System

Abstract

A system is provided.SOLUTION: A system comprising: means for inputting a desired condition of a real estate by a user in a chat form; means for transmitting the condition input by a terminal to a server; means for analyzing data received by the server and extracting the desired condition; means for searching a real estate database and listing properties matching the condition; means for transmitting matching property information to the user; means for inputting feedback on a proposed property by the user; means for transmitting an additional condition to the server; and means for learning the additional condition and searching and proposing a property again.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] Users searching for real estate on traditional real estate websites had to set parameters and check each property listing one by one, which required a lot of time and effort. Furthermore, when hiring a real estate agent, users are pressured to make a decision quickly, which often means they don't have enough time to fully consider the property. Another issue is the lack of systems that can efficiently reflect users' specific needs and desired conditions. [Means for solving the problem]

[0005] This invention provides a means for users to input their desired real estate conditions in a chat format. This means users can freely input their desired conditions, such as "within a 10-minute walk from the station," "within a 30-minute door-to-door commute to work," "2LDK," "priority on sunlight," and "rent under 100,000 yen."

[0006] The terminal sends the entered conditions to the server, which has a means for analyzing the received data and extracting desired conditions. The server has a means for searching a real estate database based on the extracted desired conditions and listing properties that match the conditions. Furthermore, the server sends matching property information to the user and provides a means for the user to input feedback on the proposed properties.

[0007] The server has the means to learn additional conditions from users and search for and propose properties again, allowing it to continually propose properties that best suit the user's specific needs. It also has a means for users to continually submit their desired real estate conditions, and a means for the server to periodically search for and provide new property information based on premium member information. The server uses natural language processing technology and AI models to accurately analyze and learn the user's desired conditions and additional conditions, allowing it to propose properties that are best suited to the user. It provides a system that significantly improves the efficiency of real estate searches and reduces the pressure felt by users.

[0008] "User" means any person or entity seeking real estate.

[0009] A "terminal" is an electronic device used by a user, including a smartphone, tablet, or PC.

[0010] A "server" is a central computer that operates a real estate search system and plays an important role in analyzing data and providing property information.

[0011] "Desired conditions" refer to specific conditions or requests that a user has for a real estate property, such as "within a 10-minute walk from the station" or "rent under 100,000 yen."

[0012] "Chat style" refers to an interface that allows users to interactively input information through text.

[0013] A "real estate database" is a database that stores information on multiple real estate properties and is the object of search by the server.

[0014] A "property that meets the conditions" refers to a real estate property that meets the user's desired conditions.

[0015] "Feedback" means any additional requests or opinions provided by a user regarding a proposed property.

[0016] "Additional conditions" refer to new desired conditions or modifications that the user makes to the proposed property.

[0017] "Natural language processing technology" is a technology that enables computers to understand and analyze human language.

[0018] An "AI model" is an algorithm built using machine learning and deep learning that learns user behavior and desired conditions to provide optimal results.

[0019] A "premium member" is a user who has subscribed to a specific membership service and can receive better services than regular users. [Brief explanation of the drawings]

[0020] [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

[0021] 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.

[0022] First, the terms used in the following description will be explained.

[0023] 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).

[0024] 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.

[0025] 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.

[0026] 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.

[0027] 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."

[0028] [First embodiment]

[0029] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0030] 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.

[0031] 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).

[0032] 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.

[0033] 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.

[0034] 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.

[0035] 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.

[0036] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0037] 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.

[0038] 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.

[0039] 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.

[0040] 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."

[0041] This invention is a system in which a user inputs desired real estate conditions in a chat format, and the server automatically proposes the most suitable property. The specific program processing procedure and its operation will be explained below, along with specific examples.

[0042] Program processing overview

[0043] 1. The user enters the desired conditions

[0044] The user opens the chat input screen on their device and enters their desired conditions, such as "within 10 minutes' walk from the station, within 30 minutes door-to-door to work, 2LDK, sunlight-oriented, rent under 100,000 yen."

[0045] 2. The device sends the input data to the server

[0046] The terminal transmits the desired conditions entered by the user to the server as text data.

[0047] 3. The server analyzes the input data

[0048] The server passes the received text data to a natural language processing (NLP) engine, which analyzes and extracts desired conditions, such as "within a 10-minute walk from the station," "within a 30-minute door-to-door drive to the office," "2LDK," "high priority on sunlight," and "rent under 100,000 yen."

[0049] 4. The server searches the database

[0050] The server searches a real estate database based on the analyzed desired conditions, lists properties that match the conditions, and retrieves detailed information about those properties.

[0051] 5. The server sends the property information to the user

[0052] The server then sends the selected property information to the user in the form of a chat message, including details such as photos, layout, rent, and location of each property.

[0053] 6. User enters additional conditions

[0054] The user reviews the proposed property list and enters feedback in chat format, such as, "I like this layout, but I don't like the unit bath."

[0055] 7. The device sends the additional conditions to the server.

[0056] The terminal transmits the additional conditions input by the user to the server.

[0057] 8. The server learns additional conditions

[0058] The server then trains the AI ​​model with the newly received additional conditions. The AI ​​model then understands the user's preferences and searches again for new properties that match the conditions.

[0059] 9. The server will suggest properties again

[0060] The server then suggests suitable properties to the user from the search results again, and this process continues with repeated feedback until the user finds a property that satisfies them.

[0061] Specific examples

[0062] For example, if a user searches for a property with the conditions "within a 10-minute walk from the station, 2LDK, rent under 100,000 yen," the server will list properties that meet these conditions and suggest them to the user. If the user gives feedback such as "This property is good, but I don't want a unit bath," the server will learn the additional conditions and search again to suggest properties with separate bathrooms and toilets.

[0063] In addition, premium members are provided with a system that regularly provides them with new property information on an ongoing basis. This is achieved by the server periodically searching the database based on the premium member information and notifying them of new property information via email or chat.

[0064] This system flexibly responds to user needs while efficiently providing suitable real estate properties, saving users a great deal of effort. In particular, the learning function using an AI model makes it possible to make highly accurate property suggestions that match the user's preferences.

[0065] The processing flow will be explained below.

[0066] Step 1:

[0067] The user inputs the desired conditions.

[0068] The user opens the chat input screen on their device and enters their desired conditions, such as "within 10 minutes' walk from the station, within 30 minutes door-to-door to work, 2LDK, sunlight-oriented, rent under 100,000 yen."

[0069] Step 2:

[0070] The terminal sends the input data to the server.

[0071] The terminal transmits the desired conditions entered by the user to the server as text data.

[0072] Step 3:

[0073] The server parses the input data.

[0074] The server passes the received text data to a natural language processing (NLP) engine, which analyzes and extracts desired conditions, such as "within a 10-minute walk from the station," "within a 30-minute door-to-door drive to the office," "2LDK," "high priority on sunlight," and "rent under 100,000 yen."

[0075] Step 4:

[0076] The server searches the database.

[0077] The server searches a real estate database based on the analyzed desired conditions, lists properties that match the conditions, and retrieves detailed information about those properties.

[0078] Step 5:

[0079] The server sends the property information to the user.

[0080] The server then sends the selected property information to the user in the form of a chat message, including details such as photos, layout, rent, and location of each property.

[0081] Step 6:

[0082] The user enters additional conditions.

[0083] The user reviews the proposed property list and enters feedback in chat format, such as, "I like this layout, but I don't like the unit bath."

[0084] Step 7:

[0085] The terminal transmits the additional conditions to the server.

[0086] The terminal again transmits the additional conditions input by the user to the server.

[0087] Step 8:

[0088] The server learns additional conditions.

[0089] The server then trains the AI ​​model with the newly received additional conditions. The AI ​​model then understands the user's preferences and searches again for new properties that match the conditions.

[0090] Step 9:

[0091] The server will suggest the property again.

[0092] The server then suggests suitable properties to the user from the search results again, and this process continues with repeated feedback until the user finds a property that satisfies them.

[0093] Step 10:

[0094] Provide ongoing proposals for premium members.

[0095] The server searches for new properties periodically (for example, every week or at the beginning of the month) based on the premium member's information and notifies the user via email or chat.

[0096] Example 1

[0097] 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."

[0098] In modern real estate searches, it takes a lot of time and effort for users to find properties that meet their desired criteria. In particular, automating search and suggestion processes that respond to changes in user preferences and additional criteria is difficult, requiring a lot of manual work. Furthermore, there is a lack of systems that efficiently and flexibly suggest properties that are optimal for users. As a result, users often feel stressed during the process of finding a property that suits them. The present invention aims to solve these problems and more efficiently and effectively suggest real estate properties that meet users' desired criteria.

[0099] 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.

[0100] In this invention, the server includes means for a user to input desired real estate conditions in a chat format, means for a terminal to transmit the input conditions to the server, means for analyzing the received data and extracting the desired conditions, means for searching a real estate database and listing properties that match the conditions, means for transmitting matching property information to the user, means for the user to input feedback on the proposed property, means for the terminal to transmit additional conditions to the server, means for learning the additional conditions and searching for and proposing properties again, and means for using an artificial intelligence model that learns the user's preferences and adaptively improves the accuracy of property proposals. This makes it possible to efficiently propose properties that match the desired conditions based on the desired conditions input by the user in a chat format, and to provide the user with the most suitable property by continuing to learn the additional conditions through feedback.

[0101] "User" means an individual or corporation that uses the system to search for and receive suggestions on real estate properties.

[0102] A "terminal" is an electronic device used by a user, such as a computer, smartphone, or tablet, that has the ability to communicate with a server.

[0103] A "server" is a computer system that processes data received from a user, searches a real estate database, and suggests suitable properties to the user.

[0104] "Chat format" is a communication method that allows users to interact with the system in natural language and input desired conditions.

[0105] "Desired conditions" refer to the specific requirements or preferences that a user has for a real estate property, and examples include "within a 10-minute walk from the station," "2LDK," and "rent under 100,000 yen."

[0106] "Text data" refers to sentence data in natural language that the user inputs as desired conditions, and is data in a format that is sent from the terminal to the server.

[0107] A "natural language processing (NLP) engine" is a software component that analyzes text data entered by the user and extracts desired conditions.

[0108] A "real estate database" is a collection of stored information about real estate properties, including detailed information such as property addresses, floor plans, rents, and photos.

[0109] "Listing" refers to the process by which the server compiles property information retrieved from the database and extracts properties that meet the criteria in a list format.

[0110] "Feedback" refers to additional opinions or requests for conditions that users enter regarding proposed properties, such as "I like the layout, but I don't like the unit bath."

[0111] An "artificial intelligence model" is a machine learning algorithm used to learn a user's additional requirements and preferences and improve the accuracy of property suggestions based on them.

[0112] A "generative AI model" is a model that learns based on user interaction data and incorporates user feedback to make adaptive property suggestions.

[0113] A "prompt sentence" is text that is input into a generative AI model, and includes a specific request, such as "I'm looking for a property within a 10-minute walk from the station, with a 2LDK and rent of less than 100,000 yen."

[0114] This invention is a system in which a user inputs desired real estate conditions in a chat format, and a server automatically proposes the most suitable property. Specific embodiments for carrying out this invention are described below.

[0115] System Overview

[0116] First, the user opens a chat-style interface using a dedicated application or web browser and enters their desired real estate requirements, such as "within a 10-minute walk from the station, within a 30-minute door-to-door commute to work, 2LDK, with priority on sunlight, rent under 100,000 yen."

[0117] Hardware and Software Configuration

[0118] The system uses the following main hardware and software:

[0119] Device: A computing device used by a user, such as a smartphone, tablet, or PC.

[0120] Server: A computer system that processes data and suggests properties suitable for users.

[0121] Real estate database: A database that stores detailed information about real estate properties. Relational databases such as MySQL and PostgreSQL are likely to be used.

[0122] Natural language processing engine: Software for analyzing text data entered by users. Specifically, Google Cloud Natural Language API and Microsoft Azure Text Analytics are used.

[0123] Generative AI model: An artificial intelligence model that learns from user input and feedback. This could be a model built using TensorFlow or PyTorch.

[0124] Processing Description

[0125] 1. Enter your desired conditions and submit

[0126] The user enters their desired conditions in a chat format and sends them to the server via their device. At this time, the device converts the entered desired conditions into text data in JSON format and sends it to the server using the secure HTTPS protocol.

[0127] 2. Data Analysis

[0128] The server passes the received JSON-formatted text data to a natural language processing engine, which analyzes the desired conditions. Specifically, it extracts conditions such as "within a 10-minute walk from the station," "within a 30-minute door-to-door drive to the office," "2LDK," "priority on sunlight," and "rent under 100,000 yen."

[0129] 3. Database Search

[0130] The server searches the real estate database based on the analyzed criteria, executes an SQL query to list properties that match the criteria, and retrieves detailed information about each property (address, layout, rent, photo pass, etc.).

[0131] 4. Submit property information

[0132] The server then formats the search results into chat messages and sends them to the device, including details such as property photo URLs, floor plan, rent, and location.

[0133] 5. User Feedback

[0134] The user checks the displayed property list and again enters feedback in chat format, such as "This property is good, but I don't like the unit bath."

[0135] 6. Submitting and Learning Additional Terms

[0136] The device sends the user's additional conditions to the server, and the newly received additional conditions are learned by the AI ​​model. The AI ​​model updates the user's preferences and reflects them in the next search results.

[0137] 7. Re-proposal

[0138] The server re-searches the real estate database based on the updated preferences, generates a new property list, and sends this list to the user again in the form of a chat message, repeating this process until the user finds a property that satisfies them.

[0139] Specific examples

[0140] For example, if a user searches for a property with conditions such as "within a 10-minute walk from the station, 2LDK, rent under 100,000 yen," the server will list properties that meet those conditions and suggest them to the user. If the user gives feedback such as "This property is good, but I don't like the unit bath," the server will learn the additional conditions and search again to suggest properties with separate bathrooms and toilets.

[0141] Prompt Sentence Examples

[0142] "I'm looking for a property within a 10-minute walk from the station, with a 2LDK and rent of less than 100,000 yen. I'd also like to avoid a unit bath. What kind of properties are available?"

[0143] In this way, a system is realized that can efficiently suggest properties that meet the desired conditions based on the desired conditions entered by the user in chat format, and by continuing to learn additional conditions through feedback, it can provide the user with the property that is most suitable for them.

[0144] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0145] Step 1:

[0146] The user inputs the desired conditions for the property.

[0147] Input: The user uses a dedicated application or web browser to enter desired conditions into a chat-style interface, such as "within 10 minutes' walk from the station, within 30 minutes' door-to-door to the office, 2LDK, sunlight-oriented, rent under 100,000 yen."

[0148] Data processing: Desired conditions are entered in text format.

[0149] Output: The desired conditions are saved as text data on the terminal.

[0150] Step 2:

[0151] The terminal sends the input data to the server.

[0152] Input: Text data of desired conditions saved on the device.

[0153] Data processing: The terminal converts the entered desired conditions into JSON format.

[0154] Output: Text data in JSON format is sent to the server using the HTTPS protocol.

[0155] Step 3:

[0156] The server parses the input data.

[0157] Input: JSON formatted text data received by the server.

[0158] Data processing: The server passes the received data to a natural language processing engine (e.g., Google Cloud Natural Language API), which analyzes and extracts the desired conditions.

[0159] Output: The extracted desired conditions (for example, "within 10 minutes' walk from the station," "within 30 minutes' door-to-door drive to work," "2LDK," "priority on sunlight," "rent under 100,000 yen") are saved on the server.

[0160] Step 4:

[0161] The server searches the database.

[0162] Input: Parsed desired conditions.

[0163] Data processing: The server generates SQL queries and searches the real estate database (MySQL or PostgreSQL).

[0164] Output: A list of properties that match the criteria (property ID, address, layout, rent, photo pass, etc.) will be obtained.

[0165] Step 5:

[0166] The server sends the property information to the user.

[0167] Input: Acquired property list.

[0168] Data processing: The server formats the property information into a chat message format.

[0169] Output: A chat-style message (including the property's photo URL, floor plan, rent, address, etc.) is sent to the user's device.

[0170] Step 6:

[0171] The user enters additional conditions.

[0172] Input: The user checks the displayed property list and enters additional conditions as feedback in chat format (e.g., "This property is good, but I don't like the unit bath").

[0173] Data processing: Additional conditions are entered in text format.

[0174] Output: The additional conditions are saved as text data on the terminal.

[0175] Step 7:

[0176] The terminal transmits the additional conditions to the server.

[0177] Input: Text data of additional conditions saved on the device.

[0178] Data processing: The terminal converts the additional conditions into JSON format.

[0179] Output: Text data in JSON format is sent to the server using the HTTPS protocol.

[0180] Step 8:

[0181] The server learns additional conditions.

[0182] Input: Additional conditions received by the server in JSON format.

[0183] Data processing: The server uses the received additional conditions to train an AI model (e.g., a TensorFlow-based model) and updates the user's preferences.

[0184] Output: The updated user preferences are saved in the server.

[0185] Step 9:

[0186] The server will suggest the property again.

[0187] Input: Updated user preferences.

[0188] Data processing: The server re-searches the real estate database based on the updated preferences and generates a new property list.

[0189] Output: New listings of properties that match the criteria are sent to the user in the form of a chat message.

[0190] (Application example 1)

[0191] 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."

[0192] Conventional real estate search systems are inefficient because they require users to input search criteria and load large amounts of information to confirm property details. Furthermore, it takes a lot of time to search again based on property feedback, and suggestions that reflect the user's preferences are not always obtained. Furthermore, the system requires users to visit the property in person, which creates significant barriers in terms of physical distance and time.

[0193] 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.

[0194] In this invention, the server includes a means for a user to input desired real estate conditions in a chat format, a means for a terminal to transmit the input conditions to the server, and a means for the server to analyze the received data and extract the desired conditions. This enables efficient property search and proposals based on the user's desired conditions. Furthermore, by including a means for viewing properties in a virtual reality environment using a smart device, it is possible to remotely check and experience the property in detail, overcoming the barriers of physical distance and time.

[0195] "Means for users to input desired conditions for real estate in chat format" is a function that allows users to input requests for real estate such as housing into a terminal using written dialogue.

[0196] The "means for transmitting conditions entered by the terminal to the server" is a function for transmitting desired conditions entered by the user to the computer server.

[0197] "Means for analyzing data received by the server and extracting desired conditions" refers to a function that analyzes the data of the user's desired conditions received by the server, and identifies and extracts specific conditions from that data.

[0198] "Means for the server to search the real estate database and list properties that meet the conditions" is a function that allows the server to search the database for real estate information that meets the conditions and create a list of relevant properties.

[0199] The "means for the server to send matching property information to the user" is a function for sending information about the found property to the user.

[0200] The "means for users to input feedback on proposed properties" is a function that allows users to input opinions and additional conditions regarding proposed properties.

[0201] The "means for the terminal to transmit additional conditions to the server" is a function for transmitting the additional desired conditions input by the user back to the server.

[0202] "Means for the server to learn additional conditions and search for and suggest properties again" is a function that allows the server to learn newly received conditions and search the database again based on them to suggest properties.

[0203] "Means for viewing properties in a virtual reality environment using a smart device" refers to the ability to visually inspect real estate properties in detail in a virtual reality environment using advanced devices such as smart glasses.

[0204] "Natural language processing technology" is a technology that enables computers to understand, analyze, and manipulate human language.

[0205] An "AI model" is a data model that uses artificial intelligence technology to perform specific tasks.

[0206] A "real estate database" is a database in which information about residential and commercial real estate is systematically stored.

[0207] A "virtual reality environment" is an environment that uses virtual reality technology to recreate a real-life three-dimensional space.

[0208] The present invention is a system in which a user inputs desired real estate conditions in a chat format, and a server automatically proposes the most suitable property. Specific embodiments of this system will be described in detail below.

[0209] System program generation

[0210] To realize this system, the following program will be constructed: The user inputs their desired conditions using a smart device (such as smart glasses), and the system analyzes those conditions and suggests the most suitable property.

[0211] Hardware and Software Use

[0212] 1. Hardware:

[0213] Smart devices (e.g. smart glasses)

[0214] 2. Software:

[0215] Natural language processing technology (NLP engine, e.g., Google Cloud NLP)

[0216] Real Estate Database

[0217] AI model

[0218] System processing flow

[0219] 1. Receiving user input:

[0220] The user inputs desired conditions into the smart glasses by voice, which is converted into text data using the smart glasses' voice recognition function.

[0221] 2. Analysis of desired conditions:

[0222] The smart glasses transmit the desired conditions in text data form to a server, which then analyzes the desired conditions using natural language processing technology and extracts specific conditions (e.g., "within a 10-minute walk from the station," "2LDK," "rent under 100,000 yen").

[0223] 3. Database Search:

[0224] The server searches a real estate database and lists properties that match the extracted desired conditions. The server then obtains detailed information about the listed properties (photos, floor plan, rent, location, etc.).

[0225] 4. User Submission of Property Information:

[0226] The server sends the listed property information to the smart glasses, through which the user can view the property in a virtual reality environment.

[0227] 5. Feedback Processing:

[0228] When the user enters feedback on the proposed property, the device sends this feedback back to the server, which then uses the feedback to train the AI ​​model and conducts another property search, taking additional criteria into account.

[0229] Specific examples

[0230] For example, suppose a user verbally inputs their desired conditions into the smart glasses, such as "within a 10-minute walk from the station, 2LDK, rent under 100,000 yen." The smart glasses convert this voice into text data and send it to the server. The server analyzes the desired conditions, searches for properties that match, and then sends a list of properties to the smart glasses. The user browses each property in the VR environment and provides feedback such as "This property is good, but I don't like the unit bath." Based on this, the server re-learns the conditions and searches for and suggests new properties that meet the conditions.

[0231] Prompt Sentence Examples

[0232] An example prompt might be, "The user puts on the smart glasses and searches for properties that meet the specified real estate criteria. The criteria are within a 10-minute walk from the station, 2LDK, and rent of less than 100,000 yen. Find the perfect property and view it in a virtual tour. The user can then enter feedback on additional criteria and update the search results."

[0233] In this way, the system of the present invention can efficiently search for properties based on the user's desired conditions and can remotely check the details of the properties using virtual reality technology.

[0234] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0235] Step 1:

[0236] Receiving User Input

[0237] The user puts on the smart glasses and inputs their desired real estate conditions by voice (e.g., "within 10 minutes' walk from the station, 2LDK, rent under 100,000 yen"). This voice input is converted into text data using the smart glasses' voice recognition function. The input data is output in text format as the user's desired conditions.

[0238] Step 2:

[0239] Send your desired conditions

[0240] The terminal sends the desired conditions entered by the user as text data to the server. This transmission is performed via the Internet. The desired conditions received as input data are transferred to the server and sent as data for analysis.

[0241] Step 3:

[0242] Analysis of desired conditions

[0243] The server analyzes the received text data of desired conditions using a natural language processing (NLP) engine and extracts specific desired conditions (e.g., "within 10 minutes' walk," "2LDK," "rent under 100,000 yen"). The input is the desired conditions in text format, and the output is the analyzed specific conditions.

[0244] Step 4:

[0245] Database search

[0246] The server searches the real estate database based on the analyzed desired conditions. It lists properties that match the conditions and obtains detailed information about those properties (photos, floor plan, rent, location, etc.). The input is the analyzed desired conditions, and the output is a list of properties that match the conditions.

[0247] Step 5:

[0248] Submit property information

[0249] The server sends the listed property information to the smart glasses, through which the user can view the properties in a virtual reality environment. The input is the property list, and the output is the display of the property information in a virtual reality environment.

[0250] Step 6:

[0251] Receiving Feedback

[0252] The user can provide feedback about the proposed property by voice (e.g., "This property is good, but I don't like the unit bath"). This feedback is converted into text data using the smart glasses' voice recognition function. The input is the feedback content, and the output is text-based feedback.

[0253] Step 7:

[0254] Submitting additional conditions

[0255] The terminal sends the user's feedback (additional conditions) to the server. The input is the feedback in text format, and the output is the data sent to the server.

[0256] Step 8:

[0257] Learning additional conditions and re-searching

[0258] The server trains the AI ​​model on the additional conditions and understands the user's new preferences. Based on this learning, it searches the real estate database again and lists properties that meet the new conditions. The input is the text data of the additional conditions, and the output is a list of properties that meet the new conditions.

[0259] Step 9:

[0260] Re-proposal

[0261] The server sends the re-listed property information to the smart glasses. The user then browses, rates, and views the properties again in a virtual reality environment. The input is the new property list, and the output is a virtual reality display of the re-suggested property.

[0262] 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.

[0263] This invention is a system in which a user inputs desired real estate conditions in a chat format, and the server automatically suggests the most suitable property. This system also incorporates an emotion engine that recognizes the user's emotions, allowing it to make suggestions according to the user's emotional state. The specific program processing procedures and their operation are explained below, along with specific examples.

[0264] Program processing overview

[0265] 1. The user enters the desired conditions

[0266] The user opens the chat input screen on their device and enters their desired conditions, such as "within 10 minutes' walk from the station, within 30 minutes door-to-door to work, 2LDK, sunlight-oriented, rent under 100,000 yen."

[0267] 2. The device sends the input data to the server

[0268] The terminal transmits the desired conditions entered by the user to the server as text data.

[0269] 3. The server analyzes the input data

[0270] The server passes the received text data to a natural language processing (NLP) engine, which analyzes and extracts desired conditions, such as "within a 10-minute walk from the station," "within a 30-minute door-to-door drive to the office," "2LDK," "high priority on sunlight," and "rent under 100,000 yen."

[0271] 4. The server recognizes emotions using the emotion engine

[0272] The server uses an emotion engine to analyze the emotions from the user's input data. For example, it can recognize whether the user is feeling dissatisfied, satisfied, or hopeful based on the wording and expressions in the text.

[0273] 5. The server searches the database

[0274] The server searches a real estate database based on the analyzed desired conditions and emotion recognition results, lists properties that meet the conditions and take the user's emotions into consideration, and retrieves detailed information about those properties.

[0275] 6. The server sends the property information to the user

[0276] The server then sends the selected property information to the user in the form of a chat message, including details such as photos, layout, rent, and location of each property.

[0277] 7. User enters additional conditions

[0278] The user reviews the proposed property list and enters feedback in chat format, such as, "I like this floor plan, but I don't like the unit bath." The emotion engine then analyzes the user's emotions in real time.

[0279] 8. The device sends additional conditions to the server

[0280] The terminal again transmits the additional conditions input by the user to the server.

[0281] 9. The server learns additional conditions

[0282] The server then trains the AI ​​model with the newly received additional conditions, allowing the AI ​​model to understand the user's preferences and emotions and search again for new properties that match the conditions.

[0283] 10. The server will suggest properties again

[0284] The server then suggests suitable properties to the user from the search results again, and this process continues with repeated feedback until the user finds a property that satisfies them.

[0285] Specific examples

[0286] For example, if a user searches for a property with the conditions "within a 10-minute walk from the station, 2LDK, rent under 100,000 yen," the server will list properties that meet these criteria and suggest them to the user. If the user gives feedback such as "This property is good, but I don't like the unit bath," the emotion engine will recognize the dissatisfaction from the user's text, and the server will learn the additional conditions and search again to suggest properties with separate bathrooms and toilets.

[0287] Furthermore, the emotion engine monitors changes in the user's emotions and analyzes whether the user is satisfied with the suggestions, thereby improving the accuracy of property suggestions in a way that satisfies the user. Premium members are also provided with a system that regularly provides them with new property information. This is achieved by the server periodically searching the database based on premium member information and notifying them of new property information via email or chat.

[0288] This system flexibly responds to the user's needs and emotions, providing a wide range of suitable real estate properties, saving the user a great deal of effort. In particular, the learning function using an emotion engine and AI model makes it possible to make highly accurate property suggestions based on the user's preferences and emotions.

[0289] The processing flow will be explained below.

[0290] Step 1:

[0291] The user inputs the desired conditions.

[0292] The user opens the chat input screen on their device and enters their desired conditions, such as "within 10 minutes' walk from the station, within 30 minutes door-to-door to work, 2LDK, sunlight-oriented, rent under 100,000 yen."

[0293] Step 2:

[0294] The terminal sends the input data to the server.

[0295] The terminal transmits the desired conditions entered by the user to the server as text data.

[0296] Step 3:

[0297] The server parses the input data.

[0298] The server passes the received text data to a natural language processing (NLP) engine, which analyzes and extracts desired conditions, such as "within a 10-minute walk from the station," "within a 30-minute door-to-door drive to the office," "2LDK," "high priority on sunlight," and "rent under 100,000 yen."

[0299] Step 4:

[0300] The server recognizes emotions using an emotion engine.

[0301] The server uses an emotion engine to analyze the emotions from the user's input data. For example, it recognizes whether the user is feeling dissatisfied, satisfied, or hopeful based on the wording and expressions in the text.

[0302] Step 5:

[0303] The server searches the database.

[0304] The server searches a real estate database based on the analyzed desired conditions and emotion recognition results, and lists properties that match the conditions and take the user's emotions into consideration.

[0305] Step 6:

[0306] The server sends the property information to the user.

[0307] The server then sends the selected property information to the user in the form of a chat message, including details such as photos, layout, rent, and location of each property.

[0308] Step 7:

[0309] The user enters additional conditions.

[0310] The user checks the proposed property list and inputs additional desired conditions as feedback, such as "I like this floor plan, but I don't want a unit bath." At this time, the emotion engine also continuously analyzes the user's emotions.

[0311] Step 8:

[0312] The terminal transmits the additional conditions to the server.

[0313] The terminal again transmits the additional conditions input by the user to the server.

[0314] Step 9:

[0315] The server learns additional conditions.

[0316] The server then trains the AI ​​model with the newly received additional conditions, allowing the AI ​​model to understand the user's preferences and emotions and search again for new properties that match the conditions.

[0317] Step 10:

[0318] The server will suggest the property again.

[0319] The server then proposes suitable properties to the user from the search results again, and continues providing feedback and suggestions using an emotion engine until the user is satisfied with the suggestions.

[0320] Step 11:

[0321] Provide ongoing proposals for premium members.

[0322] The server periodically (for example, weekly or monthly) searches for new properties based on premium member information and notifies users via email or chat. The emotion engine also analyzes user feedback and emotions to continually provide better suggestions.

[0323] Example 2

[0324] 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."

[0325] In conventional real estate search systems, when users input their desired conditions, they simply analyze the conditions as text, and are unable to make suggestions that reflect the user's emotions and preferences. Furthermore, there was no system that could understand user feedback in real time and re-make optimal suggestions. As a result, users had to spend a lot of time and effort searching for real estate properties.

[0326] The specification process by the specification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes: means for a user to input desired conditions for real estate in a chat format; means for a terminal to transmit the input conditions to the server; means for the server to analyze the received data and extract the desired conditions; means for the server to analyze the desired conditions using natural language processing technology; means for the server to recognize the user's emotions using an emotion engine; means for the server to search a real estate database and list properties that match the conditions; means for the server to transmit matching property information to the user; means for the user to input feedback on the proposed property; means for the terminal to transmit additional conditions to the server; and means for the server to learn the user's additional conditions using an AI model and search for and propose properties again.

[0327] This makes it possible to analyze the user's desired conditions and emotions in real time and propose the most suitable real estate property that suits the user's preferences.

[0328] "User" refers to a person who uses the real estate search system to input desired conditions and receive property proposals.

[0329] A "terminal" is a device used by a user to input desired conditions in chat format, and includes a smartphone, tablet, PC, etc.

[0330] "Server" refers to a central management device that receives, analyzes, and searches user input data and makes real estate property suggestions.

[0331] "Natural language processing technology" refers to technology that analyzes text data entered by the user and extracts desired conditions based on that data.

[0332] An "emotion engine" is an engine that recognizes emotions from user input data and optimizes property suggestions based on those emotions.

[0333] A "real estate database" is a database that stores various real estate property information, and refers to a collection of property information that can be searched.

[0334] "AI model" refers to a machine learning model that learns the user's desired conditions and additional conditions, and searches for and suggests the most suitable properties.

[0335] "Premium Member" refers to a user who has the right to receive special services under certain conditions.

[0336] System Overview

[0337] This invention is a system in which a user inputs desired real estate conditions in a chat format, and the server automatically suggests the most suitable property. This system incorporates an emotion engine that recognizes the user's emotions, and can make suggestions according to the user's emotional state.

[0338] Hardware and software used

[0339] User devices: smartphones, tablets, computers, etc.

[0340] Server: A central management device that analyzes desired conditions, searches real estate databases, recognizes emotions, and trains AI models.

[0341] Natural Language Processing (NLP) engine: Technology that analyzes user requirements

[0342] Emotion engine: An engine that analyzes emotions from user input data

[0343] Real estate database: Contains information on various real estate properties

[0344] AI model: A machine learning model that learns the user's desired conditions and additional conditions to suggest the most suitable properties.

[0345] System Operation

[0346] 1. Input method: Users use a device such as a smartphone or PC to input their desired real estate requirements in chat format. For example, they can input specific requirements such as "within a 10-minute walk from the station, within a 30-minute door-to-door commute to work, 2LDK, sunlight-oriented, rent under 100,000 yen."

[0347] 2. Data transmission means: The user's terminal transmits the entered desired conditions to the server as text data.

[0348] 3. Data analysis method: The server passes the received text data to a natural language processing (NLP) engine, which analyzes and extracts the desired conditions. Specifically, it breaks down conditions such as "within a 10-minute walk from the station," "within a 30-minute door-to-door drive to the office," "2LDK," "priority on sunlight," and "rent under 100,000 yen."

[0349] 4. Emotion Recognition: The server uses an emotion engine to recognize emotions from the user's input data. For example, it can read emotions such as dissatisfaction, satisfaction, and expectation from the wording and expressions in the text.

[0350] 5. Database search method: The server searches the real estate database based on the analyzed desired conditions and emotion recognition results, and lists properties that meet the conditions and take the user's emotions into consideration.

[0351] 6. Result transmission method: The server sends the selected property information to the user in the form of a chat message, including photos, floor plan, rent, location, access information, etc.

[0352] 7. Feedback: The user reviews the proposed property list and enters feedback in chat format, such as "I like this floor plan, but I don't like the unit bathroom." The emotion engine continues to analyze the user's emotions in real time and learns the user's preferences based on the feedback.

[0353] 8. Additional condition sending means: The terminal sends the additional conditions entered by the user back to the server.

[0354] 9. Learning method: The server trains the AI ​​model with the newly received additional conditions. The AI ​​model then gains a more detailed understanding of the user's preferences and emotions and searches for new properties.

[0355] 10. Re-proposal method: The server proposes a newly selected property to the user, and the process continues with repeated feedback until the user finds a property that satisfies them.

[0356] Specific examples

[0357] For example, if a user enters the conditions "within a 10-minute walk from the station, 2LDK, rent under 100,000 yen," the server will list properties that meet these criteria and suggest them to the user. If the user gives feedback such as "This property is good, but I don't like the unit bath," the emotion engine will recognize the dissatisfaction, and the server will search again and suggest properties with separate bathrooms and toilets.

[0358] Example prompts for generative AI models

[0359] Example prompt:

[0360] "The user entered the desired conditions: 'within 10 minutes' walk from the station, 2LDK, rent under 100,000 yen.' Please use the emotion engine to suggest the best property for this. Also, please explain how to respond if the user enters feedback such as 'I don't want a unit bath.'"

[0361] This allows the system to flexibly respond to users' needs and emotions, providing them with multifaceted, suitable real estate properties, saving them time and effort.In addition, by using an emotion engine and AI model, it is possible to provide highly accurate property suggestions that match the user's preferences and emotions.

[0362] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0363] Step 1:

[0364] The user enters the desired conditions.

[0365] The user opens the chat input screen on their device and enters their desired conditions, such as "within 10 minutes' walk from the station, within 30 minutes door-to-door to work, 2LDK, sunlight-oriented, rent under 100,000 yen."

[0366] Specifically, the user inputs each desired condition in text format and presses the send button.

[0367] Step 2:

[0368] The device sends the input data to the server.

[0369] The terminal transmits the desired conditions entered by the user to the server as text data.

[0370] Specifically, when the send button is pressed, the text data is automatically sent to the server.

[0371] Input: User's desired conditions (text format)

[0372] Output: Text data to the server

[0373] Step 3:

[0374] The server parses the input data.

[0375] The server passes the received text data to a natural language processing (NLP) engine, which analyzes and extracts the desired conditions.

[0376] Specifically, the NLP engine analyzes the text and extracts conditions such as "within 10 minutes' walk from the station," "2LDK," and "rent under 100,000 yen."

[0377] Input: User's desired conditions (text data)

[0378] Output: Parsed desired conditions (structured data)

[0379] Step 4:

[0380] The server recognizes emotions using an emotion engine.

[0381] The server uses an emotion engine to analyze emotions from the user's input data.

[0382] Specifically, it reads emotions such as dissatisfaction, satisfaction, and expectation from the wording of the input text.

[0383] Input: User's desired conditions (text data)

[0384] Output: Parsed emotion data

[0385] Step 5:

[0386] The server searches the database.

[0387] The server searches a real estate database based on the analyzed desired conditions and emotion recognition results, and lists properties that meet the conditions.

[0388] Specifically, it uses a filter to narrow down the properties in the database that meet the criteria and lists them.

[0389] Input: Analyzed desired conditions and emotion data

[0390] Output: List of properties that match the criteria

[0391] Step 6:

[0392] The server sends the property information to the user.

[0393] The server sends the selected property information to the user in the form of a chat message.

[0394] Specifically, a chat message is generated that includes photos of the property, floor plan, rent, location, access information, etc.

[0395] Input: List of properties that match the criteria

[0396] Output: Property chat message to user

[0397] Step 7:

[0398] The user enters additional criteria.

[0399] The user reviews the proposed property list and enters feedback in chat format, such as, "I like this layout, but I don't like the unit bath."

[0400] Specifically, the user inputs and sends specific complaints and additional requirements via chat.

[0401] Input: Feedback on the proposed property (text format)

[0402] Output: User feedback data

[0403] Step 8:

[0404] The device sends the additional conditions to the server.

[0405] The terminal again transmits the additional conditions input by the user to the server.

[0406] Specifically, when the send button is pressed, the feedback data is sent to the server.

[0407] Input: User feedback (text data)

[0408] Output: Feedback data to the server

[0409] Step 9:

[0410] The server learns additional conditions.

[0411] The server trains the AI ​​model on the newly received additional conditions.

[0412] Specifically, the AI ​​model learns from the feedback data to gain a more detailed understanding of the user's preferences.

[0413] Input: User feedback data

[0414] Output: Updated AI model

[0415] Step 10:

[0416] The server proposes the property again.

[0417] The server then suggests suitable properties to the user from the search results.

[0418] Specifically, the system searches again for new properties that meet the conditions and generates a proposal message.

[0419] Input: Updated AI model and new search results

[0420] Output: New property proposal chat message

[0421] (Application example 2)

[0422] 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."

[0423] On current online shopping sites, when users search for the products they want, it takes a lot of time and effort to find the products that match their criteria.In addition, there is a lack of a system that suggests optimal products based on the user's emotions and feedback, making it difficult for users to efficiently find products that satisfy them.

[0424] The identification process by the identification 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 a user to input desired product conditions in a chat format, means for a terminal to transmit the input conditions to the server, means for the server to analyze the received data and extract the desired conditions, means for the server to search a product database and list products that match the conditions, means for the server to transmit information about the matching products to the user, means for the user to input feedback on the suggested products, means for the terminal to transmit additional conditions to the server, means for the server to learn the additional conditions and search for and suggest products again, and an emotion engine for the server to analyze the user's emotional state and make suggestions based on the emotions. This enables the user to efficiently find products that match the desired conditions and can suggest optimal products based on the user's emotions and feedback.

[0425] A "user" is a person who searches for and purchases products using an online shopping site.

[0426] "Desired conditions" are requirements regarding product specifications and features that are input by the user in chat format.

[0427] A "terminal" is an electronic device used by a user, such as a computer, smartphone, or tablet.

[0428] A "server" is a central processing unit that receives data sent from a terminal and performs analysis and searches.

[0429] "Chat format" is an interface format that allows users to input text and exchange information in a conversational format.

[0430] A "product database" is a digital database in which product information is stored.

[0431] "Feedback" refers to the act of a user inputting an evaluation or opinion about a proposed product, or the content of such input.

[0432] "Additional conditions" are new conditions input by the user based on feedback.

[0433] An "emotion engine" is software that analyzes the user's emotions from their text data and makes suggestions based on their state.

[0434] "Natural language processing technology" is a technology that allows computers to understand and analyze human language.

[0435] A "generative AI model" is an artificial intelligence model that learns from users' conditions and feedback and makes more appropriate suggestions.

[0436] In this invention, a mail-order system will be described that proposes products based on desired conditions input by a user in a chat format.

[0437] Overall system configuration

[0438] This system mainly consists of a user's device, a server, a product database, and an emotion engine. Users input their desired conditions through a chat interface, and their device sends the information to the server. The server uses natural language processing technology to analyze the desired conditions and search for matching products in the product database. It then uses the emotion engine to analyze the user's emotions and makes product suggestions based on those emotions.

[0439] Hardware and Software Configuration

[0440] Hardware: Your PC, smartphone or tablet, and server

[0441] Software: Natural language processing engine (e.g., Google Cloud Natural Language), sentiment analysis engine (e.g., IBM Watson Tone Analyzer), database (e.g., MySQL), chatbot framework (e.g., Dialogflow)

[0442] What the program does

[0443] 1. User inputs desired conditions

[0444] The user enters the desired product conditions in chat format. For example, specific conditions such as "black jacket, size L, budget within 5,000 yen, casual style" are entered.

[0445] 2. Sending data from the device to the server

[0446] The terminal transmits the desired conditions entered by the user as text data to the server, using real-time communication.

[0447] 3. Data analysis by the server

[0448] The server passes the received text data to a natural language processing engine, which analyzes the desired conditions, such as "black jacket," "size L," "budget under 5,000 yen," and "casual style."

[0449] 4. Emotion analysis

[0450] The server uses an emotion engine to analyze the user's input data to determine whether the user is feeling expectations, hopes, dissatisfaction, or other emotions.

[0451] 5. Search the product database

[0452] The server searches a product database based on the analyzed desired conditions and emotions, lists products that match the conditions, and obtains their detailed information.

[0453] Adding specific examples

[0454] For example, if a user searches for a product using the criteria "black jacket, size L, budget under 5,000 yen, casual style," the server will list products that match these criteria and suggest them to the user. If the user gives feedback such as "I like this design, but I wish it was a little longer," the emotion engine will recognize the user's wishes from the text, and the server will learn the additional criteria, search again, and suggest new products that match the criteria.

[0455] Prompt Sentence Examples

[0456] User input: "Black jacket, size L, budget under 5000 yen, casual style"

[0457] Prompt: "Analyze the emotions expressed by the user's input."

[0458] This allows for more detailed proposals that meet the user's desired conditions, and also allows for optimal product proposals based on the user's emotions and feedback.

[0459] summary

[0460] By introducing this system, users can efficiently find products that meet their desired criteria, and by using an emotion engine, they can receive highly satisfying suggestions. Product recommendations are made based on data analyzed by the server and the results of emotion analysis, which increases user satisfaction.

[0461] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0462] Step 1:

[0463] The user inputs desired product specifications in chat format via a terminal. The input is specific text data such as "black jacket, size L, budget within 5,000 yen, casual style."

[0464] Step 2:

[0465] The terminal transmits the desired conditions entered by the user to the server as text data, and the transmitted data is transmitted to the server in real time.

[0466] Step 3:

[0467] The server passes the received text data to a natural language processing engine (e.g., Google Cloud Natural Language) and analyzes the desired conditions. This analysis includes text tokenization, part-of-speech tagging, and semantic analysis. For example, conditions such as "black jacket," "size L," "budget under 5,000 yen," and "casual style" are extracted.

[0468] Step 4:

[0469] The server uses an emotion engine (e.g., IBM Watson Tone Analyzer) to analyze the emotion from the user's input data. This analyzes the emotional weighting of words and the tone of the entire sentence to determine the user's emotional state (expectation, hope, etc.). It identifies whether the user is excited or disappointed, for example.

[0470] Step 5:

[0471] The server searches a product database (e.g., MySQL) based on the analyzed desired conditions and emotion recognition results, lists products that match the conditions, and retrieves detailed information about those products (images, prices, descriptions, etc.).

[0472] Step 6:

[0473] The server sends the selected product information to the user in the form of a chat message, including a photo, description, price, and rating of each product.

[0474] Step 7:

[0475] The user can review the proposed product list and provide feedback in chat, for example, by providing specific opinions such as, "The design is good, but I wish it was a little longer."

[0476] Step 8:

[0477] The terminal sends the additional conditions entered by the user to the server, and feedback is transmitted to the server in real time.

[0478] Step 9:

[0479] The server trains the generative AI model on the newly received additional conditions. This training uses supervised and unsupervised learning methods. The AI ​​model updates the conditions based on the user's preferences and feedback information and reflects them in the next search.

[0480] Step 10:

[0481] The server searches for products again and suggests new products that match the criteria. This process is repeated until a product that satisfies the user is found.

[0482] 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.

[0483] 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.

[0484] 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.

[0485] [Second embodiment]

[0486] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0487] 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.

[0488] 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).

[0489] 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.

[0490] 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.

[0491] 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).

[0492] 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.

[0493] 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.

[0494] 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.

[0495] 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.

[0496] 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.

[0497] 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."

[0498] This invention is a system in which a user inputs desired real estate conditions in a chat format, and the server automatically proposes the most suitable property. The specific program processing procedure and its operation will be explained below, along with specific examples.

[0499] Program processing overview

[0500] 1. The user enters the desired conditions

[0501] The user opens the chat input screen on their device and enters their desired conditions, such as "within 10 minutes' walk from the station, within 30 minutes door-to-door to work, 2LDK, sunlight-oriented, rent under 100,000 yen."

[0502] 2. The device sends the input data to the server

[0503] The terminal transmits the desired conditions entered by the user to the server as text data.

[0504] 3. The server analyzes the input data

[0505] The server passes the received text data to a natural language processing (NLP) engine, which analyzes and extracts desired conditions, such as "within a 10-minute walk from the station," "within a 30-minute door-to-door drive to the office," "2LDK," "high priority on sunlight," and "rent under 100,000 yen."

[0506] 4. The server searches the database

[0507] The server searches the real estate database based on the analyzed desired conditions, lists properties that match the conditions, and retrieves detailed information about those properties.

[0508] 5. The server sends the property information to the user

[0509] The server then sends the selected property information to the user in the form of a chat message, including details such as photos, layout, rent, and location of each property.

[0510] 6. User enters additional conditions

[0511] The user reviews the proposed property list and enters feedback in chat format, such as, "I like this layout, but I don't like the unit bath."

[0512] 7. The device sends the additional conditions to the server.

[0513] The terminal transmits the additional conditions input by the user to the server.

[0514] 8. The server learns additional conditions

[0515] The server then trains the AI ​​model with the newly received additional conditions. The AI ​​model then understands the user's preferences and searches again for new properties that match the conditions.

[0516] 9. The server will suggest properties again

[0517] The server then suggests suitable properties to the user from the search results again, and this process continues with repeated feedback until the user finds a property that satisfies them.

[0518] Specific examples

[0519] For example, if a user searches for a property with the conditions "within a 10-minute walk from the station, 2LDK, rent under 100,000 yen," the server will list properties that meet these conditions and suggest them to the user. If the user gives feedback such as "This property is good, but I don't want a unit bath," the server will learn the additional conditions and search again to suggest properties with separate bathrooms and toilets.

[0520] In addition, premium members are provided with a system that regularly provides them with new property information on an ongoing basis. This is achieved by the server periodically searching the database based on the premium member information and notifying them of new property information via email or chat.

[0521] This system flexibly responds to user needs while efficiently providing suitable real estate properties, saving users a great deal of effort. In particular, the learning function using an AI model makes it possible to make highly accurate property suggestions that match the user's preferences.

[0522] The processing flow will be explained below.

[0523] Step 1:

[0524] The user inputs the desired conditions.

[0525] The user opens the chat input screen on their device and enters their desired conditions, such as "within 10 minutes' walk from the station, within 30 minutes door-to-door to work, 2LDK, sunlight-oriented, rent under 100,000 yen."

[0526] Step 2:

[0527] The terminal sends the input data to the server.

[0528] The terminal transmits the desired conditions entered by the user to the server as text data.

[0529] Step 3:

[0530] The server parses the input data.

[0531] The server passes the received text data to a natural language processing (NLP) engine, which analyzes and extracts desired conditions, such as "within a 10-minute walk from the station," "within a 30-minute door-to-door drive to the office," "2LDK," "high priority on sunlight," and "rent under 100,000 yen."

[0532] Step 4:

[0533] The server searches the database.

[0534] The server searches the real estate database based on the analyzed desired conditions, lists properties that match the conditions, and retrieves detailed information about those properties.

[0535] Step 5:

[0536] The server sends the property information to the user.

[0537] The server then sends the selected property information to the user in the form of a chat message, including details such as photos, layout, rent, and location of each property.

[0538] Step 6:

[0539] The user enters additional conditions.

[0540] The user reviews the proposed property list and enters feedback in chat format, such as, "I like this layout, but I don't like the unit bath."

[0541] Step 7:

[0542] The terminal transmits the additional conditions to the server.

[0543] The terminal again transmits the additional conditions input by the user to the server.

[0544] Step 8:

[0545] The server learns additional conditions.

[0546] The server then trains the AI ​​model with the newly received additional conditions. The AI ​​model then understands the user's preferences and searches again for new properties that match the conditions.

[0547] Step 9:

[0548] The server will suggest the property again.

[0549] The server then suggests suitable properties to the user from the search results again, and this process continues with repeated feedback until the user finds a property that satisfies them.

[0550] Step 10:

[0551] Provide ongoing proposals for premium members.

[0552] The server searches for new properties periodically (for example, every week or at the beginning of the month) based on the premium member's information and notifies the user via email or chat.

[0553] Example 1

[0554] 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."

[0555] In modern real estate searches, it takes a lot of time and effort for users to find properties that meet their desired criteria. In particular, automating search and suggestion processes that respond to changes in user preferences and additional criteria is difficult, requiring a lot of manual work. Furthermore, there is a lack of systems that efficiently and flexibly suggest properties that are optimal for users. As a result, users often feel stressed during the process of finding a property that suits them. The present invention aims to solve these problems and more efficiently and effectively suggest real estate properties that meet users' desired criteria.

[0556] 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.

[0557] In this invention, the server includes means for a user to input desired real estate conditions in a chat format, means for a terminal to transmit the input conditions to the server, means for analyzing the received data and extracting the desired conditions, means for searching a real estate database and listing properties that match the conditions, means for transmitting matching property information to the user, means for the user to input feedback on the proposed property, means for the terminal to transmit additional conditions to the server, means for learning the additional conditions and searching for and proposing properties again, and means for using an artificial intelligence model that learns the user's preferences and adaptively improves the accuracy of property proposals. This makes it possible to efficiently propose properties that match the desired conditions based on the desired conditions input by the user in a chat format, and to continue to learn the additional conditions through feedback, thereby providing the user with the most suitable property.

[0558] "User" means an individual or corporation that uses the system to search for and receive suggestions on real estate properties.

[0559] A "terminal" is an electronic device used by a user, such as a computer, smartphone, or tablet, that has the ability to communicate with a server.

[0560] A "server" is a computer system that processes data received from a user, searches a real estate database, and suggests suitable properties to the user.

[0561] "Chat format" is a communication method that allows users to interact with the system in natural language and input desired conditions.

[0562] "Desired conditions" refer to the specific requirements or preferences that a user has for a real estate property, and examples include "within a 10-minute walk from the station," "2LDK," and "rent under 100,000 yen."

[0563] "Text data" refers to sentence data in natural language that the user inputs as desired conditions, and is data in a format that is sent from the terminal to the server.

[0564] A "natural language processing (NLP) engine" is a software component that analyzes text data entered by the user and extracts desired conditions.

[0565] A "real estate database" is a collection of stored information about real estate properties, including detailed information such as property addresses, floor plans, rents, and photos.

[0566] "Listing" refers to the process by which the server compiles property information retrieved from the database and extracts properties that meet the criteria in a list format.

[0567] "Feedback" refers to additional opinions or requests for conditions that users enter regarding proposed properties, such as "I like this layout, but I don't like the unit bath."

[0568] An "artificial intelligence model" is a machine learning algorithm used to learn a user's additional requirements and preferences and improve the accuracy of property suggestions based on that information.

[0569] A "generative AI model" is a model that learns from user interaction data and incorporates user feedback to make adaptive property suggestions.

[0570] A "prompt sentence" is text that is input into a generative AI model, and includes a specific request, such as "I'm looking for a property within a 10-minute walk from the station, with a 2LDK and rent of less than 100,000 yen."

[0571] This invention is a system in which a user inputs desired real estate conditions in a chat format, and a server automatically proposes the most suitable property. Specific embodiments for carrying out this invention are described below.

[0572] System Overview

[0573] First, the user opens a chat-style interface using a dedicated application or web browser and enters their desired real estate requirements, such as "within a 10-minute walk from the station, within a 30-minute door-to-door commute to work, 2LDK, with priority on sunlight, rent under 100,000 yen."

[0574] Hardware and Software Configuration

[0575] The system uses the following main hardware and software:

[0576] Device: A computing device used by a user, such as a smartphone, tablet, or PC.

[0577] Server: A computer system that processes data and suggests properties suitable for users.

[0578] Real estate database: A database that stores detailed information about real estate properties. Relational databases such as MySQL and PostgreSQL are likely to be used.

[0579] Natural language processing engine: Software for analyzing text data entered by users. Specifically, Google Cloud Natural Language API and Microsoft Azure Text Analytics are used.

[0580] Generative AI model: An artificial intelligence model that learns from user input and feedback. This could be a model built using TensorFlow or PyTorch.

[0581] Processing Description

[0582] 1. Enter your desired conditions and submit

[0583] The user enters their desired conditions in a chat format and sends them to the server via their device. At this time, the device converts the entered desired conditions into text data in JSON format and sends it to the server using the secure HTTPS protocol.

[0584] 2. Data Analysis

[0585] The server passes the received JSON-formatted text data to a natural language processing engine, which analyzes the desired conditions. Specifically, it extracts conditions such as "within a 10-minute walk from the station," "within a 30-minute door-to-door drive to the office," "2LDK," "priority on sunlight," and "rent under 100,000 yen."

[0586] 3. Database Search

[0587] The server searches the real estate database based on the analyzed criteria, executes an SQL query to list properties that match the criteria, and retrieves detailed information about each property (address, layout, rent, photo pass, etc.).

[0588] 4. Submit property information

[0589] The server then formats the search results into chat messages and sends them to the device, including details such as property photo URLs, floor plan, rent, and location.

[0590] 5. User Feedback

[0591] The user checks the displayed property list and again enters feedback in chat format, such as "This property is good, but I don't like the unit bath."

[0592] 6. Submitting and Learning Additional Terms

[0593] The device sends the user's additional conditions to the server, and the newly received additional conditions are learned by the AI ​​model. The AI ​​model updates the user's preferences and reflects them in the next search results.

[0594] 7. Re-proposal

[0595] The server re-searches the real estate database based on the updated preferences, generates a new property list, and sends this list to the user again in the form of a chat message, repeating this process until the user finds a property that satisfies them.

[0596] Specific examples

[0597] For example, if a user searches for a property with conditions such as "within a 10-minute walk from the station, 2LDK, rent under 100,000 yen," the server will list properties that meet those conditions and suggest them to the user. If the user gives feedback such as "This property is good, but I don't like the unit bath," the server will learn the additional conditions and search again for properties with separate bathrooms and toilets and suggest them.

[0598] Prompt Sentence Examples

[0599] "I'm looking for a property within a 10-minute walk from the station, with a 2LDK and rent of less than 100,000 yen. I'd also like to avoid a unit bath. What kind of properties are available?"

[0600] In this way, a system is realized that can efficiently suggest properties that meet the desired conditions based on the desired conditions entered by the user in chat format, and by continuing to learn additional conditions through feedback, it can provide the user with the property that is most suitable for them.

[0601] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0602] Step 1:

[0603] The user inputs the desired conditions for the property.

[0604] Input: The user uses a dedicated application or web browser to enter desired conditions into a chat-style interface, such as "within 10 minutes' walk from the station, within 30 minutes' door-to-door to the office, 2LDK, sunlight-oriented, rent under 100,000 yen."

[0605] Data processing: Desired conditions are entered in text format.

[0606] Output: The desired conditions are saved as text data on the terminal.

[0607] Step 2:

[0608] The terminal sends the input data to the server.

[0609] Input: Text data of desired conditions saved on the device.

[0610] Data processing: The terminal converts the entered desired conditions into JSON format.

[0611] Output: Text data in JSON format is sent to the server using the HTTPS protocol.

[0612] Step 3:

[0613] The server parses the input data.

[0614] Input: JSON formatted text data received by the server.

[0615] Data processing: The server passes the received data to a natural language processing engine (e.g., Google Cloud Natural Language API), which analyzes and extracts the desired conditions.

[0616] Output: The extracted desired conditions (for example, "within 10 minutes' walk from the station," "within 30 minutes' door-to-door drive to work," "2LDK," "priority on sunlight," "rent under 100,000 yen") are saved on the server.

[0617] Step 4:

[0618] The server searches the database.

[0619] Input: Parsed desired conditions.

[0620] Data processing: The server generates SQL queries and searches the real estate database (MySQL or PostgreSQL).

[0621] Output: A list of properties that match the criteria (property ID, address, layout, rent, photo pass, etc.) will be obtained.

[0622] Step 5:

[0623] The server sends the property information to the user.

[0624] Input: Acquired property list.

[0625] Data processing: The server formats the property information into a chat message format.

[0626] Output: A chat-style message (including the property's photo URL, floor plan, rent, address, etc.) is sent to the user's device.

[0627] Step 6:

[0628] The user enters additional conditions.

[0629] Input: The user checks the displayed property list and enters additional conditions as feedback in chat format (e.g., "This property is good, but I don't like the unit bath").

[0630] Data processing: Additional conditions are entered in text format.

[0631] Output: The additional conditions are saved as text data on the terminal.

[0632] Step 7:

[0633] The terminal transmits the additional conditions to the server.

[0634] Input: Text data of additional conditions saved on the device.

[0635] Data processing: The terminal converts the additional conditions into JSON format.

[0636] Output: Text data in JSON format is sent to the server using the HTTPS protocol.

[0637] Step 8:

[0638] The server learns additional conditions.

[0639] Input: Additional conditions received by the server in JSON format.

[0640] Data processing: The server uses the received additional conditions to train an AI model (e.g., a TensorFlow-based model) and updates the user's preferences.

[0641] Output: The updated user preferences are saved in the server.

[0642] Step 9:

[0643] The server will suggest the property again.

[0644] Input: Updated user preferences.

[0645] Data processing: The server re-searches the real estate database based on the updated preferences and generates a new property list.

[0646] Output: New listings of properties that match the criteria are sent to the user in the form of a chat message.

[0647] (Application example 1)

[0648] 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."

[0649] Conventional real estate search systems are inefficient because they require users to input search criteria and load large amounts of information to confirm property details. Furthermore, it takes a lot of time to search again based on property feedback, and suggestions that reflect the user's preferences are not always obtained. Furthermore, the system requires users to visit the property in person, which creates significant barriers in terms of physical distance and time.

[0650] 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.

[0651] In this invention, the server includes a means for a user to input desired real estate conditions in a chat format, a means for a terminal to transmit the input conditions to the server, and a means for the server to analyze the received data and extract the desired conditions. This enables efficient property search and proposals based on the user's desired conditions. Furthermore, by including a means for viewing properties in a virtual reality environment using a smart device, it is possible to remotely check and experience the property in detail, overcoming the barriers of physical distance and time.

[0652] "Means for users to input desired conditions for real estate in chat format" is a function that allows users to input requests for real estate such as housing into a terminal using written dialogue.

[0653] The "means for transmitting conditions entered by the terminal to the server" is a function for transmitting desired conditions entered by the user to the computer server.

[0654] "Means for analyzing data received by the server and extracting desired conditions" refers to a function that analyzes the data of the user's desired conditions received by the server, and identifies and extracts specific conditions from that data.

[0655] "Means for the server to search the real estate database and list properties that meet the conditions" is a function that allows the server to search the database for real estate information that meets the conditions and create a list of relevant properties.

[0656] The "means for the server to send matching property information to the user" is a function for sending information about the found property to the user.

[0657] The "means for users to input feedback on proposed properties" is a function that allows users to input opinions and additional conditions regarding proposed properties.

[0658] The "means for the terminal to transmit additional conditions to the server" is a function for transmitting the additional desired conditions input by the user back to the server.

[0659] "Means for the server to learn additional conditions and search for and suggest properties again" is a function that allows the server to learn newly received conditions and search the database again based on them to suggest properties.

[0660] "Means for viewing properties in a virtual reality environment using a smart device" refers to the ability to visually inspect real estate properties in detail in a virtual reality environment using advanced devices such as smart glasses.

[0661] "Natural language processing technology" is a technology that enables computers to understand, analyze, and manipulate human language.

[0662] An "AI model" is a data model that uses artificial intelligence technology to perform specific tasks.

[0663] A "real estate database" is a database in which information about residential and commercial real estate is systematically stored.

[0664] A "virtual reality environment" is an environment that uses virtual reality technology to recreate a real-life three-dimensional space.

[0665] The present invention is a system in which a user inputs desired real estate conditions in a chat format, and a server automatically proposes the most suitable property. Specific embodiments of this system will be described in detail below.

[0666] System program generation

[0667] To realize this system, the following program will be constructed: The user inputs their desired conditions using a smart device (such as smart glasses), and the system analyzes those conditions and suggests the most suitable property.

[0668] Hardware and Software Use

[0669] 1. Hardware:

[0670] Smart devices (e.g. smart glasses)

[0671] 2. Software:

[0672] Natural language processing technology (NLP engine, e.g., Google Cloud NLP)

[0673] Real Estate Database

[0674] AI model

[0675] System processing flow

[0676] 1. Receiving user input:

[0677] The user inputs desired conditions into the smart glasses by voice, which is converted into text data using the smart glasses' voice recognition function.

[0678] 2. Analysis of desired conditions:

[0679] The smart glasses transmit the desired conditions in text data form to a server, which then analyzes the desired conditions using natural language processing technology and extracts specific conditions (e.g., "within a 10-minute walk from the station," "2LDK," "rent under 100,000 yen").

[0680] 3. Database Search:

[0681] The server searches a real estate database and lists properties that match the extracted desired conditions. The server then obtains detailed information about the listed properties (photos, floor plan, rent, location, etc.).

[0682] 4. User Submission of Property Information:

[0683] The server sends the listed property information to the smart glasses, through which the user can view the property in a virtual reality environment.

[0684] 5. Feedback Processing:

[0685] When the user enters feedback on the proposed property, the device sends this feedback back to the server, which then uses the feedback to train the AI ​​model and conducts another property search, taking additional criteria into account.

[0686] Specific examples

[0687] For example, suppose a user verbally inputs their desired conditions into the smart glasses, such as "within a 10-minute walk from the station, 2LDK, rent under 100,000 yen." The smart glasses convert this voice into text data and send it to the server. The server analyzes the desired conditions, searches for properties that match, and then sends a list of properties to the smart glasses. The user browses each property in the VR environment and provides feedback such as "This property is good, but I don't like the unit bath." Based on this, the server re-learns the conditions and searches for and suggests new properties that meet the conditions.

[0688] Prompt Sentence Examples

[0689] An example prompt might be, "The user puts on the smart glasses and searches for properties that meet the specified real estate criteria. The criteria are within a 10-minute walk from the station, 2LDK, and rent of less than 100,000 yen. Find the perfect property and view it in a virtual tour. The user can then enter feedback on additional criteria and update the search results."

[0690] In this way, the system of the present invention can efficiently search for properties based on the user's desired conditions and can remotely check the details of the properties using virtual reality technology.

[0691] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0692] Step 1:

[0693] Receiving User Input

[0694] The user puts on the smart glasses and inputs their desired real estate conditions by voice (e.g., "within 10 minutes' walk from the station, 2LDK, rent under 100,000 yen"). This voice input is converted into text data using the smart glasses' voice recognition function. The input data is output in text format as the user's desired conditions.

[0695] Step 2:

[0696] Send your desired conditions

[0697] The terminal sends the desired conditions entered by the user as text data to the server. This transmission is performed via the Internet. The desired conditions received as input data are transferred to the server and sent as data for analysis.

[0698] Step 3:

[0699] Analysis of desired conditions

[0700] The server analyzes the received text data of desired conditions using a natural language processing (NLP) engine and extracts specific desired conditions (e.g., "within 10 minutes' walk," "2LDK," "rent under 100,000 yen"). The input is the desired conditions in text format, and the output is the analyzed specific conditions.

[0701] Step 4:

[0702] Database search

[0703] The server searches the real estate database based on the analyzed desired conditions. It lists properties that match the conditions and obtains detailed information about those properties (photos, floor plan, rent, location, etc.). The input is the analyzed desired conditions, and the output is a list of properties that match the conditions.

[0704] Step 5:

[0705] Submit property information

[0706] The server sends the listed property information to the smart glasses, through which the user can view the properties in a virtual reality environment. The input is the property list, and the output is the display of the property information in a virtual reality environment.

[0707] Step 6:

[0708] Receiving Feedback

[0709] The user can provide feedback about the proposed property by voice (e.g., "This property is good, but I don't like the unit bath"). This feedback is converted into text data using the smart glasses' voice recognition function. The input is the feedback content, and the output is text-based feedback.

[0710] Step 7:

[0711] Submitting additional conditions

[0712] The terminal sends the user's feedback (additional conditions) to the server. The input is the feedback in text format, and the output is the data sent to the server.

[0713] Step 8:

[0714] Learning additional conditions and re-searching

[0715] The server trains the AI ​​model on the additional conditions and understands the user's new preferences. Based on this learning, it searches the real estate database again and lists properties that meet the new conditions. The input is the text data of the additional conditions, and the output is a list of properties that meet the new conditions.

[0716] Step 9:

[0717] Re-proposal

[0718] The server sends the re-listed property information to the smart glasses. The user then browses, rates, and views the properties again in a virtual reality environment. The input is the new property list, and the output is a virtual reality display of the re-suggested property.

[0719] 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.

[0720] This invention is a system in which a user inputs desired real estate conditions in a chat format, and the server automatically suggests the most suitable property. This system also incorporates an emotion engine that recognizes the user's emotions, allowing it to make suggestions according to the user's emotional state. The specific program processing procedures and their operation are explained below, along with specific examples.

[0721] Program processing overview

[0722] 1. The user enters the desired conditions

[0723] The user opens the chat input screen on their device and enters their desired conditions, such as "within 10 minutes' walk from the station, within 30 minutes door-to-door to work, 2LDK, sunlight-oriented, rent under 100,000 yen."

[0724] 2. The device sends the input data to the server

[0725] The terminal transmits the desired conditions entered by the user to the server as text data.

[0726] 3. The server analyzes the input data

[0727] The server passes the received text data to a natural language processing (NLP) engine, which analyzes and extracts desired conditions, such as "within a 10-minute walk from the station," "within a 30-minute door-to-door drive to the office," "2LDK," "high priority on sunlight," and "rent under 100,000 yen."

[0728] 4. The server recognizes emotions using the emotion engine

[0729] The server uses an emotion engine to analyze the emotions from the user's input data. For example, it can recognize whether the user is feeling dissatisfied, satisfied, or hopeful based on the wording and expressions in the text.

[0730] 5. The server searches the database

[0731] The server searches a real estate database based on the analyzed desired conditions and emotion recognition results, lists properties that meet the conditions and take the user's emotions into consideration, and retrieves detailed information about those properties.

[0732] 6. The server sends the property information to the user

[0733] The server then sends the selected property information to the user in the form of a chat message, including details such as photos, layout, rent, and location of each property.

[0734] 7. User enters additional conditions

[0735] The user reviews the proposed property list and enters feedback in chat format, such as, "I like this floor plan, but I don't like the unit bath." The emotion engine then analyzes the user's emotions in real time.

[0736] 8. The device sends additional conditions to the server

[0737] The terminal again transmits the additional conditions input by the user to the server.

[0738] 9. The server learns additional conditions

[0739] The server then trains the AI ​​model with the newly received additional conditions, allowing the AI ​​model to understand the user's preferences and emotions and search again for new properties that match the conditions.

[0740] 10. The server will suggest properties again

[0741] The server then suggests suitable properties to the user from the search results again, and this process continues with repeated feedback until the user finds a property that satisfies them.

[0742] Specific examples

[0743] For example, if a user searches for a property with the conditions "within a 10-minute walk from the station, 2LDK, rent under 100,000 yen," the server will list properties that meet these criteria and suggest them to the user. If the user gives feedback such as "This property is good, but I don't like the unit bath," the emotion engine will recognize the dissatisfaction from the user's text, and the server will learn the additional conditions and search again to suggest properties with separate bathrooms and toilets.

[0744] Furthermore, the emotion engine monitors changes in the user's emotions and analyzes whether the user is satisfied with the suggestions, thereby improving the accuracy of property suggestions in a way that satisfies the user. Premium members are also provided with a system that regularly provides them with new property information. This is achieved by the server periodically searching the database based on premium member information and notifying them of new property information via email or chat.

[0745] This system flexibly responds to the user's needs and emotions, providing a wide range of suitable real estate properties, saving the user a great deal of effort. In particular, the learning function using an emotion engine and AI model makes it possible to make highly accurate property suggestions based on the user's preferences and emotions.

[0746] The processing flow will be explained below.

[0747] Step 1:

[0748] The user inputs the desired conditions.

[0749] The user opens the chat input screen on their device and enters their desired conditions, such as "within 10 minutes' walk from the station, within 30 minutes door-to-door to work, 2LDK, sunlight-oriented, rent under 100,000 yen."

[0750] Step 2:

[0751] The terminal sends the input data to the server.

[0752] The terminal transmits the desired conditions entered by the user to the server as text data.

[0753] Step 3:

[0754] The server parses the input data.

[0755] The server passes the received text data to a natural language processing (NLP) engine, which analyzes and extracts desired conditions, such as "within a 10-minute walk from the station," "within a 30-minute door-to-door drive to the office," "2LDK," "high priority on sunlight," and "rent under 100,000 yen."

[0756] Step 4:

[0757] The server recognizes emotions using an emotion engine.

[0758] The server uses an emotion engine to analyze the emotions from the user's input data, for example, recognizing whether the user is feeling dissatisfied, satisfied, or hopeful based on the language and expressions used in the text.

[0759] Step 5:

[0760] The server searches the database.

[0761] The server searches a real estate database based on the analyzed desired conditions and emotion recognition results, and lists properties that match the conditions and take the user's emotions into consideration.

[0762] Step 6:

[0763] The server sends the property information to the user.

[0764] The server then sends the selected property information to the user in the form of a chat message, including details such as photos, layout, rent, and location of each property.

[0765] Step 7:

[0766] The user enters additional conditions.

[0767] The user checks the proposed property list and inputs additional desired conditions as feedback, such as "I like this floor plan, but I don't want a unit bath." At this time, the emotion engine also continuously analyzes the user's emotions.

[0768] Step 8:

[0769] The terminal transmits the additional conditions to the server.

[0770] The terminal again transmits the additional conditions input by the user to the server.

[0771] Step 9:

[0772] The server learns additional conditions.

[0773] The server then trains the AI ​​model with the newly received additional conditions, allowing the AI ​​model to understand the user's preferences and emotions and search again for new properties that match the conditions.

[0774] Step 10:

[0775] The server will suggest the property again.

[0776] The server then proposes suitable properties to the user from the search results again, and continues providing feedback and suggestions using an emotion engine until the user is satisfied with the suggestions.

[0777] Step 11:

[0778] Provide ongoing proposals for premium members.

[0779] The server periodically (for example, weekly or monthly) searches for new properties based on premium member information and notifies users via email or chat. The emotion engine also analyzes user feedback and emotions to continually provide better suggestions.

[0780] Example 2

[0781] 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."

[0782] In conventional real estate search systems, when users input their desired conditions, they simply analyze the conditions as text, and are unable to make suggestions that reflect the user's emotions and preferences. Furthermore, there was no system that could understand user feedback in real time and re-make optimal suggestions. As a result, users had to spend a lot of time and effort searching for real estate properties.

[0783] The specification process by the specification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes: means for a user to input desired conditions for real estate in a chat format; means for a terminal to transmit the input conditions to the server; means for the server to analyze the received data and extract the desired conditions; means for the server to analyze the desired conditions using natural language processing technology; means for the server to recognize the user's emotions using an emotion engine; means for the server to search a real estate database and list properties that match the conditions; means for the server to transmit matching property information to the user; means for the user to input feedback on the proposed property; means for the terminal to transmit additional conditions to the server; and means for the server to learn the user's additional conditions using an AI model and search for and propose properties again.

[0784] This makes it possible to analyze the user's desired conditions and emotions in real time and propose the most suitable real estate property that suits the user's preferences.

[0785] "User" refers to a person who uses the real estate search system to input desired conditions and receive property proposals.

[0786] A "terminal" is a device used by a user to input desired conditions in chat format, and includes a smartphone, tablet, PC, etc.

[0787] "Server" refers to a central management device that receives, analyzes, and searches user input data and makes real estate property suggestions.

[0788] "Natural language processing technology" refers to technology that analyzes text data entered by the user and extracts desired conditions based on that data.

[0789] An "emotion engine" is an engine that recognizes emotions from user input data and optimizes property suggestions based on those emotions.

[0790] A "real estate database" is a database that stores various real estate property information, and refers to a collection of property information that can be searched.

[0791] "AI model" refers to a machine learning model that learns the user's desired conditions and additional conditions, and searches for and suggests the most suitable properties.

[0792] "Premium Member" refers to a user who has the right to receive special services under certain conditions.

[0793] System Overview

[0794] This invention is a system in which a user inputs desired real estate conditions in a chat format, and the server automatically suggests the most suitable property. This system incorporates an emotion engine that recognizes the user's emotions, and can make suggestions according to the user's emotional state.

[0795] Hardware and software used

[0796] User devices: smartphones, tablets, computers, etc.

[0797] Server: A central management device that analyzes desired conditions, searches real estate databases, recognizes emotions, and trains AI models.

[0798] Natural Language Processing (NLP) engine: Technology that analyzes user requirements

[0799] Emotion engine: An engine that analyzes emotions from user input data

[0800] Real estate database: Contains information on various real estate properties

[0801] AI model: A machine learning model that learns the user's desired conditions and additional conditions to suggest the most suitable properties.

[0802] System Operation

[0803] 1. Input method: Users use a device such as a smartphone or PC to input their desired real estate requirements in chat format. For example, they can input specific requirements such as "within a 10-minute walk from the station, within a 30-minute door-to-door commute to work, 2LDK, sunlight-oriented, rent under 100,000 yen."

[0804] 2. Data transmission means: The user's terminal transmits the entered desired conditions to the server as text data.

[0805] 3. Data analysis method: The server passes the received text data to a natural language processing (NLP) engine, which analyzes and extracts the desired conditions. Specifically, it breaks down conditions such as "within a 10-minute walk from the station," "within a 30-minute door-to-door drive to the office," "2LDK," "priority on sunlight," and "rent under 100,000 yen."

[0806] 4. Emotion Recognition: The server uses an emotion engine to recognize emotions from the user's input data. For example, it can read emotions such as dissatisfaction, satisfaction, and expectation from the wording and expressions in the text.

[0807] 5. Database search method: The server searches the real estate database based on the analyzed desired conditions and emotion recognition results, and lists properties that meet the conditions and take the user's emotions into consideration.

[0808] 6. Result transmission method: The server sends the selected property information to the user in the form of a chat message, including photos, floor plan, rent, location, access information, etc.

[0809] 7. Feedback: The user reviews the proposed property list and enters feedback in chat format, such as "I like this floor plan, but I don't like the unit bathroom." The emotion engine continues to analyze the user's emotions in real time and learns the user's preferences based on the feedback.

[0810] 8. Additional condition sending means: The terminal sends the additional conditions entered by the user back to the server.

[0811] 9. Learning method: The server trains the AI ​​model with the newly received additional conditions. The AI ​​model then gains a more detailed understanding of the user's preferences and emotions and searches for new properties.

[0812] 10. Re-proposal method: The server proposes a newly selected property to the user, and the process continues with repeated feedback until the user finds a property that satisfies them.

[0813] Specific examples

[0814] For example, if a user enters the conditions "within a 10-minute walk from the station, 2LDK, rent under 100,000 yen," the server will list properties that meet these criteria and suggest them to the user. If the user gives feedback such as "This property is good, but I don't like the unit bath," the emotion engine will recognize the dissatisfaction, and the server will search again and suggest properties with separate bathrooms and toilets.

[0815] Example prompts for generative AI models

[0816] Example prompt:

[0817] "The user entered the desired conditions: 'within 10 minutes' walk from the station, 2LDK, rent under 100,000 yen.' Please use the emotion engine to suggest the best property for this. Also, please explain how to respond if the user enters feedback such as 'I don't want a unit bath.'"

[0818] This allows the system to flexibly respond to users' needs and emotions, providing them with multifaceted, suitable real estate properties, saving them time and effort.In addition, by using an emotion engine and AI model, it is possible to provide highly accurate property suggestions that match the user's preferences and emotions.

[0819] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0820] Step 1:

[0821] The user enters the desired conditions.

[0822] The user opens the chat input screen on their device and enters their desired conditions, such as "within 10 minutes' walk from the station, within 30 minutes door-to-door to work, 2LDK, sunlight-oriented, rent under 100,000 yen."

[0823] Specifically, the user inputs each desired condition in text format and presses the send button.

[0824] Step 2:

[0825] The device sends the input data to the server.

[0826] The terminal transmits the desired conditions entered by the user to the server as text data.

[0827] Specifically, when the send button is pressed, the text data is automatically sent to the server.

[0828] Input: User's desired conditions (text format)

[0829] Output: Text data to the server

[0830] Step 3:

[0831] The server parses the input data.

[0832] The server passes the received text data to a natural language processing (NLP) engine, which analyzes and extracts the desired conditions.

[0833] Specifically, the NLP engine analyzes the text and extracts conditions such as "within 10 minutes' walk from the station," "2LDK," and "rent under 100,000 yen."

[0834] Input: User's desired conditions (text data)

[0835] Output: Parsed desired conditions (structured data)

[0836] Step 4:

[0837] The server recognizes emotions using an emotion engine.

[0838] The server uses an emotion engine to analyze emotions from the user's input data.

[0839] Specifically, it reads emotions such as dissatisfaction, satisfaction, and expectation from the wording of the input text.

[0840] Input: User's desired conditions (text data)

[0841] Output: Parsed emotion data

[0842] Step 5:

[0843] The server searches the database.

[0844] The server searches a real estate database based on the analyzed desired conditions and emotion recognition results, and lists properties that meet the conditions.

[0845] Specifically, it uses a filter to narrow down the properties in the database that meet the criteria and lists them.

[0846] Input: Analyzed desired conditions and emotion data

[0847] Output: List of properties that match the criteria

[0848] Step 6:

[0849] The server sends the property information to the user.

[0850] The server sends the selected property information to the user in the form of a chat message.

[0851] Specifically, a chat message is generated that includes photos of the property, floor plan, rent, location, access information, etc.

[0852] Input: List of properties that match the criteria

[0853] Output: Property chat message to user

[0854] Step 7:

[0855] The user enters additional criteria.

[0856] The user reviews the proposed property list and enters feedback in chat format, such as, "I like this layout, but I don't like the unit bath."

[0857] Specifically, the user inputs and sends specific complaints and additional requirements via chat.

[0858] Input: Feedback on the proposed property (text format)

[0859] Output: User feedback data

[0860] Step 8:

[0861] The device sends the additional conditions to the server.

[0862] The terminal again transmits the additional conditions input by the user to the server.

[0863] Specifically, when the send button is pressed, the feedback data is sent to the server.

[0864] Input: User feedback (text data)

[0865] Output: Feedback data to the server

[0866] Step 9:

[0867] The server learns additional conditions.

[0868] The server trains the AI ​​model on the newly received additional conditions.

[0869] Specifically, the AI ​​model learns from the feedback data to gain a more detailed understanding of the user's preferences.

[0870] Input: User feedback data

[0871] Output: Updated AI model

[0872] Step 10:

[0873] The server proposes the property again.

[0874] The server then suggests suitable properties to the user from the search results.

[0875] Specifically, the system searches again for new properties that meet the conditions and generates a proposal message.

[0876] Input: Updated AI model and new search results

[0877] Output: New property proposal chat message

[0878] (Application example 2)

[0879] 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."

[0880] On current online shopping sites, when users search for the products they want, it takes a lot of time and effort to find the products that match their criteria.In addition, there is a lack of a system that suggests optimal products based on the user's emotions and feedback, making it difficult for users to efficiently find products that satisfy them.

[0881] The identification process by the identification 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 a user to input desired product conditions in a chat format, means for a terminal to transmit the input conditions to the server, means for the server to analyze the received data and extract the desired conditions, means for the server to search a product database and list products that match the conditions, means for the server to transmit information about the matching products to the user, means for the user to input feedback on the suggested products, means for the terminal to transmit additional conditions to the server, means for the server to learn the additional conditions and search for and suggest products again, and an emotion engine for the server to analyze the user's emotional state and make suggestions based on the emotions. This enables the user to efficiently find products that match the desired conditions and can suggest optimal products based on the user's emotions and feedback.

[0882] A "user" is a person who searches for and purchases products using an online shopping site.

[0883] "Desired conditions" are requirements regarding product specifications and features that are input by the user in chat format.

[0884] A "terminal" is an electronic device used by a user, such as a computer, smartphone, or tablet.

[0885] A "server" is a central processing unit that receives data sent from a terminal and performs analysis and searches.

[0886] "Chat format" is an interface format that allows users to input text and exchange information in a conversational format.

[0887] A "product database" is a digital database in which product information is stored.

[0888] "Feedback" refers to the act of a user inputting an evaluation or opinion about a proposed product, or the content of such input.

[0889] "Additional conditions" are new conditions input by the user based on feedback.

[0890] An "emotion engine" is software that analyzes the user's emotions from their text data and makes suggestions based on their state.

[0891] "Natural language processing technology" is a technology that allows computers to understand and analyze human language.

[0892] A "generative AI model" is an artificial intelligence model that learns from users' conditions and feedback and makes more appropriate suggestions.

[0893] In this invention, a mail-order system will be described that proposes products based on desired conditions input by a user in a chat format.

[0894] Overall system configuration

[0895] This system mainly consists of a user's device, a server, a product database, and an emotion engine. Users input their desired conditions through a chat interface, and their device sends the information to the server. The server uses natural language processing technology to analyze the desired conditions and search for matching products in the product database. It then uses the emotion engine to analyze the user's emotions and makes product suggestions based on those emotions.

[0896] Hardware and Software Configuration

[0897] Hardware: Your PC, smartphone or tablet, and server

[0898] Software: Natural language processing engine (e.g., Google Cloud Natural Language), sentiment analysis engine (e.g., IBM Watson Tone Analyzer), database (e.g., MySQL), chatbot framework (e.g., Dialogflow)

[0899] What the program does

[0900] 1. User inputs desired conditions

[0901] The user enters the desired product conditions in chat format. For example, specific conditions such as "black jacket, size L, budget within 5,000 yen, casual style" are entered.

[0902] 2. Sending data from the device to the server

[0903] The terminal transmits the desired conditions entered by the user as text data to the server, using real-time communication.

[0904] 3. Data analysis by the server

[0905] The server passes the received text data to a natural language processing engine, which analyzes the desired conditions, such as "black jacket," "size L," "budget under 5,000 yen," and "casual style."

[0906] 4. Emotion analysis

[0907] The server uses an emotion engine to analyze the user's input data to determine whether the user is feeling expectations, hopes, dissatisfaction, or other emotions.

[0908] 5. Search the product database

[0909] The server searches a product database based on the analyzed desired conditions and emotions, lists products that match the conditions, and obtains their detailed information.

[0910] Adding specific examples

[0911] For example, if a user searches for a product using the criteria "black jacket, size L, budget under 5,000 yen, casual style," the server will list products that match these criteria and suggest them to the user. If the user gives feedback such as "I like this design, but I wish it was a little longer," the emotion engine will recognize the user's wishes from the text, and the server will learn the additional criteria, search again, and suggest new products that match the criteria.

[0912] Prompt Sentence Examples

[0913] User input: "Black jacket, size L, budget under 5000 yen, casual style"

[0914] Prompt: "Analyze the emotions expressed by the user's input."

[0915] This allows for more detailed proposals that meet the user's desired conditions, and also allows for optimal product proposals based on the user's emotions and feedback.

[0916] summary

[0917] By introducing this system, users can efficiently find products that meet their desired criteria, and by using an emotion engine, they can receive highly satisfying suggestions. Product recommendations are made based on data analyzed by the server and the results of emotion analysis, which increases user satisfaction.

[0918] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0919] Step 1:

[0920] The user inputs desired product specifications in chat format via a terminal. The input is specific text data such as "black jacket, size L, budget within 5,000 yen, casual style."

[0921] Step 2:

[0922] The terminal transmits the desired conditions entered by the user to the server as text data, and the transmitted data is transmitted to the server in real time.

[0923] Step 3:

[0924] The server passes the received text data to a natural language processing engine (e.g., Google Cloud Natural Language) and analyzes the desired conditions. This analysis includes text tokenization, part-of-speech tagging, and semantic analysis. For example, conditions such as "black jacket," "size L," "budget under 5,000 yen," and "casual style" are extracted.

[0925] Step 4:

[0926] The server uses an emotion engine (e.g., IBM Watson Tone Analyzer) to analyze the emotion from the user's input data. This analyzes the emotional weighting of words and the tone of the entire sentence to determine the user's emotional state (expectation, hope, etc.). It identifies whether the user is excited or disappointed, for example.

[0927] Step 5:

[0928] The server searches a product database (e.g., MySQL) based on the analyzed desired conditions and emotion recognition results, lists products that match the conditions, and retrieves detailed information about those products (images, prices, descriptions, etc.).

[0929] Step 6:

[0930] The server sends the selected product information to the user in the form of a chat message, including a photo, description, price, and rating of each product.

[0931] Step 7:

[0932] The user can review the proposed product list and provide feedback in chat, for example, by providing specific opinions such as, "The design is good, but I wish it was a little longer."

[0933] Step 8:

[0934] The terminal sends the additional conditions entered by the user to the server, and feedback is transmitted to the server in real time.

[0935] Step 9:

[0936] The server trains the generative AI model on the newly received additional conditions. This training uses supervised and unsupervised learning methods. The AI ​​model updates the conditions based on the user's preferences and feedback information and reflects them in the next search.

[0937] Step 10:

[0938] The server searches for products again and suggests new products that match the criteria. This process is repeated until a product that satisfies the user is found.

[0939] 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.

[0940] 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.

[0941] 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.

[0942] [Third embodiment]

[0943] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0944] 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.

[0945] 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).

[0946] 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.

[0947] 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.

[0948] 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).

[0949] 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.

[0950] 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.

[0951] 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.

[0952] 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.

[0953] 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.

[0954] 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."

[0955] This invention is a system in which a user inputs desired real estate conditions in a chat format, and the server automatically proposes the most suitable property. The specific program processing procedure and its operation will be explained below, along with specific examples.

[0956] Program processing overview

[0957] 1. The user enters the desired conditions

[0958] The user opens the chat input screen on their device and enters their desired conditions, such as "within 10 minutes' walk from the station, within 30 minutes door-to-door to work, 2LDK, sunlight-oriented, rent under 100,000 yen."

[0959] 2. The device sends the input data to the server

[0960] The terminal transmits the desired conditions entered by the user to the server as text data.

[0961] 3. The server analyzes the input data

[0962] The server passes the received text data to a natural language processing (NLP) engine, which analyzes and extracts desired conditions, such as "within a 10-minute walk from the station," "within a 30-minute door-to-door drive to the office," "2LDK," "high priority on sunlight," and "rent under 100,000 yen."

[0963] 4. The server searches the database

[0964] The server searches the real estate database based on the analyzed desired conditions, lists properties that match the conditions, and retrieves detailed information about those properties.

[0965] 5. The server sends the property information to the user

[0966] The server then sends the selected property information to the user in the form of a chat message, including details such as photos, layout, rent, and location of each property.

[0967] 6. User enters additional conditions

[0968] The user reviews the proposed property list and enters feedback in chat format, such as, "I like this layout, but I don't like the unit bath."

[0969] 7. The device sends the additional conditions to the server.

[0970] The terminal transmits the additional conditions input by the user to the server.

[0971] 8. The server learns additional conditions

[0972] The server then trains the AI ​​model with the newly received additional conditions. The AI ​​model then understands the user's preferences and searches again for new properties that match the conditions.

[0973] 9. The server will suggest properties again

[0974] The server then suggests suitable properties to the user from the search results again, and this process continues with repeated feedback until the user finds a property that satisfies them.

[0975] Specific examples

[0976] For example, if a user searches for a property with the conditions "within a 10-minute walk from the station, 2LDK, rent under 100,000 yen," the server will list properties that meet these conditions and suggest them to the user. If the user gives feedback such as "This property is good, but I don't want a unit bath," the server will learn the additional conditions and search again to suggest properties with separate bathrooms and toilets.

[0977] In addition, premium members are provided with a system that regularly provides them with new property information on an ongoing basis. This is achieved by the server periodically searching the database based on the premium member information and notifying them of new property information via email or chat.

[0978] This system flexibly responds to user needs while efficiently providing suitable real estate properties, saving users a great deal of effort. In particular, the learning function using an AI model makes it possible to make highly accurate property suggestions that match the user's preferences.

[0979] The processing flow will be explained below.

[0980] Step 1:

[0981] The user inputs the desired conditions.

[0982] The user opens the chat input screen on their device and enters their desired conditions, such as "within 10 minutes' walk from the station, within 30 minutes door-to-door to work, 2LDK, sunlight-oriented, rent under 100,000 yen."

[0983] Step 2:

[0984] The terminal sends the input data to the server.

[0985] The terminal transmits the desired conditions entered by the user to the server as text data.

[0986] Step 3:

[0987] The server parses the input data.

[0988] The server passes the received text data to a natural language processing (NLP) engine, which analyzes and extracts desired conditions, such as "within a 10-minute walk from the station," "within a 30-minute door-to-door drive to the office," "2LDK," "high priority on sunlight," and "rent under 100,000 yen."

[0989] Step 4:

[0990] The server searches the database.

[0991] The server searches the real estate database based on the analyzed desired conditions, lists properties that match the conditions, and retrieves detailed information about those properties.

[0992] Step 5:

[0993] The server sends the property information to the user.

[0994] The server then sends the selected property information to the user in the form of a chat message, including details such as photos, layout, rent, and location of each property.

[0995] Step 6:

[0996] The user enters additional conditions.

[0997] The user reviews the proposed property list and enters feedback in chat format, such as, "I like this layout, but I don't like the unit bath."

[0998] Step 7:

[0999] The terminal transmits the additional conditions to the server.

[1000] The terminal again transmits the additional conditions input by the user to the server.

[1001] Step 8:

[1002] The server learns additional conditions.

[1003] The server then trains the AI ​​model with the newly received additional conditions. The AI ​​model then understands the user's preferences and searches again for new properties that match the conditions.

[1004] Step 9:

[1005] The server will suggest the property again.

[1006] The server then suggests suitable properties to the user from the search results again, and this process continues with repeated feedback until the user finds a property that satisfies them.

[1007] Step 10:

[1008] Provide ongoing proposals for premium members.

[1009] The server searches for new properties periodically (for example, every week or at the beginning of the month) based on the premium member's information and notifies the user via email or chat.

[1010] Example 1

[1011] 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."

[1012] In modern real estate searches, it takes a lot of time and effort for users to find properties that meet their desired criteria. In particular, automating search and suggestion processes that respond to changes in user preferences and additional criteria is difficult, requiring a lot of manual work. Furthermore, there is a lack of systems that efficiently and flexibly suggest properties that are optimal for users. As a result, users often feel stressed during the process of finding a property that suits them. The present invention aims to solve these problems and more efficiently and effectively suggest real estate properties that meet users' desired criteria.

[1013] 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.

[1014] In this invention, the server includes means for a user to input desired real estate conditions in a chat format, means for a terminal to transmit the input conditions to the server, means for analyzing the received data and extracting the desired conditions, means for searching a real estate database and listing properties that match the conditions, means for transmitting matching property information to the user, means for the user to input feedback on the proposed property, means for the terminal to transmit additional conditions to the server, means for learning the additional conditions and searching for and proposing properties again, and means for using an artificial intelligence model that learns the user's preferences and adaptively improves the accuracy of property proposals. This makes it possible to efficiently propose properties that match the desired conditions based on the desired conditions input by the user in a chat format, and to continue to learn the additional conditions through feedback, thereby providing the user with the most suitable property.

[1015] "User" means an individual or corporation that uses the system to search for and receive suggestions on real estate properties.

[1016] A "terminal" is an electronic device used by a user, such as a computer, smartphone, or tablet, that has the ability to communicate with a server.

[1017] A "server" is a computer system that processes data received from a user, searches a real estate database, and suggests suitable properties to the user.

[1018] "Chat format" is a communication method that allows users to interact with the system in natural language and input desired conditions.

[1019] "Desired conditions" refer to the specific requirements or preferences that a user has for a real estate property, and examples include "within a 10-minute walk from the station," "2LDK," and "rent under 100,000 yen."

[1020] "Text data" refers to sentence data in natural language that the user inputs as desired conditions, and is data in a format that is sent from the terminal to the server.

[1021] A "natural language processing (NLP) engine" is a software component that analyzes text data entered by the user and extracts desired conditions.

[1022] A "real estate database" is a collection of stored information about real estate properties, including detailed information such as property addresses, floor plans, rents, and photos.

[1023] "Listing" refers to the process by which the server compiles property information retrieved from the database and extracts properties that meet the criteria in a list format.

[1024] "Feedback" refers to additional opinions or requests for conditions that users enter regarding proposed properties, such as "I like this layout, but I don't like the unit bath."

[1025] An "artificial intelligence model" is a machine learning algorithm used to learn a user's additional requirements and preferences and improve the accuracy of property suggestions based on that information.

[1026] A "generative AI model" is a model that learns from user interaction data and incorporates user feedback to make adaptive property suggestions.

[1027] A "prompt sentence" is text that is input into a generative AI model, and includes a specific request, such as "I'm looking for a property within a 10-minute walk from the station, with a 2LDK and rent of less than 100,000 yen."

[1028] This invention is a system in which a user inputs desired real estate conditions in a chat format, and a server automatically proposes the most suitable property. Specific embodiments for carrying out this invention are described below.

[1029] System Overview

[1030] First, the user opens a chat-style interface using a dedicated application or web browser and enters their desired real estate requirements, such as "within a 10-minute walk from the station, within a 30-minute door-to-door commute to work, 2LDK, with priority on sunlight, rent under 100,000 yen."

[1031] Hardware and Software Configuration

[1032] The system uses the following main hardware and software:

[1033] Device: A computing device used by a user, such as a smartphone, tablet, or PC.

[1034] Server: A computer system that processes data and suggests properties suitable for users.

[1035] Real estate database: A database that stores detailed information about real estate properties. Relational databases such as MySQL and PostgreSQL are likely to be used.

[1036] Natural language processing engine: Software for analyzing text data entered by users. Specifically, Google Cloud Natural Language API and Microsoft Azure Text Analytics are used.

[1037] Generative AI model: An artificial intelligence model that learns from user input and feedback. This could be a model built using TensorFlow or PyTorch.

[1038] Processing Description

[1039] 1. Enter your desired conditions and submit

[1040] The user enters their desired conditions in a chat format and sends them to the server via their device. At this time, the device converts the entered desired conditions into text data in JSON format and sends it to the server using the secure HTTPS protocol.

[1041] 2. Data Analysis

[1042] The server passes the received JSON-formatted text data to a natural language processing engine, which analyzes the desired conditions. Specifically, it extracts conditions such as "within a 10-minute walk from the station," "within a 30-minute door-to-door drive to the office," "2LDK," "priority on sunlight," and "rent under 100,000 yen."

[1043] 3. Database Search

[1044] The server searches the real estate database based on the analyzed criteria, executes an SQL query to list properties that match the criteria, and retrieves detailed information about each property (address, layout, rent, photo pass, etc.).

[1045] 4. Submit property information

[1046] The server then formats the search results into chat messages and sends them to the device, including details such as property photo URLs, floor plan, rent, and location.

[1047] 5. User Feedback

[1048] The user checks the displayed property list and again enters feedback in chat format, such as "This property is good, but I don't like the unit bath."

[1049] 6. Submitting and Learning Additional Terms

[1050] The device sends the user's additional conditions to the server, and the newly received additional conditions are learned by the AI ​​model. The AI ​​model updates the user's preferences and reflects them in the next search results.

[1051] 7. Re-proposal

[1052] The server re-searches the real estate database based on the updated preferences, generates a new property list, and sends this list to the user again in the form of a chat message, repeating this process until the user finds a property that satisfies them.

[1053] Specific examples

[1054] For example, if a user searches for a property with conditions such as "within a 10-minute walk from the station, 2LDK, rent under 100,000 yen," the server will list properties that meet those conditions and suggest them to the user. If the user gives feedback such as "This property is good, but I don't like the unit bath," the server will learn the additional conditions and search again for properties with separate bathrooms and toilets and suggest them.

[1055] Prompt Sentence Examples

[1056] "I'm looking for a property within a 10-minute walk from the station, with a 2LDK and rent of less than 100,000 yen. I'd also like to avoid a unit bath. What kind of properties are available?"

[1057] In this way, a system is realized that can efficiently suggest properties that meet the desired conditions based on the desired conditions entered by the user in chat format, and by continuing to learn additional conditions through feedback, it can provide the user with the property that is most suitable for them.

[1058] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1059] Step 1:

[1060] The user inputs the desired conditions for the property.

[1061] Input: The user uses a dedicated application or web browser to enter desired conditions into a chat-style interface, such as "within 10 minutes' walk from the station, within 30 minutes' door-to-door to the office, 2LDK, sunlight-oriented, rent under 100,000 yen."

[1062] Data processing: Desired conditions are entered in text format.

[1063] Output: The desired conditions are saved as text data on the terminal.

[1064] Step 2:

[1065] The terminal sends the input data to the server.

[1066] Input: Text data of desired conditions saved on the device.

[1067] Data processing: The terminal converts the entered desired conditions into JSON format.

[1068] Output: Text data in JSON format is sent to the server using the HTTPS protocol.

[1069] Step 3:

[1070] The server parses the input data.

[1071] Input: JSON formatted text data received by the server.

[1072] Data processing: The server passes the received data to a natural language processing engine (e.g., Google Cloud Natural Language API), which analyzes and extracts the desired conditions.

[1073] Output: The extracted desired conditions (for example, "within 10 minutes' walk from the station," "within 30 minutes' door-to-door drive to work," "2LDK," "priority on sunlight," "rent under 100,000 yen") are saved on the server.

[1074] Step 4:

[1075] The server searches the database.

[1076] Input: Parsed desired conditions.

[1077] Data processing: The server generates SQL queries and searches the real estate database (MySQL or PostgreSQL).

[1078] Output: A list of properties that match the criteria (property ID, address, layout, rent, photo pass, etc.) will be obtained.

[1079] Step 5:

[1080] The server sends the property information to the user.

[1081] Input: Acquired property list.

[1082] Data processing: The server formats the property information into a chat message format.

[1083] Output: A chat-style message (including the property's photo URL, floor plan, rent, address, etc.) is sent to the user's device.

[1084] Step 6:

[1085] The user enters additional conditions.

[1086] Input: The user checks the displayed property list and enters additional conditions as feedback in chat format (e.g., "This property is good, but I don't like the unit bath").

[1087] Data processing: Additional conditions are entered in text format.

[1088] Output: The additional conditions are saved as text data on the terminal.

[1089] Step 7:

[1090] The terminal transmits the additional conditions to the server.

[1091] Input: Text data of additional conditions saved on the device.

[1092] Data processing: The terminal converts the additional conditions into JSON format.

[1093] Output: Text data in JSON format is sent to the server using the HTTPS protocol.

[1094] Step 8:

[1095] The server learns additional conditions.

[1096] Input: Additional conditions received by the server in JSON format.

[1097] Data processing: The server uses the received additional conditions to train an AI model (e.g., a TensorFlow-based model) and updates the user's preferences.

[1098] Output: The updated user preferences are saved in the server.

[1099] Step 9:

[1100] The server will suggest the property again.

[1101] Input: Updated user preferences.

[1102] Data processing: The server re-searches the real estate database based on the updated preferences and generates a new property list.

[1103] Output: New listings of properties that match the criteria are sent to the user in the form of a chat message.

[1104] (Application example 1)

[1105] 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."

[1106] Conventional real estate search systems are inefficient because they require users to input search criteria and load large amounts of information to confirm property details. Furthermore, it takes a lot of time to search again based on property feedback, and suggestions that reflect the user's preferences are not always obtained. Furthermore, the system requires users to visit the property in person, which creates significant barriers in terms of physical distance and time.

[1107] 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.

[1108] In this invention, the server includes a means for a user to input desired real estate conditions in a chat format, a means for a terminal to transmit the input conditions to the server, and a means for the server to analyze the received data and extract the desired conditions. This enables efficient property search and proposals based on the user's desired conditions. Furthermore, by including a means for viewing properties in a virtual reality environment using a smart device, it is possible to remotely check and experience the property in detail, overcoming the barriers of physical distance and time.

[1109] "Means for users to input desired conditions for real estate in chat format" is a function that allows users to input requests for real estate such as housing into a terminal using written dialogue.

[1110] The "means for transmitting conditions entered by the terminal to the server" is a function for transmitting desired conditions entered by the user to the computer server.

[1111] "Means for analyzing data received by the server and extracting desired conditions" refers to a function that analyzes the data of the user's desired conditions received by the server, and identifies and extracts specific conditions from that data.

[1112] "Means for the server to search the real estate database and list properties that meet the conditions" is a function that allows the server to search the database for real estate information that meets the conditions and create a list of relevant properties.

[1113] The "means for the server to send matching property information to the user" is a function for sending information about the found property to the user.

[1114] The "means for users to input feedback on proposed properties" is a function that allows users to input opinions and additional conditions regarding proposed properties.

[1115] The "means for the terminal to transmit additional conditions to the server" is a function for transmitting the additional desired conditions input by the user back to the server.

[1116] "Means for the server to learn additional conditions and search for and suggest properties again" is a function that allows the server to learn newly received conditions and search the database again based on them to suggest properties.

[1117] "Means for viewing properties in a virtual reality environment using a smart device" refers to the ability to visually inspect real estate properties in detail in a virtual reality environment using advanced devices such as smart glasses.

[1118] "Natural language processing technology" is a technology that enables computers to understand, analyze, and manipulate human language.

[1119] An "AI model" is a data model that uses artificial intelligence technology to perform specific tasks.

[1120] A "real estate database" is a database in which information about residential and commercial real estate is systematically stored.

[1121] A "virtual reality environment" is an environment that uses virtual reality technology to recreate a real-life three-dimensional space.

[1122] The present invention is a system in which a user inputs desired real estate conditions in a chat format, and a server automatically proposes the most suitable property. Specific embodiments of this system will be described in detail below.

[1123] System program generation

[1124] To realize this system, the following program will be constructed: The user inputs their desired conditions using a smart device (such as smart glasses), and the system analyzes those conditions and suggests the most suitable property.

[1125] Hardware and Software Use

[1126] 1. Hardware:

[1127] Smart devices (e.g. smart glasses)

[1128] 2. Software:

[1129] Natural language processing technology (NLP engine, e.g., Google Cloud NLP)

[1130] Real Estate Database

[1131] AI model

[1132] System processing flow

[1133] 1. Receiving user input:

[1134] The user inputs desired conditions into the smart glasses by voice, which is converted into text data using the smart glasses' voice recognition function.

[1135] 2. Analysis of desired conditions:

[1136] The smart glasses transmit the desired conditions in text data form to a server, which then analyzes the desired conditions using natural language processing technology and extracts specific conditions (e.g., "within a 10-minute walk from the station," "2LDK," "rent under 100,000 yen").

[1137] 3. Database Search:

[1138] The server searches a real estate database and lists properties that match the extracted desired conditions. The server then obtains detailed information about the listed properties (photos, floor plan, rent, location, etc.).

[1139] 4. User Submission of Property Information:

[1140] The server sends the listed property information to the smart glasses, through which the user can view the property in a virtual reality environment.

[1141] 5. Feedback Processing:

[1142] When the user enters feedback on the proposed property, the device sends this feedback back to the server, which then uses the feedback to train the AI ​​model and conducts another property search, taking additional criteria into account.

[1143] Specific examples

[1144] For example, suppose a user verbally inputs their desired conditions into the smart glasses, such as "within a 10-minute walk from the station, 2LDK, rent under 100,000 yen." The smart glasses convert this voice into text data and send it to the server. The server analyzes the desired conditions, searches for properties that match, and then sends a list of properties to the smart glasses. The user browses each property in the VR environment and provides feedback such as "This property is good, but I don't like the unit bath." Based on this, the server re-learns the conditions and searches for and suggests new properties that meet the conditions.

[1145] Prompt Sentence Examples

[1146] An example prompt might be, "The user puts on the smart glasses and searches for properties that meet the specified real estate criteria. The criteria are within a 10-minute walk from the station, 2LDK, and rent of less than 100,000 yen. Find the perfect property and view it in a virtual tour. The user can then enter feedback on additional criteria and update the search results."

[1147] In this way, the system of the present invention can efficiently search for properties based on the user's desired conditions and can remotely check the details of the properties using virtual reality technology.

[1148] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1149] Step 1:

[1150] Receiving User Input

[1151] The user puts on the smart glasses and inputs their desired real estate conditions by voice (e.g., "within 10 minutes' walk from the station, 2LDK, rent under 100,000 yen"). This voice input is converted into text data using the smart glasses' voice recognition function. The input data is output in text format as the user's desired conditions.

[1152] Step 2:

[1153] Send your desired conditions

[1154] The terminal sends the desired conditions entered by the user as text data to the server. This transmission is performed via the Internet. The desired conditions received as input data are transferred to the server and sent as data for analysis.

[1155] Step 3:

[1156] Analysis of desired conditions

[1157] The server analyzes the received text data of desired conditions using a natural language processing (NLP) engine and extracts specific desired conditions (e.g., "within 10 minutes' walk," "2LDK," "rent under 100,000 yen"). The input is the desired conditions in text format, and the output is the analyzed specific conditions.

[1158] Step 4:

[1159] Database search

[1160] The server searches the real estate database based on the analyzed desired conditions. It lists properties that match the conditions and obtains detailed information about those properties (photos, floor plan, rent, location, etc.). The input is the analyzed desired conditions, and the output is a list of properties that match the conditions.

[1161] Step 5:

[1162] Submit property information

[1163] The server sends the listed property information to the smart glasses, through which the user can view the properties in a virtual reality environment. The input is the property list, and the output is the display of the property information in a virtual reality environment.

[1164] Step 6:

[1165] Receiving Feedback

[1166] The user can provide feedback about the proposed property by voice (e.g., "This property is good, but I don't like the unit bath"). This feedback is converted into text data using the smart glasses' voice recognition function. The input is the feedback content, and the output is text-based feedback.

[1167] Step 7:

[1168] Submitting additional conditions

[1169] The terminal sends the user's feedback (additional conditions) to the server. The input is the feedback in text format, and the output is the data sent to the server.

[1170] Step 8:

[1171] Learning additional conditions and re-searching

[1172] The server trains the AI ​​model on the additional conditions and understands the user's new preferences. Based on this learning, it searches the real estate database again and lists properties that meet the new conditions. The input is the text data of the additional conditions, and the output is a list of properties that meet the new conditions.

[1173] Step 9:

[1174] Re-proposal

[1175] The server sends the re-listed property information to the smart glasses. The user then browses, rates, and views the properties again in a virtual reality environment. The input is the new property list, and the output is a virtual reality display of the re-suggested property.

[1176] 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.

[1177] This invention is a system in which a user inputs desired real estate conditions in a chat format, and the server automatically suggests the most suitable property. This system also incorporates an emotion engine that recognizes the user's emotions, allowing it to make suggestions according to the user's emotional state. The specific program processing procedures and their operation are explained below, along with specific examples.

[1178] Program processing overview

[1179] 1. The user enters the desired conditions

[1180] The user opens the chat input screen on their device and enters their desired conditions, such as "within 10 minutes' walk from the station, within 30 minutes door-to-door to work, 2LDK, sunlight-oriented, rent under 100,000 yen."

[1181] 2. The device sends the input data to the server

[1182] The terminal transmits the desired conditions entered by the user to the server as text data.

[1183] 3. The server analyzes the input data

[1184] The server passes the received text data to a natural language processing (NLP) engine, which analyzes and extracts desired conditions, such as "within a 10-minute walk from the station," "within a 30-minute door-to-door drive to the office," "2LDK," "high priority on sunlight," and "rent under 100,000 yen."

[1185] 4. The server recognizes emotions using the emotion engine

[1186] The server uses an emotion engine to analyze the emotions from the user's input data. For example, it can recognize whether the user is feeling dissatisfied, satisfied, or hopeful based on the wording and expressions in the text.

[1187] 5. The server searches the database

[1188] The server searches a real estate database based on the analyzed desired conditions and emotion recognition results, lists properties that meet the conditions and take the user's emotions into consideration, and retrieves detailed information about those properties.

[1189] 6. The server sends the property information to the user

[1190] The server then sends the selected property information to the user in the form of a chat message, including details such as photos, layout, rent, and location of each property.

[1191] 7. User enters additional conditions

[1192] The user reviews the proposed property list and enters feedback in chat format, such as, "I like this floor plan, but I don't like the unit bath." The emotion engine then analyzes the user's emotions in real time.

[1193] 8. The device sends additional conditions to the server

[1194] The terminal again transmits the additional conditions input by the user to the server.

[1195] 9. The server learns additional conditions

[1196] The server then trains the AI ​​model with the newly received additional conditions, allowing the AI ​​model to understand the user's preferences and emotions and search again for new properties that match the conditions.

[1197] 10. The server will suggest properties again

[1198] The server then suggests suitable properties to the user from the search results again, and this process continues with repeated feedback until the user finds a property that satisfies them.

[1199] Specific examples

[1200] For example, if a user searches for a property with the conditions "within a 10-minute walk from the station, 2LDK, rent under 100,000 yen," the server will list properties that meet these criteria and suggest them to the user. If the user gives feedback such as "This property is good, but I don't like the unit bath," the emotion engine will recognize the dissatisfaction from the user's text, and the server will learn the additional conditions and search again to suggest properties with separate bathrooms and toilets.

[1201] Furthermore, the emotion engine monitors changes in the user's emotions and analyzes whether the user is satisfied with the suggestions, thereby improving the accuracy of property suggestions in a way that satisfies the user. Premium members are also provided with a system that regularly provides them with new property information. This is achieved by the server periodically searching the database based on premium member information and notifying them of new property information via email or chat.

[1202] This system flexibly responds to the user's needs and emotions, providing a wide range of suitable real estate properties, saving the user a great deal of effort. In particular, the learning function using an emotion engine and AI model makes it possible to make highly accurate property suggestions based on the user's preferences and emotions.

[1203] The processing flow will be explained below.

[1204] Step 1:

[1205] The user inputs the desired conditions.

[1206] The user opens the chat input screen on their device and enters their desired conditions, such as "within 10 minutes' walk from the station, within 30 minutes door-to-door to work, 2LDK, sunlight-oriented, rent under 100,000 yen."

[1207] Step 2:

[1208] The terminal sends the input data to the server.

[1209] The terminal transmits the desired conditions entered by the user to the server as text data.

[1210] Step 3:

[1211] The server parses the input data.

[1212] The server passes the received text data to a natural language processing (NLP) engine, which analyzes and extracts desired conditions, such as "within a 10-minute walk from the station," "within a 30-minute door-to-door drive to the office," "2LDK," "high priority on sunlight," and "rent under 100,000 yen."

[1213] Step 4:

[1214] The server recognizes emotions using an emotion engine.

[1215] The server uses an emotion engine to analyze the emotions from the user's input data, for example, recognizing whether the user is feeling dissatisfied, satisfied, or hopeful based on the language and expressions used in the text.

[1216] Step 5:

[1217] The server searches the database.

[1218] The server searches a real estate database based on the analyzed desired conditions and emotion recognition results, and lists properties that match the conditions and take the user's emotions into consideration.

[1219] Step 6:

[1220] The server sends the property information to the user.

[1221] The server then sends the selected property information to the user in the form of a chat message, including details such as photos, layout, rent, and location of each property.

[1222] Step 7:

[1223] The user enters additional conditions.

[1224] The user checks the proposed property list and inputs additional desired conditions as feedback, such as "I like this floor plan, but I don't want a unit bath." At this time, the emotion engine also continuously analyzes the user's emotions.

[1225] Step 8:

[1226] The terminal transmits the additional conditions to the server.

[1227] The terminal again transmits the additional conditions input by the user to the server.

[1228] Step 9:

[1229] The server learns additional conditions.

[1230] The server then trains the AI ​​model with the newly received additional conditions, allowing the AI ​​model to understand the user's preferences and emotions and search again for new properties that match the conditions.

[1231] Step 10:

[1232] The server will suggest the property again.

[1233] The server then proposes suitable properties to the user from the search results again, and continues providing feedback and suggestions using an emotion engine until the user is satisfied with the suggestions.

[1234] Step 11:

[1235] Provide ongoing proposals for premium members.

[1236] The server periodically (for example, weekly or monthly) searches for new properties based on premium member information and notifies users via email or chat. The emotion engine also analyzes user feedback and emotions to continually provide better suggestions.

[1237] Example 2

[1238] 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."

[1239] In conventional real estate search systems, when users input their desired conditions, they simply analyze the conditions as text, and are unable to make suggestions that reflect the user's emotions and preferences. Furthermore, there was no system that could understand user feedback in real time and re-make optimal suggestions. As a result, users had to spend a lot of time and effort searching for real estate properties.

[1240] The specification process by the specification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes: means for a user to input desired conditions for real estate in a chat format; means for a terminal to transmit the input conditions to the server; means for the server to analyze the received data and extract the desired conditions; means for the server to analyze the desired conditions using natural language processing technology; means for the server to recognize the user's emotions using an emotion engine; means for the server to search a real estate database and list properties that match the conditions; means for the server to transmit matching property information to the user; means for the user to input feedback on the proposed property; means for the terminal to transmit additional conditions to the server; and means for the server to learn the user's additional conditions using an AI model and search for and propose properties again.

[1241] This makes it possible to analyze the user's desired conditions and emotions in real time and propose the most suitable real estate property that suits the user's preferences.

[1242] "User" refers to a person who uses the real estate search system to input desired conditions and receive property proposals.

[1243] A "terminal" is a device used by a user to input desired conditions in chat format, and includes a smartphone, tablet, PC, etc.

[1244] "Server" refers to a central management device that receives, analyzes, and searches user input data and makes real estate property suggestions.

[1245] "Natural language processing technology" refers to technology that analyzes text data entered by the user and extracts desired conditions based on that data.

[1246] An "emotion engine" is an engine that recognizes emotions from user input data and optimizes property suggestions based on those emotions.

[1247] A "real estate database" is a database that stores various real estate property information, and refers to a collection of property information that can be searched.

[1248] "AI model" refers to a machine learning model that learns the user's desired conditions and additional conditions, and searches for and suggests the most suitable properties.

[1249] "Premium Member" refers to a user who has the right to receive special services under certain conditions.

[1250] System Overview

[1251] This invention is a system in which a user inputs desired real estate conditions in a chat format, and the server automatically suggests the most suitable property. This system incorporates an emotion engine that recognizes the user's emotions, and can make suggestions according to the user's emotional state.

[1252] Hardware and software used

[1253] User devices: smartphones, tablets, computers, etc.

[1254] Server: A central management device that analyzes desired conditions, searches real estate databases, recognizes emotions, and trains AI models.

[1255] Natural Language Processing (NLP) engine: Technology that analyzes user requirements

[1256] Emotion engine: An engine that analyzes emotions from user input data

[1257] Real estate database: Contains information on various real estate properties

[1258] AI model: A machine learning model that learns the user's desired conditions and additional conditions to suggest the most suitable properties.

[1259] System Operation

[1260] 1. Input method: Users use a device such as a smartphone or PC to input their desired real estate requirements in chat format. For example, they can input specific requirements such as "within a 10-minute walk from the station, within a 30-minute door-to-door commute to work, 2LDK, sunlight-oriented, rent under 100,000 yen."

[1261] 2. Data transmission means: The user's terminal transmits the entered desired conditions to the server as text data.

[1262] 3. Data analysis method: The server passes the received text data to a natural language processing (NLP) engine, which analyzes and extracts the desired conditions. Specifically, it breaks down conditions such as "within a 10-minute walk from the station," "within a 30-minute door-to-door drive to the office," "2LDK," "priority on sunlight," and "rent under 100,000 yen."

[1263] 4. Emotion Recognition: The server uses an emotion engine to recognize emotions from the user's input data. For example, it can read emotions such as dissatisfaction, satisfaction, and expectation from the wording and expressions in the text.

[1264] 5. Database search method: The server searches the real estate database based on the analyzed desired conditions and emotion recognition results, and lists properties that meet the conditions and take the user's emotions into consideration.

[1265] 6. Result transmission method: The server sends the selected property information to the user in the form of a chat message, including photos, floor plan, rent, location, access information, etc.

[1266] 7. Feedback: The user reviews the proposed property list and enters feedback in chat format, such as "I like this floor plan, but I don't like the unit bathroom." The emotion engine continues to analyze the user's emotions in real time and learns the user's preferences based on the feedback.

[1267] 8. Additional condition sending means: The terminal sends the additional conditions entered by the user back to the server.

[1268] 9. Learning method: The server trains the AI ​​model with the newly received additional conditions. The AI ​​model then gains a more detailed understanding of the user's preferences and emotions and searches for new properties.

[1269] 10. Re-proposal method: The server proposes a newly selected property to the user, and the process continues with repeated feedback until the user finds a property that satisfies them.

[1270] Specific examples

[1271] For example, if a user enters the conditions "within a 10-minute walk from the station, 2LDK, rent under 100,000 yen," the server will list properties that meet these criteria and suggest them to the user. If the user gives feedback such as "This property is good, but I don't like the unit bath," the emotion engine will recognize the dissatisfaction, and the server will search again and suggest properties with separate bathrooms and toilets.

[1272] Example prompts for generative AI models

[1273] Example prompt:

[1274] "The user entered the desired conditions: 'within 10 minutes' walk from the station, 2LDK, rent under 100,000 yen.' Please use the emotion engine to suggest the best property for this. Also, please explain how to respond if the user enters feedback such as 'I don't want a unit bath.'"

[1275] This allows the system to flexibly respond to users' needs and emotions, providing them with multifaceted, suitable real estate properties, saving them time and effort.In addition, by using an emotion engine and AI model, it is possible to provide highly accurate property suggestions that match the user's preferences and emotions.

[1276] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1277] Step 1:

[1278] The user enters the desired conditions.

[1279] The user opens the chat input screen on their device and enters their desired conditions, such as "within 10 minutes' walk from the station, within 30 minutes door-to-door to work, 2LDK, sunlight-oriented, rent under 100,000 yen."

[1280] Specifically, the user inputs each desired condition in text format and presses the send button.

[1281] Step 2:

[1282] The device sends the input data to the server.

[1283] The terminal transmits the desired conditions entered by the user to the server as text data.

[1284] Specifically, when the send button is pressed, the text data is automatically sent to the server.

[1285] Input: User's desired conditions (text format)

[1286] Output: Text data to the server

[1287] Step 3:

[1288] The server parses the input data.

[1289] The server passes the received text data to a natural language processing (NLP) engine, which analyzes and extracts the desired conditions.

[1290] Specifically, the NLP engine analyzes the text and extracts conditions such as "within 10 minutes' walk from the station," "2LDK," and "rent under 100,000 yen."

[1291] Input: User's desired conditions (text data)

[1292] Output: Parsed desired conditions (structured data)

[1293] Step 4:

[1294] The server recognizes emotions using an emotion engine.

[1295] The server uses an emotion engine to analyze emotions from the user's input data.

[1296] Specifically, it reads emotions such as dissatisfaction, satisfaction, and expectation from the wording of the input text.

[1297] Input: User's desired conditions (text data)

[1298] Output: Parsed emotion data

[1299] Step 5:

[1300] The server searches the database.

[1301] The server searches a real estate database based on the analyzed desired conditions and emotion recognition results, and lists properties that meet the conditions.

[1302] Specifically, it uses a filter to narrow down the properties in the database that meet the criteria and lists them.

[1303] Input: Analyzed desired conditions and emotion data

[1304] Output: List of properties that match the criteria

[1305] Step 6:

[1306] The server sends the property information to the user.

[1307] The server sends the selected property information to the user in the form of a chat message.

[1308] Specifically, a chat message is generated that includes photos of the property, floor plan, rent, location, access information, etc.

[1309] Input: List of properties that match the criteria

[1310] Output: Property chat message to user

[1311] Step 7:

[1312] The user enters additional criteria.

[1313] The user reviews the proposed property list and enters feedback in chat format, such as, "I like this layout, but I don't like the unit bath."

[1314] Specifically, the user inputs and sends specific complaints and additional requirements via chat.

[1315] Input: Feedback on the proposed property (text format)

[1316] Output: User feedback data

[1317] Step 8:

[1318] The device sends the additional conditions to the server.

[1319] The terminal again transmits the additional conditions input by the user to the server.

[1320] Specifically, when the send button is pressed, the feedback data is sent to the server.

[1321] Input: User feedback (text data)

[1322] Output: Feedback data to the server

[1323] Step 9:

[1324] The server learns additional conditions.

[1325] The server trains the AI ​​model on the newly received additional conditions.

[1326] Specifically, the AI ​​model learns from the feedback data to gain a more detailed understanding of the user's preferences.

[1327] Input: User feedback data

[1328] Output: Updated AI model

[1329] Step 10:

[1330] The server proposes the property again.

[1331] The server then suggests suitable properties to the user from the search results.

[1332] Specifically, the system searches again for new properties that meet the conditions and generates a proposal message.

[1333] Input: Updated AI model and new search results

[1334] Output: New property proposal chat message

[1335] (Application example 2)

[1336] 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."

[1337] On current online shopping sites, when users search for the products they want, it takes a lot of time and effort to find the products that match their criteria.In addition, there is a lack of a system that suggests optimal products based on the user's emotions and feedback, making it difficult for users to efficiently find products that satisfy them.

[1338] The identification process by the identification 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 a user to input desired product conditions in a chat format, means for a terminal to transmit the input conditions to the server, means for the server to analyze the received data and extract the desired conditions, means for the server to search a product database and list products that match the conditions, means for the server to transmit information about the matching products to the user, means for the user to input feedback on the suggested products, means for the terminal to transmit additional conditions to the server, means for the server to learn the additional conditions and search for and suggest products again, and an emotion engine for the server to analyze the user's emotional state and make suggestions based on the emotions. This enables the user to efficiently find products that match the desired conditions and can suggest optimal products based on the user's emotions and feedback.

[1339] A "user" is a person who searches for and purchases products using an online shopping site.

[1340] "Desired conditions" are requirements regarding product specifications and features that are input by the user in chat format.

[1341] A "terminal" is an electronic device used by a user, such as a computer, smartphone, or tablet.

[1342] A "server" is a central processing unit that receives data sent from a terminal and performs analysis and searches.

[1343] "Chat format" is an interface format that allows users to input text and exchange information in a conversational format.

[1344] A "product database" is a digital database in which product information is stored.

[1345] "Feedback" refers to the act of a user inputting an evaluation or opinion about a proposed product, or the content of such input.

[1346] "Additional conditions" are new conditions input by the user based on feedback.

[1347] An "emotion engine" is software that analyzes the user's emotions from their text data and makes suggestions based on their state.

[1348] "Natural language processing technology" is a technology that allows computers to understand and analyze human language.

[1349] A "generative AI model" is an artificial intelligence model that learns from users' conditions and feedback and makes more appropriate suggestions.

[1350] In this invention, a mail-order system will be described that proposes products based on desired conditions input by a user in a chat format.

[1351] Overall system configuration

[1352] This system mainly consists of a user's device, a server, a product database, and an emotion engine. Users input their desired conditions through a chat interface, and their device sends the information to the server. The server uses natural language processing technology to analyze the desired conditions and search for matching products in the product database. It then uses the emotion engine to analyze the user's emotions and makes product suggestions based on those emotions.

[1353] Hardware and Software Configuration

[1354] Hardware: Your PC, smartphone or tablet, and server

[1355] Software: Natural language processing engine (e.g., Google Cloud Natural Language), sentiment analysis engine (e.g., IBM Watson Tone Analyzer), database (e.g., MySQL), chatbot framework (e.g., Dialogflow)

[1356] What the program does

[1357] 1. User inputs desired conditions

[1358] The user enters the desired product conditions in chat format. For example, specific conditions such as "black jacket, size L, budget within 5,000 yen, casual style" are entered.

[1359] 2. Sending data from the device to the server

[1360] The terminal transmits the desired conditions entered by the user as text data to the server, using real-time communication.

[1361] 3. Data analysis by the server

[1362] The server passes the received text data to a natural language processing engine, which analyzes the desired conditions, such as "black jacket," "size L," "budget under 5,000 yen," and "casual style."

[1363] 4. Emotion analysis

[1364] The server uses an emotion engine to analyze the user's input data to determine whether the user is feeling expectations, hopes, dissatisfaction, or other emotions.

[1365] 5. Search the product database

[1366] The server searches a product database based on the analyzed desired conditions and emotions, lists products that match the conditions, and obtains their detailed information.

[1367] Adding specific examples

[1368] For example, if a user searches for a product using the criteria "black jacket, size L, budget under 5,000 yen, casual style," the server will list products that match these criteria and suggest them to the user. If the user gives feedback such as "I like this design, but I wish it was a little longer," the emotion engine will recognize the user's wishes from the text, and the server will learn the additional criteria, search again, and suggest new products that match the criteria.

[1369] Prompt Sentence Examples

[1370] User input: "Black jacket, size L, budget under 5000 yen, casual style"

[1371] Prompt: "Analyze the emotions expressed by the user's input."

[1372] This allows for more detailed proposals that meet the user's desired conditions, and also allows for optimal product proposals based on the user's emotions and feedback.

[1373] summary

[1374] By introducing this system, users can efficiently find products that meet their desired criteria, and by using an emotion engine, they can receive highly satisfying suggestions. Product recommendations are made based on data analyzed by the server and the results of emotion analysis, which increases user satisfaction.

[1375] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1376] Step 1:

[1377] The user inputs desired product specifications in chat format via a terminal. The input is specific text data such as "black jacket, size L, budget within 5,000 yen, casual style."

[1378] Step 2:

[1379] The terminal transmits the desired conditions entered by the user to the server as text data, and the transmitted data is transmitted to the server in real time.

[1380] Step 3:

[1381] The server passes the received text data to a natural language processing engine (e.g., Google Cloud Natural Language) and analyzes the desired conditions. This analysis includes text tokenization, part-of-speech tagging, and semantic analysis. For example, conditions such as "black jacket," "size L," "budget under 5,000 yen," and "casual style" are extracted.

[1382] Step 4:

[1383] The server uses an emotion engine (e.g., IBM Watson Tone Analyzer) to analyze the emotion from the user's input data. This analyzes the emotional weighting of words and the tone of the entire sentence to determine the user's emotional state (expectation, hope, etc.). It identifies whether the user is excited or disappointed, for example.

[1384] Step 5:

[1385] The server searches a product database (e.g., MySQL) based on the analyzed desired conditions and emotion recognition results, lists products that match the conditions, and retrieves detailed information about those products (images, prices, descriptions, etc.).

[1386] Step 6:

[1387] The server sends the selected product information to the user in the form of a chat message, including a photo, description, price, and rating of each product.

[1388] Step 7:

[1389] The user can review the proposed product list and provide feedback in chat, for example, by providing specific opinions such as, "The design is good, but I wish it was a little longer."

[1390] Step 8:

[1391] The terminal sends the additional conditions entered by the user to the server, and feedback is transmitted to the server in real time.

[1392] Step 9:

[1393] The server trains the generative AI model on the newly received additional conditions. This training uses supervised and unsupervised learning methods. The AI ​​model updates the conditions based on the user's preferences and feedback information and reflects them in the next search.

[1394] Step 10:

[1395] The server searches for products again and suggests new products that match the criteria. This process is repeated until a product that satisfies the user is found.

[1396] 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.

[1397] 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.

[1398] 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.

[1399] [Fourth embodiment]

[1400] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1401] 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.

[1402] 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).

[1403] 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.

[1404] 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.

[1405] 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).

[1406] 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.

[1407] 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.

[1408] 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.

[1409] 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.

[1410] 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.

[1411] 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.

[1412] 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."

[1413] This invention is a system in which a user inputs desired real estate conditions in a chat format, and the server automatically proposes the most suitable property. The specific program processing procedure and its operation will be explained below, along with specific examples.

[1414] Program processing overview

[1415] 1. The user enters the desired conditions

[1416] The user opens the chat input screen on their device and enters their desired conditions, such as "within 10 minutes' walk from the station, within 30 minutes door-to-door to work, 2LDK, sunlight-oriented, rent under 100,000 yen."

[1417] 2. The device sends the input data to the server

[1418] The terminal transmits the desired conditions entered by the user to the server as text data.

[1419] 3. The server analyzes the input data

[1420] The server passes the received text data to a natural language processing (NLP) engine, which analyzes and extracts desired conditions, such as "within a 10-minute walk from the station," "within a 30-minute door-to-door drive to the office," "2LDK," "high priority on sunlight," and "rent under 100,000 yen."

[1421] 4. The server searches the database

[1422] The server searches the real estate database based on the analyzed desired conditions, lists properties that match the conditions, and retrieves detailed information about those properties.

[1423] 5. The server sends the property information to the user

[1424] The server then sends the selected property information to the user in the form of a chat message, including details such as photos, layout, rent, and location of each property.

[1425] 6. User enters additional conditions

[1426] The user reviews the proposed property list and enters feedback in chat format, such as, "I like this layout, but I don't like the unit bath."

[1427] 7. The device sends the additional conditions to the server.

[1428] The terminal transmits the additional conditions input by the user to the server.

[1429] 8. The server learns additional conditions

[1430] The server then trains the AI ​​model with the newly received additional conditions. The AI ​​model then understands the user's preferences and searches again for new properties that match the conditions.

[1431] 9. The server will suggest properties again

[1432] The server then suggests suitable properties to the user from the search results again, and this process continues with repeated feedback until the user finds a property that satisfies them.

[1433] Specific examples

[1434] For example, if a user searches for a property with the conditions "within a 10-minute walk from the station, 2LDK, rent under 100,000 yen," the server will list properties that meet these conditions and suggest them to the user. If the user gives feedback such as "This property is good, but I don't want a unit bath," the server will learn the additional conditions and search again to suggest properties with separate bathrooms and toilets.

[1435] In addition, premium members are provided with a system that regularly provides them with new property information on an ongoing basis. This is achieved by the server periodically searching the database based on the premium member information and notifying them of new property information via email or chat.

[1436] This system flexibly responds to user needs while efficiently providing suitable real estate properties, saving users a great deal of effort. In particular, the learning function using an AI model makes it possible to make highly accurate property suggestions that match the user's preferences.

[1437] The processing flow will be explained below.

[1438] Step 1:

[1439] The user inputs the desired conditions.

[1440] The user opens the chat input screen on their device and enters their desired conditions, such as "within 10 minutes' walk from the station, within 30 minutes door-to-door to work, 2LDK, sunlight-oriented, rent under 100,000 yen."

[1441] Step 2:

[1442] The terminal sends the input data to the server.

[1443] The terminal transmits the desired conditions entered by the user to the server as text data.

[1444] Step 3:

[1445] The server parses the input data.

[1446] The server passes the received text data to a natural language processing (NLP) engine, which analyzes and extracts desired conditions, such as "within a 10-minute walk from the station," "within a 30-minute door-to-door drive to the office," "2LDK," "high priority on sunlight," and "rent under 100,000 yen."

[1447] Step 4:

[1448] The server searches the database.

[1449] The server searches the real estate database based on the analyzed desired conditions, lists properties that match the conditions, and retrieves detailed information about those properties.

[1450] Step 5:

[1451] The server sends the property information to the user.

[1452] The server then sends the selected property information to the user in the form of a chat message, including details such as photos, layout, rent, and location of each property.

[1453] Step 6:

[1454] The user enters additional conditions.

[1455] The user reviews the proposed property list and enters feedback in chat format, such as, "I like this layout, but I don't like the unit bath."

[1456] Step 7:

[1457] The terminal transmits the additional conditions to the server.

[1458] The terminal again transmits the additional conditions input by the user to the server.

[1459] Step 8:

[1460] The server learns additional conditions.

[1461] The server then trains the AI ​​model with the newly received additional conditions. The AI ​​model then understands the user's preferences and searches again for new properties that match the conditions.

[1462] Step 9:

[1463] The server will suggest the property again.

[1464] The server then suggests suitable properties to the user from the search results again, and this process continues with repeated feedback until the user finds a property that satisfies them.

[1465] Step 10:

[1466] Provide ongoing proposals for premium members.

[1467] The server searches for new properties periodically (for example, every week or at the beginning of the month) based on the premium member's information and notifies the user via email or chat.

[1468] Example 1

[1469] 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."

[1470] In modern real estate searches, it takes a lot of time and effort for users to find properties that meet their desired criteria. In particular, automating search and suggestion processes that respond to changes in user preferences and additional criteria is difficult, requiring a lot of manual work. Furthermore, there is a lack of systems that efficiently and flexibly suggest properties that are optimal for users. As a result, users often feel stressed during the process of finding a property that suits them. The present invention aims to solve these problems and more efficiently and effectively suggest real estate properties that meet users' desired criteria.

[1471] 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.

[1472] In this invention, the server includes means for a user to input desired real estate conditions in a chat format, means for a terminal to transmit the input conditions to the server, means for analyzing the received data and extracting the desired conditions, means for searching a real estate database and listing properties that match the conditions, means for transmitting matching property information to the user, means for the user to input feedback on the proposed property, means for the terminal to transmit additional conditions to the server, means for learning the additional conditions and searching for and proposing properties again, and means for using an artificial intelligence model that learns the user's preferences and adaptively improves the accuracy of property proposals. This makes it possible to efficiently propose properties that match the desired conditions based on the desired conditions input by the user in a chat format, and to continue to learn the additional conditions through feedback, thereby providing the user with the most suitable property.

[1473] "User" means an individual or corporation that uses the system to search for and receive suggestions on real estate properties.

[1474] A "terminal" is an electronic device used by a user, such as a computer, smartphone, or tablet, that has the ability to communicate with a server.

[1475] A "server" is a computer system that processes data received from a user, searches a real estate database, and suggests suitable properties to the user.

[1476] "Chat format" is a communication method that allows users to interact with the system in natural language and input desired conditions.

[1477] "Desired conditions" refer to the specific requirements or preferences that a user has for a real estate property, and examples include "within a 10-minute walk from the station," "2LDK," and "rent under 100,000 yen."

[1478] "Text data" refers to sentence data in natural language that the user inputs as desired conditions, and is data in a format that is sent from the terminal to the server.

[1479] A "natural language processing (NLP) engine" is a software component that analyzes text data entered by the user and extracts desired conditions.

[1480] A "real estate database" is a collection of stored information about real estate properties, including detailed information such as property addresses, floor plans, rents, and photos.

[1481] "Listing" refers to the process by which the server compiles property information retrieved from the database and extracts properties that meet the criteria in a list format.

[1482] "Feedback" refers to additional opinions or requests for conditions that users enter regarding proposed properties, such as "I like this layout, but I don't like the unit bath."

[1483] An "artificial intelligence model" is a machine learning algorithm used to learn a user's additional requirements and preferences and improve the accuracy of property suggestions based on that information.

[1484] A "generative AI model" is a model that learns from user interaction data and incorporates user feedback to make adaptive property suggestions.

[1485] A "prompt sentence" is text that is input into a generative AI model, and includes a specific request, such as "I'm looking for a property within a 10-minute walk from the station, with a 2LDK and rent of less than 100,000 yen."

[1486] This invention is a system in which a user inputs desired real estate conditions in a chat format, and a server automatically proposes the most suitable property. Specific embodiments for carrying out this invention are described below.

[1487] System Overview

[1488] First, the user opens a chat-style interface using a dedicated application or web browser and enters their desired real estate requirements, such as "within a 10-minute walk from the station, within a 30-minute door-to-door commute to work, 2LDK, with priority on sunlight, rent under 100,000 yen."

[1489] Hardware and Software Configuration

[1490] The system uses the following main hardware and software:

[1491] Device: A computing device used by a user, such as a smartphone, tablet, or PC.

[1492] Server: A computer system that processes data and suggests properties suitable for users.

[1493] Real estate database: A database that stores detailed information about real estate properties. Relational databases such as MySQL and PostgreSQL are likely to be used.

[1494] Natural language processing engine: Software for analyzing text data entered by users. Specifically, Google Cloud Natural Language API and Microsoft Azure Text Analytics are used.

[1495] Generative AI model: An artificial intelligence model that learns from user input and feedback. This could be a model built using TensorFlow or PyTorch.

[1496] Processing Description

[1497] 1. Enter your desired conditions and submit

[1498] The user enters their desired conditions in a chat format and sends them to the server via their device. At this time, the device converts the entered desired conditions into text data in JSON format and sends it to the server using the secure HTTPS protocol.

[1499] 2. Data Analysis

[1500] The server passes the received JSON-formatted text data to a natural language processing engine, which analyzes the desired conditions. Specifically, it extracts conditions such as "within a 10-minute walk from the station," "within a 30-minute door-to-door drive to the office," "2LDK," "priority on sunlight," and "rent under 100,000 yen."

[1501] 3. Database Search

[1502] The server searches the real estate database based on the analyzed criteria, executes an SQL query to list properties that match the criteria, and retrieves detailed information about each property (address, layout, rent, photo pass, etc.).

[1503] 4. Submit property information

[1504] The server then formats the search results into chat messages and sends them to the device, including details such as property photo URLs, floor plan, rent, and location.

[1505] 5. User Feedback

[1506] The user checks the displayed property list and again enters feedback in chat format, such as "This property is good, but I don't like the unit bath."

[1507] 6. Submitting and Learning Additional Terms

[1508] The device sends the user's additional conditions to the server, and the newly received additional conditions are learned by the AI ​​model. The AI ​​model updates the user's preferences and reflects them in the next search results.

[1509] 7. Re-proposal

[1510] The server re-searches the real estate database based on the updated preferences, generates a new property list, and sends this list to the user again in the form of a chat message, repeating this process until the user finds a property that satisfies them.

[1511] Specific examples

[1512] For example, if a user searches for a property with conditions such as "within a 10-minute walk from the station, 2LDK, rent under 100,000 yen," the server will list properties that meet those conditions and suggest them to the user. If the user gives feedback such as "This property is good, but I don't like the unit bath," the server will learn the additional conditions and search again for properties with separate bathrooms and toilets and suggest them.

[1513] Prompt Sentence Examples

[1514] "I'm looking for a property within a 10-minute walk from the station, with a 2LDK and rent of less than 100,000 yen. I'd also like to avoid a unit bath. What kind of properties are available?"

[1515] In this way, a system is realized that can efficiently suggest properties that meet the desired conditions based on the desired conditions entered by the user in chat format, and by continuing to learn additional conditions through feedback, it can provide the user with the property that is most suitable for them.

[1516] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1517] Step 1:

[1518] The user inputs the desired conditions for the property.

[1519] Input: The user uses a dedicated application or web browser to enter desired conditions into a chat-style interface, such as "within 10 minutes' walk from the station, within 30 minutes' door-to-door to the office, 2LDK, sunlight-oriented, rent under 100,000 yen."

[1520] Data processing: Desired conditions are entered in text format.

[1521] Output: The desired conditions are saved as text data on the terminal.

[1522] Step 2:

[1523] The terminal sends the input data to the server.

[1524] Input: Text data of desired conditions saved on the device.

[1525] Data processing: The terminal converts the entered desired conditions into JSON format.

[1526] Output: Text data in JSON format is sent to the server using the HTTPS protocol.

[1527] Step 3:

[1528] The server parses the input data.

[1529] Input: JSON formatted text data received by the server.

[1530] Data processing: The server passes the received data to a natural language processing engine (e.g., Google Cloud Natural Language API), which analyzes and extracts the desired conditions.

[1531] Output: The extracted desired conditions (for example, "within 10 minutes' walk from the station," "within 30 minutes' door-to-door drive to work," "2LDK," "priority on sunlight," "rent under 100,000 yen") are saved on the server.

[1532] Step 4:

[1533] The server searches the database.

[1534] Input: Parsed desired conditions.

[1535] Data processing: The server generates SQL queries and searches the real estate database (MySQL or PostgreSQL).

[1536] Output: A list of properties that match the criteria (property ID, address, layout, rent, photo pass, etc.) will be obtained.

[1537] Step 5:

[1538] The server sends the property information to the user.

[1539] Input: Acquired property list.

[1540] Data processing: The server formats the property information into a chat message format.

[1541] Output: A chat-style message (including the property's photo URL, floor plan, rent, address, etc.) is sent to the user's device.

[1542] Step 6:

[1543] The user enters additional conditions.

[1544] Input: The user checks the displayed property list and enters additional conditions as feedback in chat format (e.g., "This property is good, but I don't like the unit bath").

[1545] Data processing: Additional conditions are entered in text format.

[1546] Output: The additional conditions are saved as text data on the terminal.

[1547] Step 7:

[1548] The terminal transmits the additional conditions to the server.

[1549] Input: Text data of additional conditions saved on the device.

[1550] Data processing: The terminal converts the additional conditions into JSON format.

[1551] Output: Text data in JSON format is sent to the server using the HTTPS protocol.

[1552] Step 8:

[1553] The server learns additional conditions.

[1554] Input: Additional conditions received by the server in JSON format.

[1555] Data processing: The server uses the received additional conditions to train an AI model (e.g., a TensorFlow-based model) and updates the user's preferences.

[1556] Output: The updated user preferences are saved in the server.

[1557] Step 9:

[1558] The server will suggest the property again.

[1559] Input: Updated user preferences.

[1560] Data processing: The server re-searches the real estate database based on the updated preferences and generates a new property list.

[1561] Output: New listings of properties that match the criteria are sent to the user in the form of a chat message.

[1562] (Application example 1)

[1563] 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."

[1564] Conventional real estate search systems are inefficient because they require users to input search criteria and load large amounts of information to confirm property details. Furthermore, it takes a lot of time to search again based on property feedback, and suggestions that reflect the user's preferences are not always obtained. Furthermore, the system requires users to visit the property in person, which creates significant barriers in terms of physical distance and time.

[1565] 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.

[1566] In this invention, the server includes a means for a user to input desired real estate conditions in a chat format, a means for a terminal to transmit the input conditions to the server, and a means for the server to analyze the received data and extract the desired conditions. This enables efficient property search and proposals based on the user's desired conditions. Furthermore, by including a means for viewing properties in a virtual reality environment using a smart device, it is possible to remotely check and experience the property in detail, overcoming the barriers of physical distance and time.

[1567] "Means for users to input desired conditions for real estate in chat format" is a function that allows users to input requests for real estate such as housing into a terminal using written dialogue.

[1568] The "means for transmitting conditions entered by the terminal to the server" is a function for transmitting desired conditions entered by the user to the computer server.

[1569] "Means for analyzing data received by the server and extracting desired conditions" refers to a function that analyzes the data of the user's desired conditions received by the server, and identifies and extracts specific conditions from that data.

[1570] "Means for the server to search the real estate database and list properties that meet the conditions" is a function that allows the server to search the database for real estate information that meets the conditions and create a list of relevant properties.

[1571] The "means for the server to send matching property information to the user" is a function for sending information about the found property to the user.

[1572] The "means for users to input feedback on proposed properties" is a function that allows users to input opinions and additional conditions regarding proposed properties.

[1573] The "means for the terminal to transmit additional conditions to the server" is a function for transmitting the additional desired conditions input by the user back to the server.

[1574] "Means for the server to learn additional conditions and search for and suggest properties again" is a function that allows the server to learn newly received conditions and search the database again based on them to suggest properties.

[1575] "Means for viewing properties in a virtual reality environment using a smart device" refers to the ability to visually inspect real estate properties in detail in a virtual reality environment using advanced devices such as smart glasses.

[1576] "Natural language processing technology" is a technology that enables computers to understand, analyze, and manipulate human language.

[1577] An "AI model" is a data model that uses artificial intelligence technology to perform specific tasks.

[1578] A "real estate database" is a database in which information about residential and commercial real estate is systematically stored.

[1579] A "virtual reality environment" is an environment that uses virtual reality technology to recreate a real-life three-dimensional space.

[1580] The present invention is a system in which a user inputs desired real estate conditions in a chat format, and a server automatically proposes the most suitable property. Specific embodiments of this system will be described in detail below.

[1581] System program generation

[1582] To realize this system, the following program will be constructed: The user inputs their desired conditions using a smart device (such as smart glasses), and the system analyzes those conditions and suggests the most suitable property.

[1583] Hardware and Software Use

[1584] 1. Hardware:

[1585] Smart devices (e.g. smart glasses)

[1586] 2. Software:

[1587] Natural language processing technology (NLP engine, e.g., Google Cloud NLP)

[1588] Real Estate Database

[1589] AI model

[1590] System processing flow

[1591] 1. Receiving user input:

[1592] The user inputs desired conditions into the smart glasses by voice, which is converted into text data using the smart glasses' voice recognition function.

[1593] 2. Analysis of desired conditions:

[1594] The smart glasses transmit the desired conditions in text data form to a server, which then analyzes the desired conditions using natural language processing technology and extracts specific conditions (e.g., "within a 10-minute walk from the station," "2LDK," "rent under 100,000 yen").

[1595] 3. Database Search:

[1596] The server searches a real estate database and lists properties that match the extracted desired conditions. The server then obtains detailed information about the listed properties (photos, floor plan, rent, location, etc.).

[1597] 4. User Submission of Property Information:

[1598] The server sends the listed property information to the smart glasses, through which the user can view the property in a virtual reality environment.

[1599] 5. Feedback Processing:

[1600] When the user enters feedback on the proposed property, the device sends this feedback back to the server, which then uses the feedback to train the AI ​​model and conducts another property search, taking additional criteria into account.

[1601] Specific examples

[1602] For example, suppose a user verbally inputs their desired conditions into the smart glasses, such as "within a 10-minute walk from the station, 2LDK, rent under 100,000 yen." The smart glasses convert this voice into text data and send it to the server. The server analyzes the desired conditions, searches for properties that match, and then sends a list of properties to the smart glasses. The user browses each property in the VR environment and provides feedback such as "This property is good, but I don't like the unit bath." Based on this, the server re-learns the conditions and searches for and suggests new properties that meet the conditions.

[1603] Prompt Sentence Examples

[1604] An example prompt might be, "The user puts on the smart glasses and searches for properties that meet the specified real estate criteria. The criteria are within a 10-minute walk from the station, 2LDK, and rent of less than 100,000 yen. Find the perfect property and view it in a virtual tour. The user can then enter feedback on additional criteria and update the search results."

[1605] In this way, the system of the present invention can efficiently search for properties based on the user's desired conditions and can remotely check the details of the properties using virtual reality technology.

[1606] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1607] Step 1:

[1608] Receiving User Input

[1609] The user puts on the smart glasses and inputs their desired real estate conditions by voice (e.g., "within 10 minutes' walk from the station, 2LDK, rent under 100,000 yen"). This voice input is converted into text data using the smart glasses' voice recognition function. The input data is output in text format as the user's desired conditions.

[1610] Step 2:

[1611] Send your desired conditions

[1612] The terminal sends the desired conditions entered by the user as text data to the server. This transmission is performed via the Internet. The desired conditions received as input data are transferred to the server and sent as data for analysis.

[1613] Step 3:

[1614] Analysis of desired conditions

[1615] The server analyzes the received text data of desired conditions using a natural language processing (NLP) engine and extracts specific desired conditions (e.g., "within 10 minutes' walk," "2LDK," "rent under 100,000 yen"). The input is the desired conditions in text format, and the output is the analyzed specific conditions.

[1616] Step 4:

[1617] Database search

[1618] The server searches the real estate database based on the analyzed desired conditions. It lists properties that match the conditions and obtains detailed information about those properties (photos, floor plan, rent, location, etc.). The input is the analyzed desired conditions, and the output is a list of properties that match the conditions.

[1619] Step 5:

[1620] Submit property information

[1621] The server sends the listed property information to the smart glasses, through which the user can view the properties in a virtual reality environment. The input is the property list, and the output is the display of the property information in a virtual reality environment.

[1622] Step 6:

[1623] Receiving Feedback

[1624] The user can provide feedback about the proposed property by voice (e.g., "This property is good, but I don't like the unit bath"). This feedback is converted into text data using the smart glasses' voice recognition function. The input is the feedback content, and the output is text-based feedback.

[1625] Step 7:

[1626] Submitting additional conditions

[1627] The terminal sends the user's feedback (additional conditions) to the server. The input is the feedback in text format, and the output is the data sent to the server.

[1628] Step 8:

[1629] Learning additional conditions and re-searching

[1630] The server trains the AI ​​model on the additional conditions and understands the user's new preferences. Based on this learning, it searches the real estate database again and lists properties that meet the new conditions. The input is the text data of the additional conditions, and the output is a list of properties that meet the new conditions.

[1631] Step 9:

[1632] Re-proposal

[1633] The server sends the re-listed property information to the smart glasses. The user then browses, rates, and views the properties again in a virtual reality environment. The input is the new property list, and the output is a virtual reality display of the re-suggested property.

[1634] 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.

[1635] This invention is a system in which a user inputs desired real estate conditions in a chat format, and the server automatically suggests the most suitable property. This system also incorporates an emotion engine that recognizes the user's emotions, allowing it to make suggestions according to the user's emotional state. The specific program processing procedures and their operation are explained below, along with specific examples.

[1636] Program processing overview

[1637] 1. The user enters the desired conditions

[1638] The user opens the chat input screen on their device and enters their desired conditions, such as "within 10 minutes' walk from the station, within 30 minutes door-to-door to work, 2LDK, sunlight-oriented, rent under 100,000 yen."

[1639] 2. The device sends the input data to the server

[1640] The terminal transmits the desired conditions entered by the user to the server as text data.

[1641] 3. The server analyzes the input data

[1642] The server passes the received text data to a natural language processing (NLP) engine, which analyzes and extracts desired conditions, such as "within a 10-minute walk from the station," "within a 30-minute door-to-door drive to the office," "2LDK," "high priority on sunlight," and "rent under 100,000 yen."

[1643] 4. The server recognizes emotions using the emotion engine

[1644] The server uses an emotion engine to analyze the emotions from the user's input data. For example, it can recognize whether the user is feeling dissatisfied, satisfied, or hopeful based on the wording and expressions in the text.

[1645] 5. The server searches the database

[1646] The server searches a real estate database based on the analyzed desired conditions and emotion recognition results, lists properties that meet the conditions and take the user's emotions into consideration, and retrieves detailed information about those properties.

[1647] 6. The server sends the property information to the user

[1648] The server then sends the selected property information to the user in the form of a chat message, including details such as photos, layout, rent, and location of each property.

[1649] 7. User enters additional conditions

[1650] The user reviews the proposed property list and enters feedback in chat format, such as, "I like this floor plan, but I don't like the unit bath." The emotion engine then analyzes the user's emotions in real time.

[1651] 8. The device sends additional conditions to the server

[1652] The terminal again transmits the additional conditions input by the user to the server.

[1653] 9. The server learns additional conditions

[1654] The server then trains the AI ​​model with the newly received additional conditions, allowing the AI ​​model to understand the user's preferences and emotions and search again for new properties that match the conditions.

[1655] 10. The server will suggest properties again

[1656] The server then suggests suitable properties to the user from the search results again, and this process continues with repeated feedback until the user finds a property that satisfies them.

[1657] Specific examples

[1658] For example, if a user searches for a property with the conditions "within a 10-minute walk from the station, 2LDK, rent under 100,000 yen," the server will list properties that meet these criteria and suggest them to the user. If the user gives feedback such as "This property is good, but I don't like the unit bath," the emotion engine will recognize the dissatisfaction from the user's text, and the server will learn the additional conditions and search again to suggest properties with separate bathrooms and toilets.

[1659] Furthermore, the emotion engine monitors changes in the user's emotions and analyzes whether the user is satisfied with the suggestions, thereby improving the accuracy of property suggestions in a way that satisfies the user. Premium members are also provided with a system that regularly provides them with new property information. This is achieved by the server periodically searching the database based on premium member information and notifying them of new property information via email or chat.

[1660] This system flexibly responds to the user's needs and emotions, providing a wide range of suitable real estate properties, saving the user a great deal of effort. In particular, the learning function using an emotion engine and AI model makes it possible to make highly accurate property suggestions based on the user's preferences and emotions.

[1661] The processing flow will be explained below.

[1662] Step 1:

[1663] The user inputs the desired conditions.

[1664] The user opens the chat input screen on their device and enters their desired conditions, such as "within 10 minutes' walk from the station, within 30 minutes door-to-door to work, 2LDK, sunlight-oriented, rent under 100,000 yen."

[1665] Step 2:

[1666] The terminal sends the input data to the server.

[1667] The terminal transmits the desired conditions entered by the user to the server as text data.

[1668] Step 3:

[1669] The server parses the input data.

[1670] The server passes the received text data to a natural language processing (NLP) engine, which analyzes and extracts desired conditions, such as "within a 10-minute walk from the station," "within a 30-minute door-to-door drive to the office," "2LDK," "high priority on sunlight," and "rent under 100,000 yen."

[1671] Step 4:

[1672] The server recognizes emotions using an emotion engine.

[1673] The server uses an emotion engine to analyze the emotions from the user's input data, for example, recognizing whether the user is feeling dissatisfied, satisfied, or hopeful based on the language and expressions used in the text.

[1674] Step 5:

[1675] The server searches the database.

[1676] The server searches a real estate database based on the analyzed desired conditions and emotion recognition results, and lists properties that match the conditions and take the user's emotions into consideration.

[1677] Step 6:

[1678] The server sends the property information to the user.

[1679] The server then sends the selected property information to the user in the form of a chat message, including details such as photos, layout, rent, and location of each property.

[1680] Step 7:

[1681] The user enters additional conditions.

[1682] The user checks the proposed property list and inputs additional desired conditions as feedback, such as "I like this floor plan, but I don't want a unit bath." At this time, the emotion engine also continuously analyzes the user's emotions.

[1683] Step 8:

[1684] The terminal transmits the additional conditions to the server.

[1685] The terminal again transmits the additional conditions input by the user to the server.

[1686] Step 9:

[1687] The server learns additional conditions.

[1688] The server then trains the AI ​​model with the newly received additional conditions, allowing the AI ​​model to understand the user's preferences and emotions and search again for new properties that match the conditions.

[1689] Step 10:

[1690] The server will suggest the property again.

[1691] The server then proposes suitable properties to the user from the search results again, and continues providing feedback and suggestions using an emotion engine until the user is satisfied with the suggestions.

[1692] Step 11:

[1693] Provide ongoing proposals for premium members.

[1694] The server periodically (for example, weekly or monthly) searches for new properties based on premium member information and notifies users via email or chat. The emotion engine also analyzes user feedback and emotions to continually provide better suggestions.

[1695] Example 2

[1696] 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."

[1697] In conventional real estate search systems, when users input their desired conditions, they simply analyze the conditions as text, and are unable to make suggestions that reflect the user's emotions and preferences. Furthermore, there was no system that could understand user feedback in real time and re-make optimal suggestions. As a result, users had to spend a lot of time and effort searching for real estate properties.

[1698] The specification process by the specification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes: means for a user to input desired conditions for real estate in a chat format; means for a terminal to transmit the input conditions to the server; means for the server to analyze the received data and extract the desired conditions; means for the server to analyze the desired conditions using natural language processing technology; means for the server to recognize the user's emotions using an emotion engine; means for the server to search a real estate database and list properties that match the conditions; means for the server to transmit matching property information to the user; means for the user to input feedback on the proposed property; means for the terminal to transmit additional conditions to the server; and means for the server to learn the user's additional conditions using an AI model and search for and propose properties again.

[1699] This makes it possible to analyze the user's desired conditions and emotions in real time and propose the most suitable real estate property that suits the user's preferences.

[1700] "User" refers to a person who uses the real estate search system to input desired conditions and receive property proposals.

[1701] A "terminal" is a device used by a user to input desired conditions in chat format, and includes a smartphone, tablet, PC, etc.

[1702] "Server" refers to a central management device that receives, analyzes, and searches user input data and makes real estate property suggestions.

[1703] "Natural language processing technology" refers to technology that analyzes text data entered by the user and extracts desired conditions based on that data.

[1704] An "emotion engine" is an engine that recognizes emotions from user input data and optimizes property suggestions based on those emotions.

[1705] A "real estate database" is a database that stores various real estate property information, and refers to a collection of property information that can be searched.

[1706] "AI model" refers to a machine learning model that learns the user's desired conditions and additional conditions, and searches for and suggests the most suitable properties.

[1707] "Premium Member" refers to a user who has the right to receive special services under certain conditions.

[1708] System Overview

[1709] This invention is a system in which a user inputs desired real estate conditions in a chat format, and the server automatically suggests the most suitable property. This system incorporates an emotion engine that recognizes the user's emotions, and can make suggestions according to the user's emotional state.

[1710] Hardware and software used

[1711] User devices: smartphones, tablets, computers, etc.

[1712] Server: A central management device that analyzes desired conditions, searches real estate databases, recognizes emotions, and trains AI models.

[1713] Natural Language Processing (NLP) engine: Technology that analyzes user requirements

[1714] Emotion engine: An engine that analyzes emotions from user input data

[1715] Real estate database: Contains information on various real estate properties

[1716] AI model: A machine learning model that learns the user's desired conditions and additional conditions to suggest the most suitable properties.

[1717] System Operation

[1718] 1. Input method: Users use a device such as a smartphone or PC to input their desired real estate requirements in chat format. For example, they can input specific requirements such as "within a 10-minute walk from the station, within a 30-minute door-to-door commute to work, 2LDK, sunlight-oriented, rent under 100,000 yen."

[1719] 2. Data transmission means: The user's terminal transmits the entered desired conditions to the server as text data.

[1720] 3. Data analysis method: The server passes the received text data to a natural language processing (NLP) engine, which analyzes and extracts the desired conditions. Specifically, it breaks down conditions such as "within a 10-minute walk from the station," "within a 30-minute door-to-door drive to the office," "2LDK," "priority on sunlight," and "rent under 100,000 yen."

[1721] 4. Emotion Recognition: The server uses an emotion engine to recognize emotions from the user's input data. For example, it can read emotions such as dissatisfaction, satisfaction, and expectation from the wording and expressions in the text.

[1722] 5. Database search method: The server searches the real estate database based on the analyzed desired conditions and emotion recognition results, and lists properties that meet the conditions and take the user's emotions into consideration.

[1723] 6. Result transmission method: The server sends the selected property information to the user in the form of a chat message, including photos, floor plan, rent, location, access information, etc.

[1724] 7. Feedback: The user reviews the proposed property list and enters feedback in chat format, such as "I like this floor plan, but I don't like the unit bathroom." The emotion engine continues to analyze the user's emotions in real time and learns the user's preferences based on the feedback.

[1725] 8. Additional condition sending means: The terminal sends the additional conditions entered by the user back to the server.

[1726] 9. Learning method: The server trains the AI ​​model with the newly received additional conditions. The AI ​​model then gains a more detailed understanding of the user's preferences and emotions and searches for new properties.

[1727] 10. Re-proposal method: The server proposes a newly selected property to the user, and the process continues with repeated feedback until the user finds a property that satisfies them.

[1728] Specific examples

[1729] For example, if a user enters the conditions "within a 10-minute walk from the station, 2LDK, rent under 100,000 yen," the server will list properties that meet these criteria and suggest them to the user. If the user gives feedback such as "This property is good, but I don't like the unit bath," the emotion engine will recognize the dissatisfaction, and the server will search again and suggest properties with separate bathrooms and toilets.

[1730] Example prompts for generative AI models

[1731] Example prompt:

[1732] "The user entered the desired conditions: 'within 10 minutes' walk from the station, 2LDK, rent under 100,000 yen.' Please use the emotion engine to suggest the best property for this. Also, please explain how to respond if the user enters feedback such as 'I don't want a unit bath.'"

[1733] This allows the system to flexibly respond to users' needs and emotions, providing them with multifaceted, suitable real estate properties, saving them time and effort.In addition, by using an emotion engine and AI model, it is possible to provide highly accurate property suggestions that match the user's preferences and emotions.

[1734] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1735] Step 1:

[1736] The user enters the desired conditions.

[1737] The user opens the chat input screen on their device and enters their desired conditions, such as "within 10 minutes' walk from the station, within 30 minutes door-to-door to work, 2LDK, sunlight-oriented, rent under 100,000 yen."

[1738] Specifically, the user inputs each desired condition in text format and presses the send button.

[1739] Step 2:

[1740] The device sends the input data to the server.

[1741] The terminal transmits the desired conditions entered by the user to the server as text data.

[1742] Specifically, when the send button is pressed, the text data is automatically sent to the server.

[1743] Input: User's desired conditions (text format)

[1744] Output: Text data to the server

[1745] Step 3:

[1746] The server parses the input data.

[1747] The server passes the received text data to a natural language processing (NLP) engine, which analyzes and extracts the desired conditions.

[1748] Specifically, the NLP engine analyzes the text and extracts conditions such as "within 10 minutes' walk from the station," "2LDK," and "rent under 100,000 yen."

[1749] Input: User's desired conditions (text data)

[1750] Output: Parsed desired conditions (structured data)

[1751] Step 4:

[1752] The server recognizes emotions using an emotion engine.

[1753] The server uses an emotion engine to analyze emotions from the user's input data.

[1754] Specifically, it reads emotions such as dissatisfaction, satisfaction, and expectation from the wording of the input text.

[1755] Input: User's desired conditions (text data)

[1756] Output: Parsed emotion data

[1757] Step 5:

[1758] The server searches the database.

[1759] The server searches a real estate database based on the analyzed desired conditions and emotion recognition results, and lists properties that meet the conditions.

[1760] Specifically, it uses a filter to narrow down the properties in the database that meet the criteria and lists them.

[1761] Input: Analyzed desired conditions and emotion data

[1762] Output: List of properties that match the criteria

[1763] Step 6:

[1764] The server sends the property information to the user.

[1765] The server sends the selected property information to the user in the form of a chat message.

[1766] Specifically, a chat message is generated that includes photos of the property, floor plan, rent, location, access information, etc.

[1767] Input: List of properties that match the criteria

[1768] Output: Property chat message to user

[1769] Step 7:

[1770] The user enters additional criteria.

[1771] The user reviews the proposed property list and enters feedback in chat format, such as, "I like this layout, but I don't like the unit bath."

[1772] Specifically, the user inputs and sends specific complaints and additional requirements via chat.

[1773] Input: Feedback on the proposed property (text format)

[1774] Output: User feedback data

[1775] Step 8:

[1776] The device sends the additional conditions to the server.

[1777] The terminal again transmits the additional conditions input by the user to the server.

[1778] Specifically, when the send button is pressed, the feedback data is sent to the server.

[1779] Input: User feedback (text data)

[1780] Output: Feedback data to the server

[1781] Step 9:

[1782] The server learns additional conditions.

[1783] The server trains the AI ​​model on the newly received additional conditions.

[1784] Specifically, the AI ​​model learns from the feedback data to gain a more detailed understanding of the user's preferences.

[1785] Input: User feedback data

[1786] Output: Updated AI model

[1787] Step 10:

[1788] The server proposes the property again.

[1789] The server then suggests suitable properties to the user from the search results.

[1790] Specifically, the system searches again for new properties that meet the conditions and generates a proposal message.

[1791] Input: Updated AI model and new search results

[1792] Output: New property proposal chat message

[1793] (Application example 2)

[1794] 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."

[1795] On current online shopping sites, when users search for the products they want, it takes a lot of time and effort to find the products that match their criteria.In addition, there is a lack of a system that suggests optimal products based on the user's emotions and feedback, making it difficult for users to efficiently find products that satisfy them.

[1796] The identification process by the identification 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 a user to input desired product conditions in a chat format, means for a terminal to transmit the input conditions to the server, means for the server to analyze the received data and extract the desired conditions, means for the server to search a product database and list products that match the conditions, means for the server to transmit information about the matching products to the user, means for the user to input feedback on the suggested products, means for the terminal to transmit additional conditions to the server, means for the server to learn the additional conditions and search for and suggest products again, and an emotion engine for the server to analyze the user's emotional state and make suggestions based on the emotions. This enables the user to efficiently find products that match the desired conditions and can suggest optimal products based on the user's emotions and feedback.

[1797] A "user" is a person who searches for and purchases products using an online shopping site.

[1798] "Desired conditions" are requirements regarding product specifications and features that are input by the user in chat format.

[1799] A "terminal" is an electronic device used by a user, such as a computer, smartphone, or tablet.

[1800] A "server" is a central processing unit that receives data sent from a terminal and performs analysis and searches.

[1801] "Chat format" is an interface format that allows users to input text and exchange information in a conversational format.

[1802] A "product database" is a digital database in which product information is stored.

[1803] "Feedback" refers to the act of a user inputting an evaluation or opinion about a proposed product, or the content of such input.

[1804] "Additional conditions" are new conditions input by the user based on feedback.

[1805] An "emotion engine" is software that analyzes the user's emotions from their text data and makes suggestions based on their state.

[1806] "Natural language processing technology" is a technology that allows computers to understand and analyze human language.

[1807] A "generative AI model" is an artificial intelligence model that learns from users' conditions and feedback and makes more appropriate suggestions.

[1808] In this invention, a mail-order system will be described that proposes products based on desired conditions input by a user in a chat format.

[1809] Overall system configuration

[1810] This system mainly consists of a user's device, a server, a product database, and an emotion engine. Users input their desired conditions through a chat interface, and their device sends the information to the server. The server uses natural language processing technology to analyze the desired conditions and search for matching products in the product database. It then uses the emotion engine to analyze the user's emotions and makes product suggestions based on those emotions.

[1811] Hardware and Software Configuration

[1812] Hardware: Your PC, smartphone or tablet, and server

[1813] Software: Natural language processing engine (e.g., Google Cloud Natural Language), sentiment analysis engine (e.g., IBM Watson Tone Analyzer), database (e.g., MySQL), chatbot framework (e.g., Dialogflow)

[1814] What the program does

[1815] 1. User inputs desired conditions

[1816] The user enters the desired product conditions in chat format. For example, specific conditions such as "black jacket, size L, budget within 5,000 yen, casual style" are entered.

[1817] 2. Sending data from the device to the server

[1818] The terminal transmits the desired conditions entered by the user as text data to the server, using real-time communication.

[1819] 3. Data analysis by the server

[1820] The server passes the received text data to a natural language processing engine, which analyzes the desired conditions, such as "black jacket," "size L," "budget under 5,000 yen," and "casual style."

[1821] 4. Emotion analysis

[1822] The server uses an emotion engine to analyze the user's input data to determine whether the user is feeling expectations, hopes, dissatisfaction, or other emotions.

[1823] 5. Search the product database

[1824] The server searches a product database based on the analyzed desired conditions and emotions, lists products that match the conditions, and obtains their detailed information.

[1825] Adding specific examples

[1826] For example, if a user searches for a product using the criteria "black jacket, size L, budget under 5,000 yen, casual style," the server will list products that match these criteria and suggest them to the user. If the user gives feedback such as "I like this design, but I wish it was a little longer," the emotion engine will recognize the user's wishes from the text, and the server will learn the additional criteria, search again, and suggest new products that match the criteria.

[1827] Prompt Sentence Examples

[1828] User input: "Black jacket, size L, budget under 5000 yen, casual style"

[1829] Prompt: "Analyze the emotions expressed by the user's input."

[1830] This allows for more detailed proposals that meet the user's desired conditions, and also allows for optimal product proposals based on the user's emotions and feedback.

[1831] summary

[1832] By introducing this system, users can efficiently find products that meet their desired criteria, and by using an emotion engine, they can receive highly satisfying suggestions. Product recommendations are made based on data analyzed by the server and the results of emotion analysis, which increases user satisfaction.

[1833] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1834] Step 1:

[1835] The user inputs desired product specifications in chat format via a terminal. The input is specific text data such as "black jacket, size L, budget within 5,000 yen, casual style."

[1836] Step 2:

[1837] The terminal transmits the desired conditions entered by the user to the server as text data, and the transmitted data is transmitted to the server in real time.

[1838] Step 3:

[1839] The server passes the received text data to a natural language processing engine (e.g., Google Cloud Natural Language) and analyzes the desired conditions. This analysis includes text tokenization, part-of-speech tagging, and semantic analysis. For example, conditions such as "black jacket," "size L," "budget under 5,000 yen," and "casual style" are extracted.

[1840] Step 4:

[1841] The server uses an emotion engine (e.g., IBM Watson Tone Analyzer) to analyze the emotion from the user's input data. This analyzes the emotional weighting of words and the tone of the entire sentence to determine the user's emotional state (expectation, hope, etc.). It identifies whether the user is excited or disappointed, for example.

[1842] Step 5:

[1843] The server searches a product database (e.g., MySQL) based on the analyzed desired conditions and emotion recognition results, lists products that match the conditions, and retrieves detailed information about those products (images, prices, descriptions, etc.).

[1844] Step 6:

[1845] The server sends the selected product information to the user in the form of a chat message, including a photo, description, price, and rating of each product.

[1846] Step 7:

[1847] The user can review the proposed product list and provide feedback in chat, for example, by providing specific opinions such as, "The design is good, but I wish it was a little longer."

[1848] Step 8:

[1849] The terminal sends the additional conditions entered by the user to the server, and feedback is transmitted to the server in real time.

[1850] Step 9:

[1851] The server trains the generative AI model on the newly received additional conditions. This training uses supervised and unsupervised learning methods. The AI ​​model updates the conditions based on the user's preferences and feedback information and reflects them in the next search.

[1852] Step 10:

[1853] The server searches for products again and suggests new products that match the criteria. This process is repeated until a product that satisfies the user is found.

[1854] 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.

[1855] 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.

[1856] 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.

[1857] 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.

[1858] 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.

[1859] 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.

[1860] 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).

[1861] 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.

[1862] 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."

[1863] 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.

[1864] 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).

[1865] 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.

[1866] 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.

[1867] 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.

[1868] 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.

[1869] 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.

[1870] 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.

[1871] 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.

[1872] 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.

[1873] 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.

[1874] 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.

[1875] The following is further disclosed regarding the above embodiment.

[1876] (Claim 1)

[1877] A means for users to input desired real estate conditions in a chat format;

[1878] A means for transmitting the input conditions from the terminal to the server;

[1879] A means for analyzing the data received by the server and extracting desired conditions;

[1880] A means for the server to search a real estate database and list properties that match the criteria;

[1881] a means for the server to send matching property information to the user;

[1882] a means for the user to input feedback on the proposed property;

[1883] A means for the terminal to transmit the additional conditions to the server;

[1884] The server learns the additional conditions and searches for and proposes properties again.

[1885] A system including:

[1886] (Claim 2)

[1887] A means by which users can continuously submit their real estate preferences;

[1888] The server periodically searches for and provides new property information based on the information of Yahoo! Premium members, and

[1889] 10. The system of claim 1, comprising:

[1890] (Claim 3)

[1891] A means for the server to analyze desired conditions using natural language processing technology;

[1892] A means for the server to learn the user's additional conditions using an AI model;

[1893] 10. The system of claim 1, comprising:

[1894] "Example 1"

[1895] (Claim 1)

[1896] A means for users to input desired real estate conditions in a chat format;

[1897] A means for transmitting the input conditions from the terminal to the server;

[1898] A means for analyzing the data received by the server and extracting desired conditions;

[1899] A means for the server to search a real estate database and list properties that match the criteria;

[1900] a means for the server to send matching property information to the user;

[1901] a means for the user to input feedback on the proposed property;

[1902] A means for the terminal to transmit the additional conditions to the server;

[1903] The server learns the additional conditions and searches for and proposes properties again.

[1904] A means for using an artificial intelligence model that learns user preferences and adaptively improves the accuracy of property suggestions;

[1905] A system including:

[1906] (Claim 2)

[1907] A means by which users can continuously submit their real estate preferences;

[1908] The server periodically searches for and provides new property information based on the information of premium members.

[1909] 10. The system of claim 1, comprising:

[1910] (Claim 3)

[1911] A means for the server to analyze desired conditions using natural language processing technology;

[1912] A means for the server to learn the user's additional conditions using the generative AI model;

[1913] 10. The system of claim 1, comprising:

[1914] "Application Example 1"

[1915] (Claim 1)

[1916] A means for users to input desired real estate conditions in a chat format;

[1917] A means for transmitting the input conditions from the terminal to the server;

[1918] A means for analyzing the data received by the server and extracting desired conditions;

[1919] A means for the server to search a real estate database and list properties that match the criteria;

[1920] a means for the server to send matching property information to the user;

[1921] a means for the user to input feedback on the proposed property;

[1922] A means for the terminal to transmit the additional conditions to the server;

[1923] The server learns the additional conditions and searches for and proposes properties again.

[1924] a means for viewing the property in a virtual reality environment using a smart device;

[1925] A system including:

[1926] (Claim 2)

[1927] A means by which users can continuously submit their real estate preferences;

[1928] The server periodically searches for and provides new property information based on the information of premium members.

[1929] 10. The system of claim 1, comprising:

[1930] (Claim 3)

[1931] A means for the server to analyze desired conditions using natural language processing technology;

[1932] A means for the server to learn the user's additional conditions using an AI model;

[1933] 10. The system of claim 1, comprising:

[1934] "Example 2: Combining Emotion Engines"

[1935] (Claim 1)

[1936] A means for users to input desired real estate conditions in a chat format;

[1937] A means for transmitting the input conditions from the terminal to the server;

[1938] A means for analyzing the data received by the server and extracting desired conditions;

[1939] A means for the server to analyze desired conditions using natural language processing technology;

[1940] A means for the server to recognize the user's emotion using an emotion engine;

[1941] A means for the server to search a real estate database and list properties that match the criteria;

[1942] a means for the server to send matching property information to the user;

[1943] a means for the user to input feedback on the proposed property;

[1944] A means for the terminal to transmit the additional conditions to the server;

[1945] The server uses an AI model to learn the user's additional conditions, and then searches for and proposes properties again.

[1946] A system including:

[1947] (Claim 2)

[1948] A means by which users can continuously submit their real estate preferences;

[1949] The server periodically searches for and provides new property information based on the information of premium members.

[1950] 10. The system of claim 1, comprising:

[1951] (Claim 3)

[1952] The server uses the generated AI model to analyze the desired conditions and additional conditions and propose the most suitable real estate property.

[1953] 10. The system of claim 1, comprising:

[1954] "Application example 2 when combining emotion engines"

[1955] (Claim 1)

[1956] A means for users to input desired product conditions in a chat format;

[1957] A means for transmitting the input conditions from the terminal to the server;

[1958] A means for analyzing the data received by the server and extracting desired conditions;

[1959] A means for the server to search a product database and list products that match the conditions;

[1960] A means for the server to send matching product information to the user;

[1961] a means for the user to input feedback on the proposed product;

[1962] A means for the terminal to transmit the additional conditions to the server;

[1963] The server learns the additional conditions and searches for and suggests products again.

[1964] A server includes an emotion engine that analyzes the user's emotional state and makes suggestions according to the user's emotions;

[1965] A system including:

[1966] (Claim 2)

[1967] A means for allowing users to continuously transmit desired product conditions;

[1968] A means for the server to periodically search for and provide new product information based on member information;

[1969] 10. The system of claim 1, comprising:

[1970] (Claim 3)

[1971] A means for the server to analyze desired conditions using natural language processing technology;

[1972] A means for the server to learn the user's additional conditions using the generative AI model;

[1973] 10. The system of claim 1, comprising: [Explanation of symbols]

[1974] 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 for users to input desired real estate conditions in a chat format; A means for transmitting the input conditions from the terminal to the server; A means for analyzing the data received by the server and extracting desired conditions; A means for the server to search a real estate database and list properties that match the criteria; a means for the server to send matching property information to the user; a means for the user to input feedback on the proposed property; A means for the terminal to transmit the additional conditions to the server; The server learns the additional conditions and searches for and proposes properties again. A system including:

2. A means by which users can continuously submit their real estate preferences; The server periodically searches for and provides new property information based on the information of Yahoo! Premium members, and The system of claim 1 , comprising:

3. A means for the server to analyze desired conditions using natural language processing technology; A means for the server to learn the user's additional conditions using an AI model; The system of claim 1 , comprising:

Images