and budget. For example, user enters: Family size: 4 people, Workplace: Shinjuku, School commute: Ueno Ward Daiichi Elementary School, Hobbies: Jogging, Requirements for property purchase: 4LDK, with parking, pets allowed, Budget: 70 million yen.

[1073] Step 2:

[1074] Emotion engine: When a user enters information, the camera and microphone are used to analyze the user's facial expressions and tone of voice in real time. It determines whether the user is expressing emotions such as satisfaction, dissatisfaction, or expectation, and captures this as emotional data. For example, if the user shows a smile or a relieved expression while entering information, that emotional data is collected.

[1075] Step 3:

[1076] On the device: When the user clicks the "Search" button, the entered information and the emotion data generated by the emotion engine are sent to the server. This transmission is encrypted using the SSL / TLS protocol.

[1077] Step 4:

[1078] Server: Receives data sent from the device. The received data is parsed in JSON or XML format, and the data such as family structure, place of employment, school attended, hobbies, purchasing conditions, budget, and emotional data is analyzed and stored in a database.

[1079] Step 5:

[1080] Server: Accesses real estate and local information databases to retrieve relevant local and property data that meet the user's criteria. For example, searches for property listings with 4 bedrooms, kitchens, parking spaces, and pet-friendly rooms, as well as information on parks, schools, and public transportation.

[1081] Step 6:

[1082] Server: Uses sophisticated AI algorithms to analyze acquired property and area data. Specific processes include:

[1083] 1. Score the degree of match between the user's conditions and the property.

[1084] 2. Evaluate the area's amenities, taking into account commute times and access to public facilities.

[1085] 3. Taking into account emotional data, adjustments are made, such as prioritizing properties that users feel more comfortable with.

[1086] 4. Look at past user feedback data and apply a model to predict satisfaction.

[1087] 5. Calculate an overall score and rank the best properties and neighborhoods.

[1088] Step 7:

[1089] Server: Based on the results of the AI ​​algorithm, the server generates a list of properties and areas to recommend to the user, including detailed information about each property (price, location, photos, etc.).

[1090] Step 8:

[1091] Server: The recommendation results are sent to the user's device in JSON or XML format, and the data is encrypted using the SSL / TLS protocol.

[1092] Step 9:

[1093] Device: Receives the recommendation results sent from the server and displays them in an easy-to-understand manner to the user. For example, Property A: Within budget, easy commute, 10 minutes walk to the park; Property B: Within school district, pets allowed, 15 minutes to Shinjuku.

[1094] ---

[1095] We have explained in detail the processing steps of a real estate search system that combines an emotion engine. This system allows users to receive more personalized property recommendations based on their emotional state.

[1096] Example 2

[1097] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1098] Conventional real estate search systems recommend properties based only on the user's simple criteria (such as family composition and budget), making it difficult to provide personalized recommendations that reflect the user's emotions and lifestyle. Furthermore, general AI algorithms do not take the user's emotional state into account, making it difficult to find properties that will satisfy them. This makes it difficult for users to quickly and efficiently find the perfect property, often resulting in frustration.

[1099] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes: means for receiving information input by the user regarding family composition, place of work, educational institutions, daily range of activities, hobbies, important conditions for the purchase request, and budget; means for analyzing the information and saving the analysis results in a database; means for acquiring area and property data from the database; means for acquiring user emotion data through facial expression recognition and voice analysis; means for using the emotion data to recommend optimal properties and areas using an AI algorithm; and means for displaying the recommendation results on the user terminal. This enables more personalized real estate property recommendations that reflect the user's emotional state and individual lifestyle.

[1100] "User Information" means information entered by a User through the web interface or mobile application, such as family composition, place of employment, educational institution, daily activities, hobbies, important purchase requirements, and budget.

[1101] "Analysis" refers to storing user information in a database, extracting the necessary items, and processing them.

[1102] A "database" is a collection of data used to store and manage real estate property information, local information, user information, etc.

[1103] "Local data" refers to information about public facilities, transportation, infrastructure, etc. in a particular local area.

[1104] "Property data" refers to detailed information about a real estate property, including, for example, floor plan, price, location, facilities, etc.

[1105] "Facial expression recognition" is a technology that uses a camera to analyze a user's facial expressions in real time and identify their emotional state.

[1106] "Voice analysis" is a technology that analyzes a user's tone of voice and speaking style in real time through a microphone to identify their emotional state.

[1107] "Emotional data" is information about the user's emotional state obtained through facial expression recognition and voice analysis.

[1108] An "AI algorithm" is a mathematical method that uses machine learning and data analysis to process input data and recommend the most suitable properties and areas.

[1109] "Recommendation Results" refers to a list of properties and areas suitable for the user, derived through analysis by an AI algorithm.

[1110] This real estate search system recommends the best properties and areas based on information entered by the user and sentiment data collected in real time. The system is implemented using the following hardware and software:

[1111] Hardware and software used

[1112] 1. Device: A device such as a PC, smartphone, or tablet with a web browser is used. Users enter information using these devices.

[1113] 2. Facial Recognition Camera: Uses a camera to analyze the user's facial expressions in real time.

[1114] 3. Voice Analysis Microphone: Uses a microphone to analyze the user's tone of voice and speaking style.

[1115] 4. Server: A server is used to run high-performance AI algorithms. This server also handles database management and calculations.

[1116] Specific operation of the system

[1117] Entering user information

[1118] Users enter information about their family, place of employment, educational institution, daily activities, hobbies, important purchase requirements, and budget through a web interface or mobile application. This information is important for improving the system's recommendation accuracy. For example, suppose a user enters the following information:

[1119] Family size: 4 people (2 adults, 2 children)

[1120] Location: Shinjuku

[1121] Educational institution: Ueno Ward Daiichi Elementary School

[1122] Daily range of activities: Tokyo's 23 wards

[1123] Hobbies: Jogging in the park

[1124] Important conditions for purchase: 4LDK, parking space, pets allowed

[1125] Budget: 70 million yen

[1126] Emotion recognition by emotion engine

[1127] The emotion engine uses a facial recognition camera and a voice analysis microphone to analyze the user's emotions in real time while they are entering information. For example, if a user looks anxious, the emotional state will be recorded as "anxiety." This emotional data is reflected in the recommendation algorithm to increase user satisfaction.

[1128] Sending and Receiving Data

[1129] When a user clicks the "Search" button, the entered information and emotion data are encrypted using the SSL / TLS protocol and sent to the server, which then analyzes the data and stores it in a database using each item as a key.

[1130] Obtaining area and property data

[1131] The server accesses the database and searches for suitable areas and properties based on the user's criteria and emotional data. Property details (e.g., floor plan, price, location, etc.) are obtained from the real estate database, and information on public facilities and infrastructure is obtained from the local information database.

[1132] AI recommendation algorithm

[1133] The server analyzes the acquired data using a high-performance AI algorithm and performs the following processes:

[1134] 1. Score the degree of match between the input conditions and the property.

[1135] 2. Evaluate the area's amenities, considering factors such as commute time, access to schools, and proximity to parks suitable for jogging.

[1136] 3. Use emotional data to tailor recommendation results based on the user's emotional state.

[1137] 4. Look at past user feedback data and apply a model to predict satisfaction.

[1138] 5. Calculate an overall score and display the best properties and areas in ranked order.

[1139] Generating and displaying recommendations

[1140] Based on the results of the AI ​​algorithm, the server generates a list of properties and areas to recommend to the user. The recommendations are ranked and include detailed information about each property (e.g., price, location, photos, etc.). The device receives this information and displays it in a user-friendly format.

[1141] Examples and prompts

[1142] Specific examples

[1143] Family size: 4 people (2 adults, 2 children)

[1144] Location: Shinjuku

[1145] Educational institution: Ueno Ward Daiichi Elementary School

[1146] Daily range of activities: Tokyo's 23 wards

[1147] Hobbies: Jogging in the park

[1148] Important conditions for purchase: 4LDK, parking space, pets allowed

[1149] Budget: 70 million yen

[1150] Prompt Sentence Examples

[1151] "Enter your family composition, place of employment, school, and other requirements. We will recommend the ideal property for you."

[1152] "We take into account emotional data to recommend properties that users can feel comfortable with."

[1153] "We will design a system that uses user criteria and emotional data to recommend the best real estate properties."

[1154] As a result, this real estate search system is able to provide personalized recommendations that reflect the user's feelings and requirements.

[1155] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1156] Step 1: Enter your user information

[1157] Users open a web interface or mobile application and enter information about their family composition, workplace, educational institution, daily activities, hobbies, key purchase criteria, and budget. Examples of input information include "Family composition: 4 people (2 adults, 2 children)," "Workplace: Shinjuku," "Educational institution: Ueno Ward Daiichi Elementary School," "Daily activities: Within Tokyo's 23 wards," "Hobbies: Jogging in the park," "Key purchase criteria: 4LDK, parking, pets allowed," and "Budget: 70 million yen." This information serves as input data and is important for specifically reflecting the user's needs and criteria.

[1158] Step 2: Emotion recognition by the emotion engine

[1159] The server uses a facial expression recognition camera and a voice analysis microphone to analyze the user's facial expressions and voice as they are input. The facial expression recognition camera captures the subtle movements of the user's face, and the voice analysis microphone analyzes the user's tone of voice and speaking style. For example, if the user has an anxious expression or a low tone of voice, the emotional state of "anxiety" is detected. This emotional data is used as input data for the next processing step.

[1160] Step 3: Sending data

[1161] When the user clicks the "Search" button, the device encrypts the information entered by the user and the emotion data obtained by the emotion engine using the SSL / TLS protocol and sends it to the server. This sent data becomes the input data for the server.

[1162] Step 4: Receiving and storing data

[1163] The server receives the data sent from the device and parses it in JSON or XML format. The received data includes the user's family structure, place of work, educational institution, daily range of activities, hobbies, important conditions for desired purchases, budget, and emotional state. This data is analyzed and each item is stored as a key in the database. The analysis results are stored in the database and used for search processing in the next step.

[1164] Step 5: Obtaining location and property data

[1165] The server accesses the real estate database and local information database based on the user's criteria and emotion data stored in the database. Specifically, it extracts property listings that match the user's filter criteria and obtains information about the surrounding area (nearby parks, schools, public transportation, etc.). This obtained data becomes input data for the AI ​​algorithm in the next step.

[1166] Step 6: AI recommendation algorithm

[1167] The server uses a high-performance AI algorithm to analyze the input data and perform the following processes: First, it scores the degree of match between the user's input criteria and the property. It also evaluates the convenience of the area, taking into account factors such as commute time, access to schools, and proximity to public facilities. It also uses emotional data to adjust the recommendation results based on the user's emotional state. It references past user feedback data and applies a model to predict satisfaction. By processing and calculating these data, it calculates an overall score and ranks the properties.

[1168] Step 7: Generate and display recommendations

[1169] Based on the results of the AI ​​algorithm, the server generates a list of properties and areas to recommend to the user. The generated list also includes detailed property information (price, location, photos, etc.). These recommendation results become the output data from the server. The device receives the recommended results and displays them in a format that is easy for the user to understand. Based on this information, the user can intuitively and efficiently select the most suitable property.

[1170] Detailed processing is carried out at each step, resulting in personalized property recommendations that reflect the user's needs and emotions.

[1171] (Application example 2)

[1172] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1173] While conventional real estate search systems can recommend properties based on user input, they are limited in providing personalized recommendations that take into account the user's emotional state. Furthermore, viewing property details requires a site visit to get a real feel for the property, which is time-consuming and labor-intensive for users. The present invention aims to solve these problems and provide a real estate search system that enables more personalized property recommendations and property viewing using virtual reality technology.

[1174] The specification processing by the specification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving information and emotional state input by the user regarding family composition, place of work, school, daily range of activities, hobbies, important conditions for a property to be purchased, and budget; means for analyzing the information and emotional state and saving the analysis results in a database; means for acquiring area and property data from the database; means for recommending optimal properties and areas based on the acquired data using an AI algorithm; and means for displaying the recommendation results on the user terminal and allowing the user to view properties using virtual reality technology. This allows for more personalized property recommendations based on the user's emotional state, and allows the user to view properties using virtual reality technology through a smartphone application.

[1175] "User information" refers to information regarding family composition, place of employment, school attended, daily range of activities, hobbies, important conditions required for a property to be purchased, and budget.

[1176] "Emotional state" is data that represents the user's emotions, and is information obtained by analyzing the user's facial expressions, tone of voice, etc.

[1177] "Analysis" is the process of processing user information and emotional states as data to understand their content.

[1178] "Database" means a digital repository for organizing and storing information, including user information, emotional states, local area information, and property information.

[1179] "Local and Property Data" means detailed information about the recommended real estate and its surrounding area.

[1180] "AI Algorithm" refers to an artificial intelligence calculation method used to recommend optimal properties and areas based on user information and emotional state.

[1181] "Virtual reality technology" is a technology that provides users with a sense of realism through a computer-generated virtual space.

[1182] "Recommendation results" are a list of the best properties and areas selected by an AI algorithm.

[1183] The real estate search system of the present invention is a system that uses an AI algorithm to recommend optimal properties and areas based on the user's input information and emotional state, and allows the user to view properties using virtual reality technology. Specific embodiments of the system are described below.

[1184] 1. Entering user information and acquiring emotional state

[1185] Users enter information about their family, place of employment, school, daily activities, hobbies, important requirements for a property to purchase, and budget through a smartphone application. The smartphone's front camera and microphone are used to analyze the user's facial expressions and tone of voice. Specifically, OpenCV is used for facial recognition, and the Google Cloud Speech-to-Text API is used to analyze emotions from voice.

[1186] Examples:

[1187] A user enters information into an input field within an application.

[1188] Example prompt: "Please tell us about your family structure, place of work, daily activities, hobbies, property requirements, and budget."

[1189] 2. Data transmission to the server and analysis

[1190] The device sends the entered user information and analyzed emotional state data to the server using SSL / TLS encryption, which then analyzes the data and stores it in a database. This process is performed using Python and Django.

[1191] 3. Obtaining area and property data

[1192] The server accesses a real estate database to retrieve property and area information that matches the user's criteria, using a relational database such as PostgreSQL.

[1193] 4. Recommendation of optimal properties using AI

[1194] The server uses AI algorithms using TensorFlow or PyTorch to score and recommend the best properties and neighborhoods based on user information and emotional state, taking into account commute times, school districts, access to public facilities, past user feedback, and emotional data.

[1195] 5. Recommendation results and virtual reality property viewing

[1196] The server generates recommendations and sends them to a smartphone application that allows users to view properties using virtual reality technology. Within the app, users can view the list of recommended properties and view each property in detail in VR mode.

[1197] Examples:

[1198] Recommended properties will be displayed on your smartphone.

[1199] Example prompt: "The following properties are recommended for you. Property A: Within your budget, easy commute, 10 minutes' walk to the park. Property B: Within the school district, pet-friendly, 15 minutes' walk to Shinjuku."

[1200] This system not only enables personalized property recommendations that reflect the user's emotional state, but also allows users to intuitively experience detailed property information using virtual reality, which is expected to significantly reduce the time and effort required when selecting a property.

[1201] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1202] Step 1:

[1203] Users use a smartphone application to input information about their family structure, place of work, school, daily range of activities, hobbies, important requirements for a property to purchase, and budget. At this time, the smartphone's front camera and microphone are used to analyze the user's facial expressions and tone of voice. Text information is entered as input data, and audio and video data acquired from the camera and microphone are used for emotion analysis. The input data consists of the user's desired conditions (text) and emotional data (video and audio). The output data after analysis is the user's information and emotional state.

[1204] Step 2:

[1205] The terminal transmits the information entered by the user and the analyzed emotion data to the server using the SSL / TLS encryption protocol. The specific process involves packaging and encrypting the user information and emotion data and transmitting them to the server. The input data is the user's desired conditions and emotion data, and the output data is the encrypted transmission data.

[1206] Step 3:

[1207] The server analyzes the received user information and emotion data and stores them in a database. This process uses Django and involves parsing the data and storing it in the database. The input data is encrypted user information and emotion data, and the output data is the user information and emotion data stored in the database.

[1208] Step 4:

[1209] The server retrieves area and property data from a real estate database. PostgreSQL is used as the database. A query is executed for properties that match the criteria, and relevant property information is retrieved. The input data is the user's desired criteria, and the output data is a list of candidate property data.

[1210] Step 5:

[1211] The server uses an AI algorithm using TensorFlow or PyTorch to score the best properties and areas based on the user's desired conditions and emotional state. The algorithm includes commute time, school district, access to public facilities, past user feedback, and emotional data. The input data is user information, emotional data, and property data, and the output data is a list of scored properties.

[1212] Step 6:

[1213] The server generates recommendation results and sends them to the user's device. The recommendation results include a list of scored properties with detailed information. The input data is the property scoring results, and the output data is the list of recommendation results.

[1214] Step 7:

[1215] Users receive recommendation results on their smartphone application and can view properties using virtual reality technology. Users can check the list of recommended properties within the app and view each property in detail in VR mode. The input data is the recommendation results, and the output data is property information displayed in virtual reality.

[1216] Through the above processing steps, users can receive personalized property recommendations that take into account their emotional state and can experience detailed property information using virtual reality technology.

[1217] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1218] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1219] 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 the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1220] [Fourth embodiment]

[1221] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1222] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1223] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1224] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1225] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1226] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1227] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1228] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1229] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1230] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific 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.

[1231] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1232] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1233] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1234] ---

[1235] The real estate search system of the present invention uses an AI algorithm to recommend optimal properties and areas based on information entered by the user. The system aims to quickly and accurately meet the user's needs and resolve the problems associated with conventional real estate search services. Specific embodiments of the present invention are described below.

[1236] Entering user information

[1237] Users: Through a web interface or mobile application, users enter information about their family structure, place of employment, school attendance, daily activities, hobbies, important requirements for a property, and budget. This information accurately reflects the user's desires and lifestyle patterns, and is necessary to improve the system's recommendations.

[1238] Sending and Receiving Data

[1239] Terminal: After the user has finished entering the information, they click the "Search" button, which sends the entered data to the server. The data is encrypted using SSL / TLS protocol for security.

[1240] Server: Receives data sent from the device and begins analysis. The analysis process parses the received data in JSON or XML format and stores each item as a key in a database.

[1241] Obtaining area and property data

[1242] Server: Based on the user's criteria, the server accesses real estate and local information databases to retrieve local and property data that matches the user's input. This data includes property details (such as floor plan, price, and location) and information about local public facilities and infrastructure (such as schools, parks, and public transportation).

[1243] AI recommendation algorithm

[1244] Server: The server uses a sophisticated AI algorithm to analyze the acquired property and area data. The AI ​​algorithm works as follows:

[1245] 1. Score the degree of match between the user's conditions and the property.

[1246] 2. Evaluate the area's amenities, considering commute times, school districts, and access to public facilities.

[1247] 3. Look at past user feedback data and apply a model to predict satisfaction.

[1248] 4. Calculate an overall score and rank the best properties and neighborhoods.

[1249] Generating and displaying recommendations

[1250] Server: Based on the results of the AI ​​algorithm, the server generates a list of properties and areas to recommend to the user. The list of recommended properties is sorted by ranking and includes detailed information about each property (price, location, photos, etc.).

[1251] Terminal: Receives the recommendation results sent from the server and displays them in a user-friendly format, allowing users to intuitively identify the most suitable property.

[1252] Specific examples

[1253] Entering user information

[1254] User: Family composition: 4 people (2 adults, 2 children), Workplace: Shinjuku, School: Ueno Ward Daiichi Elementary School, Daily range of activities: Within Tokyo's 23 wards, Hobbies: Jogging in the park, Important conditions for a property to purchase: 4LDK, parking, pets allowed, Budget: 70 million yen.

[1255] Sending and Receiving Data

[1256] Terminal: Input data is sent to the server using the https protocol.

[1257] Server: Receives the data and parses information such as family size: 4 people, workplace: Shinjuku, school attended: Ueno Ward Daiichi Elementary School, and saves it in a database.

[1258] Obtaining area and property data

[1259] Server: Accesses the real estate database to retrieve property listings that match the criteria and information about the surrounding area (nearby parks, schools, public transportation, etc.).

[1260] AI recommendation algorithm

[1261] Server: Uses AI to score every property, evaluating factors like commute convenience, access to schools, proximity to parks, etc., and calculates an overall score that also takes into account past feedback data.

[1262] Generating and displaying recommendations

[1263] Server: Generates a list of properties A-E, including detailed information about each property (e.g. price, location, photos, etc.).

[1264] Device: Receives the recommendation results and displays them to the user, such as Property A: within budget, convenient commute, 10-minute walk to the park, Property B: within school district, pet-friendly, 15-minute walk to Shinjuku.

[1265] In this way, the real estate search system of the present invention can quickly and accurately recommend the most suitable properties and areas for users and corporations. This system meets the diverse needs of users and provides a stress-free property search.

[1266] The processing flow will be explained below.

[1267] ---

[1268] Step 1: Enter your user information

[1269] Users: Enter information about their family, place of employment, school attendance, daily activities, hobbies, key requirements for the property they are looking for, and budget via a web interface or mobile application.

[1270] Step 2: Submit input data

[1271] On the device: When the user clicks the "Search" button, all entered information is sent to the server, where the data is encrypted using the SSL / TLS protocol.

[1272] Step 3: Receiving and analyzing data

[1273] Server: Receives user input data sent from the device. The data is received in JSON or XML format and parsed. Then, the parsed data is saved in the database.

[1274] Step 4: Obtaining location and property data

[1275] Server: Based on the user's criteria, the server accesses real estate and local information databases to search for suitable areas and properties. From the databases, the server obtains detailed information about the properties (floor plan, price, location, etc.) and information about local public facilities and infrastructure (schools, parks, public transportation, etc.).

[1276] Step 5: Run the AI ​​recommendation algorithm

[1277] Server: Using high-performance AI algorithms, the acquired property and area data is analyzed. Specifically, the following processes are performed:

[1278] 1. Score the degree of match between the user's conditions and the property.

[1279] 2. Evaluate the area's amenities (commute time, school district, access to public facilities).

[1280] 3. Look at past user feedback data and apply a model to predict satisfaction.

[1281] 4. Calculate an overall score and rank the best properties and neighborhoods.

[1282] Step 6: Generate recommendations

[1283] Server: Based on the results of the AI ​​algorithm, the server generates a list of properties and areas to recommend to users, including detailed information about each property (price, location, photos, etc.).

[1284] Step 7: Submit and view your nominations

[1285] Server: The recommendation results are sent to the user's device in JSON or XML format, and the data is encrypted using SSL / TLS protocol.

[1286] Device: Receives the recommendations sent from the server and displays them in a user-friendly format, presented as a list with property images and details.

[1287] ---

[1288] Above, we have explained the specific steps of the program's processing flow. This system allows users to quickly and accurately find properties that meet their needs.

[1289] Example 1

[1290] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1291] Conventional real estate search systems have the problem of making it difficult to quickly and accurately find properties that meet users' requirements. Furthermore, they are unable to properly evaluate commute times, school districts, and access to public facilities, and are therefore unable to adequately meet the diverse needs of users. In particular, they lack a means to increase user satisfaction by utilizing AI algorithms.

[1292] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1293] In this invention, the server includes means for receiving information input by a user, means for analyzing the information and saving it in a database, means for encrypting the input information using the SSL / TLS protocol and sending it to the server, means for parsing the information in JSON or XML format, means for acquiring area and property data from the database, means for analyzing the acquired property data and area data using an AI algorithm based on the user's conditions and recommending optimal properties and areas, and means for displaying the recommendation results on the user's terminal, thereby making it possible to quickly and accurately recommend optimal properties and areas that meet the user's conditions.

[1294] "Information entered by the user" refers to information regarding family composition, place of employment, school attended, range of daily activities, hobbies, important conditions required for a property to be purchased, and budget.

[1295] The "means for analyzing information and storing it in a database" refers to the means for converting information received from a user into a structured data format and executing the process of storing it in a database.

[1296] "Means of encrypting using the SSL / TLS protocol and sending it to the server" is a mechanism for encrypting data entered by the user using the SSL / TLS protocol and transmitting it securely to the server.

[1297] The "means for parsing in JSON or XML format" is a process for converting received data into JSON or XML format and parsing it to obtain each item.

[1298] "Means for retrieving locality and property data" means a mechanism for querying and retrieving relevant data from real estate and locality databases based on stored user criteria.

[1299] "Means for analyzing property data and area data obtained using AI algorithms and recommending optimal properties and areas" refers to a means for using AI to evaluate data collected based on the user's conditions and carry out a process to select the optimal property and area.

[1300] "Means for displaying recommendation results on the user's device" refers to a mechanism for displaying recommendation results selected by AI on the user's device in an easy-to-understand format.

[1301] This invention is a real estate search system that uses an AI algorithm to recommend optimal properties and areas based on information entered by the user. The system aims to quickly and accurately meet user needs and resolve the problems of conventional real estate search services.

[1302] System Overview

[1303] The system includes the following elements:

[1304] 1. A means of receiving information entered by the user

[1305] 2. Means for analyzing the information and storing it in a database

[1306] 3. Encrypting data using the SSL / TLS protocol and sending it to the server

[1307] 4. Means for parsing said information in JSON or XML format

[1308] 5. How to obtain area and property data

[1309] 6. A method to use AI algorithms to analyze acquired property and area data and recommend the most suitable property and area

[1310] 7. Means for displaying the recommendation results on the user terminal

[1311] Hardware and Software Configuration

[1312] This embodiment uses a dedicated server and user terminals, which can be PCs, smartphones, tablets, etc. Users input information through a web interface or mobile application, which provides basic UI elements including a data entry form and a submit button.

[1313] The server is composed of a high-performance computer and is equipped with a communication module compatible with the SSL / TLS protocol, as well as a data analysis module, an AI algorithm module, and a database management system.

[1314] Specific processing of the program

[1315] Using a web interface or mobile application, users enter details such as family size, place of employment, school attendance, daily activities, hobbies, key requirements for a property, and budget. This information is entered in a form format, with required and optional fields clearly indicated. For example, a family size might be "4 people (2 adults, 2 children)," a place of employment might be "Shinjuku," and a school attendance might be "Ueno Ward First Elementary School."

[1316] When the user clicks the "Search" button, the device encrypts the entered data using the SSL / TLS protocol and sends it to the server. The server receives this data, parses it in JSON or XML format, and stores each item in a database. For example, data such as family size "4 people," workplace "Shinjuku," and school attended "Ueno Ward First Elementary School" are stored in the respective fields.

[1317] The server then accesses a real estate database and a local area information database to retrieve property and local area data that matches the user's criteria, including property details (such as floor plan, price, and location) and information about local public facilities and infrastructure (such as schools, parks, and public transportation).

[1318] The AI ​​algorithm scores properties based on user criteria, taking into account commute time, school districts, and access to public facilities, and then calculates an overall score by looking at past user feedback data and applying a model to predict satisfaction, then ranks the best properties and neighborhoods.

[1319] Finally, based on the results of the AI ​​algorithm, a list of properties and areas recommended to the user is generated and sent to the device along with detailed information (price, location, photos, etc.). The device receives these recommendations and displays them to the user in an intuitive format. For example, it might say, "Property A is within your budget, convenient for commuting, and a 10-minute walk to the park," or "Property B is within the school district, pet-friendly, and 15 minutes to Shinjuku."

[1320] Specific examples

[1321] As an example of user information input, let's assume a family of four (two adults, two children), workplace in Shinjuku, school attended by Ueno Ward Daiichi Elementary School, daily activities within Tokyo's 23 wards, hobby jogging in parks, requirements for a property to be purchased: 4LDK, with parking, pets allowed, budget of 70 million yen.

[1322] An example of a prompt sentence is, "Please recommend a property with a 4LDK, parking, pet-friendly, and a budget of 70 million yen for a family of four, who commute to Shinjuku and have a child attending Ueno Ward Daiichi Elementary School."

[1323] As described above, the real estate search system of the present invention can meet the diverse needs of users, quickly and accurately recommend the most suitable properties and areas, and provide a stress-free real estate search.

[1324] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1325] Step 1:

[1326] Using a web interface or mobile application, users enter details such as family composition, place of employment, school attendance, daily activities, hobbies, key requirements for a property to purchase, budget, etc. This information is entered in a form format, with required and optional fields clearly marked.

[1327] Input: User information such as family composition, place of employment, school attended, etc.

[1328] Output: A set of form data

[1329] Step 2:

[1330] When the user clicks the "Search" button, the terminal encrypts the data entered in the form using SSL / TLS protocol and sends it to the server, ensuring secure communication during this process.

[1331] Input: User details entered

[1332] Output: Encrypted data packet

[1333] Step 3:

[1334] The server receives the data sent from the device and decrypts it using SSL / TLS. It then parses the data in JSON or XML format, extracts each item, and stores it in a database. For example, information such as family size ("4 people"), workplace ("Shinjuku"), and school attended ("Ueno Ward First Elementary School") are stored in each field of the database.

[1335] Input: Encrypted data

[1336] Output: Structured data stored in a database

[1337] Step 4:

[1338] The server accesses the database and queries relevant neighborhood and property data based on the user's criteria. In this process, the data retrieved includes property details and local infrastructure information (schools, parks, public transportation, etc.).

[1339] Input: A query based on the user's criteria

[1340] Output: Property and area information as query results

[1341] Step 5:

[1342] The server uses AI algorithms to analyze the acquired property and area data, including the following specific operations:

[1343] 1. Scoring: Scoring the property's suitability to the user's criteria.

[1344] 2. Convenience evaluation: Evaluate by taking into account commute time, school districts, and access to public facilities.

[1345] 3. Feedback reference: Apply a model to predict satisfaction based on past user feedback data.

[1346] 4. Overall score calculation: Each evaluation item is combined to calculate an overall score and rank the property.

[1347] Input: Property data and area data

[1348] Output: Ranked property list

[1349] Step 6:

[1350] Based on the results of the AI ​​algorithm, the server generates a list of properties and areas recommended to the user, and adds detailed information (price, location, photos, etc.).

[1351] Input: A list of properties with an overall score

[1352] Output: Recommendation result list

[1353] Step 7:

[1354] The device receives the recommendation results sent from the server and displays them in a format that is easy for the user to understand. Specifically, information such as property price, location, and photos is displayed in a dashboard format, and the properties are ranked.

[1355] Input: Recommendation results received from the server

[1356] Output: The displayed property list and details

[1357] In this way, each step works in tandem, allowing users to quickly and accurately find the perfect property and area.

[1358] (Application example 1)

[1359] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1360] With conventional real estate search systems, users must search on websites or mobile applications to obtain property information, which requires processing large amounts of information at once. Furthermore, when viewing properties on-site, users must carry a separate device (such as a smartphone or tablet) to refer to information they have previously researched. Furthermore, there are limited ways to view detailed information about properties and surrounding facilities at a glance. There is a need for a system that can solve these problems and enable users to more intuitively obtain and view real estate information in real time.

[1361] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1362] In this invention, the server includes means for receiving information entered by the user regarding family composition, place of work, school, daily range of activities, hobbies, important conditions for a property to be purchased, and budget, means for analyzing the information and saving the analysis results in a database, means for acquiring area and property data from the database, means for recommending optimal properties and areas based on the acquired data using an AI algorithm, and means for displaying the recommendation results on the user's terminal and displaying area and property information using augmented reality via a smart device.This allows users to visually acquire and check property information and surrounding facility information on site in real time, enabling them to use real estate information more intuitively and efficiently.

[1363] "Information entered by the user" refers to information entered by the user regarding family composition, place of work, school attended, range of daily activities, hobbies, important conditions required for a property to be purchased, and budget.

[1364] "Analysis" refers to the process of analyzing the information received from users and using that information to identify the best properties and areas.

[1365] A "database" is a system for storing user information and area / property data, and retrieving it as needed.

[1366] "Retrieval" refers to pulling the required data from the database.

[1367] An "AI algorithm" is an algorithm that uses artificial intelligence to solve complex problems and recommend the best properties and areas.

[1368] "Recommendation" means presenting the best options based on the information entered by the user.

[1369] "Display" means providing information visually to a user terminal or smart device.

[1370] "Smart devices" refers to high-function devices such as smart glasses and head-mounted displays.

[1371] "Augmented reality" is a technology that displays digital information overlaid on real-world scenery.

[1372] The system of the present invention recommends optimal real estate properties based on information entered by the user, and allows the user to obtain and confirm that information in real time. The system includes a server, a user terminal, and a smart device.

[1373] 1. Input and Receipt of User Information

[1374] Server: Using a web interface, mobile application, or smart device, the user enters information about their family, place of employment, school attendance, daily activities, hobbies, key property requirements, and budget. This information is provided via voice or text input.

[1375] 2. Data submission and analysis

[1376] On the device: The information entered by the user is sent to the server in JSON format, encrypted using the HTTPS protocol.

[1377] Server: Analyzes the received data and stores it in a database, which contains real estate property data and local information data (e.g., MySQL).

[1378] 3. Obtaining area and property data

[1379] Server: Accesses real estate databases and local information databases based on the user's requirements and retrieves the required data.

[1380] 4. AI-based data analysis and recommendations

[1381] Server: Runs AI algorithms using TensorFlow and Scikit-learn to analyze the acquired property and neighborhood data, taking into account commute times, school districts, access to public facilities, and past user feedback.

[1382] 5. Generating and Displaying Recommendations

[1383] Server: Based on the analysis results of the AI ​​algorithm, the server generates a ranking of the most suitable properties and areas, and sends the recommendation results to the user's terminal or smart device.

[1384] 6. Display and operation using smart devices

[1385] Terminal: Smart devices (e.g., smart glasses or head-mounted displays) use Unity or ARKit to display property information using augmented reality (AR). Users can use the device on-site to visually check property information and surrounding facilities in real time.

[1386] Examples of concrete examples and prompts

[1387] Example: While a user is walking around Shinjuku Station, the smart glasses display information such as "Shinjuku Building 5, 4LDK, 68 million yen, 5-minute walk from the station, 3-minute walk to the supermarket." If the user uses the device's microphone to ask, "Where is the nearest park from here?", the location of the park will be displayed in the user's field of vision.

[1388] Example prompt sentence:

[1389] User input prompt: "Find properties within 15 minutes of Shinjuku Station, suitable for families, and budgets under 70 million yen."

[1390] Prompt for generative AI model: "Based on the user's input, please have the algorithm recommend the best family-friendly properties in the Shinjuku area."

[1391] This makes it possible to specifically implement the form of the invention, and allows users to intuitively use real estate information on-site.

[1392] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1393] Step 1:

[1394] Enter and submit user information

[1395] Users: Through a web interface, mobile application, or smart device, they enter information about their family composition, place of employment, school attendance, daily activities, hobbies, key requirements for a property, and budget.

[1396] Input: Family composition, place of work, school, daily activities, hobbies, important conditions, budget

[1397] Output: User information in JSON format

[1398] Specific operation: The user provides information by voice or touch input. The device converts the input data into JSON format and sends it to the server via HTTPS protocol.

[1399] Step 2:

[1400] Data reception and analysis

[1401] Server: Receives JSON formatted data sent by the user and parses it using a JSON parser.

[1402] Input: User information in JSON format

[1403] Output: Structured user information (e.g., information as variables and fields)

[1404] What happens: The server receives the HTTPS request, decodes the JSON data, converts it into a structured format, and saves it in the database.

[1405] Step 3:

[1406] Obtaining area and property data

[1407] Server: Based on the user information, accesses the real estate database and area information database to retrieve the appropriate area and property data.

[1408] Input: Structured user information

[1409] Output: Area and property data

[1410] What it does: Executes a database query to retrieve properties and locality information that match the user's criteria. For example, it uses a MySQL query to pull the required information from the database.

[1411] Step 4:

[1412] AI-based data analysis and recommendations

[1413] Server: Using TensorFlow and Scikit-learn, the acquired property data and area data are analyzed using AI algorithms.

[1414] Input: Area and property data

[1415] Output: Recommended property list (with scores)

[1416] How it works: Data is fed into an AI model, which scores properties based on factors such as commute time, school district, access to public facilities, and past user feedback, generating a ranked list of recommended properties.

[1417] Step 5:

[1418] Generating and sending recommendations

[1419] Server: Generates a list of recommended properties and sends it to the user's terminal or smart device.

[1420] Input: Recommended property list

[1421] Output: Recommendation results in JSON format

[1422] Specific operation: Convert the recommended property list into JSON format and send it to the user terminal or smart device using the HTTPS protocol.

[1423] Step 6:

[1424] Display and operation using a smart device

[1425] Device: Uses the AR function of your smart device to display recommended property information and local information.

[1426] Input: Recommendation results in JSON format

[1427] Output: Property information displayed in AR

[1428] How it works: Using Unity and ARKit, property information is overlaid on the smart device's camera view. Users can view the information in real time through their device, and additional information is displayed in response to input prompts.

[1429] This will enable users to more intuitively obtain and check real estate property information using their smart devices, enabling them to make real-time decisions.

[1430] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1431] ---

[1432] The real estate search system of the present invention uses an AI algorithm to recommend optimal properties and areas based on information entered by a user, and also combines an emotion engine that recognizes the user's emotions to provide more personalized recommendations. Specific embodiments of the present invention are described below.

[1433] Entering user information

[1434] Users: Through a web interface or mobile application, users enter information about their family structure, place of employment, school attendance, daily activities, hobbies, important requirements for a property, and budget. This information is important for improving the system's recommendation accuracy, as it accurately reflects the user's desires and lifestyle patterns.

[1435] Emotion recognition by emotion engine

[1436] Emotion engine: When a user inputs information, it uses a facial recognition camera and a voice analysis microphone to recognize the user's emotions in real time. It analyzes the input data, facial expressions during operation, and tone of voice to determine the user's emotional state (e.g., satisfaction, dissatisfaction, expectation, excitement, etc.).

[1437] Sending and Receiving Data

[1438] On the device: When the user clicks the "Search" button, the entered data and the emotion data from the emotion engine are sent to the server, encrypted using the SSL / TLS protocol.

[1439] Server: Receives data sent from the device and begins analysis. The analysis process parses the received data in JSON or XML format and stores each item as a key in a database.

[1440] Obtaining area and property data

[1441] Server: Based on the user's criteria and emotion data, the server accesses the real estate database and local information database to search for suitable areas and properties. From the database, the server obtains detailed information about the property (floor plan, price, location, etc.) and information about local public facilities and infrastructure (schools, parks, public transportation, etc.).

[1442] AI recommendation algorithm

[1443] Server: The server uses sophisticated AI algorithms to analyze the acquired property and area data. The AI ​​algorithms work as follows:

[1444] 1. Score the degree of match between the user's conditions and the property.

[1445] 2. Evaluate the area's amenities, considering commute times, school districts, and access to public facilities.

[1446] 3. Emotional data obtained from the emotion engine is added to adjust the recommendation results based on the user's emotional state.

[1447] 4. Look at past user feedback data and apply a model to predict satisfaction.

[1448] 5. Calculate an overall score and rank the best properties and neighborhoods.

[1449] Generating and displaying recommendations

[1450] Server: Based on the results of the AI ​​algorithm, the server generates a list of properties and areas to recommend to users. The recommended properties are sorted by ranking and include detailed information about each property (price, location, photos, etc.).

[1451] Terminal: Receives the recommendation results sent from the server and displays them in a user-friendly format, allowing users to intuitively identify the most suitable property.

[1452] Specific examples

[1453] Entering user information

[1454] User: Family composition: 4 people (2 adults, 2 children), Workplace: Shinjuku, School: Ueno Ward Daiichi Elementary School, Daily range of activities: Within Tokyo's 23 wards, Hobbies: Jogging in the park, Important conditions for a property to purchase: 4LDK, parking, pets allowed, Budget: 70 million yen.

[1455] Emotion recognition by emotion engine

[1456] Emotion engine: Analyzes the user's facial expressions and tone of voice when inputting information to identify the user's current emotional state. For example, if the user looks anxious, the system will capture that information as emotion data.

[1457] Sending and Receiving Data

[1458] Device: When the user presses the "Search" button, the input data and emotion data are sent to the server using the https protocol.

[1459] Server: Receives the data, parses the user's family size: 4 people, workplace: Shinjuku, school attended: Ueno Ward Daiichi Elementary School, and emotional state (e.g., anxiety) and stores it in a database.

[1460] Obtaining area and property data

[1461] Server: Accesses the real estate database to retrieve property listings that match the criteria and information about the surrounding area (e.g., nearby parks, schools, public transportation, etc.).

[1462] AI recommendation algorithm

[1463] Server: Using AI, the server scores all properties, evaluating factors such as commute convenience, access to schools, and proximity to parks suitable for jogging. It also takes into account sentiment data, prioritizing safer neighborhoods to alleviate anxiety. It also takes into account past feedback data to calculate an overall score.

[1464] Generating and displaying recommendations

[1465] Server: Generates a list of properties A-E, including detailed information about each property (e.g. price, location, photos, etc.).

[1466] Device: Receives the recommendation results and displays them to the user, such as Property A: within budget, convenient commute, 10-minute walk to the park, Property B: within school district, pet-friendly, 15-minute walk to Shinjuku.

[1467] In this way, the real estate search system of the present invention can provide more personalized property recommendations that reflect the user's needs and emotional state, allowing users to find the property that best suits them without stress and improving the quality of service.

[1468] The processing flow will be explained below.

[1469] ---

[1470] Step 1:

[1471] User: Using the web interface or mobile application, user enters information about family size, workplace, school commute, daily activities, hobbies, important requirements for a property, and budget. For example, user enters: Family size: 4 people, Workplace: Shinjuku, School commute: Ueno Ward Daiichi Elementary School, Hobbies: Jogging, Requirements for property purchase: 4LDK, with parking, pets allowed, Budget: 70 million yen.

[1472] Step 2:

[1473] Emotion engine: When a user enters information, the camera and microphone are used to analyze the user's facial expressions and tone of voice in real time. It determines whether the user is expressing emotions such as satisfaction, dissatisfaction, or expectation, and captures this as emotional data. For example, if the user shows a smile or a relieved expression while entering information, that emotional data is collected.

[1474] Step 3:

[1475] On the device: When the user clicks the "Search" button, the entered information and the emotion data generated by the emotion engine are sent to the server. This transmission is encrypted using the SSL / TLS protocol.

[1476] Step 4:

[1477] Server: Receives data sent from the device. The received data is parsed in JSON or XML format, and the data such as family structure, place of employment, school attended, hobbies, purchasing conditions, budget, and emotional data is analyzed and stored in a database.

[1478] Step 5:

[1479] Server: Accesses real estate and local information databases to retrieve relevant local and property data that meet the user's criteria. For example, searches for property listings with 4 bedrooms, kitchens, parking spaces, and pet-friendly rooms, as well as information on parks, schools, and public transportation.

[1480] Step 6:

[1481] Server: Uses sophisticated AI algorithms to analyze acquired property and area data. Specific processes include:

[1482] 1. Score the degree of match between the user's conditions and the property.

[1483] 2. Evaluate the area's amenities, taking into account commute times and access to public facilities.

[1484] 3. Taking into account emotional data, adjustments are made, such as prioritizing properties that users feel more comfortable with.

[1485] 4. Look at past user feedback data and apply a model to predict satisfaction.

[1486] 5. Calculate an overall score and rank the best properties and neighborhoods.

[1487] Step 7:

[1488] Server: Based on the results of the AI ​​algorithm, the server generates a list of properties and areas to recommend to the user, including detailed information about each property (price, location, photos, etc.).

[1489] Step 8:

[1490] Server: The recommendation results are sent to the user's device in JSON or XML format, and the data is encrypted using the SSL / TLS protocol.

[1491] Step 9:

[1492] Device: Receives the recommendation results sent from the server and displays them in an easy-to-understand manner to the user. For example, Property A: Within budget, easy commute, 10 minutes walk to the park; Property B: Within school district, pets allowed, 15 minutes to Shinjuku.

[1493] ---

[1494] We have explained in detail the processing steps of a real estate search system that combines an emotion engine. This system allows users to receive more personalized property recommendations based on their emotional state.

[1495] Example 2

[1496] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1497] Conventional real estate search systems recommend properties based only on the user's simple criteria (such as family composition and budget), making it difficult to provide personalized recommendations that reflect the user's emotions and lifestyle. Furthermore, general AI algorithms do not take the user's emotional state into account, making it difficult to find properties that will satisfy them. This makes it difficult for users to quickly and efficiently find the perfect property, often resulting in frustration.

[1498] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes: means for receiving information input by the user regarding family composition, place of work, educational institutions, daily range of activities, hobbies, important conditions for the purchase request, and budget; means for analyzing the information and saving the analysis results in a database; means for acquiring area and property data from the database; means for acquiring user emotion data through facial expression recognition and voice analysis; means for using the emotion data to recommend optimal properties and areas using an AI algorithm; and means for displaying the recommendation results on the user terminal. This enables more personalized real estate property recommendations that reflect the user's emotional state and individual lifestyle.

[1499] "User Information" means information entered by a User through the web interface or mobile application, such as family composition, place of employment, educational institution, daily activities, hobbies, important purchase requirements, and budget.

[1500] "Analysis" refers to storing user information in a database, extracting the necessary items, and processing them.

[1501] A "database" is a collection of data used to store and manage real estate property information, local information, user information, etc.

[1502] "Local data" refers to information about public facilities, transportation, infrastructure, etc. in a particular local area.

[1503] "Property data" refers to detailed information about a real estate property, including, for example, floor plan, price, location, facilities, etc.

[1504] "Facial expression recognition" is a technology that uses a camera to analyze a user's facial expressions in real time and identify their emotional state.

[1505] "Voice analysis" is a technology that analyzes a user's tone of voice and speaking style in real time through a microphone to identify their emotional state.

[1506] "Emotional data" is information about the user's emotional state obtained through facial expression recognition and voice analysis.

[1507] An "AI algorithm" is a mathematical method that uses machine learning and data analysis to process input data and recommend the most suitable properties and areas.

[1508] "Recommendation Results" refers to a list of properties and areas suitable for the user, derived through analysis by an AI algorithm.

[1509] This real estate search system recommends the best properties and areas based on information entered by the user and sentiment data collected in real time. The system is implemented using the following hardware and software:

[1510] Hardware and software used

[1511] 1. Device: A device such as a PC, smartphone, or tablet with a web browser is used. Users enter information using these devices.

[1512] 2. Facial Recognition Camera: Uses a camera to analyze the user's facial expressions in real time.

[1513] 3. Voice Analysis Microphone: Uses a microphone to analyze the user's tone of voice and speaking style.

[1514] 4. Server: A server is used to run high-performance AI algorithms. This server also handles database management and calculations.

[1515] Specific operation of the system

[1516] Entering user information

[1517] Users enter information about their family, place of employment, educational institution, daily activities, hobbies, important purchase requirements, and budget through a web interface or mobile application. This information is important for improving the system's recommendation accuracy. For example, suppose a user enters the following information:

[1518] Family size: 4 people (2 adults, 2 children)

[1519] Location: Shinjuku

[1520] Educational institution: Ueno Ward Daiichi Elementary School

[1521] Daily range of activities: Tokyo's 23 wards

[1522] Hobbies: Jogging in the park

[1523] Important conditions for purchase: 4LDK, parking space, pets allowed

[1524] Budget: 70 million yen

[1525] Emotion recognition by emotion engine

[1526] The emotion engine uses a facial recognition camera and a voice analysis microphone to analyze the user's emotions in real time while they are entering information. For example, if a user looks anxious, the emotional state will be recorded as "anxiety." This emotional data is reflected in the recommendation algorithm to increase user satisfaction.

[1527] Sending and Receiving Data

[1528] When a user clicks the "Search" button, the entered information and emotion data are encrypted using the SSL / TLS protocol and sent to the server, which then analyzes the data and stores it in a database using each item as a key.

[1529] Obtaining area and property data

[1530] The server accesses the database and searches for suitable areas and properties based on the user's criteria and emotional data. Property details (e.g., floor plan, price, location, etc.) are obtained from the real estate database, and information on public facilities and infrastructure is obtained from the local information database.

[1531] AI recommendation algorithm

[1532] The server analyzes the acquired data using a high-performance AI algorithm and performs the following processes:

[1533] 1. Score the degree of match between the input conditions and the property.

[1534] 2. Evaluate the area's amenities, considering factors such as commute time, access to schools, and proximity to parks suitable for jogging.

[1535] 3. Use emotional data to tailor recommendation results based on the user's emotional state.

[1536] 4. Look at past user feedback data and apply a model to predict satisfaction.

[1537] 5. Calculate an overall score and display the best properties and areas in ranked order.

[1538] Generating and displaying recommendations

[1539] Based on the results of the AI ​​algorithm, the server generates a list of properties and areas to recommend to the user. The recommendations are ranked and include detailed information about each property (e.g., price, location, photos, etc.). The device receives this information and displays it in a user-friendly format.

[1540] Examples and prompts

[1541] Specific examples

[1542] Family size: 4 people (2 adults, 2 children)

[1543] Location: Shinjuku

[1544] Educational institution: Ueno Ward Daiichi Elementary School

[1545] Daily range of activities: Tokyo's 23 wards

[1546] Hobbies: Jogging in the park

[1547] Important conditions for purchase: 4LDK, parking space, pets allowed

[1548] Budget: 70 million yen

[1549] Prompt Sentence Examples

[1550] "Enter your family composition, place of employment, school, and other requirements. We will recommend the ideal property for you."

[1551] "We take into account emotional data to recommend properties that users can feel comfortable with."

[1552] "We will design a system that uses user criteria and emotional data to recommend the best real estate properties."

[1553] As a result, this real estate search system is able to provide personalized recommendations that reflect the user's feelings and requirements.

[1554] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1555] Step 1: Enter your user information

[1556] Users open a web interface or mobile application and enter information about their family composition, workplace, educational institution, daily activities, hobbies, key purchase criteria, and budget. Examples of input information include "Family composition: 4 people (2 adults, 2 children)," "Workplace: Shinjuku," "Educational institution: Ueno Ward Daiichi Elementary School," "Daily activities: Within Tokyo's 23 wards," "Hobbies: Jogging in the park," "Key purchase criteria: 4LDK, parking, pets allowed," and "Budget: 70 million yen." This information serves as input data and is important for specifically reflecting the user's needs and criteria.

[1557] Step 2: Emotion recognition by the emotion engine

[1558] The server uses a facial expression recognition camera and a voice analysis microphone to analyze the user's facial expressions and voice as they are input. The facial expression recognition camera captures the subtle movements of the user's face, and the voice analysis microphone analyzes the user's tone of voice and speaking style. For example, if the user has an anxious expression or a low tone of voice, the emotional state of "anxiety" is detected. This emotional data is used as input data for the next processing step.

[1559] Step 3: Sending data

[1560] When the user clicks the "Search" button, the device encrypts the information entered by the user and the emotion data obtained by the emotion engine using the SSL / TLS protocol and sends it to the server. This sent data becomes the input data for the server.

[1561] Step 4: Receiving and storing data

[1562] The server receives the data sent from the device and parses it in JSON or XML format. The received data includes the user's family structure, place of work, educational institution, daily range of activities, hobbies, important conditions for desired purchases, budget, and emotional state. This data is analyzed and each item is stored as a key in the database. The analysis results are stored in the database and used for search processing in the next step.

[1563] Step 5: Obtaining location and property data

[1564] The server accesses the real estate database and local information database based on the user's criteria and emotion data stored in the database. Specifically, it extracts property listings that match the user's filter criteria and obtains information about the surrounding area (nearby parks, schools, public transportation, etc.). This obtained data becomes input data for the AI ​​algorithm in the next step.

[1565] Step 6: AI recommendation algorithm

[1566] The server uses a high-performance AI algorithm to analyze the input data and perform the following processes: First, it scores the degree of match between the user's input criteria and the property. It also evaluates the convenience of the area, taking into account factors such as commute time, access to schools, and proximity to public facilities. It also uses emotional data to adjust the recommendation results based on the user's emotional state. It references past user feedback data and applies a model to predict satisfaction. By processing and calculating these data, it calculates an overall score and ranks the properties.

[1567] Step 7: Generate and display recommendations

[1568] Based on the results of the AI ​​algorithm, the server generates a list of properties and areas to recommend to the user. The generated list also includes detailed property information (price, location, photos, etc.). These recommendation results become the output data from the server. The device receives the recommended results and displays them in a format that is easy for the user to understand. Based on this information, the user can intuitively and efficiently select the most suitable property.

[1569] Detailed processing is carried out at each step, resulting in personalized property recommendations that reflect the user's needs and emotions.

[1570] (Application example 2)

[1571] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1572] While conventional real estate search systems can recommend properties based on user input, they are limited in providing personalized recommendations that take into account the user's emotional state. Furthermore, viewing property details requires a site visit to get a real feel for the property, which is time-consuming and labor-intensive for users. The present invention aims to solve these problems and provide a real estate search system that enables more personalized property recommendations and property viewing using virtual reality technology.

[1573] The specification processing by the specification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving information and emotional state input by the user regarding family composition, place of work, school, daily range of activities, hobbies, important conditions for a property to be purchased, and budget; means for analyzing the information and emotional state and saving the analysis results in a database; means for acquiring area and property data from the database; means for recommending optimal properties and areas based on the acquired data using an AI algorithm; and means for displaying the recommendation results on the user terminal and allowing the user to view properties using virtual reality technology. This allows for more personalized property recommendations based on the user's emotional state, and allows the user to view properties using virtual reality technology through a smartphone application.

[1574] "User information" refers to information regarding family composition, place of employment, school attended, daily range of activities, hobbies, important conditions required for a property to be purchased, and budget.

[1575] "Emotional state" is data that represents the user's emotions, and is information obtained by analyzing the user's facial expressions, tone of voice, etc.

[1576] "Analysis" is the process of processing user information and emotional states as data to understand their content.

[1577] "Database" means a digital repository for organizing and storing information, including user information, emotional states, local area information, and property information.

[1578] "Local and Property Data" means detailed information about the recommended real estate and its surrounding area.

[1579] "AI Algorithm" refers to an artificial intelligence calculation method used to recommend optimal properties and areas based on user information and emotional state.

[1580] "Virtual reality technology" is a technology that provides users with a sense of realism through a computer-generated virtual space.

[1581] "Recommendation results" are a list of the best properties and areas selected by an AI algorithm.

[1582] The real estate search system of the present invention is a system that uses an AI algorithm to recommend optimal properties and areas based on the user's input information and emotional state, and allows the user to view properties using virtual reality technology. Specific embodiments of the system are described below.

[1583] 1. Entering user information and acquiring emotional state

[1584] Users enter information about their family, place of employment, school, daily activities, hobbies, important requirements for a property to purchase, and budget through a smartphone application. The smartphone's front camera and microphone are used to analyze the user's facial expressions and tone of voice. Specifically, OpenCV is used for facial recognition, and the Google Cloud Speech-to-Text API is used to analyze emotions from voice.

[1585] Examples:

[1586] A user enters information into an input field within an application.

[1587] Example prompt: "Please tell us about your family structure, place of work, daily activities, hobbies, property requirements, and budget."

[1588] 2. Data transmission to the server and analysis

[1589] The device sends the entered user information and analyzed emotional state data to the server using SSL / TLS encryption, which then analyzes the data and stores it in a database. This process is performed using Python and Django.

[1590] 3. Obtaining area and property data

[1591] The server accesses a real estate database to retrieve property and area information that matches the user's criteria, using a relational database such as PostgreSQL.

[1592] 4. Recommendation of optimal properties using AI

[1593] The server uses AI algorithms using TensorFlow or PyTorch to score and recommend the best properties and neighborhoods based on user information and emotional state, taking into account commute times, school districts, access to public facilities, past user feedback, and emotional data.

[1594] 5. Recommendation results and virtual reality property viewing

[1595] The server generates recommendations and sends them to a smartphone application that allows users to view properties using virtual reality technology. Within the app, users can view the list of recommended properties and view each property in detail in VR mode.

[1596] Examples:

[1597] Recommended properties will be displayed on your smartphone.

[1598] Example prompt: "The following properties are recommended for you. Property A: Within your budget, easy commute, 10 minutes' walk to the park. Property B: Within the school district, pet-friendly, 15 minutes' walk to Shinjuku."

[1599] This system not only enables personalized property recommendations that reflect the user's emotional state, but also allows users to intuitively experience detailed property information using virtual reality, which is expected to significantly reduce the time and effort required when selecting a property.

[1600] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1601] Step 1:

[1602] Users use a smartphone application to input information about their family structure, place of work, school, daily range of activities, hobbies, important requirements for a property to purchase, and budget. At this time, the smartphone's front camera and microphone are used to analyze the user's facial expressions and tone of voice. Text information is entered as input data, and audio and video data acquired from the camera and microphone are used for emotion analysis. The input data consists of the user's desired conditions (text) and emotional data (video and audio). The output data after analysis is the user's information and emotional state.

[1603] Step 2:

[1604] The terminal transmits the information entered by the user and the analyzed emotion data to the server using the SSL / TLS encryption protocol. The specific process involves packaging and encrypting the user information and emotion data and transmitting them to the server. The input data is the user's desired conditions and emotion data, and the output data is the encrypted transmission data.

[1605] Step 3:

[1606] The server analyzes the received user information and emotion data and stores them in a database. This process uses Django and involves parsing the data and storing it in the database. The input data is encrypted user information and emotion data, and the output data is the user information and emotion data stored in the database.

[1607] Step 4:

[1608] The server retrieves area and property data from a real estate database. PostgreSQL is used as the database. A query is executed for properties that match the criteria, and relevant property information is retrieved. The input data is the user's desired criteria, and the output data is a list of candidate property data.

[1609] Step 5:

[1610] The server uses an AI algorithm using TensorFlow or PyTorch to score the best properties and areas based on the user's desired conditions and emotional state. The algorithm includes commute time, school district, access to public facilities, past user feedback, and emotional data. The input data is user information, emotional data, and property data, and the output data is a list of scored properties.

[1611] Step 6:

[1612] The server generates recommendation results and sends them to the user's device. The recommendation results include a list of scored properties with detailed information. The input data is the property scoring results, and the output data is the list of recommendation results.

[1613] Step 7:

[1614] Users receive recommendation results on their smartphone application and can view properties using virtual reality technology. Users can check the list of recommended properties within the app and view each property in detail in VR mode. The input data is the recommendation results, and the output data is property information displayed in virtual reality.

[1615] Through the above processing steps, users can receive personalized property recommendations that take into account their emotional state and can experience detailed property information using virtual reality technology.

[1616] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1617] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1618] 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 the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1619] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1620] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1621] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1622] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1623] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1624] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1625] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1626] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1627] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1628] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1629] 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.

[1630] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1631] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1632] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1633] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1634] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1635] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1636] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1637] The following is further disclosed regarding the above embodiment.

[1638] ---

[1639] (Claim 1)

[1640] A means to receive information entered by users regarding family structure, place of work, school attendance, range of daily activities, hobbies, important conditions for a property to be purchased, and budget;

[1641] means for analyzing the information and storing the analysis results in a database;

[1642] a means for retrieving area and property data from the database;

[1643] A means for recommending optimal properties and areas using an AI algorithm based on the acquired data;

[1644] means for displaying the recommendation results on a user terminal;

[1645] A system including:

[1646] (Claim 2)

[1647] 10. The system of claim 1, wherein the means for receiving the user information is provided by a web interface or a mobile application.

[1648] (Claim 3)

[1649] The system of claim 1, wherein the AI ​​algorithm recommends optimal properties taking into account commute times, school districts, access to public facilities, and past user feedback.

[1650] ---

[1651] The above is a draft of the patent claims based on the distinctive features of the system.

[1652] "Example 1"

[1653] (Claim 1)

[1654] A means to receive information entered by users regarding family structure, place of employment, school attendance, range of daily activities, hobbies, important conditions for a property to be purchased, and budget;

[1655] means for analyzing the information and storing the analysis results in a database;

[1656] A means of encrypting input information using the SSL / TLS protocol and sending it to the server;

[1657] means for parsing said information in JSON or XML format;

[1658] a means for retrieving area and property data from the database;

[1659] A means to analyze property and area data obtained using AI algorithms based on the user's conditions and recommend the most suitable property and area;

[1660] means for displaying the recommendation results on a user terminal;

[1661] A system including:

[1662] (Claim 2)

[1663] 10. The system of claim 1, wherein the means for receiving the user information is provided by a web interface or a mobile application.

[1664] (Claim 3)

[1665] The system of claim 1, characterized in that the AI ​​algorithm recommends optimal properties taking into account commute times, access to educational institutions, public facilities, and past user feedback.

[1666] "Application Example 1"

[1667] (Claim 1)

[1668] A means to receive information entered by users regarding family structure, place of work, school attendance, range of daily activities, hobbies, important conditions for a property to be purchased, and budget;

[1669] means for analyzing the information and storing the analysis results in a database;

[1670] a means for retrieving area and property data from the database;

[1671] A means for recommending optimal properties and areas using an AI algorithm based on the acquired data;

[1672] a means for displaying the recommendation results on a user terminal and displaying area and property information through augmented reality via a smart device;

[1673] A system including:

[1674] (Claim 2)

[1675] 2. The system of claim 1, wherein the means for receiving the user information is provided by a web interface, a mobile application, or a smart device.

[1676] (Claim 3)

[1677] The system described in claim 1, characterized in that the AI ​​algorithm recommends the most suitable property taking into account commute times, school districts, access to public facilities, and past user feedback, and displays property information and information about surrounding facilities in the user's field of view using a smart device.

[1678] "Example 2: Combining Emotion Engines"

[1679] (Claim 1)

[1680] A means for receiving information entered by the user regarding family structure, place of employment, educational institution, range of daily activities, hobbies, important conditions for purchases, and budget;

[1681] means for analyzing the information and storing the analysis results in a database;

[1682] a means for retrieving area and property data from the database;

[1683] A means for acquiring user emotional data through facial expression recognition and voice analysis;

[1684] A means for recommending optimal properties and areas using an AI algorithm based on the emotion data;

[1685] means for displaying the recommendation results on a user terminal;

[1686] A system including:

[1687] (Claim 2)

[1688] 10. The system of claim 1, wherein the means for receiving the user information is provided by a web interface or a mobile application.

[1689] (Claim 3)

[1690] The system of claim 1, wherein the AI ​​algorithm recommends optimal properties taking into account commute times, school districts, access to public facilities, past user feedback, and the user's emotional state.

[1691] "Application example 2 when combining emotion engines"

[1692] (Claim 1)

[1693] A means to receive information entered by users regarding family structure, place of work, school attendance, range of daily activities, hobbies, important conditions for a property to be purchased, and budget;

[1694] means for analyzing the information and the user's emotional state and storing the analysis results in a database;

[1695] a means for retrieving area and property data from the database;

[1696] A means for recommending optimal properties and areas using an AI algorithm based on the acquired data;

[1697] a means for displaying the recommendation results on a user terminal and allowing the user to view properties using virtual reality technology;

[1698] A system including:

[1699] (Claim 2)

[1700] 10. The system of claim 1, wherein the means for receiving the user information and emotional state uses a web interface, a smartphone application, or a voice analyzer.

[1701] (Claim 3)

[1702] The system of claim 1, wherein the AI ​​algorithm considers commute times, school districts, access to public facilities, past user feedback, and sentiment data to recommend optimal properties. [Explanation of symbols]

[1703] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. A means to receive information entered by users regarding family structure, place of work, school attendance, range of daily activities, hobbies, important conditions for a property to be purchased, and budget; means for analyzing the information and storing the analysis results in a database; a means for retrieving area and property data from the database; A means for recommending optimal properties and areas using an AI algorithm based on the acquired data; means for displaying the recommendation results on a user terminal; A system including:

2. 10. The system of claim 1, wherein the means for receiving the user information is provided by a web interface or a mobile application.

3. The system of claim 1, wherein the AI ​​algorithm recommends optimal properties taking into account commute times, school districts, access to public facilities, and past user feedback.

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