System

Abstract

A system is provided.SOLUTION: This system is provided with a means for inputting a place and a condition where the user wants to live, a means for collecting data based on the condition inputted by the user from a plurality of information sources on the Internet, a means for analyzing the collected data, and for selecting housing information optimal to the user, and a means for presenting the selected housing information to a user terminal.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] In conventional real estate search systems, even if users input their detailed desired conditions, they could only narrow down their search based on pre-set search criteria, which often resulted in properties being presented that were different from the user's expectations. In addition, real estate information is scattered across multiple sites, and checking all the options takes time and effort. This makes it difficult for users to find the housing information they really want. Furthermore, it is not possible to obtain the latest reviews or reputation information, which is insufficient as a basis for making a decision. [Means for solving the problem]

[0005] To solve the above problems, the present invention provides a system including a means for a user to input desired living locations and conditions, a means for collecting data based on the conditions entered by the user from multiple information sources on the Internet, a means for analyzing the collected data and selecting the most suitable housing information for the user, and a means for presenting the selected housing information on a user terminal. Furthermore, the system assigns an evaluation score based on the collected data and ranks properties based on the evaluation score, thereby presenting the information in a form that is easy for the user to understand. Furthermore, the system collects information from real estate information websites and social networking services, and selects properties based on the latest word-of-mouth and reputation information, thereby providing useful information to the user.

[0006] A "user" is someone who uses the system to input the desired location and conditions for living and obtain the most suitable housing information.

[0007] "Input means" refers to the interface that users use to communicate their desired location and conditions to the system, specifically referring to web forms and application input screens.

[0008] "Source" means a website or service that collects data based on user-entered criteria, including real estate listing sites and social networking services.

[0009] A "real estate information site" is a website that provides users with information about real estate properties, allowing them to browse and search for a large number of property listings.

[0010] "Social Networking Service" refers to an online platform that allows users to share and obtain information, such as reviews and ratings.

[0011] "Collection Method" means a program or algorithm used to obtain specified data from multiple sources on the Internet.

[0012] "Data" refers to all information collected and analyzed by the system, such as the housing conditions desired by the user, property information, word-of-mouth reviews, and reputation.

[0013] "Analysis means" refers to the programs and algorithms used to select the most suitable housing information that meets the user's requirements based on the collected data.

[0014] "Selection method" refers to the program or algorithm used to select the most suitable property information from the analyzed data.

[0015] "Presentation means" refers to an interface for displaying the selected residential information on a user terminal, specifically a web page or a mobile application display screen.

[0016] "Evaluation score" refers to a numerical value used to evaluate and rank properties based on collected data.

[0017] "Ranking method" refers to a program or algorithm used to rank property information collected and analyzed based on evaluation scores. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

[0026] [First embodiment]

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

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

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

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

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

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

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

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

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

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

[0037] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

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

[0039] This invention relates to a system that allows users to input desired locations and conditions for living, and then collects and analyzes related data from multiple information sources on the Internet to provide optimal housing information. Specifically, it is configured as follows.

[0040] Basic system configuration

[0041] This system consists of a "terminal" used by the user, a "server" that collects and analyzes data, and multiple information sources (real estate information sites and social networking services).

[0042] User side

[0043] 1. Input Method

[0044] A form is displayed on the user's device for entering the desired place to live and conditions (area, budget, floor plan, other conditions, etc.) For example, the user enters "Shinjuku-ku, Tokyo, under 100,000 yen, 2LDK, nearby supermarket, pets allowed."

[0045] Server side

[0046] 2. Data Collection Methods

[0047] The server uses web scraping technologies such as Python, BeautifulSoup, and Selenium to collect property information from real estate information websites. It also collects reviews and reputation information related to the user's criteria through social media APIs (e.g., Twitter API and Instagram Graph API).

[0048] 3. Temporary storage of data

[0049] The collected data is temporarily stored in a database, including the property's address, rent, floor plan, age, distance to the nearest station, and the latest related reviews.

[0050] 4. Data Analysis Methods

[0051] The server analyzes the collected data and filters the property information that matches the user's criteria, such as rent under 100,000 yen, a 2LDK layout, and pet-friendly properties.

[0052] 5. Evaluation Score Assignment Method

[0053] The filtered property information is assigned a rating score based on the collected reviews. Properties with many positive reviews are given a high score, while properties with many negative reviews are given a low score.

[0054] 6. Ranking Methods

[0055] Property information is ranked based on the rating score, with properties that best match the user's criteria and have the highest ratings displayed at the top.

[0056] Presentation of results

[0057] 7. Presentation means

[0058] The server sends a list of selected housing information to the user's device, and the user's browser or application displays information such as rent, floor plan, location, distance to the nearest station, and the latest reviews.

[0059] Specific operation example

[0060] For example, if a user enters conditions such as "Shinjuku-ku, Tokyo, under 100,000 yen, 2LDK, nearby supermarket, pets allowed," the server will operate as follows:

[0061] The server scrapes multiple real estate information sites using keywords such as "Shinjuku-ku 2LDK rental."

[0062] At the same time, information is collected on social media using hashtags such as "Shinjuku Ward Rentals" and "Pets Allowed."

[0063] The collected data is stored in a database and property information that matches the user's criteria is filtered.

[0064] The filtered properties are assigned an evaluation score based on the collected reviews and ranked.

[0065] Finally, the top-ranked property information is displayed on the user's device, allowing the user to check detailed information and contact details.

[0066] This system allows users to easily obtain housing information that matches their preferences and select the most suitable property.

[0067] The processing flow will be explained below.

[0068] Step 1:

[0069] The user enters the desired location and conditions into a form displayed on the user's device. For example, the user might enter "Shinjuku-ku, Tokyo, under 100,000 yen, 2LDK, nearby supermarket, pets allowed."

[0070] Step 2:

[0071] The conditions entered by the user are transmitted. At this time, the user terminal transmits the input data to the server.

[0072] Step 3:

[0073] Based on the user's conditions received by the server, web scraping is initiated against multiple real estate information websites on the Internet. Using tools such as Python, BeautifulSoup, and Selenium, searches are performed using keywords such as "Shinjuku Ward, 2LDK for rent" and "under 100,000 yen" to collect property information.

[0074] Step 4:

[0075] The server temporarily stores the property information it collects in a database, which includes basic information such as address, rent, floor plan, age of the building, and distance to the nearest station.

[0076] Step 5:

[0077] The server uses SNS APIs (e.g., Twitter API or Instagram Graph API) to collect relevant reviews on SNS. For example, it searches for hashtags such as "Shinjuku-ku rental" and "pets allowed" to obtain the latest reputations and reviews.

[0078] Step 6:

[0079] The server also stores the collected social media data in a database, including user ratings and comments.

[0080] Step 7:

[0081] The server filters the real estate information in the database based on the user's input criteria, such as rent under 100,000 yen, a 2LDK layout, and pet-friendly properties.

[0082] Step 8:

[0083] The server assigns a rating score to the filtered property information based on the collected reviews, raising the score for properties with many positive comments and lowering the score for properties with many negative comments.

[0084] Step 9:

[0085] The server ranks properties based on their rating scores and sorts the property information so that properties with higher scores are displayed at the top.

[0086] Step 10:

[0087] The server formats the ranked property information and sends it to the user's device. The formatted information includes rent, floor plan, location, distance to the nearest station, and the latest reviews.

[0088] Step 11:

[0089] The property information received by the user's device is displayed on a browser or application. The user can check the displayed property information and click to view detailed information or contact information.

[0090] The above is the specific flow of program processing for this system.

[0091] Example 1

[0092] 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."

[0093] In recent years, it has become common for users to use the Internet to search for housing information in the real estate market. However, it is difficult and time-consuming to efficiently collect housing information that meets the user's desired criteria and to perform accurate filtering and evaluation. In addition, the collected data is diverse, and there is a lack of means to integrate and appropriately evaluate it, making it difficult to provide users with the housing information that is most suitable for them.

[0094] 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.

[0095] In this invention, the server includes means for a user to input desired living locations and conditions, means for collecting data based on the conditions entered by the user from multiple data sources on the Internet, means for temporarily storing the collected data in a database, means for analyzing the collected data and filtering out housing information that matches the user's conditions, means for assigning evaluation scores to the filtered housing information, means for ranking the housing information based on the evaluation scores, means for presenting the selected housing information to a user terminal, and means for collecting word-of-mouth and reputation information from social networking services, thereby enabling users to quickly and accurately obtain optimal housing information that matches their desired conditions.

[0096] "User" refers to someone who uses this system to input the desired location and conditions to search for housing information.

[0097] "Desired place and conditions to live" refers to the specific requirements of the user, such as the area, budget, and floor plan they desire.

[0098] "Data source" refers to various information sources that provide data that meets the user's requirements, such as real estate information sites on the Internet and social networking services (SNS).

[0099] A "database" refers to a digital storage system for temporarily storing and managing collected data (property information and reviews).

[0100] "Rating score" refers to the points or rating value assigned to each property based on collected reviews and other indicators.

[0101] "Ranking" refers to the process of ranking property information based on evaluation scores and presenting it to the user in order of priority.

[0102] "Social Networking Service" refers to an online service (e.g., a social networking platform) that enables users to post and share information.

[0103] "Filtering" refers to the process of extracting information that meets a user's criteria from collected data and excluding unnecessary information.

[0104] A "real estate information site" refers to a site that provides information on the rental and sale of properties on the Internet.

[0105] This invention relates to a system that allows users to input desired locations and conditions for living, and then collects and analyzes related data from multiple data sources on the Internet to provide optimal housing information. Specifically, it is configured as follows.

[0106] Basic system configuration

[0107] This system consists of a "terminal" used by the user, a "server" that collects and analyzes data, and multiple data sources (real estate information sites and social networking services).

[0108] User side

[0109] 1. Input Method

[0110] The user uses a browser on their device (e.g., a PC or smartphone) or a dedicated application to input the desired location and conditions for living. For example, they can input conditions such as "Shinjuku-ku, Tokyo, under 100,000 yen, 2LDK, nearby supermarket, pets allowed." The input data is sent to the server.

[0111] Server side

[0112] 2. Data Collection Methods

[0113] The server uses web scraping technologies such as Python, BeautifulSoup, and Selenium to collect property information from real estate information websites, and also uses APIs of social networking services (e.g., Twitter API and other SNS APIs) to collect reviews and reputation information related to the user's criteria.

[0114] 3. Temporarily save to database

[0115] The server temporarily stores the collected data in a database (e.g., MySQL), including the property's address, rent, floor plan, age, distance to the nearest station, and the latest related reviews.

[0116] 4. Data Analysis Methods

[0117] The server analyzes the temporarily saved data and filters the property information that matches the conditions entered by the user. For example, it extracts only properties that match conditions such as rent under 100,000 yen, floor plan of 2LDK, and pet-friendly.

[0118] 5. Evaluation Score Assignment Method

[0119] The server assigns a rating score to the filtered property information based on the collected reviews, giving a high score to properties with many positive reviews and a low score to properties with many negative reviews.

[0120] 6. Ranking Methods

[0121] The server ranks the property information based on the evaluation score, so that the highly rated properties that best match the user's criteria are displayed at the top.

[0122] Presentation of results

[0123] 7. Presentation means

[0124] The server sends a list of selected housing information to the user's device. The user's browser or application displays information such as rent, floor plan, location, distance to the nearest station, and the latest reviews. This information is presented visually and easily understandable to the user.

[0125] Specific operation example

[0126] For example, if a user enters conditions such as "Shinjuku-ku, Tokyo, under 100,000 yen, 2LDK, nearby supermarket, pets allowed," the server will operate as follows:

[0127] The server scrapes multiple real estate information sites using keywords such as "Shinjuku-ku 2LDK rental."

[0128] At the same time, the server collects information on social media using hashtags such as "Shinjuku Ward Rentals" and "Pets Allowed."

[0129] The server temporarily stores the collected data in a database.

[0130] The server filters the property listings that match the user's criteria.

[0131] The server assigns an evaluation score to the filtered properties based on the collected reviews and ranks them.

[0132] The server displays the top-ranked property information on the user's device, allowing the user to check detailed information and contact information. This system allows users to easily obtain information about housing that matches their preferences and select the property that best suits them.

[0133] Examples of prompt statements

[0134] "Build a system that allows users to input criteria and collects and analyzes data from multiple sources to provide the most suitable housing information. Use Python, BeautifulSoup, Selenium, MySQL, and SNS APIs as the technologies to be used. Include a function to assign an evaluation score based on the user's criteria and create a ranking."

[0135] This invention is a system that efficiently provides housing information that matches a user's desires from a vast amount of data on the Internet, and supports the user in making the most suitable choice.

[0136] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0137] Step 1:

[0138] The user inputs the desired location and conditions into the device.

[0139] Input: The user enters conditions such as "Shinjuku-ku, Tokyo, under 100,000 yen, 2LDK, nearby supermarket, pets allowed" into the input form.

[0140] Output: The entered conditions are sent to the server.

[0141] Specific operation: The user uses a browser or application to enter the desired conditions through an input form and clicks the submit button. This information is sent to the server in JSON format.

[0142] Step 2:

[0143] The server collects data from real estate information sites and social networking sites.

[0144] Input: Conditions received from the user (e.g., "Shinjuku-ku, Tokyo, under 100,000 yen, 2LDK, nearby supermarket, pets allowed").

[0145] Output: The collected property information and reviews are stored in a database for temporary storage.

[0146] How it works: The server uses Python's BeautifulSoup and Selenium to crawl multiple real estate information websites and collect property information that matches the search criteria. At the same time, it uses Twitter APIs and other social media APIs to collect reviews using related hashtags and keywords.

[0147] Step 3:

[0148] The server temporarily stores the collected data in a database.

[0149] Input: Collected property information and reviews.

[0150] Output: Raw data stored in a database.

[0151] Specific operation: The collected property address, rent, floor plan, age, distance to the nearest station, and related reviews are organized and stored in MySQL or other databases.

[0152] Step 4:

[0153] The server analyzes the collected data and filters property information that matches the user's criteria.

[0154] Input: Property information and reviews stored in the database.

[0155] Output: Filtered listings that match the user's criteria.

[0156] Specific operation: The server extracts only matching property information from the database based on the conditions entered by the user (e.g., rent less than 100,000 yen, layout 2LDK, pets allowed, etc.).

[0157] Step 5:

[0158] The server assigns an evaluation score to the filtered property information.

[0159] Input: Filtered property information and collected reviews.

[0160] Output: Property information with rating scores.

[0161] Specific operation: The server analyzes the collected review information for the filtered properties and assigns a high score to properties with many positive reviews and a low score to properties with many negative reviews.

[0162] Step 6:

[0163] The server ranks the property information based on the evaluation score.

[0164] Input: Property information with rating scores.

[0165] Output: A ranked list of properties.

[0166] Specific operation: The server ranks property information based on the rating score and creates a list so that properties with higher scores are displayed at the top.

[0167] Step 7:

[0168] The server transmits a list of the selected residence information to the user terminal and displays it.

[0169] Input: Ranked property list.

[0170] Output: Residence information displayed on the user's device.

[0171] Specific operation: The server sends the ranked property information to the user's device, and displays the rent, floor plan, location, distance to the nearest station, and related reviews on the user's browser or application. The user can then view detailed information and contact details.

[0172] (Application example 1)

[0173] 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."

[0174] Currently, searching for real estate information requires manually collecting data from multiple sources and conducting detailed research on each property, which is extremely time-consuming and labor-intensive. Furthermore, it is difficult to evaluate properties using word-of-mouth information on social media, making it difficult for users to efficiently select the housing information that is best for them.

[0175] 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.

[0176] In this invention, the server includes means for a user to input desired living locations and conditions, means for collecting data based on the conditions input by the user from multiple information sources on the Internet, means for analyzing the collected word-of-mouth information on SNS and assigning evaluation scores, means for ranking properties based on the evaluation scores, means for inputting prompt sentences to a generative AI model based on the conditions input by the user and having the AI ​​model evaluate the properties, and means for presenting selected housing information to a user terminal, thereby enabling users to efficiently and effectively obtain optimal housing information.

[0177] The "place and conditions in which the user wishes to live" are specific requirements regarding the residence the user desires, such as the area, budget, layout, and the presence or absence of nearby facilities.

[0178] "Multiple sources of information on the Internet" refers to multiple platforms that provide housing information and word-of-mouth information on the web, such as real estate information sites and social networking services.

[0179] "Means of collection" refers to the means of obtaining data from sources on the Internet, such as using web scraping techniques or APIs.

[0180] The "means of analysis" refers to the means of storing the collected data in a database, interpreting the data using statistical analysis and natural language processing, and assigning an evaluation score.

[0181] The "evaluation score" is a numerical value of positive and negative evaluations that is generated by analyzing word-of-mouth information collected based on the user's conditions.

[0182] The "ranking means" is a means for ranking properties based on the evaluation scores so that the properties most suitable for the user are displayed at the top.

[0183] A "user terminal" is a device, such as a smartphone, tablet, or PC, that a user operates to input information and check the results.

[0184] A "generative AI model" is an artificial intelligence model that generates prompt statements based on user input and automatically evaluates properties using those prompt statements.

[0185] A "prompt" is an instruction entered into a generative AI model to make it perform a specific task.

[0186] The present invention relates to a system for efficiently acquiring residential information desired by a user, and its embodiment is as follows: This system is mainly composed of a user terminal, a server, and multiple information sources on the Internet.

[0187] User terminal

[0188] Users access the system using their own devices such as smartphones, tablets, or PCs. Using a dedicated application or web browser, users input the desired location and conditions (area, budget, floor plan, availability of nearby facilities, etc.).

[0189] Data collection

[0190] The server uses Python-based web scraping technology (such as BeautifulSoup or Selenium) to collect property data based on user criteria from multiple real estate information websites on the Internet, and also collects related reviews from social networking services via the Twitter API and Instagram Graph API.

[0191] Data analysis

[0192] The server stores the collected data in an SQL database (e.g., MySQL) and analyzes it using natural language processing technology (e.g., TextBlob), classifying reviews as positive or negative, and assigning a rating score to each property based on that.

[0193] Property ranking

[0194] To rank the properties based on their rating scores, the server runs a ranking algorithm that ensures that the properties that best meet the user's criteria are displayed at the top of the list.

[0195] Presentation of information

[0196] The server sends the selected housing information to the user's device and displays it in an easy-to-read format, including the property's rent, layout, location, distance to the nearest public transport, and user reviews.

[0197] Generative AI Models

[0198] The server inputs the user's criteria as a prompt into the generative AI model. This generative AI model is optimized to perform a specific task and automatically evaluates properties. An example of a prompt might be, "Please search for a 2LDK home in Shinjuku Ward with a budget of less than 100,000 yen, gather information on properties with a nearby supermarket and that allow pets, and rank them based on reviews on social media."

[0199] In this way, the system collects data from multiple sources, analyzes, evaluates, and ranks them, and provides users with the most suitable housing information.Specific hardware and software used include Python, BeautifulSoup, Selenium, MySQL, Twitter API, Instagram Graph API, TextBlob, and React Native.

[0200] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0201] Step 1:

[0202] Users input their desired location and conditions (e.g., area, budget, floor plan, availability of nearby facilities, etc.) through an application or web browser on their device (smartphone, tablet, PC). The input information is sent to the server.

[0203] Step 2:

[0204] The server uses Python's BeautifulSoup and Selenium to scrape property data from multiple real estate information websites on the Internet based on user-specified criteria. The input is the criteria entered by the user, and the output is property data that matches the criteria (e.g., rent, floor plan, location, distance to the nearest station).

[0205] Step 3:

[0206] The server uses the Twitter API and Instagram Graph API to collect reviews of properties that meet the criteria. Here too, the input is the user's criteria, and the output includes reviews on social media (e.g., tweets and comments).

[0207] Step 4:

[0208] The server stores the collected property data and reviews in a MySQL database. The input is the data collected in Step 2 and Step 3, and the output is the results stored in the database.

[0209] Step 5:

[0210] The server uses natural language processing technology (e.g., TextBlob) to analyze the stored data. Specifically, it classifies reviews as positive or negative and assigns a rating score to each property. The input is the reviews retrieved from the database, and the output is the rating score for each property.

[0211] Step 6:

[0212] The server ranks the properties based on the rating score. It runs a ranking algorithm to ensure that the properties that best fit the user's criteria are displayed at the top. The input is the rating score, and the output is a ranked list of properties.

[0213] Step 7:

[0214] The server inputs the user's conditions into the generative AI model as a prompt statement, and uses the AI ​​model to make a final evaluation of the property. The prompt statement is used as input, and the output is the property evaluation result by the generative AI model. For example, a prompt statement could be, "Please search for a 2LDK home in Shinjuku Ward with a budget of less than 100,000 yen, gather information on properties with a nearby supermarket and that allow pets, and rank them including reviews on social media."

[0215] Step 8:

[0216] The server sends the ranked property information to the user's device and displays it on an application or web browser. The optimal housing information is then presented to the user. The output includes information such as rent, floor plan, location, distance to the nearest station, and user reviews.

[0217] 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.

[0218] This invention relates to a system that allows users to input the location and conditions they wish to live in, and then collects and analyzes related data from multiple information sources on the Internet to provide optimal housing information. Furthermore, a specific form is shown in which user satisfaction can be increased by combining it with an emotion engine that recognizes the user's emotions. Specifically, the system is configured as follows:

[0219] Basic system configuration

[0220] This system consists of a "terminal" used by the user, a "server" that collects and analyzes data, multiple information sources (real estate information sites and social networking services), and an emotion engine.

[0221] User side

[0222] 1. Input Method

[0223] A form is displayed on the user's device for entering the desired place to live and conditions (area, budget, floor plan, other conditions, etc.) For example, the user enters "Shinjuku-ku, Tokyo, under 100,000 yen, 2LDK, nearby supermarket, pets allowed."

[0224] When inputting, the emotion engine recognizes emotions from the user's facial expressions and voice, and collects emotion data for the user's input conditions.

[0225] Server side

[0226] 2. Data Collection Methods

[0227] The server uses web scraping technologies such as Python, BeautifulSoup, and Selenium to collect property information from real estate information websites. It also collects reviews and reputation information related to the user's criteria through social media APIs (e.g., Twitter API and Instagram Graph API).

[0228] 3. Temporary storage of data

[0229] The collected data is temporarily stored in a database, and includes basic information such as address, rent, floor plan, age of the building, and distance to the nearest station.

[0230] 4. Data Analysis Methods

[0231] The server analyzes the collected data and filters the property information that matches the user's criteria, such as rent under 100,000 yen, a 2LDK layout, and pet-friendly properties.

[0232] 5. Evaluation Score Assignment Method

[0233] The filtered property information is assigned a rating score based on the collected reviews. Properties with many positive reviews are given a high score, while properties with many negative reviews are given a low score.

[0234] 6. Ranking Methods

[0235] The property listings are ranked based on their rating scores. The property listings are sorted so that the properties that best match the user's criteria and have the highest ratings are displayed at the top.

[0236] Use of emotion engine

[0237] 7. Collecting Emotional Data

[0238] The emotion engine recognizes emotions from the user's facial expressions and voice, collects emotional data, and quantifies, for example, the user's satisfaction and expectations regarding the input conditions.

[0239] 8. Emotion Data Analysis

[0240] The server analyzes the emotional data and identifies which conditions the user has more positive feelings about. Based on this, it adjusts the analysis results and selects the property information that is most suitable for the user.

[0241] 9. User response monitoring

[0242] The emotion engine monitors users' reactions in real time when property information is presented. If the user responds positively to the property information presented, that information is reflected in the next data analysis.

[0243] Presentation of results

[0244] 10. Presentation means

[0245] The server sends a list of selected housing information to the user's device. The formatted information includes rent, floor plan, location, distance to the nearest station, the latest reviews, and information based on the user's emotional data.

[0246] 11. User Feedback

[0247] The user terminal displays the received property information. The user can check the displayed property information and provide feedback if there are any deficiencies or problems. The server collects the feedback and reflects it in the next analysis.

[0248] Specific operation example

[0249] For example, if a user enters conditions such as "Shinjuku-ku, Tokyo, under 100,000 yen, 2LDK, nearby supermarket, pets allowed," the server will operate as follows:

[0250] The server scrapes multiple real estate information sites using keywords such as "Shinjuku-ku 2LDK rental."

[0251] At the same time, information is collected on social media using hashtags such as "Shinjuku Ward Rentals" and "Pets Allowed."

[0252] The collected data is stored in a database and property information that matches the user's criteria is filtered.

[0253] The filtered properties are assigned an evaluation score based on the collected reviews and ranked.

[0254] The emotion engine recognizes the user's emotions when entering information and when information is presented, and reflects that data in the analysis results, allowing for more accurate property selection.

[0255] Finally, the top-ranked property information and supplementary information based on emotion data are displayed on the user's device, allowing the user to check detailed information and contact details.

[0256] This system allows users to easily obtain more personalized housing information and select the most suitable property.

[0257] The processing flow will be explained below.

[0258] Step 1:

[0259] The user enters the desired location and conditions into a form displayed on the user's device. For example, the user might enter "Shinjuku-ku, Tokyo, under 100,000 yen, 2LDK, nearby supermarket, pets allowed."

[0260] Step 2:

[0261] Along with the conditions entered by the user, the emotion engine uses face recognition technology and voice analysis technology to recognize emotions from the user's facial expressions and tone of voice, and collects emotion data for the entered conditions.

[0262] Step 3:

[0263] When a user transmits input content, the user terminal transmits the input data and emotion data to the server.

[0264] Step 4:

[0265] Based on the user's conditions received by the server, web scraping is initiated against multiple real estate information websites on the Internet. Using Python, BeautifulSoup, and Selenium, searches are performed using keywords such as "Shinjuku Ward, 2LDK for rent" and "under 100,000 yen" to collect property information.

[0266] Step 5:

[0267] The server temporarily stores the property information it collects in a database, which includes basic information such as address, rent, floor plan, age of the building, and distance to the nearest station.

[0268] Step 6:

[0269] The server uses SNS APIs (e.g., Twitter API or Instagram Graph API) to collect relevant reviews on SNS. For example, it searches for hashtags such as "Shinjuku-ku rental" and "pets allowed" to obtain the latest reputations and reviews.

[0270] Step 7:

[0271] The server also stores the collected social media data in a database, including user ratings and comments.

[0272] Step 8:

[0273] The server filters the real estate information in the database based on the user's input criteria, such as rent under 100,000 yen, a 2LDK layout, and pet-friendly properties.

[0274] Step 9:

[0275] The server assigns a rating score to the filtered property information based on the collected reviews, raising the score for properties with many positive comments and lowering the score for properties with many negative comments.

[0276] Step 10:

[0277] The server analyzes the emotion data and reflects the user's feelings toward the criteria entered in the evaluation score. For example, if the user expresses particularly positive feelings toward a particular criteria, the score will be adjusted to give more weight to that criteria.

[0278] Step 11:

[0279] Ranking of property information based on evaluation scores. Sort property information so that properties with higher scores are displayed at the top.

[0280] Step 12:

[0281] The server formats the ranked property information and sends it to the user's device. The formatted information includes rent, floor plan, location, distance to the nearest station, the latest reviews, and information based on the user's emotional data.

[0282] Step 13:

[0283] The property information received by the user's device is displayed on a browser or application. The user can check the displayed property information and click to view detailed information or contact information.

[0284] Step 14:

[0285] Users can provide feedback on the displayed property information, and the server collects that feedback and reflects it in the next data analysis.

[0286] The above is the specific flow of program processing for a system that combines an emotion engine.

[0287] Example 2

[0288] 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."

[0289] Conventional housing information systems only provide property information based on the conditions entered by the user, and do not consider the user's feelings or satisfaction when selecting properties. As a result, it is not possible to fully meet the user's expectations, making it difficult to provide optimal housing information.

[0290] The identification process by the identification 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 a means for a user to input desired living conditions and locations, a means for collecting data based on the conditions input by the user from multiple information sources on the Internet, a means for temporarily storing the collected data, a means for analyzing the collected data and selecting optimal housing information for the user, a means for presenting the selected housing information to the user terminal, and a means for acquiring user emotion data using an emotion engine and reflecting the data in the analysis results. This makes it possible to provide optimal housing information that takes the user's emotions into consideration.

[0291] "Means for users to input desired location and conditions for living" refers to an interface that allows users to input desired conditions for housing (e.g., location, budget, layout, and other specific requirements) using a terminal.

[0292] "Multiple sources on the Internet" refers to various websites and online services (e.g., real estate websites, social networking services, etc.) that provide data related to the user's input.

[0293] "Means of collecting data" refers to the technical means (e.g., web scraping technology or APIs) used to automatically obtain relevant data from each source.

[0294] "Means for temporarily storing collected data" refers to a database or storage system for temporarily storing acquired data.

[0295] "Means for analyzing collected data" refers to algorithms and analytical techniques for analyzing collected data and selecting the most suitable property based on the user's conditions.

[0296] "Means for presenting selected residential information on a user terminal" refers to a method for displaying the optimal property information selected as a result of the analysis on a user device.

[0297] An "emotion engine" refers to software or hardware that recognizes and analyzes emotions from a user's facial expressions and voice and collects that data.

[0298] "Means of assigning evaluation scores" refers to the method for calculating and assigning quantitative evaluation scores to each property based on collected word-of-mouth information, etc.

[0299] "Means for ranking properties" refers to a means for prioritizing and sorting properties based on their rating scores.

[0300] "Means of acquiring emotional data and reflecting it in the analysis results" refers to technology that incorporates user emotional data acquired through an emotion engine into the analysis results to enable more accurate property selection.

[0301] The present invention relates to a system that allows users to input their desired location and conditions, collects and analyzes related data from multiple information sources on the Internet, and provides optimal housing information. This system can further increase user satisfaction by combining it with an emotion engine that recognizes the user's emotions.

[0302] Basic system configuration

[0303] This system includes "means for the user to input the location and conditions for living that the user desires," "means for collecting data," "means for temporarily storing collected data," "means for analyzing collected data," "means for presenting selected housing information on the user's terminal," and "means for obtaining the user's emotional data using an emotion engine and reflecting it in the analysis results."

[0304] User side

[0305] First, the user inputs the desired location and conditions into the input form displayed on the device. For example, the user might input conditions such as "Shinjuku-ku, Tokyo, under 100,000 yen, 2LDK, nearby supermarket, pets allowed." As the input is made, the emotion engine recognizes emotions from the user's facial expressions and voice, and collects emotional data for the user's input conditions. The emotion engine used is software that uses a camera and microphone to quantify the user's emotions.

[0306] Server side

[0307] The server uses web scraping technologies such as Python, BeautifulSoup, and Selenium to collect property information from real estate information websites. It also collects reviews and reputation information related to the user's criteria through social media APIs (e.g., Twitter API and Instagram Graph API). The collected data is temporarily stored in a database such as MySQL. The stored data includes address, rent, floor plan, age of the building, distance to the nearest station, etc.

[0308] The server then analyzes the collected data and filters the property information that matches the user's criteria. For example, an SQL query can be used to extract properties that meet criteria such as rent under 100,000 yen, a 2LDK layout, and pet-friendly. Then, based on the reviews, a scoring method is used to assign a high score to properties with many positive reviews and a low score to properties with many negative reviews. For example, a natural language processing model using TensorFlow can be used to analyze the reviews and calculate the score.

[0309] Based on the calculated score, the server runs a sorting algorithm to rank the property information. The data is then formatted so that the most highly rated properties are displayed at the top. The emotional data obtained from the emotion engine is analyzed as the user's reaction to the conditions entered by the user and their reaction when the housing information is presented. Based on this, the server determines which conditions the user has more positive feelings about and reflects this information in the next data analysis.

[0310] Presentation of results

[0311] The housing information list selected as the result of the analysis is sent from the server to the user's device in JSON format or similar. The user's device then formats and displays the received data. The displayed information includes rent, floor plan, location, distance to the nearest station, and the latest reviews. In addition, supplemental information based on emotional data is also presented. The user can review the provided information and provide feedback if necessary. This feedback is also reflected in the next data analysis, contributing to improving the accuracy of the entire system.

[0312] Specific operation example

[0313] For example, if a user enters the following criteria: "Shinjuku Ward, Tokyo, under 100,000 yen, 2LDK, nearby supermarket, pets allowed," the server will scrape multiple real estate information websites using keywords such as "Shinjuku Ward 2LDK rental." At the same time, it will collect information on social media using hashtags such as "Shinjuku Ward rental" and "pets allowed." The collected data is stored in a database and property listings that match the user's criteria are filtered. Based on the filtering results, properties are assigned an evaluation score and ranked. The emotion engine recognizes the user's emotions when entering and presenting the information, and incorporates this data into the analysis results to enable more accurate property selection. Finally, the top-ranked property listings and supplementary information based on the emotion data are displayed on the user's device, allowing the user to check detailed information and contact information.

[0314] Example prompts for generative AI models

[0315] If a user enters "Shinjuku-ku, Tokyo, under 100,000 yen, 2LDK, nearby supermarket, pets allowed," please explain what type of property would be most suitable and why.

[0316] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0317] Step 1: User Input

[0318] The user enters the desired location and conditions into an input form on the device. For example, they might enter "Shinjuku-ku, Tokyo, under 100,000 yen, 2LDK, nearby supermarket, pets allowed." At the same time, this input condition is sent to the server, the emotion engine acquires emotional data from the user's facial expressions and voice. The emotion data and the condition input data are sent to the server and stored together. The specific input data are the location, budget, floor plan, and specific requirements (near supermarket, pets allowed, etc.), and the output is the input data and emotional data.

[0319] Step 2: Data collection

[0320] The server uses web scraping technologies such as Python, BeautifulSoup, and Selenium to collect property information from real estate information websites. It also collects reviews and reputation information related to user criteria through social media APIs (e.g., Twitter API and Instagram Graph API). The input data is the location and conditions entered by the user, and the output is property information and reviews collected based on those conditions. Specifically, the server searches multiple real estate information websites for keywords such as "2LDK rental in Shinjuku Ward" and analyzes the HTML content.

[0321] Step 3: Save data

[0322] The server stores the collected property information and reviews in a database such as MySQL. The input data is information collected from web scraping and SNS APIs, and the output is a database containing that information. Specifically, the server stores the information in a table with fields such as address, rent, floor plan, age of the building, and distance to the nearest station.

[0323] Step 4: Data analysis

[0324] The server executes an SQL query to extract properties from the database that match the user's criteria. The input data is the user's desired criteria and property information in the database, and the output is a list of properties that match those criteria. Specifically, the server executes a query such as "SELECT FROM properties WHERE rent <= 100000 AND layout = '2LDK' AND pets_allowed = true."

[0325] Step 5: Assigning a rating score

[0326] The server analyzes the reviews and assigns a high score to properties with many positive reviews. For example, the reviews are analyzed using a natural language processing model using TensorFlow. The input data is the extracted property list and the reviews for that property, and the output is a property list with a score assigned. Specifically, the server judges comments such as "This place is great for families!" to be positive and assigns a high score.

[0327] Step 6: Ranking

[0328] The server runs a sorting algorithm to sort the properties by highest score. The input data is a list of scored properties, and the output is a ranked list of properties. Specifically, the server sorts the list of properties in descending order based on the score field, with the most highly rated properties at the top.

[0329] Step 7: Sentiment Data Analysis

[0330] The server analyzes the emotion data obtained from the emotion engine and identifies the conditions for which the user has positive emotions. The input data is the emotion data and the condition data entered by the user, and the output is the user's emotional evaluation of those conditions. Specifically, if the user shows a satisfied expression for the condition "there is a supermarket nearby," the importance of that condition is increased.

[0331] Step 8: Presenting the results

[0332] The housing information list selected as the analysis result is sent from the server to the user's device. The input data is a ranked property list that reflects emotional evaluations, and the output is property information displayed on the user's device. The user's device formats and displays this information, providing information such as rent, floor plan, location, distance to the nearest station, and the latest reviews. The user then enters feedback, which is reflected in the next analysis.

[0333] Example prompts for generative AI models

[0334] If a user enters "Shinjuku-ku, Tokyo, under 100,000 yen, 2LDK, nearby supermarket, pets allowed," please explain what type of property would be most suitable and why.

[0335] (Application example 2)

[0336] 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."

[0337] Currently, it is not easy for users to find the best place to live or travel based on their own conditions and emotions. In particular, there is no system that provides appropriate information in real time while traveling in an autonomous vehicle. As a result, users must manually collect and evaluate information from many sources, which takes time and effort and has low accuracy. In addition, because the information is not individually optimized taking into account the user's emotions, it may result in low satisfaction.

[0338] 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 places and conditions for living, means for collecting data based on the conditions entered by the user from multiple information sources on the Internet, means for analyzing the collected data and selecting housing information optimal for the user, means for assigning an evaluation score based on the collected data, means for ranking based on the evaluation score, means for collecting user emotions using an emotion recognition engine and reflecting them in the analysis results, and means for presenting the selected information on the user terminal. This enables users to easily obtain information on optimal housing and travel destinations based on their emotions in real time.

[0339] "Means for users to input desired living conditions and location" refers to a form or interface that allows users to input conditions such as desired area, budget, and floor plan into a terminal.

[0340] "Multiple sources on the Internet" includes information providers that are the source of data collected from websites and social networking services.

[0341] An "emotion recognition engine" refers to software or a system that analyzes emotions from a user's facial expressions and voice and collects that data.

[0342] "Means for assigning an evaluation score" refers to software or algorithms that have the function of calculating an evaluation score for an object based on collected data.

[0343] "Ranking means" refers to software or algorithms used to rank objects based on their evaluation scores.

[0344] "Means for presenting on the user terminal" refers to the interface or software for displaying the selected information on the device used by the user.

[0345] This invention relates to a system that allows users to input desired locations and conditions, collects and analyzes related data from multiple sources on the Internet, and provides optimal housing information and travel destinations. Furthermore, by combining it with an emotion recognition engine, a specific form that can increase user satisfaction is shown.

[0346] Basic system configuration

[0347] This system consists of a "terminal" used by the user, a "server" that collects and analyzes data, multiple information sources, and an emotion recognition engine.

[0348] User side

[0349] 1. Input method:

[0350] Users input their desired location and conditions for living, as well as their travel destination preferences (region, budget, tourist spots, and other conditions) on a touch panel terminal inside the self-driving vehicle.

[0351] When inputting, the emotion recognition engine recognizes emotions from the user's facial expressions and voice, and collects emotion data for the input conditions.

[0352] Server side

[0353] 2. Data collection methods:

[0354] The server uses web scraping technologies such as Python, BeautifulSoup, and Selenium to collect relevant information from travel and real estate information sites.

[0355] We also collect word-of-mouth and reputation information related to user conditions through social media APIs (such as Twitter API and Instagram Graph API).

[0356] 3. Temporary data storage:

[0357] The collected data is temporarily stored in a database. In the case of travel information, it includes basic information such as tourist spots, hotels, and restaurants.

[0358] 4. Data analysis methods:

[0359] The server analyzes the collected data and filters information that matches the user's criteria, for example, extracting tourist spots and hotels that match the user's travel preferences.

[0360] 5. Rating Score Method:

[0361] The filtered information is assigned a rating score based on the collected reviews. Information with many positive reviews is given a high rating score.

[0362] 6. Ranking Method:

[0363] The information is ranked based on the evaluation score, and the information that best matches the user's criteria and is highly rated is displayed at the top.

[0364] Use of emotion recognition engine

[0365] 7. Collecting Emotional Data:

[0366] The emotion recognition engine recognizes emotions from the user's facial expressions and voice, and collects emotional data, such as the user's satisfaction and expectations regarding the conditions, which are then quantified.

[0367] 8. Emotional Data Analysis:

[0368] The server analyzes the emotional data and identifies which information the user feels more positively about. Based on this, it adjusts the analysis results and selects the most appropriate information for the user.

[0369] 9. User response monitoring:

[0370] The emotion recognition engine monitors the user's reaction in real time when information is presented. If the user responds positively to the information presented, that information is reflected in the next data analysis.

[0371] Presentation of results

[0372] 10. Presentation means:

[0373] The server sends a list of selected information to the user's device. The formatted information includes details about each facility or spot, tourist reviews, and information based on the user's emotional data.

[0374] 11. User Feedback:

[0375] The user terminal displays the received information. The user can check the displayed information and provide feedback if there are any deficiencies or problems. The server collects the feedback and reflects it in the next analysis.

[0376] Specific operation example

[0377] For example, if a user enters conditions such as "hot spring town" or "places where you can enjoy delicious local food," the server will operate as follows:

[0378] The server scrapes multiple travel information sites using keywords such as "hot spring town sightseeing" and "local food."

[0379] At the same time, information is collected using hashtags such as "hot spring trip" and "local gourmet" on social media.

[0380] The collected data is stored in a database and information on tourist spots and restaurants that meet the user's criteria is filtered.

[0381] The filtered information is assigned an evaluation score based on collected word-of-mouth information and ranked.

[0382] The emotion recognition engine recognizes the user's emotions when inputting and presenting information, and reflects this data in the analysis results, allowing for more accurate information selection.

[0383] Finally, the top-ranked information and supplementary information based on emotion data are displayed on the user's device, allowing the user to check detailed information and contact details.

[0384] Examples and prompts

[0385] prompt

[0386] text

[0387] Please recommend a travel destination that meets the following criteria:

[0388] The user's facial expression and voice analysis data are also input, so please take emotional data into consideration.

[0389] conditions:

[0390] hot spring town

[0391] A place to enjoy delicious local food

[0392] Emotional Data:

[0393] Expression: Excitement and anticipation

[0394] Voice: Calm and relaxed tone

[0395] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0396] Step 1:

[0397] The user inputs desired conditions into a terminal inside the autonomous vehicle. Specifically, the user inputs conditions such as "hot spring town" or "place where I can enjoy delicious local food" into the touch panel terminal. At the same time as the input, the terminal's camera and microphone record the user's facial expressions and voice, and emotional data is collected. The input here consists of the desired conditions as a string of characters and emotional data quantified by an emotion recognition engine.

[0398] Step 2:

[0399] The server receives user input criteria and collects data from multiple sources on the Internet. Using a Python script, it web scrapes travel and real estate information sites to obtain information that matches the criteria. It also uses social media APIs to collect related reviews and reputation information. The input here is the user's desired criteria, and the output is a list of collected related information.

[0400] Step 3:

[0401] The server temporarily stores the collected data in a database, which contains details such as the name, location, and reviews of travel spots. The input here is the collected data, and the output is the information stored in the database.

[0402] Step 4:

[0403] The server analyzes the stored data and filters information that matches the user's criteria. Specifically, it searches using keywords based on the criteria and extracts data that matches the criteria. The input here is all data from the database, and the output is the narrowed-down data that matches the criteria.

[0404] Step 5:

[0405] The server assigns an evaluation score to the filtered data based on the collected word-of-mouth information. Specifically, it assigns a high evaluation score to information with many positive reviews and a low evaluation score to information with many negative reviews. The input here is the filtered data and word-of-mouth data, and the output is data with an evaluation score.

[0406] Step 6:

[0407] The server ranks the data based on the evaluation score. It sorts the data in order of score so that highly rated information is displayed at the top. The input here is data with evaluation scores, and the output is ranked data.

[0408] Step 7:

[0409] The server analyzes the user's emotional data and selects data based on the user's emotions. Specifically, it analyzes the data obtained by the emotion recognition engine and adjusts the data by placing emphasis on conditions where the user has positive emotions. The input here is the emotional data and ranked data, and the output is the final adjusted data.

[0410] Step 8:

[0411] The server presents the adjusted data to the user's device. Specifically, it displays a list of top travel destinations and tourist attractions on a touch panel device, allowing the user to check detailed information. The input here is the final adjusted data, and the output is the information displayed on the user's device.

[0412] Step 9:

[0413] The user checks the presented information and provides feedback. Specifically, they input their satisfaction or dissatisfaction with each travel destination or tourist spot displayed. The device collects this feedback and reflects it in the next data analysis. The input here is the user's feedback, and the output is the feedback data sent to the server.

[0414] 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.

[0415] 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.

[0416] 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.

[0417] [Second embodiment]

[0418] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0419] 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.

[0420] 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).

[0421] 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.

[0422] 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.

[0423] 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).

[0424] 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.

[0425] 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.

[0426] 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.

[0427] 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.

[0428] 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.

[0429] 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."

[0430] This invention relates to a system that allows users to input desired locations and conditions for living, and then collects and analyzes related data from multiple information sources on the Internet to provide optimal housing information. Specifically, it is configured as follows.

[0431] Basic system configuration

[0432] This system consists of a "terminal" used by the user, a "server" that collects and analyzes data, and multiple information sources (real estate information sites and social networking services).

[0433] User side

[0434] 1. Input Method

[0435] A form is displayed on the user's device for entering the desired place to live and conditions (area, budget, floor plan, other conditions, etc.) For example, the user enters "Shinjuku-ku, Tokyo, under 100,000 yen, 2LDK, nearby supermarket, pets allowed."

[0436] Server side

[0437] 2. Data Collection Methods

[0438] The server uses web scraping technologies such as Python, BeautifulSoup, and Selenium to collect property information from real estate information websites. It also collects reviews and reputation information related to the user's criteria through social media APIs (e.g., Twitter API and Instagram Graph API).

[0439] 3. Temporary storage of data

[0440] The collected data is temporarily stored in a database, including the property's address, rent, floor plan, age, distance to the nearest station, and the latest related reviews.

[0441] 4. Data Analysis Methods

[0442] The server analyzes the collected data and filters the property information that matches the user's criteria, such as rent under 100,000 yen, a 2LDK layout, and pet-friendly properties.

[0443] 5. Evaluation Score Assignment Method

[0444] The filtered property information is assigned a rating score based on the collected reviews. Properties with many positive reviews are given a high score, while properties with many negative reviews are given a low score.

[0445] 6. Ranking Methods

[0446] Property information is ranked based on the rating score, with properties that best match the user's criteria and have the highest ratings displayed at the top.

[0447] Presentation of results

[0448] 7. Presentation means

[0449] The server sends a list of selected housing information to the user's device, and the user's browser or application displays information such as rent, floor plan, location, distance to the nearest station, and the latest reviews.

[0450] Specific operation example

[0451] For example, if a user enters conditions such as "Shinjuku-ku, Tokyo, under 100,000 yen, 2LDK, nearby supermarket, pets allowed," the server will operate as follows:

[0452] The server scrapes multiple real estate information sites using keywords such as "Shinjuku-ku 2LDK rental."

[0453] At the same time, information is collected on social media using hashtags such as "Shinjuku Ward Rentals" and "Pets Allowed."

[0454] The collected data is stored in a database and property information that matches the user's criteria is filtered.

[0455] The filtered properties are assigned an evaluation score based on the collected reviews and ranked.

[0456] Finally, the top-ranked property information is displayed on the user's device, allowing the user to check detailed information and contact details.

[0457] This system allows users to easily obtain housing information that matches their preferences and select the most suitable property.

[0458] The processing flow will be explained below.

[0459] Step 1:

[0460] The user enters the desired location and conditions into a form displayed on the user's device. For example, the user might enter "Shinjuku-ku, Tokyo, under 100,000 yen, 2LDK, nearby supermarket, pets allowed."

[0461] Step 2:

[0462] The conditions entered by the user are transmitted. At this time, the user terminal transmits the input data to the server.

[0463] Step 3:

[0464] Based on the user's conditions received by the server, web scraping is initiated against multiple real estate information websites on the Internet. Using tools such as Python, BeautifulSoup, and Selenium, searches are performed using keywords such as "Shinjuku Ward, 2LDK for rent" and "under 100,000 yen" to collect property information.

[0465] Step 4:

[0466] The server temporarily stores the property information it collects in a database, which includes basic information such as address, rent, floor plan, age of the building, and distance to the nearest station.

[0467] Step 5:

[0468] The server uses SNS APIs (e.g., Twitter API or Instagram Graph API) to collect relevant reviews on SNS. For example, it searches for hashtags such as "Shinjuku-ku rental" and "pets allowed" to obtain the latest reputations and reviews.

[0469] Step 6:

[0470] The server also stores the collected social media data in a database, including user ratings and comments.

[0471] Step 7:

[0472] The server filters the real estate information in the database based on the user's input criteria, such as rent under 100,000 yen, a 2LDK layout, and pet-friendly properties.

[0473] Step 8:

[0474] The server assigns a rating score to the filtered property information based on the collected reviews, raising the score for properties with many positive comments and lowering the score for properties with many negative comments.

[0475] Step 9:

[0476] The server ranks properties based on their rating scores and sorts the property information so that properties with higher scores are displayed at the top.

[0477] Step 10:

[0478] The server formats the ranked property information and sends it to the user's device. The formatted information includes rent, floor plan, location, distance to the nearest station, and the latest reviews.

[0479] Step 11:

[0480] The property information received by the user's device is displayed on a browser or application. The user can check the displayed property information and click to view detailed information or contact information.

[0481] The above is the specific flow of program processing for this system.

[0482] Example 1

[0483] 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."

[0484] In recent years, it has become common for users to use the Internet to search for housing information in the real estate market. However, it is difficult and time-consuming to efficiently collect housing information that meets the user's desired criteria and to perform accurate filtering and evaluation. In addition, the collected data is diverse, and there is a lack of means to integrate and appropriately evaluate it, making it difficult to provide users with the housing information that is most suitable for them.

[0485] 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.

[0486] In this invention, the server includes means for a user to input desired living locations and conditions, means for collecting data based on the conditions entered by the user from multiple data sources on the Internet, means for temporarily storing the collected data in a database, means for analyzing the collected data and filtering out housing information that matches the user's conditions, means for assigning evaluation scores to the filtered housing information, means for ranking the housing information based on the evaluation scores, means for presenting the selected housing information to a user terminal, and means for collecting word-of-mouth and reputation information from social networking services, thereby enabling users to quickly and accurately obtain optimal housing information that matches their desired conditions.

[0487] "User" refers to someone who uses this system to input the desired location and conditions to search for housing information.

[0488] "Desired place and conditions to live" refers to the specific requirements of the user, such as the area, budget, and floor plan they desire.

[0489] "Data source" refers to various information sources that provide data that meets the user's requirements, such as real estate information sites on the Internet and social networking services (SNS).

[0490] A "database" refers to a digital storage system for temporarily storing and managing collected data (property information and reviews).

[0491] "Rating score" refers to the points or rating value assigned to each property based on collected reviews and other indicators.

[0492] "Ranking" refers to the process of ranking property information based on evaluation scores and presenting it to the user in order of priority.

[0493] "Social Networking Service" refers to an online service (e.g., a social networking platform) that enables users to post and share information.

[0494] "Filtering" refers to the process of extracting information that meets a user's criteria from collected data and excluding unnecessary information.

[0495] A "real estate information site" refers to a site that provides information on the rental and sale of properties on the Internet.

[0496] This invention relates to a system that allows users to input desired locations and conditions for living, and then collects and analyzes related data from multiple data sources on the Internet to provide optimal housing information. Specifically, it is configured as follows.

[0497] Basic system configuration

[0498] This system consists of a "terminal" used by the user, a "server" that collects and analyzes data, and multiple data sources (real estate information sites and social networking services).

[0499] User side

[0500] 1. Input Method

[0501] The user uses a browser on their device (e.g., a PC or smartphone) or a dedicated application to input the desired location and conditions for living. For example, they can input conditions such as "Shinjuku-ku, Tokyo, under 100,000 yen, 2LDK, nearby supermarket, pets allowed." The input data is sent to the server.

[0502] Server side

[0503] 2. Data Collection Methods

[0504] The server uses web scraping technologies such as Python, BeautifulSoup, and Selenium to collect property information from real estate information websites, and also uses APIs of social networking services (e.g., Twitter API and other SNS APIs) to collect reviews and reputation information related to the user's criteria.

[0505] 3. Temporarily save to database

[0506] The server temporarily stores the collected data in a database (e.g., MySQL), including the property's address, rent, floor plan, age, distance to the nearest station, and the latest related reviews.

[0507] 4. Data Analysis Methods

[0508] The server analyzes the temporarily saved data and filters the property information that matches the conditions entered by the user. For example, it extracts only properties that match conditions such as rent under 100,000 yen, floor plan of 2LDK, and pet-friendly.

[0509] 5. Evaluation Score Assignment Method

[0510] The server assigns a rating score to the filtered property information based on the collected reviews, giving a high score to properties with many positive reviews and a low score to properties with many negative reviews.

[0511] 6. Ranking Methods

[0512] The server ranks the property information based on the evaluation score, so that the highly rated properties that best match the user's criteria are displayed at the top.

[0513] Presentation of results

[0514] 7. Presentation means

[0515] The server sends a list of selected housing information to the user's device. The user's browser or application displays information such as rent, floor plan, location, distance to the nearest station, and the latest reviews. This information is presented visually and easily understandable to the user.

[0516] Specific operation example

[0517] For example, if a user enters conditions such as "Shinjuku-ku, Tokyo, under 100,000 yen, 2LDK, nearby supermarket, pets allowed," the server will operate as follows:

[0518] The server scrapes multiple real estate information sites using keywords such as "Shinjuku-ku 2LDK rental."

[0519] At the same time, the server collects information on social media using hashtags such as "Shinjuku Ward Rentals" and "Pets Allowed."

[0520] The server temporarily stores the collected data in a database.

[0521] The server filters the property listings that match the user's criteria.

[0522] The server assigns an evaluation score to the filtered properties based on the collected reviews and ranks them.

[0523] The server displays the top-ranked property information on the user's device, allowing the user to check detailed information and contact information. This system allows users to easily obtain information about housing that matches their preferences and select the property that best suits them.

[0524] Examples of prompt statements

[0525] "Build a system that allows users to input criteria and collects and analyzes data from multiple sources to provide the most suitable housing information. Use Python, BeautifulSoup, Selenium, MySQL, and SNS APIs as the technologies to be used. Include a function to assign an evaluation score based on the user's criteria and create a ranking."

[0526] This invention is a system that efficiently provides housing information that matches a user's desires from a vast amount of data on the Internet, and supports the user in making the most suitable choice.

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

[0528] Step 1:

[0529] The user inputs the desired location and conditions into the device.

[0530] Input: The user enters conditions such as "Shinjuku-ku, Tokyo, under 100,000 yen, 2LDK, nearby supermarket, pets allowed" into the input form.

[0531] Output: The entered conditions are sent to the server.

[0532] Specific operation: The user uses a browser or application to enter the desired conditions through an input form and clicks the submit button. This information is sent to the server in JSON format.

[0533] Step 2:

[0534] The server collects data from real estate information sites and social networking sites.

[0535] Input: Conditions received from the user (e.g., "Shinjuku-ku, Tokyo, under 100,000 yen, 2LDK, nearby supermarket, pets allowed").

[0536] Output: The collected property information and reviews are stored in a database for temporary storage.

[0537] How it works: The server uses Python's BeautifulSoup and Selenium to crawl multiple real estate information websites and collect property information that matches the search criteria. At the same time, it uses Twitter APIs and other social media APIs to collect reviews using related hashtags and keywords.

[0538] Step 3:

[0539] The server temporarily stores the collected data in a database.

[0540] Input: Collected property information and reviews.

[0541] Output: Raw data stored in a database.

[0542] Specific operation: The collected property address, rent, floor plan, age, distance to the nearest station, and related reviews are organized and stored in MySQL or other databases.

[0543] Step 4:

[0544] The server analyzes the collected data and filters property information that matches the user's criteria.

[0545] Input: Property information and reviews stored in the database.

[0546] Output: Filtered listings that match the user's criteria.

[0547] Specific operation: The server extracts only matching property information from the database based on the conditions entered by the user (e.g., rent less than 100,000 yen, layout 2LDK, pets allowed, etc.).

[0548] Step 5:

[0549] The server assigns an evaluation score to the filtered property information.

[0550] Input: Filtered property information and collected reviews.

[0551] Output: Property information with rating scores.

[0552] Specific operation: The server analyzes the collected review information for the filtered properties and assigns a high score to properties with many positive reviews and a low score to properties with many negative reviews.

[0553] Step 6:

[0554] The server ranks the property information based on the evaluation score.

[0555] Input: Property information with rating scores.

[0556] Output: A ranked list of properties.

[0557] Specific operation: The server ranks property information based on the rating score and creates a list so that properties with higher scores are displayed at the top.

[0558] Step 7:

[0559] The server transmits a list of the selected residence information to the user terminal and displays it.

[0560] Input: Ranked property list.

[0561] Output: Residence information displayed on the user's device.

[0562] Specific operation: The server sends the ranked property information to the user's device, and displays the rent, floor plan, location, distance to the nearest station, and related reviews on the user's browser or application. The user can then view detailed information and contact details.

[0563] (Application example 1)

[0564] 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."

[0565] Currently, searching for real estate information requires manually collecting data from multiple sources and conducting detailed research on each property, which is extremely time-consuming and labor-intensive. Furthermore, it is difficult to evaluate properties using word-of-mouth information on social media, making it difficult for users to efficiently select the housing information that is best for them.

[0566] 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.

[0567] In this invention, the server includes means for a user to input desired living locations and conditions, means for collecting data based on the conditions input by the user from multiple information sources on the Internet, means for analyzing the collected word-of-mouth information on SNS and assigning evaluation scores, means for ranking properties based on the evaluation scores, means for inputting prompt sentences to a generative AI model based on the conditions input by the user and having the AI ​​model evaluate the properties, and means for presenting selected housing information to a user terminal, thereby enabling users to efficiently and effectively obtain optimal housing information.

[0568] The "place and conditions in which the user wishes to live" are specific requirements regarding the residence the user desires, such as the area, budget, layout, and the presence or absence of nearby facilities.

[0569] "Multiple sources of information on the Internet" refers to multiple platforms that provide housing information and word-of-mouth information on the web, such as real estate information sites and social networking services.

[0570] "Means of collection" refers to the means of obtaining data from sources on the Internet, such as using web scraping techniques or APIs.

[0571] The "means of analysis" refers to the means of storing the collected data in a database, interpreting the data using statistical analysis and natural language processing, and assigning an evaluation score.

[0572] The "evaluation score" is a numerical value of positive and negative evaluations that is generated by analyzing word-of-mouth information collected based on the user's conditions.

[0573] The "ranking means" is a means for ranking properties based on the evaluation scores so that the properties most suitable for the user are displayed at the top.

[0574] A "user terminal" is a device, such as a smartphone, tablet, or PC, that a user operates to input information and check the results.

[0575] A "generative AI model" is an artificial intelligence model that generates prompt statements based on user input and automatically evaluates properties using those prompt statements.

[0576] A "prompt" is an instruction entered into a generative AI model to make it perform a specific task.

[0577] The present invention relates to a system for efficiently acquiring residential information desired by a user, and its embodiment is as follows: This system is mainly composed of a user terminal, a server, and multiple information sources on the Internet.

[0578] User terminal

[0579] Users access the system using their own devices such as smartphones, tablets, or PCs. Using a dedicated application or web browser, users input the desired location and conditions (area, budget, floor plan, availability of nearby facilities, etc.).

[0580] Data collection

[0581] The server uses Python-based web scraping technology (such as BeautifulSoup or Selenium) to collect property data based on user criteria from multiple real estate information websites on the Internet, and also collects related reviews from social networking services via the Twitter API and Instagram Graph API.

[0582] Data analysis

[0583] The server stores the collected data in an SQL database (e.g., MySQL) and analyzes it using natural language processing technology (e.g., TextBlob), classifying reviews as positive or negative, and assigning a rating score to each property based on that.

[0584] Property ranking

[0585] To rank the properties based on their rating scores, the server runs a ranking algorithm that ensures that the properties that best meet the user's criteria are displayed at the top of the list.

[0586] Presentation of information

[0587] The server sends the selected housing information to the user's device and displays it in an easy-to-read format, including the property's rent, layout, location, distance to the nearest public transport, and user reviews.

[0588] Generative AI Models

[0589] The server inputs the user's criteria as a prompt into the generative AI model. This generative AI model is optimized to perform a specific task and automatically evaluates properties. An example of a prompt might be, "Please search for a 2LDK home in Shinjuku Ward with a budget of less than 100,000 yen, gather information on properties with a nearby supermarket and that allow pets, and rank them based on reviews on social media."

[0590] In this way, the system collects data from multiple sources, analyzes, evaluates, and ranks them, and provides users with the most suitable housing information.Specific hardware and software used include Python, BeautifulSoup, Selenium, MySQL, Twitter API, Instagram Graph API, TextBlob, and React Native.

[0591] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0592] Step 1:

[0593] Users input their desired location and conditions (e.g., area, budget, floor plan, availability of nearby facilities, etc.) through an application or web browser on their device (smartphone, tablet, PC). The input information is sent to the server.

[0594] Step 2:

[0595] The server uses Python's BeautifulSoup and Selenium to scrape property data from multiple real estate information websites on the Internet based on user-specified criteria. The input is the criteria entered by the user, and the output is property data that matches the criteria (e.g., rent, floor plan, location, distance to the nearest station).

[0596] Step 3:

[0597] The server uses the Twitter API and Instagram Graph API to collect reviews of properties that meet the criteria. Here too, the input is the user's criteria, and the output includes reviews on social media (e.g., tweets and comments).

[0598] Step 4:

[0599] The server stores the collected property data and reviews in a MySQL database. The input is the data collected in Step 2 and Step 3, and the output is the results stored in the database.

[0600] Step 5:

[0601] The server uses natural language processing technology (e.g., TextBlob) to analyze the stored data. Specifically, it classifies reviews as positive or negative and assigns a rating score to each property. The input is the reviews retrieved from the database, and the output is the rating score for each property.

[0602] Step 6:

[0603] The server ranks the properties based on the rating score. It runs a ranking algorithm to ensure that the properties that best fit the user's criteria are displayed at the top. The input is the rating score, and the output is a ranked list of properties.

[0604] Step 7:

[0605] The server inputs the user's conditions into the generative AI model as a prompt statement, and uses the AI ​​model to make a final evaluation of the property. The prompt statement is used as input, and the output is the property evaluation result by the generative AI model. For example, a prompt statement could be, "Please search for a 2LDK home in Shinjuku Ward with a budget of less than 100,000 yen, gather information on properties with a nearby supermarket and that allow pets, and rank them including reviews on social media."

[0606] Step 8:

[0607] The server sends the ranked property information to the user's device and displays it on an application or web browser. The optimal housing information is then presented to the user. The output includes information such as rent, floor plan, location, distance to the nearest station, and user reviews.

[0608] 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.

[0609] This invention relates to a system that allows users to input the location and conditions they wish to live in, and then collects and analyzes related data from multiple information sources on the Internet to provide optimal housing information. Furthermore, a specific form is shown in which user satisfaction can be increased by combining it with an emotion engine that recognizes the user's emotions. Specifically, the system is configured as follows:

[0610] Basic system configuration

[0611] This system consists of a "terminal" used by the user, a "server" that collects and analyzes data, multiple information sources (real estate information sites and social networking services), and an emotion engine.

[0612] User side

[0613] 1. Input Method

[0614] A form is displayed on the user's device for entering the desired place to live and conditions (area, budget, floor plan, other conditions, etc.) For example, the user enters "Shinjuku-ku, Tokyo, under 100,000 yen, 2LDK, nearby supermarket, pets allowed."

[0615] When inputting, the emotion engine recognizes emotions from the user's facial expressions and voice, and collects emotion data for the user's input conditions.

[0616] Server side

[0617] 2. Data Collection Methods

[0618] The server uses web scraping technologies such as Python, BeautifulSoup, and Selenium to collect property information from real estate information websites. It also collects reviews and reputation information related to the user's criteria through social media APIs (e.g., Twitter API and Instagram Graph API).

[0619] 3. Temporary storage of data

[0620] The collected data is temporarily stored in a database, and includes basic information such as address, rent, floor plan, age of the building, and distance to the nearest station.

[0621] 4. Data Analysis Methods

[0622] The server analyzes the collected data and filters the property information that matches the user's criteria, such as rent under 100,000 yen, a 2LDK layout, and pet-friendly properties.

[0623] 5. Evaluation Score Assignment Method

[0624] The filtered property information is assigned a rating score based on the collected reviews. Properties with many positive reviews are given a high score, while properties with many negative reviews are given a low score.

[0625] 6. Ranking Methods

[0626] The property listings are ranked based on their rating scores. The property listings are sorted so that the properties that best match the user's criteria and have the highest ratings are displayed at the top.

[0627] Use of emotion engine

[0628] 7. Collecting Emotional Data

[0629] The emotion engine recognizes emotions from the user's facial expressions and voice, collects emotional data, and quantifies, for example, the user's satisfaction and expectations regarding the input conditions.

[0630] 8. Emotion Data Analysis

[0631] The server analyzes the emotional data and identifies which conditions the user has more positive feelings about. Based on this, it adjusts the analysis results and selects the property information that is most suitable for the user.

[0632] 9. User response monitoring

[0633] The emotion engine monitors users' reactions in real time when property information is presented. If the user responds positively to the property information presented, that information is reflected in the next data analysis.

[0634] Presentation of results

[0635] 10. Presentation means

[0636] The server sends a list of selected housing information to the user's device. The formatted information includes rent, floor plan, location, distance to the nearest station, the latest reviews, and information based on the user's emotional data.

[0637] 11. User Feedback

[0638] The user terminal displays the received property information. The user can check the displayed property information and provide feedback if there are any deficiencies or problems. The server collects the feedback and reflects it in the next analysis.

[0639] Specific operation example

[0640] For example, if a user enters conditions such as "Shinjuku-ku, Tokyo, under 100,000 yen, 2LDK, nearby supermarket, pets allowed," the server will operate as follows:

[0641] The server scrapes multiple real estate information sites using keywords such as "Shinjuku-ku 2LDK rental."

[0642] At the same time, information is collected on social media using hashtags such as "Shinjuku Ward Rentals" and "Pets Allowed."

[0643] The collected data is stored in a database and property information that matches the user's criteria is filtered.

[0644] The filtered properties are assigned an evaluation score based on the collected reviews and ranked.

[0645] The emotion engine recognizes the user's emotions when entering information and when information is presented, and reflects that data in the analysis results, allowing for more accurate property selection.

[0646] Finally, the top-ranked property information and supplementary information based on emotion data are displayed on the user's device, allowing the user to check detailed information and contact details.

[0647] This system allows users to easily obtain more personalized housing information and select the most suitable property.

[0648] The processing flow will be explained below.

[0649] Step 1:

[0650] The user enters the desired location and conditions into a form displayed on the user's device. For example, the user might enter "Shinjuku-ku, Tokyo, under 100,000 yen, 2LDK, nearby supermarket, pets allowed."

[0651] Step 2:

[0652] Along with the conditions entered by the user, the emotion engine uses face recognition technology and voice analysis technology to recognize emotions from the user's facial expressions and tone of voice, and collects emotion data for the entered conditions.

[0653] Step 3:

[0654] When a user transmits input content, the user terminal transmits the input data and emotion data to the server.

[0655] Step 4:

[0656] Based on the user's conditions received by the server, web scraping is initiated against multiple real estate information websites on the Internet. Using Python, BeautifulSoup, and Selenium, searches are performed using keywords such as "Shinjuku Ward, 2LDK for rent" and "under 100,000 yen" to collect property information.

[0657] Step 5:

[0658] The server temporarily stores the property information it collects in a database, which includes basic information such as address, rent, floor plan, age of the building, and distance to the nearest station.

[0659] Step 6:

[0660] The server uses SNS APIs (e.g., Twitter API or Instagram Graph API) to collect relevant reviews on SNS. For example, it searches for hashtags such as "Shinjuku-ku rental" and "pets allowed" to obtain the latest reputations and reviews.

[0661] Step 7:

[0662] The server also stores the collected social media data in a database, including user ratings and comments.

[0663] Step 8:

[0664] The server filters the real estate information in the database based on the user's input criteria, such as rent under 100,000 yen, a 2LDK layout, and pet-friendly properties.

[0665] Step 9:

[0666] The server assigns a rating score to the filtered property information based on the collected reviews, raising the score for properties with many positive comments and lowering the score for properties with many negative comments.

[0667] Step 10:

[0668] The server analyzes the emotion data and reflects the user's feelings toward the criteria entered in the evaluation score. For example, if the user expresses particularly positive feelings toward a particular criteria, the score will be adjusted to give more weight to that criteria.

[0669] Step 11:

[0670] Ranking of property information based on evaluation scores. Sort property information so that properties with higher scores are displayed at the top.

[0671] Step 12:

[0672] The server formats the ranked property information and sends it to the user's device. The formatted information includes rent, floor plan, location, distance to the nearest station, the latest reviews, and information based on the user's emotional data.

[0673] Step 13:

[0674] The property information received by the user's device is displayed on a browser or application. The user can check the displayed property information and click to view detailed information or contact information.

[0675] Step 14:

[0676] Users can provide feedback on the displayed property information, and the server collects that feedback and reflects it in the next data analysis.

[0677] The above is the specific flow of program processing for a system that combines an emotion engine.

[0678] Example 2

[0679] 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."

[0680] Conventional housing information systems only provide property information based on the conditions entered by the user, and do not consider the user's feelings or satisfaction when selecting properties. As a result, it is not possible to fully meet the user's expectations, making it difficult to provide optimal housing information.

[0681] The identification process by the identification 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 a means for a user to input desired living conditions and locations, a means for collecting data based on the conditions input by the user from multiple information sources on the Internet, a means for temporarily storing the collected data, a means for analyzing the collected data and selecting optimal housing information for the user, a means for presenting the selected housing information to the user terminal, and a means for acquiring user emotion data using an emotion engine and reflecting the data in the analysis results. This makes it possible to provide optimal housing information that takes the user's emotions into consideration.

[0682] "Means for users to input desired location and conditions for living" refers to an interface that allows users to input desired conditions for housing (e.g., location, budget, layout, and other specific requirements) using a terminal.

[0683] "Multiple sources on the Internet" refers to various websites and online services (e.g., real estate websites, social networking services, etc.) that provide data related to the user's input.

[0684] "Means of collecting data" refers to the technical means (e.g., web scraping technology or APIs) used to automatically obtain relevant data from each source.

[0685] "Means for temporarily storing collected data" refers to a database or storage system for temporarily storing acquired data.

[0686] "Means for analyzing collected data" refers to algorithms and analytical techniques for analyzing collected data and selecting the most suitable property based on the user's conditions.

[0687] "Means for presenting selected residential information on a user terminal" refers to a method for displaying the optimal property information selected as a result of the analysis on a user device.

[0688] An "emotion engine" refers to software or hardware that recognizes and analyzes emotions from a user's facial expressions and voice and collects that data.

[0689] "Means of assigning evaluation scores" refers to the method for calculating and assigning quantitative evaluation scores to each property based on collected word-of-mouth information, etc.

[0690] "Means for ranking properties" refers to a means for prioritizing and sorting properties based on their rating scores.

[0691] "Means of acquiring emotional data and reflecting it in the analysis results" refers to technology that incorporates user emotional data acquired through an emotion engine into the analysis results to enable more accurate property selection.

[0692] The present invention relates to a system that allows users to input their desired location and conditions, collects and analyzes related data from multiple information sources on the Internet, and provides optimal housing information. This system can further increase user satisfaction by combining it with an emotion engine that recognizes the user's emotions.

[0693] Basic system configuration

[0694] This system includes "means for the user to input the location and conditions for living that the user desires," "means for collecting data," "means for temporarily storing collected data," "means for analyzing collected data," "means for presenting selected housing information on the user's terminal," and "means for obtaining the user's emotional data using an emotion engine and reflecting it in the analysis results."

[0695] User side

[0696] First, the user inputs the desired location and conditions into the input form displayed on the device. For example, the user might input conditions such as "Shinjuku-ku, Tokyo, under 100,000 yen, 2LDK, nearby supermarket, pets allowed." As the input is made, the emotion engine recognizes emotions from the user's facial expressions and voice, and collects emotional data for the user's input conditions. The emotion engine used is software that uses a camera and microphone to quantify the user's emotions.

[0697] Server side

[0698] The server uses web scraping technologies such as Python, BeautifulSoup, and Selenium to collect property information from real estate information websites. It also collects reviews and reputation information related to the user's criteria through social media APIs (e.g., Twitter API and Instagram Graph API). The collected data is temporarily stored in a database such as MySQL. The stored data includes address, rent, floor plan, age of the building, distance to the nearest station, etc.

[0699] The server then analyzes the collected data and filters the property information that matches the user's criteria. For example, an SQL query can be used to extract properties that meet criteria such as rent under 100,000 yen, a 2LDK layout, and pet-friendly. Then, based on the reviews, a scoring method is used to assign a high score to properties with many positive reviews and a low score to properties with many negative reviews. For example, a natural language processing model using TensorFlow can be used to analyze the reviews and calculate the score.

[0700] Based on the calculated score, the server runs a sorting algorithm to rank the property information. The data is then formatted so that the most highly rated properties are displayed at the top. The emotional data obtained from the emotion engine is analyzed as the user's reaction to the conditions entered by the user and their reaction when the housing information is presented. Based on this, the server determines which conditions the user has more positive feelings about and reflects this information in the next data analysis.

[0701] Presentation of results

[0702] The housing information list selected as the result of the analysis is sent from the server to the user's device in JSON format or similar. The user's device then formats and displays the received data. The displayed information includes rent, floor plan, location, distance to the nearest station, and the latest reviews. In addition, supplemental information based on emotional data is also presented. The user can review the provided information and provide feedback if necessary. This feedback is also reflected in the next data analysis, contributing to improving the accuracy of the entire system.

[0703] Specific operation example

[0704] For example, if a user enters the following criteria: "Shinjuku Ward, Tokyo, under 100,000 yen, 2LDK, nearby supermarket, pets allowed," the server will scrape multiple real estate information websites using keywords such as "Shinjuku Ward 2LDK rental." At the same time, it will collect information on social media using hashtags such as "Shinjuku Ward rental" and "pets allowed." The collected data is stored in a database and property listings that match the user's criteria are filtered. Based on the filtering results, properties are assigned an evaluation score and ranked. The emotion engine recognizes the user's emotions when entering and presenting the information, and incorporates this data into the analysis results to enable more accurate property selection. Finally, the top-ranked property listings and supplementary information based on the emotion data are displayed on the user's device, allowing the user to check detailed information and contact information.

[0705] Example prompts for generative AI models

[0706] If a user enters "Shinjuku-ku, Tokyo, under 100,000 yen, 2LDK, nearby supermarket, pets allowed," please explain what type of property would be most suitable and why.

[0707] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0708] Step 1: User Input

[0709] The user enters the desired location and conditions into an input form on the device. For example, they might enter "Shinjuku-ku, Tokyo, under 100,000 yen, 2LDK, nearby supermarket, pets allowed." At the same time, this input condition is sent to the server, the emotion engine acquires emotional data from the user's facial expressions and voice. The emotion data and the condition input data are sent to the server and stored together. The specific input data are the location, budget, floor plan, and specific requirements (near supermarket, pets allowed, etc.), and the output is the input data and emotional data.

[0710] Step 2: Data collection

[0711] The server uses web scraping technologies such as Python, BeautifulSoup, and Selenium to collect property information from real estate information websites. It also collects reviews and reputation information related to user criteria through social media APIs (e.g., Twitter API and Instagram Graph API). The input data is the location and conditions entered by the user, and the output is property information and reviews collected based on those conditions. Specifically, the server searches multiple real estate information websites for keywords such as "2LDK rental in Shinjuku Ward" and analyzes the HTML content.

[0712] Step 3: Save data

[0713] The server stores the collected property information and reviews in a database such as MySQL. The input data is information collected from web scraping and SNS APIs, and the output is a database containing that information. Specifically, the server stores the information in a table with fields such as address, rent, floor plan, age of the building, and distance to the nearest station.

[0714] Step 4: Data analysis

[0715] The server executes an SQL query to extract properties from the database that match the user's criteria. The input data is the user's desired criteria and property information in the database, and the output is a list of properties that match those criteria. Specifically, the server executes a query such as "SELECT FROM properties WHERE rent <= 100000 AND layout = '2LDK' AND pets_allowed = true."

[0716] Step 5: Assigning a rating score

[0717] The server analyzes the reviews and assigns a high score to properties with many positive reviews. For example, the reviews are analyzed using a natural language processing model using TensorFlow. The input data is the extracted property list and the reviews for that property, and the output is a property list with a score assigned. Specifically, the server judges comments such as "This place is great for families!" to be positive and assigns a high score.

[0718] Step 6: Ranking

[0719] The server runs a sorting algorithm to sort the properties by highest score. The input data is a list of scored properties, and the output is a ranked list of properties. Specifically, the server sorts the list of properties in descending order based on the score field, with the most highly rated properties at the top.

[0720] Step 7: Sentiment Data Analysis

[0721] The server analyzes the emotion data obtained from the emotion engine and identifies the conditions for which the user has positive emotions. The input data is the emotion data and the condition data entered by the user, and the output is the user's emotional evaluation of those conditions. Specifically, if the user shows a satisfied expression for the condition "there is a supermarket nearby," the importance of that condition is increased.

[0722] Step 8: Presenting the results

[0723] The housing information list selected as the analysis result is sent from the server to the user's device. The input data is a ranked property list that reflects emotional evaluations, and the output is property information displayed on the user's device. The user's device formats and displays this information, providing information such as rent, floor plan, location, distance to the nearest station, and the latest reviews. The user then enters feedback, which is reflected in the next analysis.

[0724] Example prompts for generative AI models

[0725] If a user enters "Shinjuku-ku, Tokyo, under 100,000 yen, 2LDK, nearby supermarket, pets allowed," please explain what type of property would be most suitable and why.

[0726] (Application example 2)

[0727] 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."

[0728] Currently, it is not easy for users to find the best place to live or travel based on their own conditions and emotions. In particular, there is no system that provides appropriate information in real time while traveling in an autonomous vehicle. As a result, users must manually collect and evaluate information from many sources, which takes time and effort and has low accuracy. In addition, because the information is not individually optimized taking into account the user's emotions, it may result in low satisfaction.

[0729] 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 places and conditions for living, means for collecting data based on the conditions entered by the user from multiple information sources on the Internet, means for analyzing the collected data and selecting housing information optimal for the user, means for assigning an evaluation score based on the collected data, means for ranking based on the evaluation score, means for collecting user emotions using an emotion recognition engine and reflecting them in the analysis results, and means for presenting the selected information on the user terminal. This enables users to easily obtain information on optimal housing and travel destinations based on their emotions in real time.

[0730] "Means for users to input desired living conditions and location" refers to a form or interface that allows users to input conditions such as desired area, budget, and floor plan into a terminal.

[0731] "Multiple sources on the Internet" includes information providers that are the source of data collected from websites and social networking services.

[0732] An "emotion recognition engine" refers to software or a system that analyzes emotions from a user's facial expressions and voice and collects that data.

[0733] "Means for assigning an evaluation score" refers to software or algorithms that have the function of calculating an evaluation score for an object based on collected data.

[0734] "Ranking means" refers to software or algorithms used to rank objects based on their evaluation scores.

[0735] "Means for presenting on the user terminal" refers to the interface or software for displaying the selected information on the device used by the user.

[0736] This invention relates to a system that allows users to input desired locations and conditions, collects and analyzes related data from multiple sources on the Internet, and provides optimal housing information and travel destinations. Furthermore, by combining it with an emotion recognition engine, a specific form that can increase user satisfaction is shown.

[0737] Basic system configuration

[0738] This system consists of a "terminal" used by the user, a "server" that collects and analyzes data, multiple information sources, and an emotion recognition engine.

[0739] User side

[0740] 1. Input method:

[0741] Users input their desired location and conditions for living, as well as their travel destination preferences (region, budget, tourist spots, and other conditions) on a touch panel terminal inside the self-driving vehicle.

[0742] When inputting, the emotion recognition engine recognizes emotions from the user's facial expressions and voice, and collects emotion data for the input conditions.

[0743] Server side

[0744] 2. Data collection methods:

[0745] The server uses web scraping technologies such as Python, BeautifulSoup, and Selenium to collect relevant information from travel and real estate information sites.

[0746] We also collect word-of-mouth and reputation information related to user conditions through social media APIs (such as Twitter API and Instagram Graph API).

[0747] 3. Temporary data storage:

[0748] The collected data is temporarily stored in a database. In the case of travel information, it includes basic information such as tourist spots, hotels, and restaurants.

[0749] 4. Data analysis methods:

[0750] The server analyzes the collected data and filters information that matches the user's criteria, for example, extracting tourist spots and hotels that match the user's travel preferences.

[0751] 5. Rating Score Method:

[0752] The filtered information is assigned a rating score based on the collected reviews. Information with many positive reviews is given a high rating score.

[0753] 6. Ranking Method:

[0754] The information is ranked based on the evaluation score, and the information that best matches the user's criteria and is highly rated is displayed at the top.

[0755] Use of emotion recognition engine

[0756] 7. Collecting Emotional Data:

[0757] The emotion recognition engine recognizes emotions from the user's facial expressions and voice, and collects emotional data, such as the user's satisfaction and expectations regarding the conditions, which are then quantified.

[0758] 8. Emotional Data Analysis:

[0759] The server analyzes the emotional data and identifies which information the user feels more positively about. Based on this, it adjusts the analysis results and selects the most appropriate information for the user.

[0760] 9. User response monitoring:

[0761] The emotion recognition engine monitors the user's reaction in real time when information is presented. If the user responds positively to the information presented, that information is reflected in the next data analysis.

[0762] Presentation of results

[0763] 10. Presentation means:

[0764] The server sends a list of selected information to the user's device. The formatted information includes details about each facility or spot, tourist reviews, and information based on the user's emotional data.

[0765] 11. User Feedback:

[0766] The user terminal displays the received information. The user can check the displayed information and provide feedback if there are any deficiencies or problems. The server collects the feedback and reflects it in the next analysis.

[0767] Specific operation example

[0768] For example, if a user enters conditions such as "hot spring town" or "places where you can enjoy delicious local food," the server will operate as follows:

[0769] The server scrapes multiple travel information sites using keywords such as "hot spring town sightseeing" and "local food."

[0770] At the same time, information is collected using hashtags such as "hot spring trip" and "local gourmet" on social media.

[0771] The collected data is stored in a database and information on tourist spots and restaurants that meet the user's criteria is filtered.

[0772] The filtered information is assigned an evaluation score based on collected word-of-mouth information and ranked.

[0773] The emotion recognition engine recognizes the user's emotions when inputting and presenting information, and reflects this data in the analysis results, allowing for more accurate information selection.

[0774] Finally, the top-ranked information and supplementary information based on emotion data are displayed on the user's device, allowing the user to check detailed information and contact details.

[0775] Examples and prompts

[0776] prompt

[0777] text

[0778] Please recommend a travel destination that meets the following criteria:

[0779] The user's facial expression and voice analysis data are also input, so please take emotional data into consideration.

[0780] conditions:

[0781] hot spring town

[0782] A place to enjoy delicious local food

[0783] Emotional Data:

[0784] Expression: Excitement and anticipation

[0785] Voice: Calm and relaxed tone

[0786] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0787] Step 1:

[0788] The user inputs desired conditions into a terminal inside the autonomous vehicle. Specifically, the user inputs conditions such as "hot spring town" or "place where I can enjoy delicious local food" into the touch panel terminal. At the same time as the input, the terminal's camera and microphone record the user's facial expressions and voice, and emotional data is collected. The input here consists of the desired conditions as a string of characters and emotional data quantified by an emotion recognition engine.

[0789] Step 2:

[0790] The server receives user input criteria and collects data from multiple sources on the Internet. Using a Python script, it web scrapes travel and real estate information sites to obtain information that matches the criteria. It also uses social media APIs to collect related reviews and reputation information. The input here is the user's desired criteria, and the output is a list of collected related information.

[0791] Step 3:

[0792] The server temporarily stores the collected data in a database, which contains details such as the name, location, and reviews of travel spots. The input here is the collected data, and the output is the information stored in the database.

[0793] Step 4:

[0794] The server analyzes the stored data and filters information that matches the user's criteria. Specifically, it searches using keywords based on the criteria and extracts data that matches the criteria. The input here is all data from the database, and the output is the narrowed-down data that matches the criteria.

[0795] Step 5:

[0796] The server assigns an evaluation score to the filtered data based on the collected word-of-mouth information. Specifically, it assigns a high evaluation score to information with many positive reviews and a low evaluation score to information with many negative reviews. The input here is the filtered data and word-of-mouth data, and the output is data with an evaluation score.

[0797] Step 6:

[0798] The server ranks the data based on the evaluation score. It sorts the data in order of score so that highly rated information is displayed at the top. The input here is data with evaluation scores, and the output is ranked data.

[0799] Step 7:

[0800] The server analyzes the user's emotional data and selects data based on the user's emotions. Specifically, it analyzes the data obtained by the emotion recognition engine and adjusts the data by placing emphasis on conditions where the user has positive emotions. The input here is the emotional data and ranked data, and the output is the final adjusted data.

[0801] Step 8:

[0802] The server presents the adjusted data to the user's device. Specifically, it displays a list of top travel destinations and tourist attractions on a touch panel device, allowing the user to check detailed information. The input here is the final adjusted data, and the output is the information displayed on the user's device.

[0803] Step 9:

[0804] The user checks the presented information and provides feedback. Specifically, they input their satisfaction or dissatisfaction with each travel destination or tourist spot displayed. The device collects this feedback and reflects it in the next data analysis. The input here is the user's feedback, and the output is the feedback data sent to the server.

[0805] 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.

[0806] 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.

[0807] 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.

[0808] [Third embodiment]

[0809] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0810] 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.

[0811] 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).

[0812] 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.

[0813] 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.

[0814] 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).

[0815] 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.

[0816] 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.

[0817] 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.

[0818] 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.

[0819] 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.

[0820] 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."

[0821] This invention relates to a system that allows users to input desired locations and conditions for living, and then collects and analyzes related data from multiple information sources on the Internet to provide optimal housing information. Specifically, it is configured as follows.

[0822] Basic system configuration

[0823] This system consists of a "terminal" used by the user, a "server" that collects and analyzes data, and multiple information sources (real estate information sites and social networking services).

[0824] User side

[0825] 1. Input Method

[0826] A form is displayed on the user's device for entering the desired place to live and conditions (area, budget, floor plan, other conditions, etc.) For example, the user enters "Shinjuku-ku, Tokyo, under 100,000 yen, 2LDK, nearby supermarket, pets allowed."

[0827] Server side

[0828] 2. Data Collection Methods

[0829] The server uses web scraping technologies such as Python, BeautifulSoup, and Selenium to collect property information from real estate information websites. It also collects reviews and reputation information related to the user's criteria through social media APIs (e.g., Twitter API and Instagram Graph API).

[0830] 3. Temporary storage of data

[0831] The collected data is temporarily stored in a database, including the property's address, rent, floor plan, age, distance to the nearest station, and the latest related reviews.

[0832] 4. Data Analysis Methods

[0833] The server analyzes the collected data and filters the property information that matches the user's criteria, such as rent under 100,000 yen, a 2LDK layout, and pet-friendly properties.

[0834] 5. Evaluation Score Assignment Method

[0835] The filtered property information is assigned a rating score based on the collected reviews. Properties with many positive reviews are given a high score, while properties with many negative reviews are given a low score.

[0836] 6. Ranking Methods

[0837] Property information is ranked based on the rating score, with properties that best match the user's criteria and have the highest ratings displayed at the top.

[0838] Presentation of results

[0839] 7. Presentation means

[0840] The server sends a list of selected housing information to the user's device, and the user's browser or application displays information such as rent, floor plan, location, distance to the nearest station, and the latest reviews.

[0841] Specific operation example

[0842] For example, if a user enters conditions such as "Shinjuku-ku, Tokyo, under 100,000 yen, 2LDK, nearby supermarket, pets allowed," the server will operate as follows:

[0843] The server scrapes multiple real estate information sites using keywords such as "Shinjuku-ku 2LDK rental."

[0844] At the same time, information is collected on social media using hashtags such as "Shinjuku Ward Rentals" and "Pets Allowed."

[0845] The collected data is stored in a database and property information that matches the user's criteria is filtered.

[0846] The filtered properties are assigned an evaluation score based on the collected reviews and ranked.

[0847] Finally, the top-ranked property information is displayed on the user's device, allowing the user to check detailed information and contact details.

[0848] This system allows users to easily obtain housing information that matches their preferences and select the most suitable property.

[0849] The processing flow will be explained below.

[0850] Step 1:

[0851] The user enters the desired location and conditions into a form displayed on the user's device. For example, the user might enter "Shinjuku-ku, Tokyo, under 100,000 yen, 2LDK, nearby supermarket, pets allowed."

[0852] Step 2:

[0853] The conditions entered by the user are transmitted. At this time, the user terminal transmits the input data to the server.

[0854] Step 3:

[0855] Based on the user's conditions received by the server, web scraping is initiated against multiple real estate information websites on the Internet. Using tools such as Python, BeautifulSoup, and Selenium, searches are performed using keywords such as "Shinjuku Ward, 2LDK for rent" and "under 100,000 yen" to collect property information.

[0856] Step 4:

[0857] The server temporarily stores the property information it collects in a database, which includes basic information such as address, rent, floor plan, age of the building, and distance to the nearest station.

[0858] Step 5:

[0859] The server uses SNS APIs (e.g., Twitter API or Instagram Graph API) to collect relevant reviews on SNS. For example, it searches for hashtags such as "Shinjuku-ku rental" and "pets allowed" to obtain the latest reputations and reviews.

[0860] Step 6:

[0861] The server also stores the collected social media data in a database, including user ratings and comments.

[0862] Step 7:

[0863] The server filters the real estate information in the database based on the user's input criteria, such as rent under 100,000 yen, a 2LDK layout, and pet-friendly properties.

[0864] Step 8:

[0865] The server assigns a rating score to the filtered property information based on the collected reviews, raising the score for properties with many positive comments and lowering the score for properties with many negative comments.

[0866] Step 9:

[0867] The server ranks properties based on their rating scores and sorts the property information so that properties with higher scores are displayed at the top.

[0868] Step 10:

[0869] The server formats the ranked property information and sends it to the user's device. The formatted information includes rent, floor plan, location, distance to the nearest station, and the latest reviews.

[0870] Step 11:

[0871] The property information received by the user's device is displayed on a browser or application. The user can check the displayed property information and click to view detailed information or contact information.

[0872] The above is the specific flow of program processing for this system.

[0873] Example 1

[0874] 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."

[0875] In recent years, it has become common for users to use the Internet to search for housing information in the real estate market. However, it is difficult and time-consuming to efficiently collect housing information that meets the user's desired criteria and to perform accurate filtering and evaluation. In addition, the collected data is diverse, and there is a lack of means to integrate and appropriately evaluate it, making it difficult to provide users with the housing information that is most suitable for them.

[0876] 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.

[0877] In this invention, the server includes means for a user to input desired living locations and conditions, means for collecting data based on the conditions entered by the user from multiple data sources on the Internet, means for temporarily storing the collected data in a database, means for analyzing the collected data and filtering out housing information that matches the user's conditions, means for assigning evaluation scores to the filtered housing information, means for ranking the housing information based on the evaluation scores, means for presenting the selected housing information to a user terminal, and means for collecting word-of-mouth and reputation information from social networking services, thereby enabling users to quickly and accurately obtain optimal housing information that matches their desired conditions.

[0878] "User" refers to someone who uses this system to input the desired location and conditions to search for housing information.

[0879] "Desired place and conditions to live" refers to the specific requirements of the user, such as the area, budget, and floor plan they desire.

[0880] "Data source" refers to various information sources that provide data that meets the user's requirements, such as real estate information sites on the Internet and social networking services (SNS).

[0881] A "database" refers to a digital storage system for temporarily storing and managing collected data (property information and reviews).

[0882] "Rating score" refers to the points or rating value assigned to each property based on collected reviews and other indicators.

[0883] "Ranking" refers to the process of ranking property information based on evaluation scores and presenting it to the user in order of priority.

[0884] "Social Networking Service" refers to an online service (e.g., a social networking platform) that enables users to post and share information.

[0885] "Filtering" refers to the process of extracting information that meets a user's criteria from collected data and excluding unnecessary information.

[0886] A "real estate information site" refers to a site that provides information on the rental and sale of properties on the Internet.

[0887] This invention relates to a system that allows users to input desired locations and conditions for living, and then collects and analyzes related data from multiple data sources on the Internet to provide optimal housing information. Specifically, it is configured as follows.

[0888] Basic system configuration

[0889] This system consists of a "terminal" used by the user, a "server" that collects and analyzes data, and multiple data sources (real estate information sites and social networking services).

[0890] User side

[0891] 1. Input Method

[0892] The user uses a browser on their device (e.g., a PC or smartphone) or a dedicated application to input the desired location and conditions for living. For example, they can input conditions such as "Shinjuku-ku, Tokyo, under 100,000 yen, 2LDK, nearby supermarket, pets allowed." The input data is sent to the server.

[0893] Server side

[0894] 2. Data Collection Methods

[0895] The server uses web scraping technologies such as Python, BeautifulSoup, and Selenium to collect property information from real estate information websites, and also uses APIs of social networking services (e.g., Twitter API and other SNS APIs) to collect reviews and reputation information related to the user's criteria.

[0896] 3. Temporarily save to database

[0897] The server temporarily stores the collected data in a database (e.g., MySQL), including the property's address, rent, floor plan, age, distance to the nearest station, and the latest related reviews.

[0898] 4. Data Analysis Methods

[0899] The server analyzes the temporarily saved data and filters the property information that matches the conditions entered by the user. For example, it extracts only properties that match conditions such as rent under 100,000 yen, floor plan of 2LDK, and pet-friendly.

[0900] 5. Evaluation Score Assignment Method

[0901] The server assigns a rating score to the filtered property information based on the collected reviews, giving a high score to properties with many positive reviews and a low score to properties with many negative reviews.

[0902] 6. Ranking Methods

[0903] The server ranks the property information based on the evaluation score, so that the highly rated properties that best match the user's criteria are displayed at the top.

[0904] Presentation of results

[0905] 7. Presentation means

[0906] The server sends a list of selected housing information to the user's device. The user's browser or application displays information such as rent, floor plan, location, distance to the nearest station, and the latest reviews. This information is presented visually and easily understandable to the user.

[0907] Specific operation example

[0908] For example, if a user enters conditions such as "Shinjuku-ku, Tokyo, under 100,000 yen, 2LDK, nearby supermarket, pets allowed," the server will operate as follows:

[0909] The server scrapes multiple real estate information sites using keywords such as "Shinjuku-ku 2LDK rental."

[0910] At the same time, the server collects information on social media using hashtags such as "Shinjuku Ward Rentals" and "Pets Allowed."

[0911] The server temporarily stores the collected data in a database.

[0912] The server filters the property listings that match the user's criteria.

[0913] The server assigns an evaluation score to the filtered properties based on the collected reviews and ranks them.

[0914] The server displays the top-ranked property information on the user's device, allowing the user to check detailed information and contact information. This system allows users to easily obtain information about housing that matches their preferences and select the property that best suits them.

[0915] Examples of prompt statements

[0916] "Build a system that allows users to input criteria and collects and analyzes data from multiple sources to provide the most suitable housing information. Use Python, BeautifulSoup, Selenium, MySQL, and SNS APIs as the technologies to be used. Include a function to assign an evaluation score based on the user's criteria and create a ranking."

[0917] This invention is a system that efficiently provides housing information that matches a user's desires from a vast amount of data on the Internet, and supports the user in making the most suitable choice.

[0918] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0919] Step 1:

[0920] The user inputs the desired location and conditions into the device.

[0921] Input: The user enters conditions such as "Shinjuku-ku, Tokyo, under 100,000 yen, 2LDK, nearby supermarket, pets allowed" into the input form.

[0922] Output: The entered conditions are sent to the server.

[0923] Specific operation: The user uses a browser or application to enter the desired conditions through an input form and clicks the submit button. This information is sent to the server in JSON format.

[0924] Step 2:

[0925] The server collects data from real estate information sites and social networking sites.

[0926] Input: Conditions received from the user (e.g., "Shinjuku-ku, Tokyo, under 100,000 yen, 2LDK, nearby supermarket, pets allowed").

[0927] Output: The collected property information and reviews are stored in a database for temporary storage.

[0928] How it works: The server uses Python's BeautifulSoup and Selenium to crawl multiple real estate information websites and collect property information that matches the search criteria. At the same time, it uses Twitter APIs and other social media APIs to collect reviews using related hashtags and keywords.

[0929] Step 3:

[0930] The server temporarily stores the collected data in a database.

[0931] Input: Collected property information and reviews.

[0932] Output: Raw data stored in a database.

[0933] Specific operation: The collected property address, rent, floor plan, age, distance to the nearest station, and related reviews are organized and stored in MySQL or other databases.

[0934] Step 4:

[0935] The server analyzes the collected data and filters property information that matches the user's criteria.

[0936] Input: Property information and reviews stored in the database.

[0937] Output: Filtered listings that match the user's criteria.

[0938] Specific operation: The server extracts only matching property information from the database based on the conditions entered by the user (e.g., rent less than 100,000 yen, layout 2LDK, pets allowed, etc.).

[0939] Step 5:

[0940] The server assigns an evaluation score to the filtered property information.

[0941] Input: Filtered property information and collected reviews.

[0942] Output: Property information with rating scores.

[0943] Specific operation: The server analyzes the collected review information for the filtered properties and assigns a high score to properties with many positive reviews and a low score to properties with many negative reviews.

[0944] Step 6:

[0945] The server ranks the property information based on the evaluation score.

[0946] Input: Property information with rating scores.

[0947] Output: A ranked list of properties.

[0948] Specific operation: The server ranks property information based on the rating score and creates a list so that properties with higher scores are displayed at the top.

[0949] Step 7:

[0950] The server transmits a list of the selected residence information to the user terminal and displays it.

[0951] Input: Ranked property list.

[0952] Output: Residence information displayed on the user's device.

[0953] Specific operation: The server sends the ranked property information to the user's device, and displays the rent, floor plan, location, distance to the nearest station, and related reviews on the user's browser or application. The user can then view detailed information and contact details.

[0954] (Application example 1)

[0955] 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."

[0956] Currently, searching for real estate information requires manually collecting data from multiple sources and conducting detailed research on each property, which is extremely time-consuming and labor-intensive. Furthermore, it is difficult to evaluate properties using word-of-mouth information on social media, making it difficult for users to efficiently select the housing information that is best for them.

[0957] 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.

[0958] In this invention, the server includes means for a user to input desired living locations and conditions, means for collecting data based on the conditions input by the user from multiple information sources on the Internet, means for analyzing the collected word-of-mouth information on SNS and assigning evaluation scores, means for ranking properties based on the evaluation scores, means for inputting prompt sentences to a generative AI model based on the conditions input by the user and having the AI ​​model evaluate the properties, and means for presenting selected housing information to a user terminal, thereby enabling users to efficiently and effectively obtain optimal housing information.

[0959] The "place and conditions in which the user wishes to live" are specific requirements regarding the residence the user desires, such as the area, budget, layout, and the presence or absence of nearby facilities.

[0960] "Multiple sources of information on the Internet" refers to multiple platforms that provide housing information and word-of-mouth information on the web, such as real estate information sites and social networking services.

[0961] "Means of collection" refers to the means of obtaining data from sources on the Internet, such as using web scraping techniques or APIs.

[0962] The "means of analysis" refers to the means of storing the collected data in a database, interpreting the data using statistical analysis and natural language processing, and assigning an evaluation score.

[0963] The "evaluation score" is a numerical value of positive and negative evaluations that is generated by analyzing word-of-mouth information collected based on the user's conditions.

[0964] The "ranking means" is a means for ranking properties based on the evaluation scores so that the properties most suitable for the user are displayed at the top.

[0965] A "user terminal" is a device, such as a smartphone, tablet, or PC, that a user operates to input information and check the results.

[0966] A "generative AI model" is an artificial intelligence model that generates prompt statements based on user input and automatically evaluates properties using those prompt statements.

[0967] A "prompt" is an instruction entered into a generative AI model to make it perform a specific task.

[0968] The present invention relates to a system for efficiently acquiring residential information desired by a user, and its embodiment is as follows: This system is mainly composed of a user terminal, a server, and multiple information sources on the Internet.

[0969] User terminal

[0970] Users access the system using their own devices such as smartphones, tablets, or PCs. Using a dedicated application or web browser, users input the desired location and conditions (area, budget, floor plan, availability of nearby facilities, etc.).

[0971] Data collection

[0972] The server uses Python-based web scraping technology (such as BeautifulSoup or Selenium) to collect property data based on user criteria from multiple real estate information websites on the Internet, and also collects related reviews from social networking services via the Twitter API and Instagram Graph API.

[0973] Data analysis

[0974] The server stores the collected data in an SQL database (e.g., MySQL) and analyzes it using natural language processing technology (e.g., TextBlob), classifying reviews as positive or negative, and assigning a rating score to each property based on that.

[0975] Property ranking

[0976] To rank the properties based on their rating scores, the server runs a ranking algorithm that ensures that the properties that best meet the user's criteria are displayed at the top of the list.

[0977] Presentation of information

[0978] The server sends the selected housing information to the user's device and displays it in an easy-to-read format, including the property's rent, layout, location, distance to the nearest public transport, and user reviews.

[0979] Generative AI Models

[0980] The server inputs the user's criteria as a prompt into the generative AI model. This generative AI model is optimized to perform a specific task and automatically evaluates properties. An example of a prompt might be, "Please search for a 2LDK home in Shinjuku Ward with a budget of less than 100,000 yen, gather information on properties with a nearby supermarket and that allow pets, and rank them based on reviews on social media."

[0981] In this way, the system collects data from multiple sources, analyzes, evaluates, and ranks them, and provides users with the most suitable housing information.Specific hardware and software used include Python, BeautifulSoup, Selenium, MySQL, Twitter API, Instagram Graph API, TextBlob, and React Native.

[0982] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0983] Step 1:

[0984] Users input their desired location and conditions (e.g., area, budget, floor plan, availability of nearby facilities, etc.) through an application or web browser on their device (smartphone, tablet, PC). The input information is sent to the server.

[0985] Step 2:

[0986] The server uses Python's BeautifulSoup and Selenium to scrape property data from multiple real estate information websites on the Internet based on user-specified criteria. The input is the criteria entered by the user, and the output is property data that matches the criteria (e.g., rent, floor plan, location, distance to the nearest station).

[0987] Step 3:

[0988] The server uses the Twitter API and Instagram Graph API to collect reviews of properties that meet the criteria. Here too, the input is the user's criteria, and the output includes reviews on social media (e.g., tweets and comments).

[0989] Step 4:

[0990] The server stores the collected property data and reviews in a MySQL database. The input is the data collected in Step 2 and Step 3, and the output is the results stored in the database.

[0991] Step 5:

[0992] The server uses natural language processing technology (e.g., TextBlob) to analyze the stored data. Specifically, it classifies reviews as positive or negative and assigns a rating score to each property. The input is the reviews retrieved from the database, and the output is the rating score for each property.

[0993] Step 6:

[0994] The server ranks the properties based on the rating score. It runs a ranking algorithm to ensure that the properties that best fit the user's criteria are displayed at the top. The input is the rating score, and the output is a ranked list of properties.

[0995] Step 7:

[0996] The server inputs the user's conditions into the generative AI model as a prompt statement, and uses the AI ​​model to make a final evaluation of the property. The prompt statement is used as input, and the output is the property evaluation result by the generative AI model. For example, a prompt statement could be, "Please search for a 2LDK home in Shinjuku Ward with a budget of less than 100,000 yen, gather information on properties with a nearby supermarket and that allow pets, and rank them including reviews on social media."

[0997] Step 8:

[0998] The server sends the ranked property information to the user's device and displays it on an application or web browser. The optimal housing information is then presented to the user. The output includes information such as rent, floor plan, location, distance to the nearest station, and user reviews.

[0999] 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.

[1000] This invention relates to a system that allows users to input the location and conditions they wish to live in, and then collects and analyzes related data from multiple information sources on the Internet to provide optimal housing information. Furthermore, a specific form is shown in which user satisfaction can be increased by combining it with an emotion engine that recognizes the user's emotions. Specifically, the system is configured as follows:

[1001] Basic system configuration

[1002] This system consists of a "terminal" used by the user, a "server" that collects and analyzes data, multiple information sources (real estate information sites and social networking services), and an emotion engine.

[1003] User side

[1004] 1. Input Method

[1005] A form is displayed on the user's device for entering the desired place to live and conditions (area, budget, floor plan, other conditions, etc.) For example, the user enters "Shinjuku-ku, Tokyo, under 100,000 yen, 2LDK, nearby supermarket, pets allowed."

[1006] When inputting, the emotion engine recognizes emotions from the user's facial expressions and voice, and collects emotion data for the user's input conditions.

[1007] Server side

[1008] 2. Data Collection Methods

[1009] The server uses web scraping technologies such as Python, BeautifulSoup, and Selenium to collect property information from real estate information websites. It also collects reviews and reputation information related to the user's criteria through social media APIs (e.g., Twitter API and Instagram Graph API).

[1010] 3. Temporary storage of data

[1011] The collected data is temporarily stored in a database, and includes basic information such as address, rent, floor plan, age of the building, and distance to the nearest station.

[1012] 4. Data Analysis Methods

[1013] The server analyzes the collected data and filters the property information that matches the user's criteria, such as rent under 100,000 yen, a 2LDK layout, and pet-friendly properties.

[1014] 5. Evaluation Score Assignment Method

[1015] The filtered property information is assigned a rating score based on the collected reviews. Properties with many positive reviews are given a high score, while properties with many negative reviews are given a low score.

[1016] 6. Ranking Methods

[1017] The property listings are ranked based on their rating scores. The property listings are sorted so that the properties that best match the user's criteria and have the highest ratings are displayed at the top.

[1018] Use of emotion engine

[1019] 7. Collecting Emotional Data

[1020] The emotion engine recognizes emotions from the user's facial expressions and voice, collects emotional data, and quantifies, for example, the user's satisfaction and expectations regarding the input conditions.

[1021] 8. Emotion Data Analysis

[1022] The server analyzes the emotional data and identifies which conditions the user has more positive feelings about. Based on this, it adjusts the analysis results and selects the property information that is most suitable for the user.

[1023] 9. User response monitoring

[1024] The emotion engine monitors users' reactions in real time when property information is presented. If the user responds positively to the property information presented, that information is reflected in the next data analysis.

[1025] Presentation of results

[1026] 10. Presentation means

[1027] The server sends a list of selected housing information to the user's device. The formatted information includes rent, floor plan, location, distance to the nearest station, the latest reviews, and information based on the user's emotional data.

[1028] 11. User Feedback

[1029] The user terminal displays the received property information. The user can check the displayed property information and provide feedback if there are any deficiencies or problems. The server collects the feedback and reflects it in the next analysis.

[1030] Specific operation example

[1031] For example, if a user enters conditions such as "Shinjuku-ku, Tokyo, under 100,000 yen, 2LDK, nearby supermarket, pets allowed," the server will operate as follows:

[1032] The server scrapes multiple real estate information sites using keywords such as "Shinjuku-ku 2LDK rental."

[1033] At the same time, information is collected on social media using hashtags such as "Shinjuku Ward Rentals" and "Pets Allowed."

[1034] The collected data is stored in a database and property information that matches the user's criteria is filtered.

[1035] The filtered properties are assigned an evaluation score based on the collected reviews and ranked.

[1036] The emotion engine recognizes the user's emotions when entering information and when information is presented, and reflects that data in the analysis results, allowing for more accurate property selection.

[1037] Finally, the top-ranked property information and supplementary information based on emotion data are displayed on the user's device, allowing the user to check detailed information and contact details.

[1038] This system allows users to easily obtain more personalized housing information and select the most suitable property.

[1039] The processing flow will be explained below.

[1040] Step 1:

[1041] The user enters the desired location and conditions into a form displayed on the user's device. For example, the user might enter "Shinjuku-ku, Tokyo, under 100,000 yen, 2LDK, nearby supermarket, pets allowed."

[1042] Step 2:

[1043] Along with the conditions entered by the user, the emotion engine uses face recognition technology and voice analysis technology to recognize emotions from the user's facial expressions and tone of voice, and collects emotion data for the entered conditions.

[1044] Step 3:

[1045] When a user transmits input content, the user terminal transmits the input data and emotion data to the server.

[1046] Step 4:

[1047] Based on the user's conditions received by the server, web scraping is initiated against multiple real estate information websites on the Internet. Using Python, BeautifulSoup, and Selenium, searches are performed using keywords such as "Shinjuku Ward, 2LDK for rent" and "under 100,000 yen" to collect property information.

[1048] Step 5:

[1049] The server temporarily stores the property information it collects in a database, which includes basic information such as address, rent, floor plan, age of the building, and distance to the nearest station.

[1050] Step 6:

[1051] The server uses SNS APIs (e.g., Twitter API or Instagram Graph API) to collect relevant reviews on SNS. For example, it searches for hashtags such as "Shinjuku-ku rental" and "pets allowed" to obtain the latest reputations and reviews.

[1052] Step 7:

[1053] The server also stores the collected social media data in a database, including user ratings and comments.

[1054] Step 8:

[1055] The server filters the real estate information in the database based on the user's input criteria, such as rent under 100,000 yen, a 2LDK layout, and pet-friendly properties.

[1056] Step 9:

[1057] The server assigns a rating score to the filtered property information based on the collected reviews, raising the score for properties with many positive comments and lowering the score for properties with many negative comments.

[1058] Step 10:

[1059] The server analyzes the emotion data and reflects the user's feelings toward the criteria entered in the evaluation score. For example, if the user expresses particularly positive feelings toward a particular criteria, the score will be adjusted to give more weight to that criteria.

[1060] Step 11:

[1061] Ranking of property information based on evaluation scores. Sort property information so that properties with higher scores are displayed at the top.

[1062] Step 12:

[1063] The server formats the ranked property information and sends it to the user's device. The formatted information includes rent, floor plan, location, distance to the nearest station, the latest reviews, and information based on the user's emotional data.

[1064] Step 13:

[1065] The property information received by the user's device is displayed on a browser or application. The user can check the displayed property information and click to view detailed information or contact information.

[1066] Step 14:

[1067] Users can provide feedback on the displayed property information, and the server collects that feedback and reflects it in the next data analysis.

[1068] The above is the specific flow of program processing for a system that combines an emotion engine.

[1069] Example 2

[1070] 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."

[1071] Conventional housing information systems only provide property information based on the conditions entered by the user, and do not consider the user's feelings or satisfaction when selecting properties. As a result, it is not possible to fully meet the user's expectations, making it difficult to provide optimal housing information.

[1072] The identification process by the identification 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 a means for a user to input desired living conditions and locations, a means for collecting data based on the conditions input by the user from multiple information sources on the Internet, a means for temporarily storing the collected data, a means for analyzing the collected data and selecting optimal housing information for the user, a means for presenting the selected housing information to the user terminal, and a means for acquiring user emotion data using an emotion engine and reflecting the data in the analysis results. This makes it possible to provide optimal housing information that takes the user's emotions into consideration.

[1073] "Means for users to input desired location and conditions for living" refers to an interface that allows users to input desired conditions for housing (e.g., location, budget, layout, and other specific requirements) using a terminal.

[1074] "Multiple sources on the Internet" refers to various websites and online services (e.g., real estate websites, social networking services, etc.) that provide data related to the user's input.

[1075] "Means of collecting data" refers to the technical means (e.g., web scraping technology or APIs) used to automatically obtain relevant data from each source.

[1076] "Means for temporarily storing collected data" refers to a database or storage system for temporarily storing acquired data.

[1077] "Means for analyzing collected data" refers to algorithms and analytical techniques for analyzing collected data and selecting the most suitable property based on the user's conditions.

[1078] "Means for presenting selected residential information on a user terminal" refers to a method for displaying the optimal property information selected as a result of the analysis on a user device.

[1079] An "emotion engine" refers to software or hardware that recognizes and analyzes emotions from a user's facial expressions and voice and collects that data.

[1080] "Means of assigning evaluation scores" refers to the method for calculating and assigning quantitative evaluation scores to each property based on collected word-of-mouth information, etc.

[1081] "Means for ranking properties" refers to a means for prioritizing and sorting properties based on their rating scores.

[1082] "Means of acquiring emotional data and reflecting it in the analysis results" refers to technology that incorporates user emotional data acquired through an emotion engine into the analysis results to enable more accurate property selection.

[1083] The present invention relates to a system that allows users to input their desired location and conditions, collects and analyzes related data from multiple information sources on the Internet, and provides optimal housing information. This system can further increase user satisfaction by combining it with an emotion engine that recognizes the user's emotions.

[1084] Basic system configuration

[1085] This system includes "means for the user to input the location and conditions for living that the user desires," "means for collecting data," "means for temporarily storing collected data," "means for analyzing collected data," "means for presenting selected housing information on the user's terminal," and "means for obtaining the user's emotional data using an emotion engine and reflecting it in the analysis results."

[1086] User side

[1087] First, the user inputs the desired location and conditions into the input form displayed on the device. For example, the user might input conditions such as "Shinjuku-ku, Tokyo, under 100,000 yen, 2LDK, nearby supermarket, pets allowed." As the input is made, the emotion engine recognizes emotions from the user's facial expressions and voice, and collects emotional data for the user's input conditions. The emotion engine used is software that uses a camera and microphone to quantify the user's emotions.

[1088] Server side

[1089] The server uses web scraping technologies such as Python, BeautifulSoup, and Selenium to collect property information from real estate information websites. It also collects reviews and reputation information related to the user's criteria through social media APIs (e.g., Twitter API and Instagram Graph API). The collected data is temporarily stored in a database such as MySQL. The stored data includes address, rent, floor plan, age of the building, distance to the nearest station, etc.

[1090] The server then analyzes the collected data and filters the property information that matches the user's criteria. For example, an SQL query can be used to extract properties that meet criteria such as rent under 100,000 yen, a 2LDK layout, and pet-friendly. Then, based on the reviews, a scoring method is used to assign a high score to properties with many positive reviews and a low score to properties with many negative reviews. For example, a natural language processing model using TensorFlow can be used to analyze the reviews and calculate the score.

[1091] Based on the calculated score, the server runs a sorting algorithm to rank the property information. The data is then formatted so that the most highly rated properties are displayed at the top. The emotional data obtained from the emotion engine is analyzed as the user's reaction to the conditions entered by the user and their reaction when the housing information is presented. Based on this, the server determines which conditions the user has more positive feelings about and reflects this information in the next data analysis.

[1092] Presentation of results

[1093] The housing information list selected as the result of the analysis is sent from the server to the user's device in JSON format or similar. The user's device then formats and displays the received data. The displayed information includes rent, floor plan, location, distance to the nearest station, and the latest reviews. In addition, supplemental information based on emotional data is also presented. The user can review the provided information and provide feedback if necessary. This feedback is also reflected in the next data analysis, contributing to improving the accuracy of the entire system.

[1094] Specific operation example

[1095] For example, if a user enters the following criteria: "Shinjuku Ward, Tokyo, under 100,000 yen, 2LDK, nearby supermarket, pets allowed," the server will scrape multiple real estate information websites using keywords such as "Shinjuku Ward 2LDK rental." At the same time, it will collect information on social media using hashtags such as "Shinjuku Ward rental" and "pets allowed." The collected data is stored in a database and property listings that match the user's criteria are filtered. Based on the filtering results, properties are assigned an evaluation score and ranked. The emotion engine recognizes the user's emotions when entering and presenting the information, and incorporates this data into the analysis results to enable more accurate property selection. Finally, the top-ranked property listings and supplementary information based on the emotion data are displayed on the user's device, allowing the user to check detailed information and contact information.

[1096] Example prompts for generative AI models

[1097] If a user enters "Shinjuku-ku, Tokyo, under 100,000 yen, 2LDK, nearby supermarket, pets allowed," please explain what type of property would be most suitable and why.

[1098] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1099] Step 1: User Input

[1100] The user enters the desired location and conditions into an input form on the device. For example, they might enter "Shinjuku-ku, Tokyo, under 100,000 yen, 2LDK, nearby supermarket, pets allowed." At the same time, this input condition is sent to the server, the emotion engine acquires emotional data from the user's facial expressions and voice. The emotion data and the condition input data are sent to the server and stored together. The specific input data are the location, budget, floor plan, and specific requirements (near supermarket, pets allowed, etc.), and the output is the input data and emotional data.

[1101] Step 2: Data collection

[1102] The server uses web scraping technologies such as Python, BeautifulSoup, and Selenium to collect property information from real estate information websites. It also collects reviews and reputation information related to user criteria through social media APIs (e.g., Twitter API and Instagram Graph API). The input data is the location and conditions entered by the user, and the output is property information and reviews collected based on those conditions. Specifically, the server searches multiple real estate information websites for keywords such as "2LDK rental in Shinjuku Ward" and analyzes the HTML content.

[1103] Step 3: Save data

[1104] The server stores the collected property information and reviews in a database such as MySQL. The input data is information collected from web scraping and SNS APIs, and the output is a database containing that information. Specifically, the server stores the information in a table with fields such as address, rent, floor plan, age of the building, and distance to the nearest station.

[1105] Step 4: Data analysis

[1106] The server executes an SQL query to extract properties from the database that match the user's criteria. The input data is the user's desired criteria and property information in the database, and the output is a list of properties that match those criteria. Specifically, the server executes a query such as "SELECT FROM properties WHERE rent <= 100000 AND layout = '2LDK' AND pets_allowed = true."

[1107] Step 5: Assigning a rating score

[1108] The server analyzes the reviews and assigns a high score to properties with many positive reviews. For example, the reviews are analyzed using a natural language processing model using TensorFlow. The input data is the extracted property list and the reviews for that property, and the output is a property list with a score assigned. Specifically, the server judges comments such as "This place is great for families!" to be positive and assigns a high score.

[1109] Step 6: Ranking

[1110] The server runs a sorting algorithm to sort the properties by highest score. The input data is a list of scored properties, and the output is a ranked list of properties. Specifically, the server sorts the list of properties in descending order based on the score field, with the most highly rated properties at the top.

[1111] Step 7: Sentiment Data Analysis

[1112] The server analyzes the emotion data obtained from the emotion engine and identifies the conditions for which the user has positive emotions. The input data is the emotion data and the condition data entered by the user, and the output is the user's emotional evaluation of those conditions. Specifically, if the user shows a satisfied expression for the condition "there is a supermarket nearby," the importance of that condition is increased.

[1113] Step 8: Presenting the results

[1114] The housing information list selected as the analysis result is sent from the server to the user's device. The input data is a ranked property list that reflects emotional evaluations, and the output is property information displayed on the user's device. The user's device formats and displays this information, providing information such as rent, floor plan, location, distance to the nearest station, and the latest reviews. The user then enters feedback, which is reflected in the next analysis.

[1115] Example prompts for generative AI models

[1116] If a user enters "Shinjuku-ku, Tokyo, under 100,000 yen, 2LDK, nearby supermarket, pets allowed," please explain what type of property would be most suitable and why.

[1117] (Application example 2)

[1118] 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."

[1119] Currently, it is not easy for users to find the best place to live or travel based on their own conditions and emotions. In particular, there is no system that provides appropriate information in real time while traveling in an autonomous vehicle. As a result, users must manually collect and evaluate information from many sources, which takes time and effort and has low accuracy. In addition, because the information is not individually optimized taking into account the user's emotions, it may result in low satisfaction.

[1120] 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 places and conditions for living, means for collecting data based on the conditions entered by the user from multiple information sources on the Internet, means for analyzing the collected data and selecting housing information optimal for the user, means for assigning an evaluation score based on the collected data, means for ranking based on the evaluation score, means for collecting user emotions using an emotion recognition engine and reflecting them in the analysis results, and means for presenting the selected information on the user terminal. This enables users to easily obtain information on optimal housing and travel destinations based on their emotions in real time.

[1121] "Means for users to input desired living conditions and location" refers to a form or interface that allows users to input conditions such as desired area, budget, and floor plan into a terminal.

[1122] "Multiple sources on the Internet" includes information providers that are the source of data collected from websites and social networking services.

[1123] An "emotion recognition engine" refers to software or a system that analyzes emotions from a user's facial expressions and voice and collects that data.

[1124] "Means for assigning an evaluation score" refers to software or algorithms that have the function of calculating an evaluation score for an object based on collected data.

[1125] "Ranking means" refers to software or algorithms used to rank objects based on their evaluation scores.

[1126] "Means for presenting on the user terminal" refers to the interface or software for displaying the selected information on the device used by the user.

[1127] This invention relates to a system that allows users to input desired locations and conditions, collects and analyzes related data from multiple sources on the Internet, and provides optimal housing information and travel destinations. Furthermore, by combining it with an emotion recognition engine, a specific form that can increase user satisfaction is shown.

[1128] Basic system configuration

[1129] This system consists of a "terminal" used by the user, a "server" that collects and analyzes data, multiple information sources, and an emotion recognition engine.

[1130] User side

[1131] 1. Input method:

[1132] Users input their desired location and conditions for living, as well as their travel destination preferences (region, budget, tourist spots, and other conditions) on a touch panel terminal inside the self-driving vehicle.

[1133] When inputting, the emotion recognition engine recognizes emotions from the user's facial expressions and voice, and collects emotion data for the input conditions.

[1134] Server side

[1135] 2. Data collection methods:

[1136] The server uses web scraping technologies such as Python, BeautifulSoup, and Selenium to collect relevant information from travel and real estate information sites.

[1137] We also collect word-of-mouth and reputation information related to user conditions through social media APIs (such as Twitter API and Instagram Graph API).

[1138] 3. Temporary data storage:

[1139] The collected data is temporarily stored in a database. In the case of travel information, it includes basic information such as tourist spots, hotels, and restaurants.

[1140] 4. Data analysis methods:

[1141] The server analyzes the collected data and filters information that matches the user's criteria, for example, extracting tourist spots and hotels that match the user's travel preferences.

[1142] 5. Rating Score Method:

[1143] The filtered information is assigned a rating score based on the collected reviews. Information with many positive reviews is given a high rating score.

[1144] 6. Ranking Method:

[1145] The information is ranked based on the evaluation score, and the information that best matches the user's criteria and is highly rated is displayed at the top.

[1146] Use of emotion recognition engine

[1147] 7. Collecting Emotional Data:

[1148] The emotion recognition engine recognizes emotions from the user's facial expressions and voice, and collects emotional data, such as the user's satisfaction and expectations regarding the conditions, which are then quantified.

[1149] 8. Emotional Data Analysis:

[1150] The server analyzes the emotional data and identifies which information the user feels more positively about. Based on this, it adjusts the analysis results and selects the most appropriate information for the user.

[1151] 9. User response monitoring:

[1152] The emotion recognition engine monitors the user's reaction in real time when information is presented. If the user responds positively to the information presented, that information is reflected in the next data analysis.

[1153] Presentation of results

[1154] 10. Presentation means:

[1155] The server sends a list of selected information to the user's device. The formatted information includes details about each facility or spot, tourist reviews, and information based on the user's emotional data.

[1156] 11. User Feedback:

[1157] The user terminal displays the received information. The user can check the displayed information and provide feedback if there are any deficiencies or problems. The server collects the feedback and reflects it in the next analysis.

[1158] Specific operation example

[1159] For example, if a user enters conditions such as "hot spring town" or "places where you can enjoy delicious local food," the server will operate as follows:

[1160] The server scrapes multiple travel information sites using keywords such as "hot spring town sightseeing" and "local food."

[1161] At the same time, information is collected using hashtags such as "hot spring trip" and "local gourmet" on social media.

[1162] The collected data is stored in a database and information on tourist spots and restaurants that meet the user's criteria is filtered.

[1163] The filtered information is assigned an evaluation score based on collected word-of-mouth information and ranked.

[1164] The emotion recognition engine recognizes the user's emotions when inputting and presenting information, and reflects this data in the analysis results, allowing for more accurate information selection.

[1165] Finally, the top-ranked information and supplementary information based on emotion data are displayed on the user's device, allowing the user to check detailed information and contact details.

[1166] Examples and prompts

[1167] prompt

[1168] text

[1169] Please recommend a travel destination that meets the following criteria:

[1170] The user's facial expression and voice analysis data are also input, so please take emotional data into consideration.

[1171] conditions:

[1172] hot spring town

[1173] A place to enjoy delicious local food

[1174] Emotional Data:

[1175] Expression: Excitement and anticipation

[1176] Voice: Calm and relaxed tone

[1177] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1178] Step 1:

[1179] The user inputs desired conditions into a terminal inside the autonomous vehicle. Specifically, the user inputs conditions such as "hot spring town" or "place where I can enjoy delicious local food" into the touch panel terminal. At the same time as the input, the terminal's camera and microphone record the user's facial expressions and voice, and emotional data is collected. The input here consists of the desired conditions as a string of characters and emotional data quantified by an emotion recognition engine.

[1180] Step 2:

[1181] The server receives user input criteria and collects data from multiple sources on the Internet. Using a Python script, it web scrapes travel and real estate information sites to obtain information that matches the criteria. It also uses social media APIs to collect related reviews and reputation information. The input here is the user's desired criteria, and the output is a list of collected related information.

[1182] Step 3:

[1183] The server temporarily stores the collected data in a database, which contains details such as the name, location, and reviews of travel spots. The input here is the collected data, and the output is the information stored in the database.

[1184] Step 4:

[1185] The server analyzes the stored data and filters information that matches the user's criteria. Specifically, it searches using keywords based on the criteria and extracts data that matches the criteria. The input here is all data from the database, and the output is the narrowed-down data that matches the criteria.

[1186] Step 5:

[1187] The server assigns an evaluation score to the filtered data based on the collected word-of-mouth information. Specifically, it assigns a high evaluation score to information with many positive reviews and a low evaluation score to information with many negative reviews. The input here is the filtered data and word-of-mouth data, and the output is data with an evaluation score.

[1188] Step 6:

[1189] The server ranks the data based on the evaluation score. It sorts the data in order of score so that highly rated information is displayed at the top. The input here is data with evaluation scores, and the output is ranked data.

[1190] Step 7:

[1191] The server analyzes the user's emotional data and selects data based on the user's emotions. Specifically, it analyzes the data obtained by the emotion recognition engine and adjusts the data by placing emphasis on conditions where the user has positive emotions. The input here is the emotional data and ranked data, and the output is the final adjusted data.

[1192] Step 8:

[1193] The server presents the adjusted data to the user's device. Specifically, it displays a list of top travel destinations and tourist attractions on a touch panel device, allowing the user to check detailed information. The input here is the final adjusted data, and the output is the information displayed on the user's device.

[1194] Step 9:

[1195] The user checks the presented information and provides feedback. Specifically, they input their satisfaction or dissatisfaction with each travel destination or tourist spot displayed. The device collects this feedback and reflects it in the next data analysis. The input here is the user's feedback, and the output is the feedback data sent to the server.

[1196] 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.

[1197] 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.

[1198] 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.

[1199] [Fourth embodiment]

[1200] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1201] 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.

[1202] 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).

[1203] 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.

[1204] 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.

[1205] 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).

[1206] 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.

[1207] 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.

[1208] 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.

[1209] 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.

[1210] 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.

[1211] 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.

[1212] 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."

[1213] This invention relates to a system that allows users to input desired locations and conditions for living, and then collects and analyzes related data from multiple information sources on the Internet to provide optimal housing information. Specifically, it is configured as follows.

[1214] Basic system configuration

[1215] This system consists of a "terminal" used by the user, a "server" that collects and analyzes data, and multiple information sources (real estate information sites and social networking services).

[1216] User side

[1217] 1. Input Method

[1218] A form is displayed on the user's device for entering the desired place to live and conditions (area, budget, floor plan, other conditions, etc.) For example, the user enters "Shinjuku-ku, Tokyo, under 100,000 yen, 2LDK, nearby supermarket, pets allowed."

[1219] Server side

[1220] 2. Data Collection Methods

[1221] The server uses web scraping technologies such as Python, BeautifulSoup, and Selenium to collect property information from real estate information websites. It also collects reviews and reputation information related to the user's criteria through social media APIs (e.g., Twitter API and Instagram Graph API).

[1222] 3. Temporary storage of data

[1223] The collected data is temporarily stored in a database, including the property's address, rent, floor plan, age, distance to the nearest station, and the latest related reviews.

[1224] 4. Data Analysis Methods

[1225] The server analyzes the collected data and filters the property information that matches the user's criteria, such as rent under 100,000 yen, a 2LDK layout, and pet-friendly properties.

[1226] 5. Evaluation Score Assignment Method

[1227] The filtered property information is assigned a rating score based on the collected reviews. Properties with many positive reviews are given a high score, while properties with many negative reviews are given a low score.

[1228] 6. Ranking Methods

[1229] Property information is ranked based on the rating score, with properties that best match the user's criteria and have the highest ratings displayed at the top.

[1230] Presentation of results

[1231] 7. Presentation means

[1232] The server sends a list of selected housing information to the user's device, and the user's browser or application displays information such as rent, floor plan, location, distance to the nearest station, and the latest reviews.

[1233] Specific operation example

[1234] For example, if a user enters conditions such as "Shinjuku-ku, Tokyo, under 100,000 yen, 2LDK, nearby supermarket, pets allowed," the server will operate as follows:

[1235] The server scrapes multiple real estate information sites using keywords such as "Shinjuku-ku 2LDK rental."

[1236] At the same time, information is collected on social media using hashtags such as "Shinjuku Ward Rentals" and "Pets Allowed."

[1237] The collected data is stored in a database and property information that matches the user's criteria is filtered.

[1238] The filtered properties are assigned an evaluation score based on the collected reviews and ranked.

[1239] Finally, the top-ranked property information is displayed on the user's device, allowing the user to check detailed information and contact details.

[1240] This system allows users to easily obtain housing information that matches their preferences and select the most suitable property.

[1241] The processing flow will be explained below.

[1242] Step 1:

[1243] The user enters the desired location and conditions into a form displayed on the user's device. For example, the user might enter "Shinjuku-ku, Tokyo, under 100,000 yen, 2LDK, nearby supermarket, pets allowed."

[1244] Step 2:

[1245] The conditions entered by the user are transmitted. At this time, the user terminal transmits the input data to the server.

[1246] Step 3:

[1247] Based on the user's conditions received by the server, web scraping is initiated against multiple real estate information websites on the Internet. Using tools such as Python, BeautifulSoup, and Selenium, searches are performed using keywords such as "Shinjuku Ward, 2LDK for rent" and "under 100,000 yen" to collect property information.

[1248] Step 4:

[1249] The server temporarily stores the property information it collects in a database, which includes basic information such as address, rent, floor plan, age of the building, and distance to the nearest station.

[1250] Step 5:

[1251] The server uses SNS APIs (e.g., Twitter API or Instagram Graph API) to collect relevant reviews on SNS. For example, it searches for hashtags such as "Shinjuku-ku rental" and "pets allowed" to obtain the latest reputations and reviews.

[1252] Step 6:

[1253] The server also stores the collected social media data in a database, including user ratings and comments.

[1254] Step 7:

[1255] The server filters the real estate information in the database based on the user's input criteria, such as rent under 100,000 yen, a 2LDK layout, and pet-friendly properties.

[1256] Step 8:

[1257] The server assigns a rating score to the filtered property information based on the collected reviews, raising the score for properties with many positive comments and lowering the score for properties with many negative comments.

[1258] Step 9:

[1259] The server ranks properties based on their rating scores and sorts the property information so that properties with higher scores are displayed at the top.

[1260] Step 10:

[1261] The server formats the ranked property information and sends it to the user's device. The formatted information includes rent, floor plan, location, distance to the nearest station, and the latest reviews.

[1262] Step 11:

[1263] The property information received by the user's device is displayed on a browser or application. The user can check the displayed property information and click to view detailed information or contact information.

[1264] The above is the specific flow of program processing for this system.

[1265] Example 1

[1266] 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."

[1267] In recent years, it has become common for users to use the Internet to search for housing information in the real estate market. However, it is difficult and time-consuming to efficiently collect housing information that meets the user's desired criteria and to perform accurate filtering and evaluation. In addition, the collected data is diverse, and there is a lack of means to integrate and appropriately evaluate it, making it difficult to provide users with the housing information that is most suitable for them.

[1268] 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.

[1269] In this invention, the server includes means for a user to input desired living locations and conditions, means for collecting data based on the conditions entered by the user from multiple data sources on the Internet, means for temporarily storing the collected data in a database, means for analyzing the collected data and filtering out housing information that matches the user's conditions, means for assigning evaluation scores to the filtered housing information, means for ranking the housing information based on the evaluation scores, means for presenting the selected housing information to a user terminal, and means for collecting word-of-mouth and reputation information from social networking services, thereby enabling users to quickly and accurately obtain optimal housing information that matches their desired conditions.

[1270] "User" refers to someone who uses this system to input the desired location and conditions to search for housing information.

[1271] "Desired place and conditions to live" refers to the specific requirements of the user, such as the area, budget, and floor plan they desire.

[1272] "Data source" refers to various information sources that provide data that meets the user's requirements, such as real estate information sites on the Internet and social networking services (SNS).

[1273] A "database" refers to a digital storage system for temporarily storing and managing collected data (property information and reviews).

[1274] "Rating score" refers to the points or rating value assigned to each property based on collected reviews and other indicators.

[1275] "Ranking" refers to the process of ranking property information based on evaluation scores and presenting it to the user in order of priority.

[1276] "Social Networking Service" refers to an online service (e.g., a social networking platform) that enables users to post and share information.

[1277] "Filtering" refers to the process of extracting information that meets a user's criteria from collected data and excluding unnecessary information.

[1278] A "real estate information site" refers to a site that provides information on the rental and sale of properties on the Internet.

[1279] This invention relates to a system that allows users to input desired locations and conditions for living, and then collects and analyzes related data from multiple data sources on the Internet to provide optimal housing information. Specifically, it is configured as follows.

[1280] Basic system configuration

[1281] This system consists of a "terminal" used by the user, a "server" that collects and analyzes data, and multiple data sources (real estate information sites and social networking services).

[1282] User side

[1283] 1. Input Method

[1284] The user uses a browser on their device (e.g., a PC or smartphone) or a dedicated application to input the desired location and conditions for living. For example, they can input conditions such as "Shinjuku-ku, Tokyo, under 100,000 yen, 2LDK, nearby supermarket, pets allowed." The input data is sent to the server.

[1285] Server side

[1286] 2. Data Collection Methods

[1287] The server uses web scraping technologies such as Python, BeautifulSoup, and Selenium to collect property information from real estate information websites, and also uses APIs of social networking services (e.g., Twitter API and other SNS APIs) to collect reviews and reputation information related to the user's criteria.

[1288] 3. Temporarily save to database

[1289] The server temporarily stores the collected data in a database (e.g., MySQL), including the property's address, rent, floor plan, age, distance to the nearest station, and the latest related reviews.

[1290] 4. Data Analysis Methods

[1291] The server analyzes the temporarily saved data and filters the property information that matches the conditions entered by the user. For example, it extracts only properties that match conditions such as rent under 100,000 yen, floor plan of 2LDK, and pet-friendly.

[1292] 5. Evaluation Score Assignment Method

[1293] The server assigns a rating score to the filtered property information based on the collected reviews, giving a high score to properties with many positive reviews and a low score to properties with many negative reviews.

[1294] 6. Ranking Methods

[1295] The server ranks the property information based on the evaluation score, so that the highly rated properties that best match the user's criteria are displayed at the top.

[1296] Presentation of results

[1297] 7. Presentation means

[1298] The server sends a list of selected housing information to the user's device. The user's browser or application displays information such as rent, floor plan, location, distance to the nearest station, and the latest reviews. This information is presented visually and easily understandable to the user.

[1299] Specific operation example

[1300] For example, if a user enters conditions such as "Shinjuku-ku, Tokyo, under 100,000 yen, 2LDK, nearby supermarket, pets allowed," the server will operate as follows:

[1301] The server scrapes multiple real estate information sites using keywords such as "Shinjuku-ku 2LDK rental."

[1302] At the same time, the server collects information on social media using hashtags such as "Shinjuku Ward Rentals" and "Pets Allowed."

[1303] The server temporarily stores the collected data in a database.

[1304] The server filters the property listings that match the user's criteria.

[1305] The server assigns an evaluation score to the filtered properties based on the collected reviews and ranks them.

[1306] The server displays the top-ranked property information on the user's device, allowing the user to check detailed information and contact information. This system allows users to easily obtain information about housing that matches their preferences and select the property that best suits them.

[1307] Examples of prompt statements

[1308] "Build a system that allows users to input criteria and collects and analyzes data from multiple sources to provide the most suitable housing information. Use Python, BeautifulSoup, Selenium, MySQL, and SNS APIs as the technologies to be used. Include a function to assign an evaluation score based on the user's criteria and create a ranking."

[1309] This invention is a system that efficiently provides housing information that matches a user's desires from a vast amount of data on the Internet, and supports the user in making the most suitable choice.

[1310] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1311] Step 1:

[1312] The user inputs the desired location and conditions into the device.

[1313] Input: The user enters conditions such as "Shinjuku-ku, Tokyo, under 100,000 yen, 2LDK, nearby supermarket, pets allowed" into the input form.

[1314] Output: The entered conditions are sent to the server.

[1315] Specific operation: The user uses a browser or application to enter the desired conditions through an input form and clicks the submit button. This information is sent to the server in JSON format.

[1316] Step 2:

[1317] The server collects data from real estate information sites and social networking sites.

[1318] Input: Conditions received from the user (e.g., "Shinjuku-ku, Tokyo, under 100,000 yen, 2LDK, nearby supermarket, pets allowed").

[1319] Output: The collected property information and reviews are stored in a database for temporary storage.

[1320] How it works: The server uses Python's BeautifulSoup and Selenium to crawl multiple real estate information websites and collect property information that matches the search criteria. At the same time, it uses Twitter APIs and other social media APIs to collect reviews using related hashtags and keywords.

[1321] Step 3:

[1322] The server temporarily stores the collected data in a database.

[1323] Input: Collected property information and reviews.

[1324] Output: Raw data stored in a database.

[1325] Specific operation: The collected property address, rent, floor plan, age, distance to the nearest station, and related reviews are organized and stored in MySQL or other databases.

[1326] Step 4:

[1327] The server analyzes the collected data and filters property information that matches the user's criteria.

[1328] Input: Property information and reviews stored in the database.

[1329] Output: Filtered listings that match the user's criteria.

[1330] Specific operation: The server extracts only matching property information from the database based on the conditions entered by the user (e.g., rent less than 100,000 yen, layout 2LDK, pets allowed, etc.).

[1331] Step 5:

[1332] The server assigns an evaluation score to the filtered property information.

[1333] Input: Filtered property information and collected reviews.

[1334] Output: Property information with rating scores.

[1335] Specific operation: The server analyzes the collected review information for the filtered properties and assigns a high score to properties with many positive reviews and a low score to properties with many negative reviews.

[1336] Step 6:

[1337] The server ranks the property information based on the evaluation score.

[1338] Input: Property information with rating scores.

[1339] Output: A ranked list of properties.

[1340] Specific operation: The server ranks property information based on the rating score and creates a list so that properties with higher scores are displayed at the top.

[1341] Step 7:

[1342] The server transmits a list of the selected residence information to the user terminal and displays it.

[1343] Input: Ranked property list.

[1344] Output: Residence information displayed on the user's device.

[1345] Specific operation: The server sends the ranked property information to the user's device, and displays the rent, floor plan, location, distance to the nearest station, and related reviews on the user's browser or application. The user can then view detailed information and contact details.

[1346] (Application example 1)

[1347] 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."

[1348] Currently, searching for real estate information requires manually collecting data from multiple sources and conducting detailed research on each property, which is extremely time-consuming and labor-intensive. Furthermore, it is difficult to evaluate properties using word-of-mouth information on social media, making it difficult for users to efficiently select the housing information that is best for them.

[1349] 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.

[1350] In this invention, the server includes means for a user to input desired living locations and conditions, means for collecting data based on the conditions input by the user from multiple information sources on the Internet, means for analyzing the collected word-of-mouth information on SNS and assigning evaluation scores, means for ranking properties based on the evaluation scores, means for inputting prompt sentences to a generative AI model based on the conditions input by the user and having the AI ​​model evaluate the properties, and means for presenting selected housing information to a user terminal, thereby enabling users to efficiently and effectively obtain optimal housing information.

[1351] The "place and conditions in which the user wishes to live" are specific requirements regarding the residence the user desires, such as the area, budget, layout, and the presence or absence of nearby facilities.

[1352] "Multiple sources of information on the Internet" refers to multiple platforms that provide housing information and word-of-mouth information on the web, such as real estate information sites and social networking services.

[1353] "Means of collection" refers to the means of obtaining data from sources on the Internet, such as using web scraping techniques or APIs.

[1354] The "means of analysis" refers to the means of storing the collected data in a database, interpreting the data using statistical analysis and natural language processing, and assigning an evaluation score.

[1355] The "evaluation score" is a numerical value of positive and negative evaluations that is generated by analyzing word-of-mouth information collected based on the user's conditions.

[1356] The "ranking means" is a means for ranking properties based on the evaluation scores so that the properties most suitable for the user are displayed at the top.

[1357] A "user terminal" is a device, such as a smartphone, tablet, or PC, that a user operates to input information and check the results.

[1358] A "generative AI model" is an artificial intelligence model that generates prompt statements based on user input and automatically evaluates properties using those prompt statements.

[1359] A "prompt" is an instruction entered into a generative AI model to make it perform a specific task.

[1360] The present invention relates to a system for efficiently acquiring residential information desired by a user, and its embodiment is as follows: This system is mainly composed of a user terminal, a server, and multiple information sources on the Internet.

[1361] User terminal

[1362] Users access the system using their own devices such as smartphones, tablets, or PCs. Using a dedicated application or web browser, users input the desired location and conditions (area, budget, floor plan, availability of nearby facilities, etc.).

[1363] Data collection

[1364] The server uses Python-based web scraping technology (such as BeautifulSoup or Selenium) to collect property data based on user criteria from multiple real estate information websites on the Internet, and also collects related reviews from social networking services via the Twitter API and Instagram Graph API.

[1365] Data analysis

[1366] The server stores the collected data in an SQL database (e.g., MySQL) and analyzes it using natural language processing technology (e.g., TextBlob), classifying reviews as positive or negative, and assigning a rating score to each property based on that.

[1367] Property ranking

[1368] To rank the properties based on their rating scores, the server runs a ranking algorithm that ensures that the properties that best meet the user's criteria are displayed at the top of the list.

[1369] Presentation of information

[1370] The server sends the selected housing information to the user's device and displays it in an easy-to-read format, including the property's rent, layout, location, distance to the nearest public transport, and user reviews.

[1371] Generative AI Models

[1372] The server inputs the user's criteria as a prompt into the generative AI model. This generative AI model is optimized to perform a specific task and automatically evaluates properties. An example of a prompt might be, "Please search for a 2LDK home in Shinjuku Ward with a budget of less than 100,000 yen, gather information on properties with a nearby supermarket and that allow pets, and rank them based on reviews on social media."

[1373] In this way, the system collects data from multiple sources, analyzes, evaluates, and ranks them, and provides users with the most suitable housing information.Specific hardware and software used include Python, BeautifulSoup, Selenium, MySQL, Twitter API, Instagram Graph API, TextBlob, and React Native.

[1374] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1375] Step 1:

[1376] Users input their desired location and conditions (e.g., area, budget, floor plan, availability of nearby facilities, etc.) through an application or web browser on their device (smartphone, tablet, PC). The input information is sent to the server.

[1377] Step 2:

[1378] The server uses Python's BeautifulSoup and Selenium to scrape property data from multiple real estate information websites on the Internet based on user-specified criteria. The input is the criteria entered by the user, and the output is property data that matches the criteria (e.g., rent, floor plan, location, distance to the nearest station).

[1379] Step 3:

[1380] The server uses the Twitter API and Instagram Graph API to collect reviews of properties that meet the criteria. Here too, the input is the user's criteria, and the output includes reviews on social media (e.g., tweets and comments).

[1381] Step 4:

[1382] The server stores the collected property data and reviews in a MySQL database. The input is the data collected in Step 2 and Step 3, and the output is the results stored in the database.

[1383] Step 5:

[1384] The server uses natural language processing technology (e.g., TextBlob) to analyze the stored data. Specifically, it classifies reviews as positive or negative and assigns a rating score to each property. The input is the reviews retrieved from the database, and the output is the rating score for each property.

[1385] Step 6:

[1386] The server ranks the properties based on the rating score. It runs a ranking algorithm to ensure that the properties that best fit the user's criteria are displayed at the top. The input is the rating score, and the output is a ranked list of properties.

[1387] Step 7:

[1388] The server inputs the user's conditions into the generative AI model as a prompt statement, and uses the AI ​​model to make a final evaluation of the property. The prompt statement is used as input, and the output is the property evaluation result by the generative AI model. For example, a prompt statement could be, "Please search for a 2LDK home in Shinjuku Ward with a budget of less than 100,000 yen, gather information on properties with a nearby supermarket and that allow pets, and rank them including reviews on social media."

[1389] Step 8:

[1390] The server sends the ranked property information to the user's device and displays it on an application or web browser. The optimal housing information is then presented to the user. The output includes information such as rent, floor plan, location, distance to the nearest station, and user reviews.

[1391] 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.

[1392] This invention relates to a system that allows users to input the location and conditions they wish to live in, and then collects and analyzes related data from multiple information sources on the Internet to provide optimal housing information. Furthermore, a specific form is shown in which user satisfaction can be increased by combining it with an emotion engine that recognizes the user's emotions. Specifically, the system is configured as follows:

[1393] Basic system configuration

[1394] This system consists of a "terminal" used by the user, a "server" that collects and analyzes data, multiple information sources (real estate information sites and social networking services), and an emotion engine.

[1395] User side

[1396] 1. Input Method

[1397] A form is displayed on the user's device for entering the desired place to live and conditions (area, budget, floor plan, other conditions, etc.) For example, the user enters "Shinjuku-ku, Tokyo, under 100,000 yen, 2LDK, nearby supermarket, pets allowed."

[1398] When inputting, the emotion engine recognizes emotions from the user's facial expressions and voice, and collects emotion data for the user's input conditions.

[1399] Server side

[1400] 2. Data Collection Methods

[1401] The server uses web scraping technologies such as Python, BeautifulSoup, and Selenium to collect property information from real estate information websites. It also collects reviews and reputation information related to the user's criteria through social media APIs (e.g., Twitter API and Instagram Graph API).

[1402] 3. Temporary storage of data

[1403] The collected data is temporarily stored in a database, and includes basic information such as address, rent, floor plan, age of the building, and distance to the nearest station.

[1404] 4. Data Analysis Methods

[1405] The server analyzes the collected data and filters the property information that matches the user's criteria, such as rent under 100,000 yen, a 2LDK layout, and pet-friendly properties.

[1406] 5. Evaluation Score Assignment Method

[1407] The filtered property information is assigned a rating score based on the collected reviews. Properties with many positive reviews are given a high score, while properties with many negative reviews are given a low score.

[1408] 6. Ranking Methods

[1409] The property listings are ranked based on their rating scores. The property listings are sorted so that the properties that best match the user's criteria and have the highest ratings are displayed at the top.

[1410] Use of emotion engine

[1411] 7. Collecting Emotional Data

[1412] The emotion engine recognizes emotions from the user's facial expressions and voice, collects emotional data, and quantifies, for example, the user's satisfaction and expectations regarding the input conditions.

[1413] 8. Emotion Data Analysis

[1414] The server analyzes the emotional data and identifies which conditions the user has more positive feelings about. Based on this, it adjusts the analysis results and selects the property information that is most suitable for the user.

[1415] 9. User response monitoring

[1416] The emotion engine monitors users' reactions in real time when property information is presented. If the user responds positively to the property information presented, that information is reflected in the next data analysis.

[1417] Presentation of results

[1418] 10. Presentation means

[1419] The server sends a list of selected housing information to the user's device. The formatted information includes rent, floor plan, location, distance to the nearest station, the latest reviews, and information based on the user's emotional data.

[1420] 11. User Feedback

[1421] The user terminal displays the received property information. The user can check the displayed property information and provide feedback if there are any deficiencies or problems. The server collects the feedback and reflects it in the next analysis.

[1422] Specific operation example

[1423] For example, if a user enters conditions such as "Shinjuku-ku, Tokyo, under 100,000 yen, 2LDK, nearby supermarket, pets allowed," the server will operate as follows:

[1424] The server scrapes multiple real estate information sites using keywords such as "Shinjuku-ku 2LDK rental."

[1425] At the same time, information is collected on social media using hashtags such as "Shinjuku Ward Rentals" and "Pets Allowed."

[1426] The collected data is stored in a database and property information that matches the user's criteria is filtered.

[1427] The filtered properties are assigned an evaluation score based on the collected reviews and ranked.

[1428] The emotion engine recognizes the user's emotions when entering information and when information is presented, and reflects that data in the analysis results, allowing for more accurate property selection.

[1429] Finally, the top-ranked property information and supplementary information based on emotion data are displayed on the user's device, allowing the user to check detailed information and contact details.

[1430] This system allows users to easily obtain more personalized housing information and select the most suitable property.

[1431] The processing flow will be explained below.

[1432] Step 1:

[1433] The user enters the desired location and conditions into a form displayed on the user's device. For example, the user might enter "Shinjuku-ku, Tokyo, under 100,000 yen, 2LDK, nearby supermarket, pets allowed."

[1434] Step 2:

[1435] Along with the conditions entered by the user, the emotion engine uses face recognition technology and voice analysis technology to recognize emotions from the user's facial expressions and tone of voice, and collects emotion data for the entered conditions.

[1436] Step 3:

[1437] When a user transmits input content, the user terminal transmits the input data and emotion data to the server.

[1438] Step 4:

[1439] Based on the user's conditions received by the server, web scraping is initiated against multiple real estate information websites on the Internet. Using Python, BeautifulSoup, and Selenium, searches are performed using keywords such as "Shinjuku Ward, 2LDK for rent" and "under 100,000 yen" to collect property information.

[1440] Step 5:

[1441] The server temporarily stores the property information it collects in a database, which includes basic information such as address, rent, floor plan, age of the building, and distance to the nearest station.

[1442] Step 6:

[1443] The server uses SNS APIs (e.g., Twitter API or Instagram Graph API) to collect relevant reviews on SNS. For example, it searches for hashtags such as "Shinjuku-ku rental" and "pets allowed" to obtain the latest reputations and reviews.

[1444] Step 7:

[1445] The server also stores the collected social media data in a database, including user ratings and comments.

[1446] Step 8:

[1447] The server filters the real estate information in the database based on the user's input criteria, such as rent under 100,000 yen, a 2LDK layout, and pet-friendly properties.

[1448] Step 9:

[1449] The server assigns a rating score to the filtered property information based on the collected reviews, raising the score for properties with many positive comments and lowering the score for properties with many negative comments.

[1450] Step 10:

[1451] The server analyzes the emotion data and reflects the user's feelings toward the criteria entered in the evaluation score. For example, if the user expresses particularly positive feelings toward a particular criteria, the score will be adjusted to give more weight to that criteria.

[1452] Step 11:

[1453] Ranking of property information based on evaluation scores. Sort property information so that properties with higher scores are displayed at the top.

[1454] Step 12:

[1455] The server formats the ranked property information and sends it to the user's device. The formatted information includes rent, floor plan, location, distance to the nearest station, the latest reviews, and information based on the user's emotional data.

[1456] Step 13:

[1457] The property information received by the user's device is displayed on a browser or application. The user can check the displayed property information and click to view detailed information or contact information.

[1458] Step 14:

[1459] Users can provide feedback on the displayed property information, and the server collects that feedback and reflects it in the next data analysis.

[1460] The above is the specific flow of program processing for a system that combines an emotion engine.

[1461] Example 2

[1462] 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."

[1463] Conventional housing information systems only provide property information based on the conditions entered by the user, and do not consider the user's feelings or satisfaction when selecting properties. As a result, it is not possible to fully meet the user's expectations, making it difficult to provide optimal housing information.

[1464] The identification process by the identification 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 a means for a user to input desired living conditions and locations, a means for collecting data based on the conditions input by the user from multiple information sources on the Internet, a means for temporarily storing the collected data, a means for analyzing the collected data and selecting optimal housing information for the user, a means for presenting the selected housing information to the user terminal, and a means for acquiring user emotion data using an emotion engine and reflecting the data in the analysis results. This makes it possible to provide optimal housing information that takes the user's emotions into consideration.

[1465] "Means for users to input desired location and conditions for living" refers to an interface that allows users to input desired conditions for housing (e.g., location, budget, layout, and other specific requirements) using a terminal.

[1466] "Multiple sources on the Internet" refers to various websites and online services (e.g., real estate websites, social networking services, etc.) that provide data related to the user's input.

[1467] "Means of collecting data" refers to the technical means (e.g., web scraping technology or APIs) used to automatically obtain relevant data from each source.

[1468] "Means for temporarily storing collected data" refers to a database or storage system for temporarily storing acquired data.

[1469] "Means for analyzing collected data" refers to algorithms and analytical techniques for analyzing collected data and selecting the most suitable property based on the user's conditions.

[1470] "Means for presenting selected residential information on a user terminal" refers to a method for displaying the optimal property information selected as a result of the analysis on a user device.

[1471] An "emotion engine" refers to software or hardware that recognizes and analyzes emotions from a user's facial expressions and voice and collects that data.

[1472] "Means of assigning evaluation scores" refers to the method for calculating and assigning quantitative evaluation scores to each property based on collected word-of-mouth information, etc.

[1473] "Means for ranking properties" refers to a means for prioritizing and sorting properties based on their rating scores.

[1474] "Means of acquiring emotional data and reflecting it in the analysis results" refers to technology that incorporates user emotional data acquired through an emotion engine into the analysis results to enable more accurate property selection.

[1475] The present invention relates to a system that allows users to input their desired location and conditions, collects and analyzes related data from multiple information sources on the Internet, and provides optimal housing information. This system can further increase user satisfaction by combining it with an emotion engine that recognizes the user's emotions.

[1476] Basic system configuration

[1477] This system includes "means for the user to input the location and conditions for living that the user desires," "means for collecting data," "means for temporarily storing collected data," "means for analyzing collected data," "means for presenting selected housing information on the user's terminal," and "means for obtaining the user's emotional data using an emotion engine and reflecting it in the analysis results."

[1478] User side

[1479] First, the user inputs the desired location and conditions into the input form displayed on the device. For example, the user might input conditions such as "Shinjuku-ku, Tokyo, under 100,000 yen, 2LDK, nearby supermarket, pets allowed." As the input is made, the emotion engine recognizes emotions from the user's facial expressions and voice, and collects emotional data for the user's input conditions. The emotion engine used is software that uses a camera and microphone to quantify the user's emotions.

[1480] Server side

[1481] The server uses web scraping technologies such as Python, BeautifulSoup, and Selenium to collect property information from real estate information websites. It also collects reviews and reputation information related to the user's criteria through social media APIs (e.g., Twitter API and Instagram Graph API). The collected data is temporarily stored in a database such as MySQL. The stored data includes address, rent, floor plan, age of the building, distance to the nearest station, etc.

[1482] The server then analyzes the collected data and filters the property information that matches the user's criteria. For example, an SQL query can be used to extract properties that meet criteria such as rent under 100,000 yen, a 2LDK layout, and pet-friendly. Then, based on the reviews, a scoring method is used to assign a high score to properties with many positive reviews and a low score to properties with many negative reviews. For example, a natural language processing model using TensorFlow can be used to analyze the reviews and calculate the score.

[1483] Based on the calculated score, the server runs a sorting algorithm to rank the property information. The data is then formatted so that the most highly rated properties are displayed at the top. The emotional data obtained from the emotion engine is analyzed as the user's reaction to the conditions entered by the user and their reaction when the housing information is presented. Based on this, the server determines which conditions the user has more positive feelings about and reflects this information in the next data analysis.

[1484] Presentation of results

[1485] The housing information list selected as the result of the analysis is sent from the server to the user's device in JSON format or similar. The user's device then formats and displays the received data. The displayed information includes rent, floor plan, location, distance to the nearest station, and the latest reviews. In addition, supplemental information based on emotional data is also presented. The user can review the provided information and provide feedback if necessary. This feedback is also reflected in the next data analysis, contributing to improving the accuracy of the entire system.

[1486] Specific operation example

[1487] For example, if a user enters the following criteria: "Shinjuku Ward, Tokyo, under 100,000 yen, 2LDK, nearby supermarket, pets allowed," the server will scrape multiple real estate information websites using keywords such as "Shinjuku Ward 2LDK rental." At the same time, it will collect information on social media using hashtags such as "Shinjuku Ward rental" and "pets allowed." The collected data is stored in a database and property listings that match the user's criteria are filtered. Based on the filtering results, properties are assigned an evaluation score and ranked. The emotion engine recognizes the user's emotions when entering and presenting the information, and incorporates this data into the analysis results to enable more accurate property selection. Finally, the top-ranked property listings and supplementary information based on the emotion data are displayed on the user's device, allowing the user to check detailed information and contact information.

[1488] Example prompts for generative AI models

[1489] If a user enters "Shinjuku-ku, Tokyo, under 100,000 yen, 2LDK, nearby supermarket, pets allowed," please explain what type of property would be most suitable and why.

[1490] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1491] Step 1: User Input

[1492] The user enters the desired location and conditions into an input form on the device. For example, they might enter "Shinjuku-ku, Tokyo, under 100,000 yen, 2LDK, nearby supermarket, pets allowed." At the same time, this input condition is sent to the server, the emotion engine acquires emotional data from the user's facial expressions and voice. The emotion data and the condition input data are sent to the server and stored together. The specific input data are the location, budget, floor plan, and specific requirements (near supermarket, pets allowed, etc.), and the output is the input data and emotional data.

[1493] Step 2: Data collection

[1494] The server uses web scraping technologies such as Python, BeautifulSoup, and Selenium to collect property information from real estate information websites. It also collects reviews and reputation information related to user criteria through social media APIs (e.g., Twitter API and Instagram Graph API). The input data is the location and conditions entered by the user, and the output is property information and reviews collected based on those conditions. Specifically, the server searches multiple real estate information websites for keywords such as "2LDK rental in Shinjuku Ward" and analyzes the HTML content.

[1495] Step 3: Save data

[1496] The server stores the collected property information and reviews in a database such as MySQL. The input data is information collected from web scraping and SNS APIs, and the output is a database containing that information. Specifically, the server stores the information in a table with fields such as address, rent, floor plan, age of the building, and distance to the nearest station.

[1497] Step 4: Data analysis

[1498] The server executes an SQL query to extract properties from the database that match the user's criteria. The input data is the user's desired criteria and property information in the database, and the output is a list of properties that match those criteria. Specifically, the server executes a query such as "SELECT FROM properties WHERE rent <= 100000 AND layout = '2LDK' AND pets_allowed = true."

[1499] Step 5: Assigning a rating score

[1500] The server analyzes the reviews and assigns a high score to properties with many positive reviews. For example, the reviews are analyzed using a natural language processing model using TensorFlow. The input data is the extracted property list and the reviews for that property, and the output is a property list with a score assigned. Specifically, the server judges comments such as "This place is great for families!" to be positive and assigns a high score.

[1501] Step 6: Ranking

[1502] The server runs a sorting algorithm to sort the properties by highest score. The input data is a list of scored properties, and the output is a ranked list of properties. Specifically, the server sorts the list of properties in descending order based on the score field, with the most highly rated properties at the top.

[1503] Step 7: Sentiment Data Analysis

[1504] The server analyzes the emotion data obtained from the emotion engine and identifies the conditions for which the user has positive emotions. The input data is the emotion data and the condition data entered by the user, and the output is the user's emotional evaluation of those conditions. Specifically, if the user shows a satisfied expression for the condition "there is a supermarket nearby," the importance of that condition is increased.

[1505] Step 8: Presenting the results

[1506] The housing information list selected as the analysis result is sent from the server to the user's device. The input data is a ranked property list that reflects emotional evaluations, and the output is property information displayed on the user's device. The user's device formats and displays this information, providing information such as rent, floor plan, location, distance to the nearest station, and the latest reviews. The user then enters feedback, which is reflected in the next analysis.

[1507] Example prompts for generative AI models

[1508] If a user enters "Shinjuku-ku, Tokyo, under 100,000 yen, 2LDK, nearby supermarket, pets allowed," please explain what type of property would be most suitable and why.

[1509] (Application example 2)

[1510] 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."

[1511] Currently, it is not easy for users to find the best place to live or travel based on their own conditions and emotions. In particular, there is no system that provides appropriate information in real time while traveling in an autonomous vehicle. As a result, users must manually collect and evaluate information from many sources, which takes time and effort and has low accuracy. In addition, because the information is not individually optimized taking into account the user's emotions, it may result in low satisfaction.

[1512] 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 places and conditions for living, means for collecting data based on the conditions entered by the user from multiple information sources on the Internet, means for analyzing the collected data and selecting housing information optimal for the user, means for assigning an evaluation score based on the collected data, means for ranking based on the evaluation score, means for collecting user emotions using an emotion recognition engine and reflecting them in the analysis results, and means for presenting the selected information on the user terminal. This enables users to easily obtain information on optimal housing and travel destinations based on their emotions in real time.

[1513] "Means for users to input desired living conditions and location" refers to a form or interface that allows users to input conditions such as desired area, budget, and floor plan into a terminal.

[1514] "Multiple sources on the Internet" includes information providers that are the source of data collected from websites and social networking services.

[1515] An "emotion recognition engine" refers to software or a system that analyzes emotions from a user's facial expressions and voice and collects that data.

[1516] "Means for assigning an evaluation score" refers to software or algorithms that have the function of calculating an evaluation score for an object based on collected data.

[1517] "Ranking means" refers to software or algorithms used to rank objects based on their evaluation scores.

[1518] "Means for presenting on the user terminal" refers to the interface or software for displaying the selected information on the device used by the user.

[1519] This invention relates to a system that allows users to input desired locations and conditions, collects and analyzes related data from multiple sources on the Internet, and provides optimal housing information and travel destinations. Furthermore, by combining it with an emotion recognition engine, a specific form that can increase user satisfaction is shown.

[1520] Basic system configuration

[1521] This system consists of a "terminal" used by the user, a "server" that collects and analyzes data, multiple information sources, and an emotion recognition engine.

[1522] User side

[1523] 1. Input method:

[1524] Users input their desired location and conditions for living, as well as their travel destination preferences (region, budget, tourist spots, and other conditions) on a touch panel terminal inside the self-driving vehicle.

[1525] When inputting, the emotion recognition engine recognizes emotions from the user's facial expressions and voice, and collects emotion data for the input conditions.

[1526] Server side

[1527] 2. Data collection methods:

[1528] The server uses web scraping technologies such as Python, BeautifulSoup, and Selenium to collect relevant information from travel and real estate information sites.

[1529] We also collect word-of-mouth and reputation information related to user conditions through social media APIs (such as Twitter API and Instagram Graph API).

[1530] 3. Temporary data storage:

[1531] The collected data is temporarily stored in a database. In the case of travel information, it includes basic information such as tourist spots, hotels, and restaurants.

[1532] 4. Data analysis methods:

[1533] The server analyzes the collected data and filters information that matches the user's criteria, for example, extracting tourist spots and hotels that match the user's travel preferences.

[1534] 5. Rating Score Method:

[1535] The filtered information is assigned a rating score based on the collected reviews. Information with many positive reviews is given a high rating score.

[1536] 6. Ranking Method:

[1537] The information is ranked based on the evaluation score, and the information that best matches the user's criteria and is highly rated is displayed at the top.

[1538] Use of emotion recognition engine

[1539] 7. Collecting Emotional Data:

[1540] The emotion recognition engine recognizes emotions from the user's facial expressions and voice, and collects emotional data, such as the user's satisfaction and expectations regarding the conditions, which are then quantified.

[1541] 8. Emotional Data Analysis:

[1542] The server analyzes the emotional data and identifies which information the user feels more positively about. Based on this, it adjusts the analysis results and selects the most appropriate information for the user.

[1543] 9. User response monitoring:

[1544] The emotion recognition engine monitors the user's reaction in real time when information is presented. If the user responds positively to the information presented, that information is reflected in the next data analysis.

[1545] Presentation of results

[1546] 10. Presentation means:

[1547] The server sends a list of selected information to the user's device. The formatted information includes details about each facility or spot, tourist reviews, and information based on the user's emotional data.

[1548] 11. User Feedback:

[1549] The user terminal displays the received information. The user can check the displayed information and provide feedback if there are any deficiencies or problems. The server collects the feedback and reflects it in the next analysis.

[1550] Specific operation example

[1551] For example, if a user enters conditions such as "hot spring town" or "places where you can enjoy delicious local food," the server will operate as follows:

[1552] The server scrapes multiple travel information sites using keywords such as "hot spring town sightseeing" and "local food."

[1553] At the same time, information is collected using hashtags such as "hot spring trip" and "local gourmet" on social media.

[1554] The collected data is stored in a database and information on tourist spots and restaurants that meet the user's criteria is filtered.

[1555] The filtered information is assigned an evaluation score based on collected word-of-mouth information and ranked.

[1556] The emotion recognition engine recognizes the user's emotions when inputting and presenting information, and reflects this data in the analysis results, allowing for more accurate information selection.

[1557] Finally, the top-ranked information and supplementary information based on emotion data are displayed on the user's device, allowing the user to check detailed information and contact details.

[1558] Examples and prompts

[1559] prompt

[1560] text

[1561] Please recommend a travel destination that meets the following criteria:

[1562] The user's facial expression and voice analysis data are also input, so please take emotional data into consideration.

[1563] conditions:

[1564] hot spring town

[1565] A place to enjoy delicious local food

[1566] Emotional Data:

[1567] Expression: Excitement and anticipation

[1568] Voice: Calm and relaxed tone

[1569] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1570] Step 1:

[1571] The user inputs desired conditions into a terminal inside the autonomous vehicle. Specifically, the user inputs conditions such as "hot spring town" or "place where I can enjoy delicious local food" into the touch panel terminal. At the same time as the input, the terminal's camera and microphone record the user's facial expressions and voice, and emotional data is collected. The input here consists of the desired conditions as a string of characters and emotional data quantified by an emotion recognition engine.

[1572] Step 2:

[1573] The server receives user input criteria and collects data from multiple sources on the Internet. Using a Python script, it web scrapes travel and real estate information sites to obtain information that matches the criteria. It also uses social media APIs to collect related reviews and reputation information. The input here is the user's desired criteria, and the output is a list of collected related information.

[1574] Step 3:

[1575] The server temporarily stores the collected data in a database, which contains details such as the name, location, and reviews of travel spots. The input here is the collected data, and the output is the information stored in the database.

[1576] Step 4:

[1577] The server analyzes the stored data and filters information that matches the user's criteria. Specifically, it searches using keywords based on the criteria and extracts data that matches the criteria. The input here is all data from the database, and the output is the narrowed-down data that matches the criteria.

[1578] Step 5:

[1579] The server assigns an evaluation score to the filtered data based on the collected word-of-mouth information. Specifically, it assigns a high evaluation score to information with many positive reviews and a low evaluation score to information with many negative reviews. The input here is the filtered data and word-of-mouth data, and the output is data with an evaluation score.

[1580] Step 6:

[1581] The server ranks the data based on the evaluation score. It sorts the data in order of score so that highly rated information is displayed at the top. The input here is data with evaluation scores, and the output is ranked data.

[1582] Step 7:

[1583] The server analyzes the user's emotional data and selects data based on the user's emotions. Specifically, it analyzes the data obtained by the emotion recognition engine and adjusts the data by placing emphasis on conditions where the user has positive emotions. The input here is the emotional data and ranked data, and the output is the final adjusted data.

[1584] Step 8:

[1585] The server presents the adjusted data to the user's device. Specifically, it displays a list of top travel destinations and tourist attractions on a touch panel device, allowing the user to check detailed information. The input here is the final adjusted data, and the output is the information displayed on the user's device.

[1586] Step 9:

[1587] The user checks the presented information and provides feedback. Specifically, they input their satisfaction or dissatisfaction with each travel destination or tourist spot displayed. The device collects this feedback and reflects it in the next data analysis. The input here is the user's feedback, and the output is the feedback data sent to the server.

[1588] 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.

[1589] 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.

[1590] 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.

[1591] 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.

[1592] 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.

[1593] 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.

[1594] 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).

[1595] 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.

[1596] 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."

[1597] 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.

[1598] 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).

[1599] 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.

[1600] 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.

[1601] 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.

[1602] 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.

[1603] 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.

[1604] 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.

[1605] 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.

[1606] 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.

[1607] 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.

[1608] 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.

[1609] The following is further disclosed regarding the above embodiment.

[1610] (Claim 1)

[1611] A means for users to input the location and conditions they want to live in,

[1612] means for collecting data from multiple sources on the Internet based on user-entered criteria;

[1613] A means for analyzing the collected data and selecting the most suitable housing information for the user;

[1614] The system includes a means for presenting the selected residence information to a user terminal.

[1615] (Claim 2)

[1616] means for assigning a rating score based on the collected data;

[1617] 10. The system of claim 1, further comprising means for ranking properties based on their rating scores.

[1618] (Claim 3)

[1619] 2. The system according to claim 1, wherein the information sources include real estate information sites and social networking services.

[1620] "Example 1"

[1621] (Claim 1)

[1622] A means for users to input the location and conditions they want to live in,

[1623] means for collecting data from a plurality of data sources on the Internet based on conditions input by a user;

[1624] A means for temporarily storing the collected data in a database;

[1625] A means for analyzing the collected data and filtering out residential information that meets the user's requirements;

[1626] means for assigning an evaluation score to the filtered residential information;

[1627] a means for ranking the housing information based on the evaluation score;

[1628] The system includes a means for presenting the selected residence information to a user terminal.

[1629] (Claim 2)

[1630] 10. The system according to claim 1, further comprising means for collecting word-of-mouth and reputation information from social networking services.

[1631] (Claim 3)

[1632] 2. The system of claim 1, further comprising: means for assigning an evaluation score based on the collected data; and means for ranking the housing information based on the evaluation score.

[1633] "Application Example 1"

[1634] (Claim 1)

[1635] A means for users to input the location and conditions they want to live in,

[1636] means for collecting data from multiple sources on the Internet based on user-entered criteria;

[1637] A means for analyzing the collected data and selecting the most suitable housing information for the user;

[1638] means for assigning an evaluation score based on the analyzed data;

[1639] A means of ranking properties based on their rating scores;

[1640] The system includes a means for presenting the selected residence information to a user terminal.

[1641] (Claim 2)

[1642] 2. The system according to claim 1, wherein the collected word-of-mouth information on SNS is analyzed and an evaluation score is assigned.

[1643] (Claim 3)

[1644] The system of claim 1 inputs a prompt sentence to the generative AI model based on conditions input by the user, and the AI ​​model evaluates the property.

[1645] "Example 2: Combining Emotion Engines"

[1646] (Claim 1)

[1647] A means for users to input the location and conditions they want to live in,

[1648] means for collecting data from multiple sources on the Internet based on user-entered criteria;

[1649] A means for temporarily storing the collected data;

[1650] A means for analyzing the collected data and selecting the most suitable housing information for the user;

[1651] means for presenting the selected residence information to a user terminal;

[1652] A means for acquiring user emotion data using an emotion engine and reflecting the data in the analysis results;

[1653] A system including:

[1654] (Claim 2)

[1655] means for assigning a rating score based on the collected data;

[1656] 10. The system of claim 1, further comprising means for ranking properties based on their rating scores.

[1657] (Claim 3)

[1658] 10. The system of claim 1, wherein the information sources include real estate information websites and social networking services.

[1659] (Claim 4)

[1660] 10. The system of claim 1, wherein the emotion engine includes means for collecting emotion data from the user's facial expressions and voice.

[1661] (Claim 5)

[1662] 2. The system according to claim 1, further comprising means for monitoring the user's reaction when property information is presented in real time and reflecting the reaction in the next data analysis.

[1663] "Application example 2 when combining emotion engines"

[1664] (Claim 1)

[1665] A means for users to input the location and conditions they want to live in,

[1666] means for collecting data from multiple sources on the Internet based on user-entered criteria;

[1667] A means for analyzing the collected data and selecting the most suitable housing information for the user;

[1668] means for assigning a rating score based on the collected data;

[1669] a means for ranking based on the evaluation scores;

[1670] A means for collecting user emotions using an emotion recognition engine and reflecting them in the analysis results;

[1671] The system includes means for presenting the selected information to a user terminal.

[1672] (Claim 2)

[1673] 2. The system according to claim 1, further comprising means for monitoring the user's emotions in real time using an emotion recognition engine and reflecting the emotions in the analysis results.

[1674] (Claim 3)

[1675] 2. The system according to claim 1, wherein the information sources include real estate information sites and social networking services. [Explanation of symbols]

[1676] 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 the location and conditions they want to live in, means for collecting data from multiple sources on the Internet based on user-entered criteria; A means for analyzing the collected data and selecting the most suitable housing information for the user; The system includes a means for presenting the selected residence information to a user terminal.

2. means for assigning a rating score based on the collected data; 10. The system of claim 1, further comprising means for ranking properties based on their evaluation scores.

3. The system according to claim 1 , wherein the information sources include real estate information sites and social networking services.

Images