system

Abstract

Provide a system. 【Solution means】 Means for receiving desired conditions from users, Means for obtaining information from an external database, Means for storing the obtained information in a data storage device, Means for searching within the information storage device based on the desired conditions, Means for notifying the user of the search results, Means for automatically making service reservations, Means for assisting in immediate schedule adjustment based on the presented information, A system including the above.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional search for rental properties, there is a problem that users have to manually search for properties that meet their own conditions from a vast amount of property information, which takes time and effort. Also, the update of information is likely to be delayed, and often users have to compromise on their desired conditions. Furthermore, when arranging an in-person viewing reservation by themselves, a lot of communication is required, which also places a burden on the real estate agent side.

Means for Solving the Problems

[0005] This invention proposes a system that automatically retrieves property information from an external real estate database based on the user's pre-set preferences, searches the database, and provides the most suitable property. When a property is found, the user is notified, and a viewing appointment can be automatically made. Furthermore, by providing information on properties that do not perfectly match the user's criteria but still offer advantages, the system presents a wider range of options. This system allows users to efficiently find properties that meet their requirements and reduces the workload for real estate agents.

[0006] "User" refers to an individual or group that uses this system to search for rental properties.

[0007] "Desired conditions" refer to the specific conditions or preferences that the user sets for a rental property in advance.

[0008] An "external real estate information database" refers to a third-party database that aggregates and provides information on rental properties.

[0009] "Property information" refers to information that includes detailed data about rental properties, such as address, rent, floor plan, and year of construction.

[0010] A "database" refers to a collection of data that allows for the storage, management, and retrieval of acquired property information.

[0011] "Searching methods" refer to methods and technologies for identifying suitable properties by referencing information within a database based on the user's desired criteria.

[0012] "Means of notification" refers to methods and functions for informing users of search results.

[0013] "Methods for automatically scheduling viewings" refers to methods or systems that efficiently arrange property viewings based on the user's preferences.

[0014] "Advantages" refer to characteristics or attributes of a property that are considered beneficial to the user.

[0015] "Feedback" refers to information regarding evaluations and reactions received from real estate agents after an in-person viewing.

Brief Explanation of Drawings

[0016] [Figure 1] It is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] It shows an emotion map to which multiple emotions are mapped. [Figure 10] It shows an emotion map to which multiple emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14]It is a sequence diagram showing the processing flow of a data processing system in Application Example 2 when a sentiment engine is combined.

Embodiments for Carrying Out the Invention

[0017] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0018] First, the terms used in the following description will be explained.

[0019] In the following embodiments, a processor with a reference number (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0020] In the following embodiments, a RAM (Random Access Memory) with a reference number is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0021] In the following embodiments, a storage with a reference number is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc.

[0022] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0023] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0024] [First Embodiment]

[0025] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0026] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0027] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0028] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0029] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0030] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0031] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0032] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0033] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0034] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0035] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0036] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0037] This invention is a system that enables users to efficiently search for rental properties and book viewings. A specific embodiment of this system is shown below.

[0038] First, the user accesses the application on their device and enters their desired criteria as part of the initial setup. These criteria include detailed items such as location, maximum rent, floor plan, year of construction, and distance from the nearest station. This information serves as the basic data for searching for properties based on the user's individual needs.

[0039] Next, the server retrieves the latest property information from an external real estate database. The retrieved information is continuously updated and stored in the server's database. This accumulated property information can be easily accessed via search queries.

[0040] The server periodically searches the database for property information, matching it against the user's desired criteria, either at regular intervals or upon user request. This is done using multiple filters with defined conditions, and properties that match the criteria are listed.

[0041] Search results are notified to the device, and users can view property details within the app. Even if no properties matching the criteria are found, the server can suggest properties with advantages outside of the specified criteria to the user.

[0042] Furthermore, the server automates the application process once a property the user wishes to view has been identified. The user selects the property they wish to view through their device, and the server makes the viewing reservation on their behalf with the real estate agent. Once the viewing reservation is confirmed, the details are notified to the user's device.

[0043] This system allows users to efficiently manage the process from property search to scheduling viewings. Real estate agents also benefit from the improved quality of viewings and reduced workload due to pre-matching of criteria.

[0044] For example, if a user requests a 1LDK apartment in Tokyo with a rent of 100,000 yen or less and within a 10-minute walk from a train station, the server searches an external database for suitable properties and notifies the user of that information. The user then selects a property they like, books a viewing, and the process is complete. Automating this entire process significantly reduces the time and effort required to choose an apartment.

[0045] The following describes the processing flow.

[0046] Step 1:

[0047] The user launches the application installed on their device and creates an account. After creating the account, they enter detailed information such as location, maximum rent, floor plan, building age, and distance from the station as their desired conditions.

[0048] Step 2:

[0049] The terminal sends the entered preferences to the server, which stores this information in a database to create a profile for each user.

[0050] Step 3:

[0051] The server connects to an external real estate information database using an API and periodically retrieves the latest property information. The retrieved property information is stored in a database on the server.

[0052] Step 4:

[0053] The server executes a search query by comparing the property information stored in the database with the user's desired conditions. The search results list properties that match the criteria.

[0054] Step 5:

[0055] The server sends the search results to the device. The device then sends a push notification to the user, displaying the details of matching properties within the app.

[0056] Step 6:

[0057] Users check the property information notified on their device and select any properties they like to view.

[0058] Step 7:

[0059] The terminal sends the user's request for a viewing to the server, and the server automatically sends a request to the real estate agent to schedule a viewing.

[0060] Step 8:

[0061] When the server receives confirmation of a viewing appointment from the real estate agent, it sends the schedule to the terminal. The terminal then notifies the user of the viewing date.

[0062] Step 9:

[0063] After the viewing, if the real estate agent provides feedback, the server receives that information and notifies the user via the terminal.

[0064] (Example 1)

[0065] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0066] Conventional information retrieval and reservation systems made it difficult for users to efficiently find the most suitable options based on their individual needs and to smoothly make reservations for those options. Furthermore, they lacked the functionality to provide information that didn't perfectly match the criteria but offered other advantages, failing to suggest options that users might overlook. Additionally, the lack of a mechanism for receiving feedback from vendors meant users were unaware of the subsequent situation, potentially leading to a decline in the quality of the experience.

[0067] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0068] In this invention, the server includes means for receiving desired conditions from the user, means for obtaining target information from an external information database, and means for storing the obtained target information in data storage. This enables the user to efficiently perform searches based on their own conditions and automated reservation procedures. Furthermore, by providing a function to suggest information that does not match the conditions but has other advantages, a wider range of choices can be offered, and by receiving feedback from vendors and notifying them of the situation after use, the overall quality of the experience can be improved.

[0069] "Means for receiving desired conditions" refers to a function that collects requests and conditions entered by users via digital devices and transmits that information to the server.

[0070] "Means of obtaining target information from an information database" refers to the process of accessing an external database, obtaining the latest information, and incorporating it into the system.

[0071] "Means for storing acquired target information in data storage" refers to a system that organizes and saves information in a storage device on a server, making it available for quick retrieval and use as needed.

[0072] A "search method" is an algorithm or process that efficiently finds data that matches specific criteria from information that has been collected and stored in advance.

[0073] "Means of notifying users" refer to mechanisms for presenting search results and other important information to users, and these are usually done through digital interfaces.

[0074] "Methods for automating reservations" refers to a function that automatically executes reservation procedures for selected items with minimal user input.

[0075] To implement the invention, the user first uses an application on their terminal. The user inputs their desired criteria for property selection, and this information is sent from the user to the server. The desired criteria include detailed conditions such as region, rent limit, floor plan, year of construction, and distance to the nearest station.

[0076] The server accesses an external information database. Here, it retrieves information via an API, receiving the latest property information and related data. Generally, this operation is performed using a RESTful API, and the data is exchanged in JSON format. The retrieved information is organized and stored in the server's data storage. This data is stored with indexes to enable efficient searching.

[0077] The server searches its stored information based on the user's requested criteria. The search uses multiple filtering algorithms to extract data that meets the specified conditions. To notify the user of the most relevant results, the server creates a list and sends it to the terminal.

[0078] The terminal displays information received from the server on the user interface. Based on the provided information, the user reviews the properties and selects which ones to request a viewing.

[0079] For example, if a user requests a 1LDK apartment in Tokyo with a rent of 100,000 yen or less and within a 10-minute walk from a train station, the server queries an external database and notifies the user of suitable property information. This process utilizes a generative AI model to function as a search and recommendation system. Through this process, the user can smoothly reserve their desired property.

[0080] An example prompt is, "Please automate the viewing of 1LDK properties in Tokyo that meet the specified criteria," and a system has been implemented to automatically make reservations based on this prompt.

[0081] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0082] Step 1:

[0083] The user opens the terminal application and enters their desired property criteria. These criteria include location, rent limit, floor plan, year of construction, and distance from the nearest station. The entered data is sent from the terminal to the server. At this stage, no data processing is performed until the entered data is sent to the server.

[0084] Step 2:

[0085] The server generates a query to an external information database based on the user's requested conditions. The generated query is sent to the information database via an API. The latest property information is received from the database in JSON format and stored in the server's data storage. At this stage, the server adjusts the data format and indexes the information to enable quick searching.

[0086] Step 3:

[0087] The server searches the stored data for properties that match the user's desired criteria. A filtering algorithm is used to select properties that meet the specified conditions. The input is all property information in the data storage, and the output is a list of properties that match the criteria. In this process, the data is compared with the desired conditions, and the one with the highest match rate is selected.

[0088] Step 4:

[0089] The server generates a list of properties that match the specified criteria and sends it to the terminal. The input here is the matching property information, and the output is a formatted property list. The server organizes and provides the information in a user-friendly format.

[0090] Step 5:

[0091] The device displays received property information in the user interface. The user reviews the list, selects properties of interest, and examines the details. Specifically, tapping on a property's image or description displays even more detailed information.

[0092] Step 6:

[0093] After the user selects a property they wish to view, they request a viewing appointment via a terminal. The terminal sends this request to the server. The input is the property information selected by the user, and the output is the viewing appointment request.

[0094] Step 7:

[0095] The server receives a request for a viewing appointment and coordinates the viewing schedule with the real estate agent's system. Once the appointment is confirmed, the result is notified to the user's terminal. The server sends the appointment confirmation and schedule to the user.

[0096] (Application Example 1)

[0097] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0098] The process of searching for real estate information and scheduling viewings is time-consuming and cumbersome, especially when narrowing down the search criteria with multiple factors, making efficient property searching a challenge for users. Real estate agents also face the problem of wasted time and effort if property information meeting the user's criteria is not provided in advance. Therefore, there is a need for a system that allows both users and real estate agents to efficiently utilize property information.

[0099] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0100] In this invention, the server includes means for receiving desired conditions from a user, means for obtaining information from an external database, means for storing the obtained information in a data storage device, means for searching the information storage device based on the desired conditions, means for notifying the user of the search results, means for automatically making service reservations, and means for assisting with immediate schedule adjustments based on the presented information. This enables the user to efficiently search for properties that meet their criteria and quickly complete the entire process up to making a viewing reservation.

[0101] "Means for receiving user requests" refers to a function that allows users to input their desired conditions into the system and receive those conditions.

[0102] "Means of obtaining information from external databases" refers to functions for accessing external information sources and obtaining necessary data.

[0103] "Means for storing acquired information in a data storage device" refers to a function for saving acquired data to internal storage.

[0104] "Means for searching within an information storage device based on desired conditions" refers to a function that finds relevant information from stored data based on the user's criteria.

[0105] "Means of notifying users of search results" refers to functions that inform users of the information they have searched for.

[0106] "Means for automatically making service reservations" refers to a function that automatically makes reservations for specified services.

[0107] "Means to support immediate schedule adjustments based on the information provided" refers to a function that allows for quick scheduling adjustments according to the information provided.

[0108] The system for realizing this application example begins with the user entering their desired conditions via their smartphone. The device has an application installed to receive the user's desired conditions. This application allows users to enter specific conditions, such as "a 1LDK apartment in Tokyo, under 100,000 yen, within a 10-minute walk from the station."

[0109] The server accesses an external real estate database to retrieve the necessary property information. A backend system built with Node.js and Express stores this retrieved data in MongoDB. The stored data is then quickly searched based on the user's desired criteria. The search results are then communicated to the user through an application built with React Native.

[0110] This system can not only search for suitable properties but also automatically schedule viewings based on user instructions. Once a reservation is complete, the user is notified again with detailed information. Furthermore, it incorporates a function to assist with immediate schedule adjustments based on the presented property information. This makes it easier for users to schedule viewings when needed.

[0111] For example, if a user specifies that they are looking for a furnished, 3LDK apartment for under 150,000 yen, they can enter this information into the app, and matching properties will be displayed in real time. Once the customer selects a property they particularly like, a viewing appointment is immediately confirmed, and their preferred dates are presented.

[0112] An example of a prompt using a generative AI model is, "Please search for rental properties that are 3LDK, furnished, and have a rent of 150,000 yen or less, and suggest viewing dates." By using this prompt, the AI ​​can quickly provide the desired information and meet the user's needs to the greatest extent possible.

[0113] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0114] Step 1:

[0115] The terminal receives the user's desired conditions. These conditions include information such as "a 1LDK apartment in Tokyo, under 100,000 yen, within a 10-minute walk from the station." The entered data is then sent from the terminal to the server.

[0116] Step 2:

[0117] The server, based on the received request criteria, sends queries to an external real estate information database to retrieve property information. The property data obtained through this process includes detailed property information and is processed using Node.js and Express.

[0118] Step 3:

[0119] The retrieved property information is stored in a MongoDB database on the server. This data is cached for future searches and managed to maintain data consistency.

[0120] Step 4:

[0121] The server searches the stored property information again and lists properties that match the user's desired criteria. In this step, database queries are used to filter and extract information that matches the criteria, preparing the data to be presented to the user.

[0122] Step 5:

[0123] The server sends the search results to the user's device. The user can then view the property details on their device. The presented information is displayed visually within an application developed with React Native.

[0124] Step 6:

[0125] The user selects a property they like and requests a viewing appointment. This operation is performed on the terminal, and the request is then sent back to the server.

[0126] Step 7:

[0127] The server automatically makes viewing reservations for specified properties based on user instructions. This process involves communicating with the real estate agent's reservation system via API to coordinate reservation schedules.

[0128] Step 8:

[0129] Once the reservation is complete, the server notifies the user of the details. The user can then check the viewing date and details on their device. This allows the user to quickly confirm their viewing schedule and make the necessary preparations.

[0130] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0131] This invention relates to a system that recognizes a user's emotions and provides personalized rental property searches and viewing appointments based on those emotions. This system is implemented through the following operations:

[0132] First, the user launches the application on their device and enters their personal preferences. These preferences include the property's location, desired rent, floor plan, building age, and distance from the nearest station. This information is sent to the server and stored as the user's profile.

[0133] Next, the emotion engine becomes active and analyzes the user's emotions through the device. The emotion engine evaluates the user's current emotional state using voice, facial expressions, text data, etc. This emotional data is sent to the server and considered as a parameter when selecting property information.

[0134] The server connects with external real estate databases to collect the latest property information and stores it in the company's own database. The collected information serves as the basis for searching for properties that match the user's desired conditions and emotional state.

[0135] Based on the user's emotional state, the server generates personalized search results and optimizes the predicted user satisfaction. These results are sent to the device and notified to the user. For example, if the user is feeling stressed, properties that offer a more relaxing environment will be prioritized.

[0136] Furthermore, if a user finds a property they like, they can select their preference for scheduling a viewing. The server utilizes data from the emotion engine to optimize the timing and method of scheduling the viewing and automatically requests the viewing from the real estate agent. Once the date and time of the viewing are confirmed, the information is notified to the user's device.

[0137] This system enables personalized responses based on emotion recognition, thereby improving the user experience. Furthermore, by providing real estate agents with user emotion data, even more personalized services can be realized, and an increase in the closing rate can be expected. For example, when a user is searching for a property in the city center and prioritizes speed, property information that takes into consideration to reduce their stress will be presented with higher priority.

[0138] The following describes the processing flow.

[0139] Step 1:

[0140] The user opens the application on their device and creates an account. Here, they enter detailed information such as the property's location, rent range, floor plan, year of construction, and distance from the nearest station. This information is necessary to reflect the user's individual needs.

[0141] Step 2:

[0142] The device obtains permission from the user for sentiment analysis, along with the user's inputted preferences. It may also use audio or video data, and permission must be obtained to send that data to the server.

[0143] Step 3:

[0144] The emotion engine activates and analyzes the user's emotional state through the device. This analysis includes voice tone analysis and facial expression recognition, generating a score that quantifies the user's current emotions.

[0145] Step 4:

[0146] The device sends the generated emotion score and desired conditions to the server. The server stores this data in a database and uses it as parameters when selecting a property.

[0147] Step 5:

[0148] The server accesses an external real estate information database to retrieve the latest property information and update the database. This information includes property location, rent, and amenities.

[0149] Step 6:

[0150] The server searches the database based on the user's preferences and sentiment score, filtering for suitable properties. Furthermore, it generates a prioritized list of properties that the user is likely to be interested in, based on their sentiment score.

[0151] Step 7:

[0152] The server sends search results to the device, and the device sends a push notification to the user. The user can view detailed information within the app and see recommended properties based on their sentiment score.

[0153] Step 8:

[0154] When a user selects a property they wish to view, the device sends that information to the server. The server, taking sentiment data into consideration, automatically suggests and books the optimal viewing time with the real estate agent.

[0155] Step 9:

[0156] The server receives confirmation information for the viewing appointment and notifies the user via the terminal. The user can then check the appointment date and request changes if necessary.

[0157] (Example 2)

[0158] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0159] Conventional information provision systems are unable to provide information that takes into account the individual emotional state of users, making it difficult to maximize user satisfaction. Furthermore, there is a need to not only provide information that meets the user's desired conditions, but also to offer more appropriate suggestions based on their emotional state. Additionally, there is a lack of means to automatically adjust the optimal timing and method of reservations, making improved convenience a challenge.

[0160] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0161] In this invention, the server includes means for receiving desired conditions from the user, means for evaluating the user's emotional state using emotion analysis means, and means for retrieving information based on the user's desired conditions and emotional state. This enables the provision of personalized information tailored to the user's emotions and efficient reservation scheduling.

[0162] A "user" is the entity that uses this system and inputs information.

[0163] "Desired conditions" refer to the conditions that users enter for providing information, specifically including information such as location and price.

[0164] "Emotion analysis means" refers to a device or software that includes technology for evaluating emotions from a user's voice, facial expressions, text, etc.

[0165] An "external information database" is a storage device that stores information from external sources and provides the latest information in a specific field.

[0166] "Means of searching" refers to methods and devices for investigating information within a database based on desired conditions and emotional states, and for extracting appropriate data.

[0167] "Means of notification" refers to devices or processes used to inform users of search results or the confirmed date and time of reservations.

[0168] "Methods for automatically optimizing reservations" refer to processes that automatically set the optimal reservation schedule according to the user's emotional state and desired conditions.

[0169] This invention is an information provision system that takes into account the emotional state of the user, and provides the user with the most suitable information by using emotion analysis technology. It also enables efficient schedule management through automatic reservation adjustment. The following describes embodiments for carrying out this invention.

[0170] The user launches an application on their device and enters their desired conditions. The device is equipped with emotion analysis capabilities, and uses speech recognition software, facial expression analysis software, and text analysis tools to acquire the user's emotion data. This emotion data is analyzed in real time by an emotion engine and sent to a server.

[0171] The server receives user preferences and emotional data. The server also interacts with external information databases to retrieve the latest information and stores it in its own database. The server organizes the data in a standardized format and searches for information relevant to the user's preferences and emotional state.

[0172] The server generates personalized search results based on the user's emotions. For example, a user experiencing stress will be prioritized with information on relaxing facilities and quiet areas. These search results are then ranked to optimize the user's predicted satisfaction level and sent to their device.

[0173] Furthermore, the server automatically makes reservations at the optimal time based on the information selected by the user. This includes using AI technology to adjust the schedule, taking into account the user's past behavioral history and current emotional state. Once the reservation is complete, the information is notified to the user's device.

[0174] A concrete example would be prioritizing the presentation of properties with a calm atmosphere to reduce the stress users experience when searching for properties in the city center. An example of a prompt to the generating AI model would be, "If a user is urgently searching for a property in the city center, how would you prioritize recommending properties that will alleviate their stress?"

[0175] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0176] Step 1:

[0177] The user launches the application on their terminal and enters their desired criteria. These criteria include information such as the property's location, budget, floor plan, year of construction, and distance from the nearest station. The entered data undergoes preprocessing to format it before being sent to the server. Preprocessing includes handling missing data values ​​and ensuring consistent formatting.

[0178] Step 2:

[0179] The terminal activates the emotion analysis system and acquires the user's emotional data. For emotion analysis, speech recognition software analyzes the tone and speed of the user's voice, and facial expression analysis software detects facial expressions using the camera. Furthermore, a text analysis tool is used to extract emotions from the text messages entered by the user. This emotional data is processed by an emotion engine and sent to the server as numerical data indicating the user's emotional state.

[0180] Step 3:

[0181] The server receives user preferences and sentiment data, and accesses external information databases to obtain the latest information. It periodically retrieves information using APIs and stores it in its own database. The data is converted to a standardized format and stored. The database includes basic property attribute information and related environmental information.

[0182] Step 4:

[0183] The server searches the database by combining the user's desired conditions and emotional state. In addition to desired location, the search criteria include a sorting algorithm based on the emotional state. For example, if prioritizing relaxing properties, data in quiet areas will receive higher weight. Search results are scored and ranked to determine the most relevant information for the user.

[0184] Step 5:

[0185] The server sends search results to the user's device, notifying them of property details through the results. Once the user selects a property they like, the server automatically optimizes the viewing appointment schedule. This schedule optimization takes into account the user's browsing history and past booking patterns. A request is sent to the real estate agent, and once the booking is confirmed, the date and time are notified to the user's device.

[0186] (Application Example 2)

[0187] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0188] When users select rental properties, there is a need to provide a property selection service that takes into account not only their individual preferences but also their emotional state. Traditional systems are unable to respond to emotional changes, making it difficult to increase user satisfaction. Furthermore, the scheduling of property viewings is not optimized to take users' emotions into account, so improvements in convenience are desired.

[0189] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0190] In this invention, the server includes means for receiving user preferences, means for recognizing the user's emotions, and means for acquiring property information from external property information records. This enables personalized property selection and optimized viewing reservations that take into account the user's preferences and emotional state.

[0191] "User" refers to an individual who uses this system to search for rental properties and schedule viewings.

[0192] "Desired conditions" refer to the requirements that users have for rental properties, such as the property's location, desired rent, floor plan, age of the building, and distance from the station.

[0193] "Means of recognizing emotions" refers to a process or device that analyzes a user's emotional state using voice, facial expressions, text data, etc.

[0194] "Property information record" refers to a recording medium or data area that contains information about rental properties obtained from an external database.

[0195] "Means of searching within property records" refers to a process or device for extracting the most suitable property information from property records based on desired conditions and emotional state.

[0196] "Method for automatically scheduling viewings" refers to a process or device in which the system automatically arranges viewings for properties selected by the user on appropriate dates.

[0197] "Means for optimizing viewing schedules" refers to a process or device for setting efficient and user-friendly viewing schedules based on the user's emotional state and other relevant information.

[0198] This invention is a system for selecting rental properties and scheduling viewings, taking into account the user's desired conditions and emotional state. This system mainly consists of a server and terminals.

[0199] The server receives the user's desired conditions entered into the device. These conditions include personal preferences such as location, budget, and floor plan. Next, an emotion recognition engine running on the device analyzes the user's emotions and sends that data to the server. This emotion data is collected using voice and facial expression analysis software, such as Google Cloud's Speech-to-Text or Microsoft Azure's Face API.

[0200] The server accesses external property information records to retrieve the latest property information. This information is stored in the server's internal database. The server analyzes the retrieved property information along with desired conditions and sentiment information to select the most suitable property. This selection process utilizes machine learning algorithms and database search functions.

[0201] Information on selected properties is notified to the terminal and presented to the user in real time. The server also creates an optimal viewing schedule for the properties the user wishes to view and notifies the user. Time management software and traffic information APIs are used for this schedule optimization.

[0202] As a concrete example, if a user working in the city center is feeling stressed, properties located in relaxing areas will be prioritized on their smartphone, and they will be notified of viewing schedules that take their free time into consideration.

[0203] An example of a prompt to a generating AI model would be: "My current emotion is stress. I'm looking for a relaxing rental property, and my requirements are a 1K apartment near the city center with a rent of under 100,000 yen. Please suggest the best viewing schedule."

[0204] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0205] Step 1:

[0206] The user enters their desired conditions using a device. This includes information such as location, budget, and floor plan. The entered conditions are sent from the device to the server. As part of the data processing, the conditions are converted into structured data such as JSON format.

[0207] Step 2:

[0208] An emotion recognition engine running on the device analyzes the user's emotions. This step uses voice and facial expression data as input. Speech recognition software or a facial recognition API is used for emotion recognition. The resulting emotion data is sent to a server. The output is an emotional state represented as a string or numerical value.

[0209] Step 3:

[0210] The server accesses external property information records based on the received preference criteria and sentiment data to retrieve the latest property information. This retrieval process uses real estate information APIs, etc. The input is preference criteria and sentiment data, and the output is a list of property information.

[0211] Step 4:

[0212] The server stores the acquired property information in an internal database. The stored data is appropriately indexed, taking into account desired conditions and sentiment data. The output is the indexed information stored in the database.

[0213] Step 5:

[0214] The server searches the property records based on user preferences and sentiment data to select the most suitable property for the user. A machine learning algorithm is used for selection, with the property information list and user requirements as input. The output is a list of recommended properties.

[0215] Step 6:

[0216] The recommended property information is sent back to the device and the user is notified. The notification is displayed on the application's UI, allowing the user to view the property information. The output is the specific property information displayed on the user's device.

[0217] Step 7:

[0218] The server creates an optimal viewing schedule based on recommended properties. Time management software and a traffic information API are used for scheduling. Inputs are recommended property information and sentiment data, and output is the scheduled viewing dates.

[0219] Step 8:

[0220] The viewing schedule will be notified to the user's device. The user can then confirm and adjust the viewing date based on the notified information. The output will be a viewing schedule optimized for the user.

[0221] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0222] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0223] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0224] [Second Embodiment]

[0225] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0226] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0227] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0228] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0229] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0230] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0231] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0232] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0233] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0234] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0235] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0236] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0237] This invention is a system that enables users to efficiently search for rental properties and book viewings. A specific embodiment of this system is shown below.

[0238] First, the user accesses the application on their device and enters their desired criteria as part of the initial setup. These criteria include detailed items such as location, maximum rent, floor plan, year of construction, and distance from the nearest station. This information serves as the basic data for searching for properties based on the user's individual needs.

[0239] Next, the server retrieves the latest property information from an external real estate database. The retrieved information is continuously updated and stored in the server's database. This accumulated property information can be easily accessed via search queries.

[0240] The server periodically searches the database for property information, matching it against the user's desired criteria, either at regular intervals or upon user request. This is done using multiple filters with defined conditions, and properties that match the criteria are listed.

[0241] Search results are notified to the device, and users can view property details within the app. Even if no properties matching the criteria are found, the server can suggest properties with advantages outside of the specified criteria to the user.

[0242] Furthermore, the server automates the application process once a property the user wishes to view has been identified. The user selects the property they wish to view through their device, and the server makes the viewing reservation on their behalf with the real estate agent. Once the viewing reservation is confirmed, the details are notified to the user's device.

[0243] This system allows users to efficiently manage the process from property search to scheduling viewings. Real estate agents also benefit from the improved quality of viewings and reduced workload due to pre-matching of criteria.

[0244] For example, if a user requests a 1LDK apartment in Tokyo with a rent of 100,000 yen or less and within a 10-minute walk from a train station, the server searches an external database for suitable properties and notifies the user of that information. The user then selects a property they like, books a viewing, and the process is complete. Automating this entire process significantly reduces the time and effort required to choose an apartment.

[0245] The following describes the processing flow.

[0246] Step 1:

[0247] The user launches the application installed on their device and creates an account. After creating the account, they enter detailed information such as location, maximum rent, floor plan, building age, and distance from the station as their desired conditions.

[0248] Step 2:

[0249] The terminal sends the entered preferences to the server, which stores this information in a database to create a profile for each user.

[0250] Step 3:

[0251] The server connects to an external real estate information database using an API and periodically retrieves the latest property information. The retrieved property information is stored in a database on the server.

[0252] Step 4:

[0253] The server executes a search query by comparing the property information stored in the database with the user's desired conditions. The search results list properties that match the criteria.

[0254] Step 5:

[0255] The server sends the search results to the device. The device then sends a push notification to the user, displaying the details of matching properties within the app.

[0256] Step 6:

[0257] Users check the property information notified on their device and select any properties they like to view.

[0258] Step 7:

[0259] The terminal sends the user's request for a viewing to the server, and the server automatically sends a request to the real estate agent to schedule a viewing.

[0260] Step 8:

[0261] When the server receives confirmation of a viewing appointment from the real estate agent, it sends the schedule to the terminal. The terminal then notifies the user of the viewing date.

[0262] Step 9:

[0263] After the viewing, if the real estate agent provides feedback, the server receives that information and notifies the user via the terminal.

[0264] (Example 1)

[0265] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0266] Conventional information retrieval and reservation systems made it difficult for users to efficiently find the most suitable options based on their individual needs and to smoothly make reservations for those options. Furthermore, they lacked the functionality to provide information that didn't perfectly match the criteria but offered other advantages, failing to suggest options that users might overlook. Additionally, the lack of a mechanism for receiving feedback from vendors meant users were unaware of the subsequent situation, potentially leading to a decline in the quality of the experience.

[0267] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0268] In this invention, the server includes means for receiving desired conditions from the user, means for obtaining target information from an external information database, and means for storing the obtained target information in data storage. This enables the user to efficiently perform searches based on their own conditions and automated reservation procedures. Furthermore, by providing a function to suggest information that does not match the conditions but has other advantages, a wider range of choices can be offered, and by receiving feedback from vendors and notifying them of the situation after use, the overall quality of the experience can be improved.

[0269] "Means for receiving desired conditions" refers to a function that collects requests and conditions entered by users via digital devices and transmits that information to the server.

[0270] "Means of obtaining target information from an information database" refers to the process of accessing an external database, obtaining the latest information, and incorporating it into the system.

[0271] "Means for storing acquired target information in data storage" refers to a system that organizes and saves information in a storage device on a server, making it available for quick retrieval and use as needed.

[0272] A "search method" is an algorithm or process that efficiently finds data that matches specific criteria from information that has been collected and stored in advance.

[0273] "Means of notifying users" refer to mechanisms for presenting search results and other important information to users, and these are usually done through digital interfaces.

[0274] "Methods for automating reservations" refers to a function that automatically executes reservation procedures for selected items with minimal user input.

[0275] To implement the invention, the user first uses an application on their terminal. The user inputs their desired criteria for property selection, and this information is sent from the user to the server. The desired criteria include detailed conditions such as region, rent limit, floor plan, year of construction, and distance to the nearest station.

[0276] The server accesses an external information database. Here, it retrieves information via an API, receiving the latest property information and related data. Generally, this operation is performed using a RESTful API, and the data is exchanged in JSON format. The retrieved information is organized and stored in the server's data storage. This data is stored with indexes to enable efficient searching.

[0277] The server searches its stored information based on the user's requested criteria. The search uses multiple filtering algorithms to extract data that meets the specified conditions. To notify the user of the most relevant results, the server creates a list and sends it to the terminal.

[0278] The terminal displays information received from the server on the user interface. Based on the provided information, the user reviews the properties and selects which ones to request a viewing.

[0279] For example, if a user requests a 1LDK apartment in Tokyo with a rent of 100,000 yen or less and within a 10-minute walk from a train station, the server queries an external database and notifies the user of suitable property information. This process utilizes a generative AI model to function as a search and recommendation system. Through this process, the user can smoothly reserve their desired property.

[0280] An example prompt is, "Please automate the viewing of 1LDK properties in Tokyo that meet the specified criteria," and a system has been implemented to automatically make reservations based on this prompt.

[0281] The flow of the specific process in Example 1 will be described using FIG. 11.

[0282] Step 1:

[0283] The user opens the terminal application and enters the desired conditions for the property. These desired conditions include the region, rent limit, floor plan, age of construction, and distance from the nearest station. The input data is sent from the terminal to the server. At this stage, no data processing is performed until the input data is sent to the server.

[0284] Step 2:

[0285] Based on the desired conditions received from the user, the server generates a query to the external information database. The generated query is sent to the information database through the API. The latest property information is received from the database in JSON format and stored in the server's data storage. At this stage, the server adjusts the data format and indexes the information to enable rapid search.

[0286] Step 3:

[0287] The server searches for properties that match the user's desired conditions from the stored data. Here, a filtering algorithm is used to select properties that meet the conditions. The input is all the property information in the data storage, and the output is a list of properties that meet the conditions. In this process, the data is compared with the desired conditions, and the one with the highest matching rate is selected.

[0288] Step 4:

[0289] The server generates a list of properties that meet the conditions and sends it to the terminal. The input here is the property information that meets the conditions, and the output is a formatted list of properties. The server organizes and provides the information in a format that is easy for the user to understand.

[0290] Step 5:

[0291] The device displays received property information in the user interface. The user reviews the list, selects properties of interest, and examines the details. Specifically, tapping on a property's image or description displays even more detailed information.

[0292] Step 6:

[0293] After the user selects a property they wish to view, they request a viewing appointment via a terminal. The terminal sends this request to the server. The input is the property information selected by the user, and the output is the viewing appointment request.

[0294] Step 7:

[0295] The server receives a request for a viewing appointment and coordinates the viewing schedule with the real estate agent's system. Once the appointment is confirmed, the result is notified to the user's terminal. The server sends the appointment confirmation and schedule to the user.

[0296] (Application Example 1)

[0297] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0298] The process of searching for real estate information and scheduling viewings is time-consuming and cumbersome, especially when narrowing down the search criteria with multiple factors, making efficient property searching a challenge for users. Real estate agents also face the problem of wasted time and effort if property information meeting the user's criteria is not provided in advance. Therefore, there is a need for a system that allows both users and real estate agents to efficiently utilize property information.

[0299] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0300] In this invention, the server includes means for receiving desired conditions from a user, means for obtaining information from an external database, means for storing the obtained information in a data storage device, means for searching within the information storage device based on the desired conditions, means for notifying the user of the search results, means for automatically making service reservations, and means for assisting in immediate schedule adjustment based on the presented information. Thereby, the user can efficiently search for a property that meets the conditions and quickly complete a series of processes up to the viewing reservation.

[0301] The "means for receiving desired conditions from a user" is a function for the system to receive and input the conditions required by the user.

[0302] The "means for obtaining information from an external database" is a function for accessing an external information source to obtain necessary data.

[0303] The "means for storing the obtained information in a data storage device" is a function for storing the obtained data in an internal storage.

[0304] The "means for searching within the information storage device based on the desired conditions" is a function for searching for corresponding information from the stored data based on the user's conditions.

[0305] The "means for notifying the user of the search results" is a function for informing the user of the searched information.

[0306] The "means for automatically making service reservations" is a function for automatically making reservations for the specified service.

[0307] The "means for assisting in immediate schedule adjustment based on the presented information" is a function for quickly adjusting the schedule according to the provided information.

[0308] The system for realizing this application example begins with the user entering their desired conditions via their smartphone. The device has an application installed to receive the user's desired conditions. This application allows users to enter specific conditions, such as "a 1LDK apartment in Tokyo, under 100,000 yen, within a 10-minute walk from the station."

[0309] The server accesses an external real estate database to retrieve the necessary property information. A backend system built with Node.js and Express stores this retrieved data in MongoDB. The stored data is then quickly searched based on the user's desired criteria. The search results are then communicated to the user through an application built with React Native.

[0310] This system can not only search for suitable properties but also automatically schedule viewings based on user instructions. Once a reservation is complete, the user is notified again with detailed information. Furthermore, it incorporates a function to assist with immediate schedule adjustments based on the presented property information. This makes it easier for users to schedule viewings when needed.

[0311] For example, if a user specifies that they are looking for a furnished, 3LDK apartment for under 150,000 yen, they can enter this information into the app, and matching properties will be displayed in real time. Once the customer selects a property they particularly like, a viewing appointment is immediately confirmed, and their preferred dates are presented.

[0312] An example of a prompt using a generative AI model is, "Please search for rental properties that are 3LDK, furnished, and have a rent of 150,000 yen or less, and suggest viewing dates." By using this prompt, the AI ​​can quickly provide the desired information and meet the user's needs to the greatest extent possible.

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

[0314] Step 1:

[0315] The terminal receives the user's desired conditions. These conditions include information such as "a 1LDK apartment in Tokyo, under 100,000 yen, within a 10-minute walk from the station." The entered data is then sent from the terminal to the server.

[0316] Step 2:

[0317] The server, based on the received request criteria, sends queries to an external real estate information database to retrieve property information. The property data obtained through this process includes detailed property information and is processed using Node.js and Express.

[0318] Step 3:

[0319] The retrieved property information is stored in a MongoDB database on the server. This data is cached for future searches and managed to maintain data consistency.

[0320] Step 4:

[0321] The server searches the stored property information again and lists properties that match the user's desired criteria. In this step, database queries are used to filter and extract information that matches the criteria, preparing the data to be presented to the user.

[0322] Step 5:

[0323] The server sends the search results to the user's device. The user can then view the property details on their device. The presented information is displayed visually within an application developed with React Native.

[0324] Step 6:

[0325] The user selects a property they like and requests a viewing appointment. This operation is performed on the terminal, and the request is then sent back to the server.

[0326] Step 7:

[0327] The server automatically makes viewing reservations for specified properties based on user instructions. This process involves communicating with the real estate agent's reservation system via API to coordinate reservation schedules.

[0328] Step 8:

[0329] Once the reservation is complete, the server notifies the user of the details. The user can then check the viewing date and details on their device. This allows the user to quickly confirm their viewing schedule and make the necessary preparations.

[0330] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0331] This invention relates to a system that recognizes a user's emotions and provides personalized rental property searches and viewing appointments based on those emotions. This system is implemented through the following operations:

[0332] First, the user launches the application on their device and enters their personal preferences. These preferences include the property's location, desired rent, floor plan, building age, and distance from the nearest station. This information is sent to the server and stored as the user's profile.

[0333] Next, the emotion engine becomes active and analyzes the user's emotions through the device. The emotion engine evaluates the user's current emotional state using voice, facial expressions, text data, etc. This emotional data is sent to the server and considered as a parameter when selecting property information.

[0334] The server connects with external real estate databases to collect the latest property information and stores it in the company's own database. The collected information serves as the basis for searching for properties that match the user's desired conditions and emotional state.

[0335] Based on the user's emotional state, the server generates personalized search results and optimizes the predicted user satisfaction. These results are sent to the device and notified to the user. For example, if the user is feeling stressed, properties that offer a more relaxing environment will be prioritized.

[0336] Furthermore, if a user finds a property they like, they can select their preference for scheduling a viewing. The server utilizes data from the emotion engine to optimize the timing and method of scheduling the viewing and automatically requests the viewing from the real estate agent. Once the date and time of the viewing are confirmed, the information is notified to the user's device.

[0337] This system enables personalized responses based on emotion recognition, thereby improving the user experience. Furthermore, by providing real estate agents with user emotion data, even more personalized services can be realized, and an increase in the closing rate can be expected. For example, when a user is searching for a property in the city center and prioritizes speed, property information that takes into consideration to reduce their stress will be presented with higher priority.

[0338] The following describes the processing flow.

[0339] Step 1:

[0340] The user opens the application on their device and creates an account. Here, they enter detailed information such as the property's location, rent range, floor plan, year of construction, and distance from the nearest station. This information is necessary to reflect the user's individual needs.

[0341] Step 2:

[0342] The device obtains permission from the user for sentiment analysis, along with the user's inputted preferences. It may also use audio or video data, and permission must be obtained to send that data to the server.

[0343] Step 3:

[0344] The emotion engine activates and analyzes the user's emotional state through the device. This analysis includes voice tone analysis and facial expression recognition, generating a score that quantifies the user's current emotions.

[0345] Step 4:

[0346] The device sends the generated emotion score and desired conditions to the server. The server stores this data in a database and uses it as parameters when selecting a property.

[0347] Step 5:

[0348] The server accesses an external real estate information database to retrieve the latest property information and update the database. This information includes property location, rent, and amenities.

[0349] Step 6:

[0350] The server searches the database based on the user's preferences and sentiment score, filtering for suitable properties. Furthermore, it generates a prioritized list of properties that the user is likely to be interested in, based on their sentiment score.

[0351] Step 7:

[0352] The server sends search results to the device, and the device sends a push notification to the user. The user can view detailed information within the app and see recommended properties based on their sentiment score.

[0353] Step 8:

[0354] When a user selects a property they wish to view, the device sends that information to the server. The server, taking sentiment data into consideration, automatically suggests and books the optimal viewing time with the real estate agent.

[0355] Step 9:

[0356] The server receives confirmation information for the viewing appointment and notifies the user via the terminal. The user can then check the appointment date and request changes if necessary.

[0357] (Example 2)

[0358] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0359] Conventional information provision systems are unable to provide information that takes into account the individual emotional state of users, making it difficult to maximize user satisfaction. Furthermore, there is a need to not only provide information that meets the user's desired conditions, but also to offer more appropriate suggestions based on their emotional state. Additionally, there is a lack of means to automatically adjust the optimal timing and method of reservations, making improved convenience a challenge.

[0360] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0361] In this invention, the server includes means for receiving desired conditions from the user, means for evaluating the user's emotional state using emotion analysis means, and means for retrieving information based on the user's desired conditions and emotional state. This enables the provision of personalized information tailored to the user's emotions and efficient reservation scheduling.

[0362] A "user" is the entity that uses this system and inputs information.

[0363] "Desired conditions" refer to the conditions that users enter for providing information, specifically including information such as location and price.

[0364] "Emotion analysis means" refers to a device or software that includes technology for evaluating emotions from a user's voice, facial expressions, text, etc.

[0365] An "external information database" is a storage device that stores information from external sources and provides the latest information in a specific field.

[0366] "Means of searching" refers to methods and devices for investigating information within a database based on desired conditions and emotional states, and for extracting appropriate data.

[0367] "Means of notification" refers to devices or processes used to inform users of search results or the confirmed date and time of reservations.

[0368] "Methods for automatically optimizing reservations" refer to processes that automatically set the optimal reservation schedule according to the user's emotional state and desired conditions.

[0369] This invention is an information provision system that takes into account the emotional state of the user, and provides the user with the most suitable information by using emotion analysis technology. It also enables efficient schedule management through automatic reservation adjustment. The following describes embodiments for carrying out this invention.

[0370] The user launches an application on their device and enters their desired conditions. The device is equipped with emotion analysis capabilities, and uses speech recognition software, facial expression analysis software, and text analysis tools to acquire the user's emotion data. This emotion data is analyzed in real time by an emotion engine and sent to a server.

[0371] The server receives user preferences and emotional data. The server also interacts with external information databases to retrieve the latest information and stores it in its own database. The server organizes the data in a standardized format and searches for information relevant to the user's preferences and emotional state.

[0372] The server generates personalized search results based on the user's emotions. For example, a user experiencing stress will be prioritized with information on relaxing facilities and quiet areas. These search results are then ranked to optimize the user's predicted satisfaction level and sent to their device.

[0373] Furthermore, the server automatically makes reservations at the optimal time based on the information selected by the user. This includes using AI technology to adjust the schedule, taking into account the user's past behavioral history and current emotional state. Once the reservation is complete, the information is notified to the user's device.

[0374] A concrete example would be prioritizing the presentation of properties with a calm atmosphere to reduce the stress users experience when searching for properties in the city center. An example of a prompt to the generating AI model would be, "If a user is urgently searching for a property in the city center, how would you prioritize recommending properties that will alleviate their stress?"

[0375] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0376] Step 1:

[0377] The user launches the application on their terminal and enters their desired criteria. These criteria include information such as the property's location, budget, floor plan, year of construction, and distance from the nearest station. The entered data undergoes preprocessing to format it before being sent to the server. Preprocessing includes handling missing data values ​​and ensuring consistent formatting.

[0378] Step 2:

[0379] The terminal activates the emotion analysis system and acquires the user's emotional data. For emotion analysis, speech recognition software analyzes the tone and speed of the user's voice, and facial expression analysis software detects facial expressions using the camera. Furthermore, a text analysis tool is used to extract emotions from the text messages entered by the user. This emotional data is processed by an emotion engine and sent to the server as numerical data indicating the user's emotional state.

[0380] Step 3:

[0381] The server receives user preferences and sentiment data, and accesses external information databases to obtain the latest information. It periodically retrieves information using APIs and stores it in its own database. The data is converted to a standardized format and stored. The database includes basic property attribute information and related environmental information.

[0382] Step 4:

[0383] The server searches the database by combining the user's desired conditions and emotional state. In addition to desired location, the search criteria include a sorting algorithm based on the emotional state. For example, if prioritizing relaxing properties, data in quiet areas will receive higher weight. Search results are scored and ranked to determine the most relevant information for the user.

[0384] Step 5:

[0385] The server sends search results to the user's device, notifying them of property details through the results. Once the user selects a property they like, the server automatically optimizes the viewing appointment schedule. This schedule optimization takes into account the user's browsing history and past booking patterns. A request is sent to the real estate agent, and once the booking is confirmed, the date and time are notified to the user's device.

[0386] (Application Example 2)

[0387] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0388] When users select rental properties, there is a need to provide a property selection service that takes into account not only their individual preferences but also their emotional state. Traditional systems are unable to respond to emotional changes, making it difficult to increase user satisfaction. Furthermore, the scheduling of property viewings is not optimized to take users' emotions into account, so improvements in convenience are desired.

[0389] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0390] In this invention, the server includes means for receiving user preferences, means for recognizing the user's emotions, and means for acquiring property information from external property information records. This enables personalized property selection and optimized viewing reservations that take into account the user's preferences and emotional state.

[0391] "User" refers to an individual who uses this system to search for rental properties and schedule viewings.

[0392] "Desired conditions" refer to the requirements that users have for rental properties, such as the property's location, desired rent, floor plan, age of the building, and distance from the station.

[0393] "Means of recognizing emotions" refers to a process or device that analyzes a user's emotional state using voice, facial expressions, text data, etc.

[0394] "Property information record" refers to a recording medium or data area that contains information about rental properties obtained from an external database.

[0395] "Means of searching within property records" refers to a process or device for extracting the most suitable property information from property records based on desired conditions and emotional state.

[0396] "Method for automatically scheduling viewings" refers to a process or device in which the system automatically arranges viewings for properties selected by the user on appropriate dates.

[0397] "Means for optimizing viewing schedules" refers to a process or device for setting efficient and user-friendly viewing schedules based on the user's emotional state and other relevant information.

[0398] This invention is a system for selecting rental properties and scheduling viewings, taking into account the user's desired conditions and emotional state. This system mainly consists of a server and terminals.

[0399] The server receives the user's preferences entered into the device. These preferences include data such as location information, budget, and floor plan. Next, an emotion recognition engine running on the device analyzes the user's emotions and sends that data to the server. This emotion data is collected using voice and facial expression analysis software, such as Google Cloud's Speech-to-Text or Microsoft Azure's Face API.

[0400] The server accesses external property information records to retrieve the latest property information. This information is stored in the server's internal database. The server analyzes the retrieved property information along with desired conditions and sentiment information to select the most suitable property. This selection process utilizes machine learning algorithms and database search functions.

[0401] Information on selected properties is notified to the terminal and presented to the user in real time. The server also creates an optimal viewing schedule for the properties the user wishes to view and notifies the user. Time management software and traffic information APIs are used for this schedule optimization.

[0402] As a concrete example, if a user working in the city center is feeling stressed, properties located in relaxing areas will be prioritized on their smartphone, and they will be notified of viewing schedules that take their free time into consideration.

[0403] An example of a prompt to a generating AI model would be: "My current emotion is stress. I'm looking for a relaxing rental property, and my requirements are a 1K apartment near the city center with a rent of under 100,000 yen. Please suggest the best viewing schedule."

[0404] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0405] Step 1:

[0406] The user enters their desired conditions using a device. This includes information such as location, budget, and floor plan. The entered conditions are sent from the device to the server. As part of the data processing, the conditions are converted into structured data such as JSON format.

[0407] Step 2:

[0408] An emotion recognition engine running on the device analyzes the user's emotions. This step uses voice and facial expression data as input. Speech recognition software or a facial recognition API is used for emotion recognition. The resulting emotion data is sent to a server. The output is an emotional state represented as a string or numerical value.

[0409] Step 3:

[0410] The server accesses external property information records based on the received preference criteria and sentiment data to retrieve the latest property information. This retrieval process uses real estate information APIs, etc. The input is preference criteria and sentiment data, and the output is a list of property information.

[0411] Step 4:

[0412] The server stores the acquired property information in an internal database. The stored data is appropriately indexed, taking into account desired conditions and sentiment data. The output is the indexed information stored in the database.

[0413] Step 5:

[0414] The server searches the property records based on user preferences and sentiment data to select the most suitable property for the user. A machine learning algorithm is used for selection, with the property information list and user requirements as input. The output is a list of recommended properties.

[0415] Step 6:

[0416] The recommended property information is sent back to the device and the user is notified. The notification is displayed on the application's UI, allowing the user to view the property information. The output is the specific property information displayed on the user's device.

[0417] Step 7:

[0418] The server creates an optimal viewing schedule based on recommended properties. Time management software and a traffic information API are used for scheduling. Inputs are recommended property information and sentiment data, and output is the scheduled viewing dates.

[0419] Step 8:

[0420] The viewing schedule will be notified to the user's device. The user can then confirm and adjust the viewing date based on the notified information. The output will be a viewing schedule optimized for the user.

[0421] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0422] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0423] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0424] [Third Embodiment]

[0425] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0426] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

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

[0428] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0429] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

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

[0431] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0432] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0433] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

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

[0435] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0436] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0437] This invention is a system that enables users to efficiently search for rental properties and book viewings. A specific embodiment of this system is shown below.

[0438] First, the user accesses the application on their device and enters their desired criteria as part of the initial setup. These criteria include detailed items such as location, maximum rent, floor plan, year of construction, and distance from the nearest station. This information serves as the basic data for searching for properties based on the user's individual needs.

[0439] Next, the server retrieves the latest property information from an external real estate database. The retrieved information is continuously updated and stored in the server's database. This accumulated property information can be easily accessed via search queries.

[0440] The server periodically searches the database for property information, matching it against the user's desired criteria, either at regular intervals or upon user request. This is done using multiple filters with defined conditions, and properties that match the criteria are listed.

[0441] Search results are notified to the device, and users can view property details within the app. Even if no properties matching the criteria are found, the server can suggest properties with advantages outside of the specified criteria to the user.

[0442] Furthermore, the server automates the application process once a property the user wishes to view has been identified. The user selects the property they wish to view through their device, and the server makes the viewing reservation on their behalf with the real estate agent. Once the viewing reservation is confirmed, the details are notified to the user's device.

[0443] This system allows users to efficiently manage the process from property search to scheduling viewings. Real estate agents also benefit from the improved quality of viewings and reduced workload due to pre-matching of criteria.

[0444] For example, if a user requests a 1LDK apartment in Tokyo with a rent of 100,000 yen or less and within a 10-minute walk from a train station, the server searches an external database for suitable properties and notifies the user of that information. The user then selects a property they like, books a viewing, and the process is complete. Automating this entire process significantly reduces the time and effort required to choose an apartment.

[0445] The following describes the processing flow.

[0446] Step 1:

[0447] The user launches the application installed on their device and creates an account. After creating the account, they enter detailed information such as location, maximum rent, floor plan, building age, and distance from the station as their desired conditions.

[0448] Step 2:

[0449] The terminal sends the entered preferences to the server, which stores this information in a database to create a profile for each user.

[0450] Step 3:

[0451] The server connects to an external real estate information database using an API and periodically retrieves the latest property information. The retrieved property information is stored in a database on the server.

[0452] Step 4:

[0453] The server executes a search query by comparing the property information stored in the database with the user's desired conditions. The search results list properties that match the criteria.

[0454] Step 5:

[0455] The server sends the search results to the device. The device then sends a push notification to the user, displaying the details of matching properties within the app.

[0456] Step 6:

[0457] Users check the property information notified on their device and select any properties they like to view.

[0458] Step 7:

[0459] The terminal sends the user's request for a viewing to the server, and the server automatically sends a request to the real estate agent to schedule a viewing.

[0460] Step 8:

[0461] When the server receives confirmation of a viewing appointment from the real estate agent, it sends the schedule to the terminal. The terminal then notifies the user of the viewing date.

[0462] Step 9:

[0463] After the viewing, if the real estate agent provides feedback, the server receives that information and notifies the user via the terminal.

[0464] (Example 1)

[0465] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0466] Conventional information retrieval and reservation systems made it difficult for users to efficiently find the most suitable options based on their individual needs and to smoothly make reservations for those options. Furthermore, they lacked the functionality to provide information that didn't perfectly match the criteria but offered other advantages, failing to suggest options that users might overlook. Additionally, the lack of a mechanism for receiving feedback from vendors meant users were unaware of the subsequent situation, potentially leading to a decline in the quality of the experience.

[0467] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0468] In this invention, the server includes means for receiving desired conditions from the user, means for obtaining target information from an external information database, and means for storing the obtained target information in data storage. This enables the user to efficiently perform searches based on their own conditions and automated reservation procedures. Furthermore, by providing a function to suggest information that does not match the conditions but has other advantages, a wider range of choices can be offered, and by receiving feedback from vendors and notifying them of the situation after use, the overall quality of the experience can be improved.

[0469] "Means for receiving desired conditions" refers to a function that collects requests and conditions entered by users via digital devices and transmits that information to the server.

[0470] "Means of obtaining target information from an information database" refers to the process of accessing an external database, obtaining the latest information, and incorporating it into the system.

[0471] "Means for storing acquired target information in data storage" refers to a system that organizes and saves information in a storage device on a server, making it available for quick retrieval and use as needed.

[0472] A "search method" is an algorithm or process that efficiently finds data that matches specific criteria from information that has been collected and stored in advance.

[0473] "Means of notifying users" refer to mechanisms for presenting search results and other important information to users, and these are usually done through digital interfaces.

[0474] "Methods for automating reservations" refers to a function that automatically executes reservation procedures for selected items with minimal user input.

[0475] To implement the invention, the user first uses an application on their terminal. The user inputs their desired criteria for property selection, and this information is sent from the user to the server. The desired criteria include detailed conditions such as region, rent limit, floor plan, year of construction, and distance to the nearest station.

[0476] The server accesses an external information database. Here, it retrieves information via an API, receiving the latest property information and related data. Generally, this operation is performed using a RESTful API, and the data is exchanged in JSON format. The retrieved information is organized and stored in the server's data storage. This data is stored with indexes to enable efficient searching.

[0477] The server searches its stored information based on the user's requested criteria. The search uses multiple filtering algorithms to extract data that meets the specified conditions. To notify the user of the most relevant results, the server creates a list and sends it to the terminal.

[0478] The terminal displays information received from the server on the user interface. Based on the provided information, the user reviews the properties and selects which ones to request a viewing.

[0479] For example, if a user requests a 1LDK apartment in Tokyo with a rent of 100,000 yen or less and within a 10-minute walk from a train station, the server queries an external database and notifies the user of suitable property information. This process utilizes a generative AI model to function as a search and recommendation system. Through this process, the user can smoothly reserve their desired property.

[0480] An example prompt is, "Please automate the viewing of 1LDK properties in Tokyo that meet the specified criteria," and a system has been implemented to automatically make reservations based on this prompt.

[0481] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0482] Step 1:

[0483] The user opens the terminal application and enters their desired property criteria. These criteria include location, rent limit, floor plan, year of construction, and distance from the nearest station. The entered data is sent from the terminal to the server. At this stage, no data processing is performed until the entered data is sent to the server.

[0484] Step 2:

[0485] The server generates a query to an external information database based on the user's requested conditions. The generated query is sent to the information database via an API. The latest property information is received from the database in JSON format and stored in the server's data storage. At this stage, the server adjusts the data format and indexes the information to enable quick searching.

[0486] Step 3:

[0487] The server searches the stored data for properties that match the user's desired criteria. A filtering algorithm is used to select properties that meet the specified conditions. The input is all property information in the data storage, and the output is a list of properties that match the criteria. In this process, the data is compared with the desired conditions, and the one with the highest match rate is selected.

[0488] Step 4:

[0489] The server generates a list of properties that match the specified criteria and sends it to the terminal. The input here is the matching property information, and the output is a formatted property list. The server organizes and provides the information in a user-friendly format.

[0490] Step 5:

[0491] The device displays received property information in the user interface. The user reviews the list, selects properties of interest, and examines the details. Specifically, tapping on a property's image or description displays even more detailed information.

[0492] Step 6:

[0493] After the user selects a property they wish to view, they request a viewing appointment via a terminal. The terminal sends this request to the server. The input is the property information selected by the user, and the output is the viewing appointment request.

[0494] Step 7:

[0495] The server receives a request for a viewing appointment and coordinates the viewing schedule with the real estate agent's system. Once the appointment is confirmed, the result is notified to the user's terminal. The server sends the appointment confirmation and schedule to the user.

[0496] (Application Example 1)

[0497] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0498] The process of searching for real estate information and scheduling viewings is time-consuming and cumbersome, especially when narrowing down the search criteria with multiple factors, making efficient property searching a challenge for users. Real estate agents also face the problem of wasted time and effort if property information meeting the user's criteria is not provided in advance. Therefore, there is a need for a system that allows both users and real estate agents to efficiently utilize property information.

[0499] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0500] In this invention, the server includes means for receiving desired conditions from a user, means for obtaining information from an external database, means for storing the obtained information in a data storage device, means for searching the information storage device based on the desired conditions, means for notifying the user of the search results, means for automatically making service reservations, and means for assisting with immediate schedule adjustments based on the presented information. This enables the user to efficiently search for properties that meet their criteria and quickly complete the entire process up to making a viewing reservation.

[0501] "Means for receiving user requests" refers to a function that allows users to input their desired conditions into the system and receive those conditions.

[0502] "Means of obtaining information from external databases" refers to functions for accessing external information sources and obtaining necessary data.

[0503] "Means for storing acquired information in a data storage device" refers to a function for saving acquired data to internal storage.

[0504] "Means for searching within an information storage device based on desired conditions" refers to a function that finds relevant information from stored data based on the user's criteria.

[0505] "Means of notifying users of search results" refers to functions that inform users of the information they have searched for.

[0506] "Means for automatically making service reservations" refers to a function that automatically makes reservations for specified services.

[0507] "Means to support immediate schedule adjustments based on the information provided" refers to a function that allows for quick scheduling adjustments according to the information provided.

[0508] The system for realizing this application example begins with the user entering their desired conditions via their smartphone. The device has an application installed to receive the user's desired conditions. This application allows users to enter specific conditions, such as "a 1LDK apartment in Tokyo, under 100,000 yen, within a 10-minute walk from the station."

[0509] The server accesses an external real estate database to retrieve the necessary property information. A backend system built with Node.js and Express stores this retrieved data in MongoDB. The stored data is then quickly searched based on the user's desired criteria. The search results are then communicated to the user through an application built with React Native.

[0510] This system can not only search for suitable properties but also automatically schedule viewings based on user instructions. Once a reservation is complete, the user is notified again with detailed information. Furthermore, it incorporates a function to assist with immediate schedule adjustments based on the presented property information. This makes it easier for users to schedule viewings when needed.

[0511] For example, if a user specifies that they are looking for a furnished, 3LDK apartment for under 150,000 yen, they can enter this information into the app, and matching properties will be displayed in real time. Once the customer selects a property they particularly like, a viewing appointment is immediately confirmed, and their preferred dates are presented.

[0512] An example of a prompt using a generative AI model is, "Please search for rental properties that are 3LDK, furnished, and have a rent of 150,000 yen or less, and suggest viewing dates." By using this prompt, the AI ​​can quickly provide the desired information and meet the user's needs to the greatest extent possible.

[0513] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0514] Step 1:

[0515] The terminal receives the user's desired conditions. These conditions include information such as "a 1LDK apartment in Tokyo, under 100,000 yen, within a 10-minute walk from the station." The entered data is then sent from the terminal to the server.

[0516] Step 2:

[0517] The server, based on the received request criteria, sends queries to an external real estate information database to retrieve property information. The property data obtained through this process includes detailed property information and is processed using Node.js and Express.

[0518] Step 3:

[0519] The retrieved property information is stored in a MongoDB database on the server. This data is cached for future searches and managed to maintain data consistency.

[0520] Step 4:

[0521] The server searches the stored property information again and lists properties that match the user's desired criteria. In this step, database queries are used to filter and extract information that matches the criteria, preparing the data to be presented to the user.

[0522] Step 5:

[0523] The server sends the search results to the user's device. The user can then view the property details on their device. The presented information is displayed visually within an application developed with React Native.

[0524] Step 6:

[0525] The user selects a property they like and requests a viewing appointment. This operation is performed on the terminal, and the request is then sent back to the server.

[0526] Step 7:

[0527] The server automatically makes viewing reservations for specified properties based on user instructions. This process involves communicating with the real estate agent's reservation system via API to coordinate reservation schedules.

[0528] Step 8:

[0529] Once the reservation is complete, the server notifies the user of the details. The user can then check the viewing date and details on their device. This allows the user to quickly confirm their viewing schedule and make the necessary preparations.

[0530] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0531] This invention relates to a system that recognizes a user's emotions and provides personalized rental property searches and viewing appointments based on those emotions. This system is implemented through the following operations:

[0532] First, the user launches the application on their device and enters their personal preferences. These preferences include the property's location, desired rent, floor plan, building age, and distance from the nearest station. This information is sent to the server and stored as the user's profile.

[0533] Next, the emotion engine becomes active and analyzes the user's emotions through the device. The emotion engine evaluates the user's current emotional state using voice, facial expressions, text data, etc. This emotional data is sent to the server and considered as a parameter when selecting property information.

[0534] The server connects with external real estate databases to collect the latest property information and stores it in the company's own database. The collected information serves as the basis for searching for properties that match the user's desired conditions and emotional state.

[0535] Based on the user's emotional state, the server generates personalized search results and optimizes the predicted user satisfaction. These results are sent to the device and notified to the user. For example, if the user is feeling stressed, properties that offer a more relaxing environment will be prioritized.

[0536] Furthermore, if a user finds a property they like, they can select their preference for scheduling a viewing. The server utilizes data from the emotion engine to optimize the timing and method of scheduling the viewing and automatically requests the viewing from the real estate agent. Once the date and time of the viewing are confirmed, the information is notified to the user's device.

[0537] This system enables personalized responses based on emotion recognition, thereby improving the user experience. Furthermore, by providing real estate agents with user emotion data, even more personalized services can be realized, and an increase in the closing rate can be expected. For example, when a user is searching for a property in the city center and prioritizes speed, property information that takes into consideration to reduce their stress will be presented with higher priority.

[0538] The following describes the processing flow.

[0539] Step 1:

[0540] The user opens the application on their device and creates an account. Here, they enter detailed information such as the property's location, rent range, floor plan, year of construction, and distance from the nearest station. This information is necessary to reflect the user's individual needs.

[0541] Step 2:

[0542] The device obtains permission from the user for sentiment analysis, along with the user's inputted preferences. It may also use audio or video data, and permission must be obtained to send that data to the server.

[0543] Step 3:

[0544] The emotion engine activates and analyzes the user's emotional state through the device. This analysis includes voice tone analysis and facial expression recognition, generating a score that quantifies the user's current emotions.

[0545] Step 4:

[0546] The device sends the generated emotion score and desired conditions to the server. The server stores this data in a database and uses it as parameters when selecting a property.

[0547] Step 5:

[0548] The server accesses an external real estate information database to retrieve the latest property information and update the database. This information includes property location, rent, and amenities.

[0549] Step 6:

[0550] The server searches the database based on the user's preferences and sentiment score, filtering for suitable properties. Furthermore, it generates a prioritized list of properties that the user is likely to be interested in, based on their sentiment score.

[0551] Step 7:

[0552] The server sends search results to the device, and the device sends a push notification to the user. The user can view detailed information within the app and see recommended properties based on their sentiment score.

[0553] Step 8:

[0554] When a user selects a property they wish to view, the device sends that information to the server. The server, taking sentiment data into consideration, automatically suggests and books the optimal viewing time with the real estate agent.

[0555] Step 9:

[0556] The server receives confirmation information for the viewing appointment and notifies the user via the terminal. The user can then check the appointment date and request changes if necessary.

[0557] (Example 2)

[0558] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0559] Conventional information provision systems are unable to provide information that takes into account the individual emotional state of users, making it difficult to maximize user satisfaction. Furthermore, there is a need to not only provide information that meets the user's desired conditions, but also to offer more appropriate suggestions based on their emotional state. Additionally, there is a lack of means to automatically adjust the optimal timing and method of reservations, making improved convenience a challenge.

[0560] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0561] In this invention, the server includes means for receiving desired conditions from the user, means for evaluating the user's emotional state using emotion analysis means, and means for retrieving information based on the user's desired conditions and emotional state. This enables the provision of personalized information tailored to the user's emotions and efficient reservation scheduling.

[0562] A "user" is the entity that uses this system and inputs information.

[0563] "Desired conditions" refer to the conditions that users enter for providing information, specifically including information such as location and price.

[0564] "Emotion analysis means" refers to a device or software that includes technology for evaluating emotions from a user's voice, facial expressions, text, etc.

[0565] An "external information database" is a storage device that stores information from external sources and provides the latest information in a specific field.

[0566] "Means of searching" refers to methods and devices for investigating information within a database based on desired conditions and emotional states, and for extracting appropriate data.

[0567] "Means of notification" refers to devices or processes used to inform users of search results or the confirmed date and time of reservations.

[0568] "Methods for automatically optimizing reservations" refer to processes that automatically set the optimal reservation schedule according to the user's emotional state and desired conditions.

[0569] This invention is an information provision system that takes into account the emotional state of the user, and provides the user with the most suitable information by using emotion analysis technology. It also enables efficient schedule management through automatic reservation adjustment. The following describes embodiments for carrying out this invention.

[0570] The user launches an application on their device and enters their desired conditions. The device is equipped with emotion analysis capabilities, and uses speech recognition software, facial expression analysis software, and text analysis tools to acquire the user's emotion data. This emotion data is analyzed in real time by an emotion engine and sent to a server.

[0571] The server receives user preferences and emotional data. The server also interacts with external information databases to retrieve the latest information and stores it in its own database. The server organizes the data in a standardized format and searches for information relevant to the user's preferences and emotional state.

[0572] The server generates personalized search results based on the user's emotions. For example, a user experiencing stress will be prioritized with information on relaxing facilities and quiet areas. These search results are then ranked to optimize the user's predicted satisfaction level and sent to their device.

[0573] Furthermore, the server automatically makes reservations at the optimal time based on the information selected by the user. This includes using AI technology to adjust the schedule, taking into account the user's past behavioral history and current emotional state. Once the reservation is complete, the information is notified to the user's device.

[0574] A concrete example would be prioritizing the presentation of properties with a calm atmosphere to reduce the stress users experience when searching for properties in the city center. An example of a prompt to the generating AI model would be, "If a user is urgently searching for a property in the city center, how would you prioritize recommending properties that will alleviate their stress?"

[0575] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0576] Step 1:

[0577] The user launches the application on their terminal and enters their desired criteria. These criteria include information such as the property's location, budget, floor plan, year of construction, and distance from the nearest station. The entered data undergoes preprocessing to format it before being sent to the server. Preprocessing includes handling missing data values ​​and ensuring consistent formatting.

[0578] Step 2:

[0579] The terminal activates the emotion analysis system and acquires the user's emotional data. For emotion analysis, speech recognition software analyzes the tone and speed of the user's voice, and facial expression analysis software detects facial expressions using the camera. Furthermore, a text analysis tool is used to extract emotions from the text messages entered by the user. This emotional data is processed by an emotion engine and sent to the server as numerical data indicating the user's emotional state.

[0580] Step 3:

[0581] The server receives user preferences and sentiment data, and accesses external information databases to obtain the latest information. It periodically retrieves information using APIs and stores it in its own database. The data is converted to a standardized format and stored. The database includes basic property attribute information and related environmental information.

[0582] Step 4:

[0583] The server searches the database by combining the user's desired conditions and emotional state. In addition to desired location, the search criteria include a sorting algorithm based on the emotional state. For example, if prioritizing relaxing properties, data in quiet areas will receive higher weight. Search results are scored and ranked to determine the most relevant information for the user.

[0584] Step 5:

[0585] The server sends search results to the user's device, notifying them of property details through the results. Once the user selects a property they like, the server automatically optimizes the viewing appointment schedule. This schedule optimization takes into account the user's browsing history and past booking patterns. A request is sent to the real estate agent, and once the booking is confirmed, the date and time are notified to the user's device.

[0586] (Application Example 2)

[0587] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0588] When users select rental properties, there is a need to provide a property selection service that takes into account not only their individual preferences but also their emotional state. Traditional systems are unable to respond to emotional changes, making it difficult to increase user satisfaction. Furthermore, the scheduling of property viewings is not optimized to take users' emotions into account, so improvements in convenience are desired.

[0589] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0590] In this invention, the server includes means for receiving user preferences, means for recognizing the user's emotions, and means for acquiring property information from external property information records. This enables personalized property selection and optimized viewing reservations that take into account the user's preferences and emotional state.

[0591] "User" refers to an individual who uses this system to search for rental properties and schedule viewings.

[0592] "Desired conditions" refer to the requirements that users have for rental properties, such as the property's location, desired rent, floor plan, age of the building, and distance from the station.

[0593] "Means of recognizing emotions" refers to a process or device that analyzes a user's emotional state using voice, facial expressions, text data, etc.

[0594] "Property information record" refers to a recording medium or data area that contains information about rental properties obtained from an external database.

[0595] "Means of searching within property records" refers to a process or device for extracting the most suitable property information from property records based on desired conditions and emotional state.

[0596] "Method for automatically scheduling viewings" refers to a process or device in which the system automatically arranges viewings for properties selected by the user on appropriate dates.

[0597] "Means for optimizing viewing schedules" refers to a process or device for setting efficient and user-friendly viewing schedules based on the user's emotional state and other relevant information.

[0598] This invention is a system for selecting rental properties and scheduling viewings, taking into account the user's desired conditions and emotional state. This system mainly consists of a server and terminals.

[0599] The server receives the user's preferences entered into the device. These preferences include data such as location information, budget, and floor plan. Next, an emotion recognition engine running on the device analyzes the user's emotions and sends that data to the server. This emotion data is collected using voice and facial expression analysis software, such as Google Cloud's Speech-to-Text or Microsoft Azure's Face API.

[0600] The server accesses external property information records to retrieve the latest property information. This information is stored in the server's internal database. The server analyzes the retrieved property information along with desired conditions and sentiment information to select the most suitable property. This selection process utilizes machine learning algorithms and database search functions.

[0601] Information on selected properties is notified to the terminal and presented to the user in real time. The server also creates an optimal viewing schedule for the properties the user wishes to view and notifies the user. Time management software and traffic information APIs are used for this schedule optimization.

[0602] As a concrete example, if a user working in the city center is feeling stressed, properties located in relaxing areas will be prioritized on their smartphone, and they will be notified of viewing schedules that take their free time into consideration.

[0603] An example of a prompt to a generating AI model would be: "My current emotion is stress. I'm looking for a relaxing rental property, and my requirements are a 1K apartment near the city center with a rent of under 100,000 yen. Please suggest the best viewing schedule."

[0604] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0605] Step 1:

[0606] The user enters their desired conditions using a device. This includes information such as location, budget, and floor plan. The entered conditions are sent from the device to the server. As part of the data processing, the conditions are converted into structured data such as JSON format.

[0607] Step 2:

[0608] An emotion recognition engine running on the device analyzes the user's emotions. This step uses voice and facial expression data as input. Speech recognition software or a facial recognition API is used for emotion recognition. The resulting emotion data is sent to a server. The output is an emotional state represented as a string or numerical value.

[0609] Step 3:

[0610] The server accesses external property information records based on the received preference criteria and sentiment data to retrieve the latest property information. This retrieval process uses real estate information APIs, etc. The input is preference criteria and sentiment data, and the output is a list of property information.

[0611] Step 4:

[0612] The server stores the acquired property information in an internal database. The stored data is appropriately indexed, taking into account desired conditions and sentiment data. The output is the indexed information stored in the database.

[0613] Step 5:

[0614] The server searches the property records based on user preferences and sentiment data to select the most suitable property for the user. A machine learning algorithm is used for selection, with the property information list and user requirements as input. The output is a list of recommended properties.

[0615] Step 6:

[0616] The recommended property information is sent back to the device and the user is notified. The notification is displayed on the application's UI, allowing the user to view the property information. The output is the specific property information displayed on the user's device.

[0617] Step 7:

[0618] The server creates an optimal viewing schedule based on recommended properties. Time management software and a traffic information API are used for scheduling. Inputs are recommended property information and sentiment data, and output is the scheduled viewing dates.

[0619] Step 8:

[0620] The viewing schedule will be notified to the user's device. The user can then confirm and adjust the viewing date based on the notified information. The output will be a viewing schedule optimized for the user.

[0621] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0622] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0623] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0624] [Fourth Embodiment]

[0625] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0626] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0627] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0628] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0629] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0630] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0631] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0632] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0633] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0634] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0635] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0636] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0637] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0638] This invention is a system that enables users to efficiently search for rental properties and book viewings. A specific embodiment of this system is shown below.

[0639] First, the user accesses the application on their device and enters their desired criteria as part of the initial setup. These criteria include detailed items such as location, maximum rent, floor plan, year of construction, and distance from the nearest station. This information serves as the basic data for searching for properties based on the user's individual needs.

[0640] Next, the server retrieves the latest property information from an external real estate database. The retrieved information is continuously updated and stored in the server's database. This accumulated property information can be easily accessed via search queries.

[0641] The server periodically searches the database for property information, matching it against the user's desired criteria, either at regular intervals or upon user request. This is done using multiple filters with defined conditions, and properties that match the criteria are listed.

[0642] Search results are notified to the device, and users can view property details within the app. Even if no properties matching the criteria are found, the server can suggest properties with advantages outside of the specified criteria to the user.

[0643] Furthermore, the server automates the application process once a property the user wishes to view has been identified. The user selects the property they wish to view through their device, and the server makes the viewing reservation on their behalf with the real estate agent. Once the viewing reservation is confirmed, the details are notified to the user's device.

[0644] This system allows users to efficiently manage the process from property search to scheduling viewings. Real estate agents also benefit from the improved quality of viewings and reduced workload due to pre-matching of criteria.

[0645] For example, if a user requests a 1LDK apartment in Tokyo with a rent of 100,000 yen or less and within a 10-minute walk from a train station, the server searches an external database for suitable properties and notifies the user of that information. The user then selects a property they like, books a viewing, and the process is complete. Automating this entire process significantly reduces the time and effort required to choose an apartment.

[0646] The following describes the processing flow.

[0647] Step 1:

[0648] The user launches the application installed on their device and creates an account. After creating the account, they enter detailed information such as location, maximum rent, floor plan, building age, and distance from the station as their desired conditions.

[0649] Step 2:

[0650] The terminal sends the entered preferences to the server, which stores this information in a database to create a profile for each user.

[0651] Step 3:

[0652] The server connects to an external real estate information database using an API and periodically retrieves the latest property information. The retrieved property information is stored in a database on the server.

[0653] Step 4:

[0654] The server executes a search query by comparing the property information stored in the database with the user's desired conditions. The search results list properties that match the criteria.

[0655] Step 5:

[0656] The server sends the search results to the device. The device then sends a push notification to the user, displaying the details of matching properties within the app.

[0657] Step 6:

[0658] Users check the property information notified on their device and select any properties they like to view.

[0659] Step 7:

[0660] The terminal sends the user's request for a viewing to the server, and the server automatically sends a request to the real estate agent to schedule a viewing.

[0661] Step 8:

[0662] When the server receives confirmation of a viewing appointment from the real estate agent, it sends the schedule to the terminal. The terminal then notifies the user of the viewing date.

[0663] Step 9:

[0664] After the viewing, if the real estate agent provides feedback, the server receives that information and notifies the user via the terminal.

[0665] (Example 1)

[0666] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0667] Conventional information retrieval and reservation systems made it difficult for users to efficiently find the most suitable options based on their individual needs and to smoothly make reservations for those options. Furthermore, they lacked the functionality to provide information that didn't perfectly match the criteria but offered other advantages, failing to suggest options that users might overlook. Additionally, the lack of a mechanism for receiving feedback from vendors meant users were unaware of the subsequent situation, potentially leading to a decline in the quality of the experience.

[0668] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0669] In this invention, the server includes means for receiving desired conditions from the user, means for obtaining target information from an external information database, and means for storing the obtained target information in data storage. This enables the user to efficiently perform searches based on their own conditions and automated reservation procedures. Furthermore, by providing a function to suggest information that does not match the conditions but has other advantages, a wider range of choices can be offered, and by receiving feedback from vendors and notifying them of the situation after use, the overall quality of the experience can be improved.

[0670] "Means for receiving desired conditions" refers to a function that collects requests and conditions entered by users via digital devices and transmits that information to the server.

[0671] "Means of obtaining target information from an information database" refers to the process of accessing an external database, obtaining the latest information, and incorporating it into the system.

[0672] "Means for storing acquired target information in data storage" refers to a system that organizes and saves information in a storage device on a server, making it available for quick retrieval and use as needed.

[0673] A "search method" is an algorithm or process that efficiently finds data that matches specific criteria from information that has been collected and stored in advance.

[0674] "Means of notifying users" refer to mechanisms for presenting search results and other important information to users, and these are usually done through digital interfaces.

[0675] "Methods for automating reservations" refers to a function that automatically executes reservation procedures for selected items with minimal user input.

[0676] To implement the invention, the user first uses an application on their terminal. The user inputs their desired criteria for property selection, and this information is sent from the user to the server. The desired criteria include detailed conditions such as region, rent limit, floor plan, year of construction, and distance to the nearest station.

[0677] The server accesses an external information database. Here, it retrieves information via an API, receiving the latest property information and related data. Generally, this operation is performed using a RESTful API, and the data is exchanged in JSON format. The retrieved information is organized and stored in the server's data storage. This data is stored with indexes to enable efficient searching.

[0678] The server searches its stored information based on the user's requested criteria. The search uses multiple filtering algorithms to extract data that meets the specified conditions. To notify the user of the most relevant results, the server creates a list and sends it to the terminal.

[0679] The terminal displays information received from the server on the user interface. Based on the provided information, the user reviews the properties and selects which ones to request a viewing.

[0680] For example, if a user requests a 1LDK apartment in Tokyo with a rent of 100,000 yen or less and within a 10-minute walk from a train station, the server queries an external database and notifies the user of suitable property information. This process utilizes a generative AI model to function as a search and recommendation system. Through this process, the user can smoothly reserve their desired property.

[0681] An example prompt is, "Please automate the viewing of 1LDK properties in Tokyo that meet the specified criteria," and a system has been implemented to automatically make reservations based on this prompt.

[0682] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0683] Step 1:

[0684] The user opens the terminal application and enters their desired property criteria. These criteria include location, rent limit, floor plan, year of construction, and distance from the nearest station. The entered data is sent from the terminal to the server. At this stage, no data processing is performed until the entered data is sent to the server.

[0685] Step 2:

[0686] The server generates a query to an external information database based on the user's requested conditions. The generated query is sent to the information database via an API. The latest property information is received from the database in JSON format and stored in the server's data storage. At this stage, the server adjusts the data format and indexes the information to enable quick searching.

[0687] Step 3:

[0688] The server searches the stored data for properties that match the user's desired criteria. A filtering algorithm is used to select properties that meet the specified conditions. The input is all property information in the data storage, and the output is a list of properties that match the criteria. In this process, the data is compared with the desired conditions, and the one with the highest match rate is selected.

[0689] Step 4:

[0690] The server generates a list of properties that match the specified criteria and sends it to the terminal. The input here is the matching property information, and the output is a formatted property list. The server organizes and provides the information in a user-friendly format.

[0691] Step 5:

[0692] The device displays received property information in the user interface. The user reviews the list, selects properties of interest, and examines the details. Specifically, tapping on a property's image or description displays even more detailed information.

[0693] Step 6:

[0694] After the user selects a property they wish to view, they request a viewing appointment via a terminal. The terminal sends this request to the server. The input is the property information selected by the user, and the output is the viewing appointment request.

[0695] Step 7:

[0696] The server receives a request for a viewing appointment and coordinates the viewing schedule with the real estate agent's system. Once the appointment is confirmed, the result is notified to the user's terminal. The server sends the appointment confirmation and schedule to the user.

[0697] (Application Example 1)

[0698] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0699] The process of searching for real estate information and scheduling viewings is time-consuming and cumbersome, especially when narrowing down the search criteria with multiple factors, making efficient property searching a challenge for users. Real estate agents also face the problem of wasted time and effort if property information meeting the user's criteria is not provided in advance. Therefore, there is a need for a system that allows both users and real estate agents to efficiently utilize property information.

[0700] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0701] In this invention, the server includes means for receiving desired conditions from a user, means for obtaining information from an external database, means for storing the obtained information in a data storage device, means for searching the information storage device based on the desired conditions, means for notifying the user of the search results, means for automatically making service reservations, and means for assisting with immediate schedule adjustments based on the presented information. This enables the user to efficiently search for properties that meet their criteria and quickly complete the entire process up to making a viewing reservation.

[0702] "Means for receiving user requests" refers to a function that allows users to input their desired conditions into the system and receive those conditions.

[0703] "Means of obtaining information from external databases" refers to functions for accessing external information sources and obtaining necessary data.

[0704] "Means for storing acquired information in a data storage device" refers to a function for saving acquired data to internal storage.

[0705] "Means for searching within an information storage device based on desired conditions" refers to a function that finds relevant information from stored data based on the user's criteria.

[0706] "Means of notifying users of search results" refers to functions that inform users of the information they have searched for.

[0707] "Means for automatically making service reservations" refers to a function that automatically makes reservations for specified services.

[0708] "Means to support immediate schedule adjustments based on the information provided" refers to a function that allows for quick scheduling adjustments according to the information provided.

[0709] The system for realizing this application example begins with the user entering their desired conditions via their smartphone. The device has an application installed to receive the user's desired conditions. This application allows users to enter specific conditions, such as "a 1LDK apartment in Tokyo, under 100,000 yen, within a 10-minute walk from the station."

[0710] The server accesses an external real estate database to retrieve the necessary property information. A backend system built with Node.js and Express stores this retrieved data in MongoDB. The stored data is then quickly searched based on the user's desired criteria. The search results are then communicated to the user through an application built with React Native.

[0711] This system can not only search for suitable properties but also automatically schedule viewings based on user instructions. Once a reservation is complete, the user is notified again with detailed information. Furthermore, it incorporates a function to assist with immediate schedule adjustments based on the presented property information. This makes it easier for users to schedule viewings when needed.

[0712] For example, if a user specifies that they are looking for a furnished, 3LDK apartment for under 150,000 yen, they can enter this information into the app, and matching properties will be displayed in real time. Once the customer selects a property they particularly like, a viewing appointment is immediately confirmed, and their preferred dates are presented.

[0713] An example of a prompt using a generative AI model is, "Please search for rental properties that are 3LDK, furnished, and have a rent of 150,000 yen or less, and suggest viewing dates." By using this prompt, the AI ​​can quickly provide the desired information and meet the user's needs to the greatest extent possible.

[0714] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0715] Step 1:

[0716] The terminal receives the user's desired conditions. These conditions include information such as "a 1LDK apartment in Tokyo, under 100,000 yen, within a 10-minute walk from the station." The entered data is then sent from the terminal to the server.

[0717] Step 2:

[0718] The server, based on the received request criteria, sends queries to an external real estate information database to retrieve property information. The property data obtained through this process includes detailed property information and is processed using Node.js and Express.

[0719] Step 3:

[0720] The retrieved property information is stored in a MongoDB database on the server. This data is cached for future searches and managed to maintain data consistency.

[0721] Step 4:

[0722] The server searches the stored property information again and lists properties that match the user's desired criteria. In this step, database queries are used to filter and extract information that matches the criteria, preparing the data to be presented to the user.

[0723] Step 5:

[0724] The server sends the search results to the user's device. The user can then view the property details on their device. The presented information is displayed visually within an application developed with React Native.

[0725] Step 6:

[0726] The user selects a property they like and requests a viewing appointment. This operation is performed on the terminal, and the request is then sent back to the server.

[0727] Step 7:

[0728] The server automatically makes viewing reservations for specified properties based on user instructions. This process involves communicating with the real estate agent's reservation system via API to coordinate reservation schedules.

[0729] Step 8:

[0730] Once the reservation is complete, the server notifies the user of the details. The user can then check the viewing date and details on their device. This allows the user to quickly confirm their viewing schedule and make the necessary preparations.

[0731] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0732] This invention relates to a system that recognizes a user's emotions and provides personalized rental property searches and viewing appointments based on those emotions. This system is implemented through the following operations:

[0733] First, the user launches the application on their device and enters their personal preferences. These preferences include the property's location, desired rent, floor plan, building age, and distance from the nearest station. This information is sent to the server and stored as the user's profile.

[0734] Next, the emotion engine becomes active and analyzes the user's emotions through the device. The emotion engine evaluates the user's current emotional state using voice, facial expressions, text data, etc. This emotional data is sent to the server and considered as a parameter when selecting property information.

[0735] The server connects with external real estate databases to collect the latest property information and stores it in the company's own database. The collected information serves as the basis for searching for properties that match the user's desired conditions and emotional state.

[0736] Based on the user's emotional state, the server generates personalized search results and optimizes the predicted user satisfaction. These results are sent to the device and notified to the user. For example, if the user is feeling stressed, properties that offer a more relaxing environment will be prioritized.

[0737] Furthermore, if a user finds a property they like, they can select their preference for scheduling a viewing. The server utilizes data from the emotion engine to optimize the timing and method of scheduling the viewing and automatically requests the viewing from the real estate agent. Once the date and time of the viewing are confirmed, the information is notified to the user's device.

[0738] This system enables personalized responses based on emotion recognition, thereby improving the user experience. Furthermore, by providing real estate agents with user emotion data, even more personalized services can be realized, and an increase in the closing rate can be expected. For example, when a user is searching for a property in the city center and prioritizes speed, property information that takes into consideration to reduce their stress will be presented with higher priority.

[0739] The following describes the processing flow.

[0740] Step 1:

[0741] The user opens the application on their device and creates an account. Here, they enter detailed information such as the property's location, rent range, floor plan, year of construction, and distance from the nearest station. This information is necessary to reflect the user's individual needs.

[0742] Step 2:

[0743] The device obtains permission from the user for sentiment analysis, along with the user's inputted preferences. It may also use audio or video data, and permission must be obtained to send that data to the server.

[0744] Step 3:

[0745] The emotion engine activates and analyzes the user's emotional state through the device. This analysis includes voice tone analysis and facial expression recognition, generating a score that quantifies the user's current emotions.

[0746] Step 4:

[0747] The device sends the generated emotion score and desired conditions to the server. The server stores this data in a database and uses it as parameters when selecting a property.

[0748] Step 5:

[0749] The server accesses an external real estate information database to retrieve the latest property information and update the database. This information includes property location, rent, and amenities.

[0750] Step 6:

[0751] The server searches the database based on the user's preferences and sentiment score, filtering for suitable properties. Furthermore, it generates a prioritized list of properties that the user is likely to be interested in, based on their sentiment score.

[0752] Step 7:

[0753] The server sends search results to the device, and the device sends a push notification to the user. The user can view detailed information within the app and see recommended properties based on their sentiment score.

[0754] Step 8:

[0755] When a user selects a property they wish to view, the device sends that information to the server. The server, taking sentiment data into consideration, automatically suggests and books the optimal viewing time with the real estate agent.

[0756] Step 9:

[0757] The server receives confirmation information for the viewing appointment and notifies the user via the terminal. The user can then check the appointment date and request changes if necessary.

[0758] (Example 2)

[0759] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0760] Conventional information provision systems are unable to provide information that takes into account the individual emotional state of users, making it difficult to maximize user satisfaction. Furthermore, there is a need to not only provide information that meets the user's desired conditions, but also to offer more appropriate suggestions based on their emotional state. Additionally, there is a lack of means to automatically adjust the optimal timing and method of reservations, making improved convenience a challenge.

[0761] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0762] In this invention, the server includes means for receiving desired conditions from the user, means for evaluating the user's emotional state using emotion analysis means, and means for retrieving information based on the user's desired conditions and emotional state. This enables the provision of personalized information tailored to the user's emotions and efficient reservation scheduling.

[0763] A "user" is the entity that uses this system and inputs information.

[0764] "Desired conditions" refer to the conditions that users enter for providing information, specifically including information such as location and price.

[0765] "Emotion analysis means" refers to a device or software that includes technology for evaluating emotions from a user's voice, facial expressions, text, etc.

[0766] An "external information database" is a storage device that stores information from external sources and provides the latest information in a specific field.

[0767] "Means of searching" refers to methods and devices for investigating information within a database based on desired conditions and emotional states, and for extracting appropriate data.

[0768] "Means of notification" refers to devices or processes used to inform users of search results or the confirmed date and time of reservations.

[0769] "Methods for automatically optimizing reservations" refer to processes that automatically set the optimal reservation schedule according to the user's emotional state and desired conditions.

[0770] This invention is an information provision system that takes into account the emotional state of the user, and provides the user with the most suitable information by using emotion analysis technology. It also enables efficient schedule management through automatic reservation adjustment. The following describes embodiments for carrying out this invention.

[0771] The user launches an application on their device and enters their desired conditions. The device is equipped with emotion analysis capabilities, and uses speech recognition software, facial expression analysis software, and text analysis tools to acquire the user's emotion data. This emotion data is analyzed in real time by an emotion engine and sent to a server.

[0772] The server receives user preferences and emotional data. The server also interacts with external information databases to retrieve the latest information and stores it in its own database. The server organizes the data in a standardized format and searches for information relevant to the user's preferences and emotional state.

[0773] The server generates personalized search results based on the user's emotions. For example, a user experiencing stress will be prioritized with information on relaxing facilities and quiet areas. These search results are then ranked to optimize the user's predicted satisfaction level and sent to their device.

[0774] Furthermore, the server automatically makes reservations at the optimal time based on the information selected by the user. This includes using AI technology to adjust the schedule, taking into account the user's past behavioral history and current emotional state. Once the reservation is complete, the information is notified to the user's device.

[0775] A concrete example would be prioritizing the presentation of properties with a calm atmosphere to reduce the stress users experience when searching for properties in the city center. An example of a prompt to the generating AI model would be, "If a user is urgently searching for a property in the city center, how would you prioritize recommending properties that will alleviate their stress?"

[0776] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0777] Step 1:

[0778] The user launches the application on their terminal and enters their desired criteria. These criteria include information such as the property's location, budget, floor plan, year of construction, and distance from the nearest station. The entered data undergoes preprocessing to format it before being sent to the server. Preprocessing includes handling missing data values ​​and ensuring consistent formatting.

[0779] Step 2:

[0780] The terminal activates the emotion analysis system and acquires the user's emotional data. For emotion analysis, speech recognition software analyzes the tone and speed of the user's voice, and facial expression analysis software detects facial expressions using the camera. Furthermore, a text analysis tool is used to extract emotions from the text messages entered by the user. This emotional data is processed by an emotion engine and sent to the server as numerical data indicating the user's emotional state.

[0781] Step 3:

[0782] The server receives user preferences and sentiment data, and accesses external information databases to obtain the latest information. It periodically retrieves information using APIs and stores it in its own database. The data is converted to a standardized format and stored. The database includes basic property attribute information and related environmental information.

[0783] Step 4:

[0784] The server searches the database by combining the user's desired conditions and emotional state. In addition to desired location, the search criteria include a sorting algorithm based on the emotional state. For example, if prioritizing relaxing properties, data in quiet areas will receive higher weight. Search results are scored and ranked to determine the most relevant information for the user.

[0785] Step 5:

[0786] The server sends search results to the user's device, notifying them of property details through the results. Once the user selects a property they like, the server automatically optimizes the viewing appointment schedule. This schedule optimization takes into account the user's browsing history and past booking patterns. A request is sent to the real estate agent, and once the booking is confirmed, the date and time are notified to the user's device.

[0787] (Application Example 2)

[0788] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0789] When users select rental properties, there is a need to provide a property selection service that takes into account not only their individual preferences but also their emotional state. Traditional systems are unable to respond to emotional changes, making it difficult to increase user satisfaction. Furthermore, the scheduling of property viewings is not optimized to take users' emotions into account, so improvements in convenience are desired.

[0790] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0791] In this invention, the server includes means for receiving user preferences, means for recognizing the user's emotions, and means for acquiring property information from external property information records. This enables personalized property selection and optimized viewing reservations that take into account the user's preferences and emotional state.

[0792] "User" refers to an individual who uses this system to search for rental properties and schedule viewings.

[0793] "Desired conditions" refer to the requirements that users have for rental properties, such as the property's location, desired rent, floor plan, age of the building, and distance from the station.

[0794] "Means of recognizing emotions" refers to a process or device that analyzes a user's emotional state using voice, facial expressions, text data, etc.

[0795] "Property information record" refers to a recording medium or data area that contains information about rental properties obtained from an external database.

[0796] "Means of searching within property records" refers to a process or device for extracting the most suitable property information from property records based on desired conditions and emotional state.

[0797] "Method for automatically scheduling viewings" refers to a process or device in which the system automatically arranges viewings for properties selected by the user on appropriate dates.

[0798] "Means for optimizing viewing schedules" refers to a process or device for setting efficient and user-friendly viewing schedules based on the user's emotional state and other relevant information.

[0799] This invention is a system for selecting rental properties and scheduling viewings, taking into account the user's desired conditions and emotional state. This system mainly consists of a server and terminals.

[0800] The server receives the user's preferences entered into the device. These preferences include data such as location information, budget, and floor plan. Next, an emotion recognition engine running on the device analyzes the user's emotions and sends that data to the server. This emotion data is collected using voice and facial expression analysis software, such as Google Cloud's Speech-to-Text or Microsoft Azure's Face API.

[0801] The server accesses external property information records to retrieve the latest property information. This information is stored in the server's internal database. The server analyzes the retrieved property information along with desired conditions and sentiment information to select the most suitable property. This selection process utilizes machine learning algorithms and database search functions.

[0802] Information on selected properties is notified to the terminal and presented to the user in real time. The server also creates an optimal viewing schedule for the properties the user wishes to view and notifies the user. Time management software and traffic information APIs are used for this schedule optimization.

[0803] As a concrete example, if a user working in the city center is feeling stressed, properties located in relaxing areas will be prioritized on their smartphone, and they will be notified of viewing schedules that take their free time into consideration.

[0804] An example of a prompt to a generating AI model would be: "My current emotion is stress. I'm looking for a relaxing rental property, and my requirements are a 1K apartment near the city center with a rent of under 100,000 yen. Please suggest the best viewing schedule."

[0805] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0806] Step 1:

[0807] The user enters their desired conditions using a device. This includes information such as location, budget, and floor plan. The entered conditions are sent from the device to the server. As part of the data processing, the conditions are converted into structured data such as JSON format.

[0808] Step 2:

[0809] An emotion recognition engine running on the device analyzes the user's emotions. This step uses voice and facial expression data as input. Speech recognition software or a facial recognition API is used for emotion recognition. The resulting emotion data is sent to a server. The output is an emotional state represented as a string or numerical value.

[0810] Step 3:

[0811] The server accesses external property information records based on the received preference criteria and sentiment data to retrieve the latest property information. This retrieval process uses real estate information APIs, etc. The input is preference criteria and sentiment data, and the output is a list of property information.

[0812] Step 4:

[0813] The server stores the acquired property information in an internal database. The stored data is appropriately indexed, taking into account desired conditions and sentiment data. The output is the indexed information stored in the database.

[0814] Step 5:

[0815] The server searches the property records based on user preferences and sentiment data to select the most suitable property for the user. A machine learning algorithm is used for selection, with the property information list and user requirements as input. The output is a list of recommended properties.

[0816] Step 6:

[0817] The recommended property information is sent back to the device and the user is notified. The notification is displayed on the application's UI, allowing the user to view the property information. The output is the specific property information displayed on the user's device.

[0818] Step 7:

[0819] The server creates an optimal viewing schedule based on recommended properties. Time management software and a traffic information API are used for scheduling. Inputs are recommended property information and sentiment data, and output is the scheduled viewing dates.

[0820] Step 8:

[0821] The viewing schedule will be notified to the user's device. The user can then confirm and adjust the viewing date based on the notified information. The output will be a viewing schedule optimized for the user.

[0822] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0823] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0824] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0825] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0826] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0827] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0828] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0829] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0830] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0831] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0832] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0833] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0834] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0835] 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.

[0836] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0837] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0838] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0839] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0840] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0841] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0842] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0843] The following is further disclosed regarding the embodiments described above.

[0844] (Claim 1)

[0845] A means of receiving desired conditions from users,

[0846] Methods for obtaining property information from external real estate databases,

[0847] A means of storing acquired property information in a database,

[0848] A means of searching within the property database based on desired conditions,

[0849] A means of notifying users of search results,

[0850] A method for automatically scheduling viewings,

[0851] A system that includes this.

[0852] (Claim 2)

[0853] The system according to claim 1, comprising means for suggesting to the user property information that does not meet the criteria but has other advantages.

[0854] (Claim 3)

[0855] The system according to claim 1, comprising means for receiving feedback from real estate agents and notifying the user of the status after a viewing.

[0856] "Example 1"

[0857] (Claim 1)

[0858] A means of receiving desired conditions from users,

[0859] A means of obtaining target information from an external information database,

[0860] A means for storing the acquired target information in data storage,

[0861] A means of searching within data storage based on desired conditions,

[0862] A means of notifying users of search results,

[0863] A method to automatically make a reservation if you like the presented item,

[0864] A system that includes this.

[0865] (Claim 2)

[0866] The system according to claim 1, comprising means for suggesting to the user target information that does not meet the conditions but has other advantages.

[0867] (Claim 3)

[0868] The system according to claim 1, comprising means for receiving feedback from vendors and notifying users of information regarding usage status.

[0869] "Application Example 1"

[0870] (Claim 1)

[0871] A means of receiving desired conditions from users,

[0872] Means of obtaining information from an external database,

[0873] A means for storing acquired information in a data storage device,

[0874] A means of searching within an information storage device based on desired conditions,

[0875] A means of notifying users of search results,

[0876] A means of automatically making service reservations,

[0877] Based on the information provided, a means to support immediate schedule adjustments,

[0878] A system that includes this.

[0879] (Claim 2)

[0880] The system according to claim 1, comprising means for suggesting to the user information that does not meet the conditions but has other advantages.

[0881] (Claim 3)

[0882] The system according to claim 1, further comprising means for receiving feedback from a service provider and notifying the user of the status after receiving the service.

[0883] "Example 2 of combining an emotion engine"

[0884] (Claim 1)

[0885] A means of receiving desired conditions from users,

[0886] A means for evaluating the emotional state of a user using emotion analysis tools,

[0887] Means of obtaining information from external information databases,

[0888] Means for storing acquired information,

[0889] A means of searching for information based on the user's desired conditions and emotional state,

[0890] A means of notifying users of search results,

[0891] Methods for automatically optimizing reservations,

[0892] A system that includes this.

[0893] (Claim 2)

[0894] The system according to claim 1, further comprising means for suggesting to the user information that does not meet the conditions but is more appropriate to the user's emotional state.

[0895] (Claim 3)

[0896] The system according to claim 1, comprising means for receiving feedback from external parties and notifying the user of the situation.

[0897] "Application example 2 when combining with an emotional engine"

[0898] (Claim 1)

[0899] A means of receiving desired conditions from users,

[0900] Means for recognizing the user's emotions,

[0901] A means of obtaining property information from external property information records,

[0902] A means of storing acquired property information in a record,

[0903] A means of searching within property records based on desired conditions and user sentiment,

[0904] A means of notifying users of search results,

[0905] A method for automatically scheduling viewings,

[0906] A means of optimizing the viewing schedule based on the user's emotions,

[0907] A system that includes this.

[0908] (Claim 2)

[0909] The system according to claim 1, comprising means for suggesting to the user property information that does not meet the criteria but has other advantages.

[0910] (Claim 3)

[0911] The system according to claim 1, comprising means for receiving feedback from real estate agents and notifying the user of the status after a viewing. [Explanation of Symbols]

[0912] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of receiving desired conditions from users, Means of obtaining information from an external database, A means for storing acquired information in a data storage device, A means of searching within an information storage device based on desired conditions, A means of notifying users of search results, A means of automatically making service reservations, Based on the information provided, a means to support immediate schedule adjustments, A system that includes this.

2. The system according to claim 1, further comprising means for suggesting to the user information that does not meet the conditions but has other advantages.

3. The system according to claim 1, further comprising means for receiving feedback from a service provider and notifying the user of the status after receiving the service.

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