system

Abstract

Provide a system. 【Solution means】 An automation system in property management, a means for receiving information input by a user via a terminal, a means for obtaining property data related to the received information from a database, a means for analyzing the obtained property data to identify the cause of a problem, a means for proposing appropriate countermeasures based on the analysis result, a means for executing the proposed countermeasures, a means for notifying the user of the execution status of the countermeasures A system including.

Description

Technical Field

[0005] , ,

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] Real estate management operations handle a large number of property information, and different responses are required for each property, so they are very specialized and complex. In addition, these operations are often carried out manually by managers, and may lack efficiency and accuracy. Even in operations such as repair requests and rent reviews, it is difficult to respond quickly and appropriately, which may result in a decrease in user satisfaction and deterioration of management efficiency. Thus, automation and efficiency improvement of real estate management operations are required.

Means for Solving the Problems

[0005] The present invention provides an automated real estate management system that includes means for receiving information entered by a user via a terminal, means for obtaining property data related to the received information from a database, means for analyzing the obtained property data to identify the cause of a problem, means for proposing appropriate countermeasures based on the analysis results, means for executing the proposed countermeasures, and means for notifying the user of the status of the implementation of the countermeasures. This system enables tasks such as repair requests and rent reviews to be performed quickly and automatically, thereby improving the efficiency and accuracy of real estate management operations.

[0006] A "user" is a person who uses a terminal to input information into a system.

[0007] A "terminal" is an electronic device used by a user to input information, and includes computers, smartphones, tablets, and other similar devices.

[0008] "Means of receiving information" refers to the functions and processes that allow a system to receive information entered by a user via a terminal.

[0009] "Property data" refers to various types of information related to a real estate property, including information such as the property's location, area, floor plan, repair history, and rent.

[0010] A "database" is an electronic data storage system where property data is stored and can be searched and retrieved.

[0011] "Means of retrieving information from a database" refers to components or programs that search a database for specified information and retrieve the necessary data.

[0012] "Means of analysis" refers to functions that include algorithms and analysis methods for identifying the cause of a problem based on acquired property data.

[0013] "Means of suggestion" refers to a function that presents appropriate countermeasures and solutions to the user based on the analysis results.

[0014] "Means of implementation" refer to automated processes or actions taken to actually put the proposed countermeasures into action.

[0015] "Means of notification" refers to mechanisms by which the system informs the user of the status of the implementation of countermeasures, and includes, for example, email and in-app notifications. [Brief explanation of the drawing]

[0016] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This 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] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This 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] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This 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] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This 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 the emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when the emotion engine is combined.

Embodiments for Carrying out the Invention

[0017] Hereinafter, an example of an embodiment of the 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, the labeled processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), etc.

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

[0021] In the following embodiments, the labeled storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, 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] As a concrete example of this specification, a form of a system for automating real estate management operations will be described. In this system, the user inputs information via a terminal, which is received and processed by a server, which then proposes and implements appropriate countermeasures. The present invention can effectively automate tasks such as repair requests and rent reviews.

[0038] System Configuration

[0039] 1. User input

[0040] Users use a terminal to input information into the system, such as repair requests or rent review requests. This input information is received by the server, which then proceeds to the next step.

[0041] 2. The server retrieves the information.

[0042] The server retrieves detailed information about the target property from the database based on the information received from the user. This detailed information includes the property's repair history, rental history, and the condition of its facilities.

[0043] 3. The server analyzes the data.

[0044] The acquired information is analyzed to determine whether repairs are necessary or if the rent can be reviewed. This analysis also takes into account historical data and market trends.

[0045] 4. The server generates the proposal.

[0046] Based on the analysis results, we propose appropriate countermeasures to the user. For example, if repairs are needed, we will suggest a partner repair company, and if we propose a review of the rent, we will present the new rent amount.

[0047] 5. The server performs the action.

[0048] If the user approves the proposal, the server automatically sends a repair request to the contractor or renews the lease. The user is also notified of the progress.

[0049] Specific example

[0050] Automation of repair requests

[0051] This explains a scenario where a user enters "The toilet in room 203 is broken" into the terminal.

[0052] 1. User inputs into terminal: The user inputs "The toilet in room 203 is broken" into the terminal.

[0053] 2. Server receives: The server receives this information and retrieves the property data for Room 203 from the database.

[0054] 3. Server analysis: The server analyzes the acquired data to identify the cause of the failure.

[0055] 4. Server Proposal: A server that determines it needs repair will propose to the user, "Repair is needed. Shall we request a repair service?"

[0056] 5. Server takes action: If the user approves, the server automatically sends a repair request to the affiliated repair company and notifies the user of the progress.

[0057] Proposal to review rent

[0058] This explains a scenario where a user enters "I want to review the rent for room 301" into their device.

[0059] 1. User inputs into terminal: The user inputs "I would like to review the rent for room 301" into the terminal.

[0060] 2. Server receives: The server receives this information and retrieves property data for Room 301 and rental data for the surrounding area from its database.

[0061] 3. Server analysis: The server analyzes the acquired data and calculates new rental rates based on market trends.

[0062] 4. The server makes a suggestion: It presents to the user, "We suggest changing the rent for room 301 from XX yen to YY yen."

[0063] 5. Server execution: If the user approves, the new rental fee is updated in the database, and the related contract information is also automatically updated.

[0064] Thus, the present invention can automate the specialized and complex tasks of real estate management, enabling efficient and accurate handling. This dramatically improves the efficiency of management operations and contributes to increased user satisfaction.

[0065] The following describes the processing flow.

[0066] Automation of repair requests

[0067] Step 1:

[0068] The user enters "The toilet in room 203 is broken" into the terminal. This is done through the terminal's input form, and the server receives the information.

[0069] Step 2:

[0070] Based on the information received by the server, property data for Room 203 is retrieved from the database. This includes detailed information about the property's repair history and current condition.

[0071] Step 3:

[0072] The system analyzes the data acquired by the server to identify the cause of the failure. For example, it refers to past repair history for similar incidents to determine if the current failure is due to the same cause.

[0073] Step 4:

[0074] Based on the analysis results, the server sends a suggestion to the user: "Repair is needed. Would you like to request a repair service?" This suggestion is displayed on the terminal.

[0075] Step 5:

[0076] If the user approves the proposal, select "Yes" and send the decision to the server.

[0077] Step 6:

[0078] The server retrieves information on affiliated repair companies from a database and automatically generates and sends repair requests. Specifically, it sends a message to the repair company containing information about the necessary repairs and their locations.

[0079] Step 7:

[0080] The server monitors the status of repair requests and receives progress reports from repair companies.

[0081] Step 8:

[0082] The server notifies the user of the progress of the repairs. For example, it displays messages on the user's terminal such as "The repair company has arrived on site" or "The repair work has been completed."

[0083] Proposal to review rent

[0084] Step 1:

[0085] The user enters "I would like to review the rent for room 301" into the terminal. The input is made through a form on the terminal, and the server receives the information.

[0086] Step 2:

[0087] The server retrieves property data for Room 301 and rental information for surrounding properties from a database and external real estate data APIs. This includes current rental rates, past rental history, and market prices.

[0088] Step 3:

[0089] The server analyzes the collected data to calculate the optimal rental price. The analysis takes into account local real estate market trends and the rental rates of competing properties.

[0090] Step 4:

[0091] Based on the analysis results, the server notifies the user, "We propose changing the rent for room 301 from XX yen to YY yen." This proposal is displayed on the terminal.

[0092] Step 5:

[0093] If the user approves the proposal, select "Yes" and send the decision to the server.

[0094] Step 6:

[0095] The server updates the database with new rental rates. This includes automatic updates of rental contract information and advertising data.

[0096] Step 7:

[0097] The server notifies the user of the update status. A message such as "Rental fee review is complete. The new rent is YY yen." is displayed on the user's terminal.

[0098] (Example 1)

[0099] 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."

[0100] Current real estate management operations are largely manual, and important tasks such as repair requests and rent reviews are not processed quickly and efficiently. This leads to decreased user satisfaction and undermines the efficiency of management operations. Furthermore, traditional systems lack sufficient automation for data analysis, proposals, and implementation, resulting in a high reliance on human resources.

[0101] 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.

[0102] In this invention, the server includes means for receiving user input information as an HTTP request, means for obtaining detailed property information from a database, means for analyzing the data using Python to determine how to handle repairs and rent adjustments, and means for sending requests to the APIs of partner companies. This enables the automation of real estate management operations, allowing for the rapid and efficient handling of repair requests and proposals and implementation of rent adjustments.

[0103] A "user" refers to an individual or company that uses a real estate management system to request repairs, review rental rates, and perform other similar actions.

[0104] "Terminal" refers to devices such as computers, smartphones, and tablets that users use to input and transmit information.

[0105] "Information" refers to data entered by users via their devices, such as repair requests and rent review data.

[0106] A "server" refers to a computer system that receives information from users, analyzes the data, and makes and implements suggestions.

[0107] A "database" refers to a data management system used to store property information, repair history, rental history, and so on.

[0108] "Data analysis" refers to the process of identifying the cause of a problem and determining appropriate countermeasures based on data acquired by the server.

[0109] "Countermeasures" refer to actions such as repairs or rent adjustments that the server proposes to the user based on the results of data analysis.

[0110] An "HTTP request" is a type of communication protocol used by a device to send information to a server.

[0111] "Python" refers to a high-level programming language used for data analysis, machine learning, and other applications.

[0112] An "API" refers to an interface for exchanging information between different software systems.

[0113] "Suggestion" refers to the recommended course of action that the server provides to the user based on the analysis results.

[0114] "Execution" refers to the server taking action based on a proposal after the user has approved it.

[0115] One embodiment of the present invention specifically describes a system for automating real estate management operations. In this system, the user inputs information via a terminal, which is received and processed by a server, which then proposes and implements appropriate countermeasures. The aim is to automate tasks such as repair requests and rent reviews, and to respond efficiently and accurately.

[0116] System Configuration

[0117] Hardware and software

[0118] Server: A central computer that performs data processing and analysis. Python is used for analysis, and MySQL® databases are used for data management.

[0119] Terminal: A device used by a user to input information. This includes PCs, smartphones, and tablets.

[0120] software:

[0121] Frontend: Uses React.js.

[0122] Backend: Django is used.

[0123] Data analysis: Python will be used, with libraries such as Pandas and Scikit-learn.

[0124] Data processing and calculation

[0125] Receiving user input information

[0126] The server receives user input information sent from the terminal as an HTTP request.

[0127] Data acquisition

[0128] The server retrieves detailed property information from the database based on the information received from the user. This detailed information includes the property's repair history, rental history, and the condition of its facilities.

[0129] Data Analysis

[0130] The acquired information is analyzed using Python libraries to determine necessary repairs and rent adjustments. Data cleansing, statistical analysis, and machine learning techniques are used in the analysis.

[0131] Proposal generation

[0132] Based on the analysis results, the server determines whether repairs are needed or if the rental fee needs to be reviewed, and proposes appropriate solutions to the user. For example, these suggestions may include: "Repairs are needed. Shall we request a repair company?" or "We propose changing the rental fee from xx yen to yy yen."

[0133] Execute action

[0134] If the user approves the proposal, repair requests or rent updates will be automatically initiated. In the case of repair requests, an automatic request will be sent to the partner company's API, and if the user approves a rent revision, the rent information in the database will be updated. As a result, the user will be notified of the execution status.

[0135] Specific example

[0136] Automation of repair requests

[0137] As an example, let's explain a scenario where the user enters "The toilet in room 203 is broken."

[0138] Example of a prompt

[0139] User: The toilet in room 203 is out of order.

[0140] 1. User inputs into the terminal: The user inputs "The toilet in room 203 is broken" into the terminal and clicks the send button.

[0141] 2. Server receives: The server receives this information and retrieves the property data for Room 203 from the database.

[0142] 3. Server retrieves data: The server retrieves the repair history and current status of room 203 from the database.

[0143] 4. Server analyzes data: The server analyzes the repair history and determines whether repairs are necessary.

[0144] 5. Server generates a suggestion: The server suggests to the user, "Repair is needed. Would you like to contact a repair company?"

[0145] 6. Server takes action: If the user approves the proposal, the server automatically sends a repair request to the partner repair company and notifies the user of the progress.

[0146] Proposal to review rent

[0147] As an example, let's explain a scenario where a user enters "I want to review the rent for room 301."

[0148] Example of a prompt

[0149] User: I would like to review the rent for room 301.

[0150] 1. User inputs into the terminal: The user inputs "I would like to review the rent for room 301" into the terminal and clicks the send button.

[0151] 2. Server receives: The server receives this information and retrieves property data for Room 301 and rental data for the surrounding area from its database.

[0152] 3. Server retrieves data: The server retrieves the current rent for Room 301 and surrounding market trends from the database.

[0153] 4. Server analyzes data: The server analyzes market trends and calculates new rental rates.

[0154] 5. Server generates proposal: The server presents the user with the following proposal: "We propose changing the rent for room 301 from xx yen to yy yen."

[0155] 6. The server takes action: If the user approves the proposal, the server updates the database with the new rent and automatically updates the related contract information.

[0156] Thus, the present invention automates real estate management operations and enables the provision of fast and accurate services to users. This dramatically improves the efficiency of management operations and contributes to increased user satisfaction.

[0157] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0158] Step 1:

[0159] User enters information

[0160] Users use their devices to enter information such as repair requests or rent review requests, and then click the submit button.

[0161] Specific actions

[0162] The user enters "The toilet in room 203 is broken" into a web form on their device and presses the submit button. This sends the entered information to the server as an HTTP request.

[0163] Input: Repair request information stating "The toilet in room 203 is out of order."

[0164] Output: Information sent to the server as an HTTP request

[0165] Step 2:

[0166] The server receives the information.

[0167] The server receives HTTP requests from users, extracts information from the request body, and stores it in the database.

[0168] Specific actions

[0169] The server receives an HTTP request and extracts information from the request body (for example, in JSON format) stating that "the toilet in room 203 is out of order." This information is then stored in the database.

[0170] Input: User input information as an HTTP request

[0171] Output: Repair request information stored in the database

[0172] Step 3:

[0173] The server retrieves the data.

[0174] The server retrieves detailed information about the target property (Room 203) from the database.

[0175] Specific actions

[0176] The server executes a database query to retrieve detailed information about "Room 203," such as its repair history and the condition of its facilities.

[0177] Input: Property ID based on user repair request information

[0178] Output: Property details retrieved from the database

[0179] Step 4:

[0180] The server analyzes the data.

[0181] The server analyzes the acquired data to determine whether repairs are necessary.

[0182] Specific actions

[0183] The server uses Python libraries (such as Pandas and scikit-learn) to analyze repair history and equipment status. For example, it determines whether repairs are necessary based on past repair frequency and the current condition of the toilets.

[0184] Input: Property details

[0185] Output: Result of the determination of whether repairs are needed.

[0186] Step 5:

[0187] The server generates suggestions.

[0188] Based on the analysis results, the server will suggest appropriate countermeasures to the user.

[0189] Specific actions

[0190] The server generates a message saying, "Repair is needed. Do you want to contact a repair company?" and notifies the user.

[0191] Input: Result of the determination of whether repairs are needed.

[0192] Output: Suggestion message to the user

[0193] Step 6:

[0194] The server performs the action

[0195] If the user approves the proposal, the server automatically sends a repair request to the contractor and notifies the user of the progress.

[0196] Specific actions

[0197] Once the user approves the proposal, the server sends a repair request to the partner repair company's API. Furthermore, it tracks the progress and notifies the user.

[0198] Input: User authorization, API information of affiliated repair companies

[0199] Output: Sending a repair request to the repair company, user notification of progress.

[0200] Based on the steps described above, the system of the present invention can automate real estate management operations and provide users with fast and accurate services. This dramatically improves the efficiency of management operations and contributes to increased user satisfaction.

[0201] (Application Example 1)

[0202] 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."

[0203] Traditional real estate management and factory equipment management require numerous manual operations, making them inefficient and prone to errors. In particular, repair requests, rent reviews, and factory equipment maintenance are often handled independently, leading to a lack of coordination and making overall management difficult. This invention aims to solve these problems by providing a system that centrally and efficiently manages both real estate and factory equipment.

[0204] 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.

[0205] In this invention, the server includes means for receiving information entered by a user via a terminal, means for obtaining property data and equipment information related to the received information from a database, means for analyzing the obtained property data and equipment information to identify the cause of a problem, means for proposing appropriate countermeasures based on the analysis results, means for executing the proposed countermeasures, means for notifying the user of the status of the implementation of the countermeasures, and means for analyzing the maintenance status of equipment and automatically proposing and generating the next maintenance. This makes it possible to significantly improve the efficiency of real estate management and factory equipment management.

[0206] "User" refers to a person who operates a system.

[0207] A "terminal" refers to a device used by a user to input information.

[0208] "Information" refers to the data and instructions that users input through their devices.

[0209] A "server" refers to a central computer system that processes information received from users.

[0210] "Property data" refers to detailed information related to a real estate property.

[0211] "Equipment information" refers to detailed information related to factory equipment.

[0212] A "database" refers to a system that systematically stores and manages large amounts of data.

[0213] "Analysis" refers to the act of analyzing acquired data to identify the cause of a problem.

[0214] "Countermeasures" refer to the means of solving the problem proposed based on the analysis results.

[0215] "Maintenance" refers to the upkeep and inspection work of equipment and machinery.

[0216] "Proposal" refers to the countermeasures presented to the user based on the analysis results.

[0217] "Implementation" refers to actually carrying out the proposed countermeasures.

[0218] "Notification" refers to informing users about the status of the implementation of countermeasures.

[0219] "System" refers to the entire information processing structure that includes these means.

[0220] This invention relates to a system for automating real estate management operations and factory equipment maintenance management. The system functions by allowing users to input information via a terminal and transmit it to a server. The server retrieves necessary data from a database based on the input information, analyzes it, proposes appropriate countermeasures to the user, and implements them. Furthermore, it improves the efficiency of real estate management and factory equipment management by analyzing the equipment maintenance status and automatically proposing and generating the next maintenance schedule.

[0221] System Configuration

[0222] 1. User input

[0223] Users use terminals to input repair requests, rent reviews, and maintenance status of factory equipment into the system. These terminals include smartphones, smart glasses, and head-mounted displays.

[0224] 2. Receiving and acquiring information

[0225] The server retrieves property and equipment information from a database based on the information received from the user. This database includes property repair history, rental history, equipment status, and maintenance history. Possible databases used include SQLite and MySQL.

[0226] 3. Data Analysis

[0227] The server analyzes the acquired data to identify the root cause of the problem. This analysis considers historical data, market trends, and maintenance schedules. The software used includes analysis tools such as Python and R.

[0228] 4. Proposal and Implementation

[0229] The server proposes appropriate countermeasures to the user based on the analysis results. For example, if repairs are needed, it will suggest a partner repair company; if a rent review is proposed, it will present the new rent amount; and if equipment maintenance is needed, it will suggest the next maintenance date.

[0230] If the user approves a proposal, the server automatically sends a repair request or renews the lease. Similarly, for maintenance requests, the server automatically generates and executes the request. The user is notified of the execution status. Notifications are sent via email, push notifications, etc.

[0231] Specific example

[0232] Automation of repair requests

[0233] This scenario describes what happens when a user enters "The toilet in room 203 is broken" into their terminal. The server receives this information and retrieves the property data for room 203 from the database. The server analyzes the retrieved data to identify the cause of the malfunction. Based on the analysis, if the server determines that repairs are necessary, it will ask the user, "Repairs are needed. Shall we request a repair company?" If the user approves, the server will automatically send a repair request to a partner repair company and notify the user of the progress.

[0234] Proposal to review rent

[0235] This scenario shows what happens when a user enters "I want to review the rent for room 301" into their terminal. The server receives this information and retrieves property data for room 301 and rental data for the surrounding area from its database. The server analyzes the retrieved data and calculates a new rent based on market trends. It then presents to the user, "We propose changing the rent for room 301 from XX yen to YY yen." If the user approves, the new rent is updated in the database, and the related contract information is also automatically updated.

[0236] Equipment maintenance proposals

[0237] This system automatically suggests maintenance for equipment used in a factory. The server retrieves the last maintenance date and maintenance interval of the equipment from a database and calculates the next maintenance date. If necessary, it prompts the user with a message such as, "The next maintenance is approaching. Would you like to request maintenance?" If the user approves, a maintenance request is automatically generated.

[0238] Example of a prompt

[0239] Please enter the following information regarding the equipment within the factory. Example: Equipment ID, Equipment Name, Last Maintenance Date, Maintenance Interval.

[0240] Thus, the present invention provides a specific form for efficiently managing real estate and factory facilities.

[0241] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0242] Step 1:

[0243] Users input information regarding repair requests, rent reviews, and equipment maintenance via a terminal. The entered data includes specific details and information about the items involved. The terminal then transmits the entered data to the server.

[0244] Input: Repair requests, rent adjustments, or maintenance requests entered by the user.

[0245] Output: Request information sent to the server

[0246] Step 2:

[0247] Based on the information received from the user, the server retrieves relevant property and equipment data from the database. The database stores detailed information about properties and equipment, repair history, maintenance history, and more.

[0248] Input: Request information submitted by the user

[0249] Output: Property data and equipment information retrieved from the database.

[0250] Step 3:

[0251] The server analyzes the acquired data to identify the root cause of the problem related to the request and determine the optimal solution. The analysis uses a generative AI model, taking into account historical data, market trends, and maintenance schedules.

[0252] Input: Property data and equipment information obtained from the database

[0253] Output: Analysis results (causes of the problem and countermeasures)

[0254] Step 4:

[0255] The server proposes appropriate countermeasures to the user based on the analysis results. The proposal is notified to the user via the terminal, and the user reviews the proposal and approves it if necessary. Examples of prompt messages include, "The toilet in room 203 is out of order. Shall we contact a repair company?"

[0256] Input: Analysis results

[0257] Output: Suggestions notified to the user

[0258] Step 5:

[0259] If the user approves the proposal, the server automatically generates a repair or maintenance request and sends it to the relevant contractor. In the case of a rent review, the database is also updated. The user is notified of the execution status in real time.

[0260] Input: User approval

[0261] Output: Generated request information, database updates, and execution status notifications.

[0262] Step 6:

[0263] The server monitors the progress of the task and notifies the user in real time. The user can check the status through their terminal and give additional instructions as needed.

[0264] Input: Progress information of tasks

[0265] Output: Notification of progress to the user

[0266] Thus, this system starts with user input and proceeds through data acquisition, analysis, proposal, execution, and monitoring to streamline management tasks.

[0267] 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.

[0268] As a concrete example of this specification, we describe a form of system that automates real estate management tasks and combines them with an emotion engine that recognizes user emotions. In this system, the user inputs information via a terminal, which is received and processed by a server, which then proposes and implements appropriate countermeasures. Furthermore, it has a function to recognize the user's emotions and adjust the proposals and responses based on those emotions.

[0269] System Configuration

[0270] 1. User input

[0271] Users use a terminal to input information into the system, such as repair requests or rent review requests. This input information is received by the server and the process proceeds to the next step. The emotion engine recognizes the user's emotions from the input information.

[0272] 2. The server retrieves the information.

[0273] The server retrieves detailed information about the property from its database based on the information received from the user and the user's sentiment. This detailed information includes the property's repair history, rental history, and the condition of its facilities.

[0274] 3. The server analyzes the data.

[0275] The acquired information is analyzed to determine whether repairs are necessary or if the rent can be reviewed. This analysis also takes into account historical data and market trends.

[0276] 4. The server generates the proposal.

[0277] Based on the analysis results, appropriate countermeasures are proposed to the user. Here, the proposals are adjusted based on the user's emotions recognized by the emotion engine. For example, if it is determined that the user is experiencing high levels of stress, a more immediate countermeasure will be prioritized.

[0278] 5. Server Executes an Action

[0279] If the user approves the proposal, the server automatically sends a repair request to the contractor or updates the rental fee. Also, the execution status is notified to the user.

[0280] Specific Example

[0281] Automation of Repair Requests and Emotional Response

[0282] Explain the scenario when the user enters "The toilet in Room 203 is broken" into the terminal and the emotion engine detects stress.

[0283] 1. User Input to Terminal: The user enters "The toilet in Room 203 is broken" into the terminal.

[0284] 2. Server Receives: The server receives this information and retrieves the property data of Room 203 from the database.

[0285] 3. Emotion Engine Recognizes Emotion: The emotion engine detects high stress from the user's input content.

[0286] 4. Server Analyzes: The server analyzes the retrieved data to identify the cause of the failure.

[0287] 5. Server Adjusts Proposal: Considering the high stress, the server sends a proposal to the user, "It has been determined that prompt repair action is required. Should we immediately request a repair contractor?"

[0288] 6. Server Executes: If the user approves, the server automatically sends a repair request to the partnered repair contractor and notifies the user of the progress.

[0289] Rental Fee Review Proposal and Emotional Response

[0290] This explains a scenario where a user enters "I want to review the rent for room 301" into the terminal, and the emotion engine detects anxiety.

[0291] 1. User inputs into terminal: The user inputs "I would like to review the rent for room 301" into the terminal.

[0292] 2. Server receives: The server receives this information and retrieves property data for Room 301 and rental data for the surrounding area from its database or an external real estate data API.

[0293] 3. Emotion engine recognizes emotions: The emotion engine detects anxiety from the user's input.

[0294] 4. Server analysis: The server analyzes the acquired data and calculates new rental rates based on market trends.

[0295] 5. The server adjusts the proposal: Taking into consideration the user's concerns, the server notifies the user of a proposal such as, "Based on market trends, we propose changing the rent from XX yen to YY yen. We will also explain the specific reasons, so please rest assured."

[0296] 6. Server execution: If the user approves, the new rental fee is updated in the database, and the related contract information is also automatically updated.

[0297] Thus, this invention, which combines an emotion engine, not only automates the specialized and complex tasks of real estate management, but also enables responses that take into account the user's emotions, thereby achieving a higher level of user satisfaction.

[0298] The following describes the processing flow.

[0299] Automated repair request and emotional support.

[0300] Step 1:

[0301] The user inputs "The toilet in Room 203 is malfunctioning" into the terminal. This is done through the input form of the terminal, and the server receives the content.

[0302] Step 2:

[0303] The server passes the received information to the emotion engine to analyze the user's emotion. Here, the emotion engine detects what kind of emotion the user has from the input text (e.g., stress, anger, etc.).

[0304] Step 3:

[0305] The server receives the result from the emotion engine and considers special countermeasures if high stress is detected.

[0306] Step 4:

[0307] Based on the information received by the server and the user's emotion information, the server retrieves the property data of Room 203 from the database. This includes information about the repair history of the property and the current equipment status.

[0308] Step 5:

[0309] The server analyzes the retrieved data to identify the cause of the failure. For example, by referring to the past repair history, the cause of this failure is determined.

[0310] Step 6:

[0311] Based on the analysis result, the server generates a proposal considering the user's emotion recognized by the emotion engine. For example, if the user is feeling strong stress, a proposal such as "Quick repair measures are needed. Should we immediately request a repair contractor?" is sent to the user.

[0312] [[ID=​​​​

[0314] Step 8:

[0315] The server retrieves information on affiliated repair companies from its database and automatically sends repair requests. It automatically generates and sends a message to the repair company containing the necessary repair details and location information.

[0316] Step 9:

[0317] The server monitors the status of repair requests and receives progress reports from repair companies. These reports include the repair company's arrival time and completion of work.

[0318] Step 10:

[0319] The server notifies the user of the progress of the repairs. For example, it displays messages on the user's device such as "The repair company has arrived on site" or "The repair work is complete."

[0320] Proposal to review rent and emotional support.

[0321] Step 1:

[0322] The user enters "I would like to review the rent for room 301" into the terminal. The input is made through a form on the terminal, and the server receives the information.

[0323] Step 2:

[0324] The server passes the received information to the emotion engine, which analyzes the user's emotions. The emotion engine detects what kind of emotions the user is experiencing (e.g., anxiety, excitement) from the input text.

[0325] Step 3:

[0326] The server receives results from the emotion engine, and if anxiety is detected, it considers special considerations.

[0327] Step 4:

[0328] Based on the information received by the server and the user's sentiment information, property data for Room 301 and rental information for surrounding properties are retrieved from a database and external real estate data APIs. This includes current rental rates, past rental history, and market prices.

[0329] Step 5:

[0330] The server analyzes the collected data to calculate new rental rates. The analysis takes into account market trends and the rental rates of competing properties.

[0331] Step 6:

[0332] Based on the analysis results, the server generates suggestions that take into account the user's emotions as recognized by the emotion engine. For example, for a user who is feeling anxious, it might notify the user with a suggestion such as, "Based on market trends, we suggest changing the rent from XX yen to YY yen. We will also explain the specific reasons, so please rest assured."

[0333] Step 7:

[0334] The user approves the proposal from their device. The user selects "Yes," and that information is sent to the server.

[0335] Step 8:

[0336] The server updates the database with new rental rates. This includes automatic updates of rental contract information and advertising data.

[0337] Step 9:

[0338] The server notifies the user of the update status. A message such as "Rental fee review is complete. The new rent is YY yen." is displayed on the user's terminal.

[0339] (Example 2)

[0340] 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".

[0341] Traditional real estate management systems only receive user input and perform basic processing, lacking the ability to sense user emotions and optimize responses. Therefore, even when users are experiencing high levels of stress or anxiety, appropriate responses are difficult to provide, potentially leading to decreased customer satisfaction. Furthermore, the automation of crucial tasks such as repair requests and rent reviews is insufficient, highlighting the need for more efficient real estate management.

[0342] The identification processing performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for receiving information entered by the user via a terminal, means for recognizing the user's emotions from the received information, and means for obtaining the recognized emotion data and property data related to the information from a database. This makes it possible to propose appropriate countermeasures that take the user's emotions into consideration.

[0343] "A means of receiving information entered by a user via a device" refers to a function that allows a server to receive data sent from a device such as a computer or smartphone operated by a user.

[0344] "Means of recognizing user emotions from received information" refers to a function that analyzes text data entered by the user and uses natural language processing and machine learning algorithms to identify the user's emotions (e.g., stress, anxiety, joy, etc.).

[0345] "Means for retrieving recognized emotion data and related property data from a database" refers to a function that retrieves data, including related property information, from a database based on the user's emotions and input requests.

[0346] "A means of identifying the cause of a problem by analyzing acquired property data and sentiment data" refers to a function that uses the acquired property history and condition information, as well as user sentiment data, to determine whether repairs are needed or if the rent needs to be reviewed.

[0347] "A means of proposing appropriate countermeasures based on analysis results and user sentiment" refers to a function that considers the results of data analysis and the emotions expressed by the user, generates specific countermeasures (for example, repair methods or proposed revisions to rent) and presents them to the user.

[0348] "Means for implementing proposed countermeasures" refers to a function that allows users to implement proposed countermeasures (for example, sending a repair request or renewing their lease) once they have approved them.

[0349] "Means for notifying users of the status of countermeasures" refers to a function that informs users of the progress and completion status of the above-mentioned countermeasures once they have been implemented.

[0350] This invention is an automated system for real estate management that takes user emotions into consideration and can respond appropriately. The system works by having the user input information via a terminal, which is then received and processed by a server, which proposes and implements appropriate countermeasures. Furthermore, it has a function to recognize user emotions and adjust proposals and responses accordingly.

[0351] System Configuration

[0352] 1. Receiving user input

[0353] Users input information such as repair requests or rent review requests using devices like computers or smartphones. The device sends the user's input to the server. The server receives this input information, performs appropriate verification, and proceeds to the next step.

[0354] 2. Emotion recognition by an emotion engine

[0355] The server analyzes the input data received from the user and uses an emotion engine to identify the user's emotions. This emotion engine can determine what emotions the user is experiencing using natural language processing and machine learning algorithms.

[0356] 3. Obtaining detailed property information

[0357] The server retrieves relevant property data from the database based on the user's sentiment and input information. This detailed information includes the property's repair history, rental history, and information about the condition of its facilities. The server then prepares this information for analysis.

[0358] 4. Data Analysis and Proposal Generation

[0359] The server analyzes acquired property and sentiment data to generate appropriate countermeasures. The analysis considers historical data and market trends, and uses machine learning models to determine the optimal action. The server then incorporates the results of the sentiment engine to generate the most suitable suggestions for the user.

[0360] 5. Approval of proposals and implementation of actions

[0361] Once the user reviews and approves the proposal, the server automatically implements the suggested actions. This includes submitting repair requests and renewing lease payments. The server notifies the user of the implementation status and reports progress in a timely manner.

[0362] Hardware and software to be used

[0363] Server: A server with high-performance data processing capabilities. Specifically, a web server and a database server that provide a REST API.

[0364] Device: A device that can connect to the internet, such as a computer, smartphone, or tablet.

[0365] Emotion engine: Natural language processing libraries (NLTK, Transformers, etc.) and machine learning algorithms (regression models, classification models).

[0366] Specific example

[0367] Automated repair request and emotional support.

[0368] This explains a scenario where a user enters "The toilet in room 203 is broken" into the terminal, and the emotion engine detects stress.

[0369] 1. User inputs into terminal: The user inputs "The toilet in room 203 is broken" into the terminal.

[0370] 2. Server receives: The server receives this information and retrieves the property data for Room 203 from the database.

[0371] 3. Emotion engine recognizes emotions: The emotion engine detects high levels of stress from the user's input.

[0372] 4. Server analysis: The server analyzes the acquired data to identify the cause of the failure.

[0373] 5. The server adjusts the suggestion: Considering the high stress level, it sends the user a suggestion stating, "It has been determined that immediate repair is needed. Would you like to request a repair service immediately?"

[0374] 6. Server takes action: If the user approves, the server automatically sends a repair request to the service provider and notifies the user of the progress.

[0375] Proposal to review rent and emotional support.

[0376] This explains a scenario where a user enters "I want to review the rent for room 301" into the terminal, and the emotion engine detects anxiety.

[0377] 1. User inputs into terminal: The user inputs "I would like to review the rent for room 301" into the terminal.

[0378] 2. Server receives: The server receives this information and retrieves property data for Room 301 and rental data for the surrounding area from its database or an external real estate data API.

[0379] 3. Emotion engine recognizes emotions: The emotion engine detects anxiety from the user's input.

[0380] 4. Server analysis: The server analyzes the acquired data and calculates new rental rates based on market trends.

[0381] 5. The server adjusts the proposal: Taking into consideration the user's concerns, the server notifies the user of a proposal such as, "Based on market trends, we propose changing the rent from XX yen to YY yen. We will also explain the specific reasons, so please rest assured."

[0382] 6. Server execution: If the user approves, the new rental fee is updated in the database, and the related contract information is also automatically updated.

[0383] Example of a prompt

[0384] "Please generate a proposal document that takes into consideration user stress regarding prompt repairs in the event of a toilet malfunction in Room 203."

[0385] "Please generate a proposal document for revising the rent for Room 301 that takes into account the user's concerns."

[0386] Thus, the system of the present invention not only automates the specialized and complex tasks of real estate management, but also aims to improve user satisfaction by providing responses based on the user's emotions.

[0387] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0388] Step 1:

[0389] Input: The user enters information into the terminal.

[0390] Processing: The user operates the terminal, enters information such as repair requests or rent review into a form, and clicks the submit button.

[0391] Output: The information entered by the user is sent from the terminal to the server.

[0392] Specific operation: When the user enters "The toilet in room 203 is broken" and presses the submit button, the terminal sends this information to the server as an HTTP request in JSON format.

[0393] Step 2:

[0394] Input: HTTP request sent from the terminal.

[0395] Processing: The server verifies the information received from the terminal and prepares it for storage in the database.

[0396] Output: The verified information is saved to the server's database.

[0397] Specific operation: The server validates the schema of the received JSON data and checks for invalid data. If there are no problems, it saves it to the database.

[0398] Step 3:

[0399] Input: Information entered by the user.

[0400] Processing: The server activates the emotion engine and sends the user's input data for emotion analysis.

[0401] Output: Sentimental information contained in the user's input data.

[0402] Specific operation: The server uses natural language processing libraries (NLTK, Transformers) to analyze the user's input text. The analysis results are output as emotion scores such as "high stress" or "anxiety."

[0403] Step 4:

[0404] Input: Verified user information and sentiment data.

[0405] Processing: The server retrieves relevant property data from the database based on the user's emotions and input information.

[0406] Output: Detailed information about the retrieved property.

[0407] Specific operation: The server executes an SQL query to retrieve information such as the repair history, rental history, and equipment status of the relevant property from the database.

[0408] Step 5:

[0409] Input: Acquired property data and sentiment data.

[0410] Processing: The server analyzes this data, identifies the cause of the problem, and generates appropriate countermeasures.

[0411] Output: Proposed solutions to present to the user.

[0412] Specific operation: The server uses machine learning models (regression models, classification models) to analyze data. For example, along with the judgment that "prompt repair is needed," it generates a proposal statement: "Prompt repair is needed. Shall we contact a repair company immediately?"

[0413] Step 6:

[0414] Input: Server-generated suggestions.

[0415] Processing: The user reviews the proposal via their device and takes action to approve or reject it.

[0416] Output: User's approval or rejection response.

[0417] Specific action: The user reads the proposal displayed on the device and clicks the accept button.

[0418] Step 7:

[0419] Input: User's approval response.

[0420] Processing: The server will execute the proposed solution after receiving user approval.

[0421] Output: Notification of the countermeasures taken.

[0422] Specific operation: The server sends requests to repair contractors or updates the rental fee in the database via API calls. The execution status is reported to the user via email or system notification.

[0423] (Application Example 2)

[0424] 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 device 14 will be referred to as the "terminal."

[0425] Existing real estate management systems and customer service systems in stores struggle to respond flexibly to user emotions and specific situations. Furthermore, faster and more appropriate responses are required to improve customer satisfaction and convenience. To address this, a system is needed that recognizes user emotions in real time and adjusts responses accordingly.

[0426] 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. In this invention, the server includes means for receiving information entered by the user via a terminal, means for obtaining item data related to the received information from a database, and means for analyzing the obtained item data to generate optimal suggestions based on the user's emotions and usage. This makes it possible to provide optimal suggestions and responses that take the user's emotions into consideration.

[0427] "Means of receiving information entered by users via their devices" refers to a function that allows a server to receive text or audio data entered by users via devices such as smartphones, tablets, or computers.

[0428] "Means for obtaining item data related to received information from a database" refers to a function that allows the server to search and retrieve corresponding item and customer information from a database based on the information received from the user.

[0429] "A means of analyzing acquired item data to generate optimal suggestions based on user emotions and usage patterns" refers to a function in which the analysis engine generates optimal suggestions based on information acquired from the database, taking into account the user's emotions and past usage patterns.

[0430] "Means of providing proposed countermeasures to users via smart devices" refers to a function that displays countermeasures generated by the server to the user via smart devices such as smart glasses or head-mounted displays.

[0431] "Means for implementing proposed countermeasures" refers to a function that automatically implements countermeasures based on proposals approved by the user, such as requesting repairs or presenting product suggestions.

[0432] "Means of notifying users of the status of countermeasures" refers to a function that allows the server to inform users in real time about the progress and completion status of countermeasures.

[0433] This invention relates to a customer service system for physical stores, which uses smart devices to recognize user emotions in real time and provide appropriate responses. Specific embodiments for carrying out this invention are described below.

[0434] System Configuration

[0435] The system includes the following main components:

[0436] 1. Terminal: A device used by the user to input information, such as smart glasses or head-mounted displays (HMDs).

[0437] 2. Server: A device that receives and processes input information. It also interacts with databases.

[0438] 3. Emotion Recognition Engine: This is a software module for analyzing the user's emotions from the input text.

[0439] 4. Database: A system for storing item data and user information, such as SQLite.

[0440] 5. Recommendation Engine: A software module for generating optimal suggestions based on emotion recognition results.

[0441] Program processing

[0442] Terminal: Users input information through smart glasses or HMDs. For example, they might ask questions like, "Are there other colors of this product available?" via voice or text.

[0443] Server: The server performs the following processing based on the information received from the terminal.

[0444] The input information is analyzed and sent to the emotion recognition engine. The emotion recognition engine identifies the user's emotions from the input information.

[0445] Based on the emotion recognition results, information about related products (e.g., stock status, different color variations, etc.) is retrieved from the database.

[0446] Using a recommendation engine, the system generates optimal suggestions based on acquired product information and sentiment data.

[0447] Emotion recognition engine: This engine analyzes user input information and processes it to identify emotions (e.g., confusion, excitement, stress). The model used is typically a generative AI model based on natural language processing.

[0448] Database: A database system such as SQLite is used to store and manage item information and user data. The server executes SQL queries to retrieve the necessary information.

[0449] Recommendation Engine: Generates optimal suggestions based on acquired information and sentiment data. For example, if a user is confused, it will immediately suggest a different color of the product that is in stock.

[0450] Displaying results: Generated suggestions are displayed to the user in real time via a smart device. This allows store staff to respond quickly and appropriately.

[0451] Specific example

[0452] For example, if a customer asks, "Do you have this product in a different color?", the system will work as follows:

[0453] 1. Receiving user questions: The store clerk inputs the customer's questions through smart glasses.

[0454] 2. Emotion recognition: The system recognizes from the customer's questions that the customer is confused.

[0455] 3. Retrieve product information: Retrieve detailed information and inventory information for the relevant product from the database.

[0456] 4. Suggestion Generation: To alleviate customer confusion, generate suggestions that immediately introduce different color variations of the product if they are in stock.

[0457] 5. Display: The suggested content will be displayed on the smart glasses.

[0458] Example of a prompt

[0459] Question: Is this product available in other colors?

[0460] Emotion recognition: perplexed

[0461] Applicable product: blue shirt

[0462] in stock

[0463] Suggestion: To alleviate the confusion, how about introducing a different colored green shirt right away?

[0464] As described above, the system can significantly improve customer satisfaction by understanding user emotions in real time and providing optimal responses.

[0465] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0466] Step 1:

[0467] The user inputs information into the device. Smart glasses or HMDs are used as devices for users to input questions and requests. Specifically, the user inputs information such as "Are there other colors of this product available?" via voice or text. This input information becomes the input data for the system.

[0468] Step 2:

[0469] The terminal sends information to the server. Information entered by the user is sent from the terminal to the server. During this process, the input information is converted into text data and reaches the server via the communication network. The server receives this information and prepares it for analysis.

[0470] Step 3:

[0471] The server performs analysis using an emotion recognition engine. The server passes the received input information to the emotion recognition engine, and uses an emotion recognition model (generative AI model) to determine the user's emotions. For example, emotions such as "confused" or "excited" may be output. The input for this step is the user's text information, and the output is identified emotion data.

[0472] Step 4:

[0473] The server retrieves relevant information from the item database. The server issues SQL queries to the database and retrieves product data related to the input information. Specifically, product inventory status and detailed information are retrieved from the database. The input for this step is user text information, and the output is product data.

[0474] Step 5:

[0475] The server generates suggestions based on the data it acquires. The server combines the emotion recognition results with product data retrieved from the database to generate the most suitable suggestions based on the recommendation engine. For example, in the case of "confusion," a suggestion such as "immediately introduce products in different colors" is generated. The input for this step is emotion data and product data, and the output is the suggested content.

[0476] Step 6:

[0477] The server sends the proposal content to the terminal. The generated proposal content is sent from the server to the smart glasses or HMD. The input for this step is the proposal content, and the output is the proposal information displayed on the terminal.

[0478] Step 7:

[0479] The terminal displays the suggestion to the user. The user checks the suggestion sent from the server in real time via their smart device. For example, the suggestion might be, "To reduce confusion, would you like us to immediately introduce a different colored green shirt?" The input for this step is the suggestion information, and the output is a notification to the user.

[0480] Step 8:

[0481] The user provides feedback on the proposal. The user uses a smart device to provide feedback on the proposal. For example, they might respond with "Yes, please introduce it." This feedback information becomes new input data for the system.

[0482] Step 9:

[0483] The server takes action based on the feedback. Upon receiving user feedback, the server implements the suggested countermeasures. For example, it might display the details of a suggested product or re-check the inventory. The input for this step is the user's feedback, and the output is the specific action taken.

[0484] Example of a prompt

[0485] Question: Is this product available in other colors?

[0486] Emotion recognition: perplexed

[0487] Applicable product: blue shirt

[0488] in stock

[0489] Suggestion: To alleviate the confusion, how about introducing a different colored green shirt right away?

[0490] The above series of processing steps enables optimal product recommendations and responses based on user emotions, resulting in a system that improves customer satisfaction.

[0491] 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.

[0492] 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.

[0493] 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.

[0494] [Second Embodiment]

[0495] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0496] 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.

[0497] 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).

[0498] 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.

[0499] 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.

[0500] 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).

[0501] 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.

[0502] 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.

[0503] 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.

[0504] 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.

[0505] 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.

[0506] 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".

[0507] As a concrete example of this specification, a form of a system for automating real estate management operations will be described. In this system, the user inputs information via a terminal, which is received and processed by a server, which then proposes and implements appropriate countermeasures. The present invention can effectively automate tasks such as repair requests and rent reviews.

[0508] System Configuration

[0509] 1. User input

[0510] Users use a terminal to input information into the system, such as repair requests or rent review requests. This input information is received by the server, which then proceeds to the next step.

[0511] 2. The server retrieves the information.

[0512] The server retrieves detailed information about the target property from the database based on the information received from the user. This detailed information includes the property's repair history, rental history, and the condition of its facilities.

[0513] 3. The server analyzes the data.

[0514] The acquired information is analyzed to determine whether repairs are necessary or if the rent can be reviewed. This analysis also takes into account historical data and market trends.

[0515] 4. The server generates the proposal.

[0516] Based on the analysis results, we propose appropriate countermeasures to the user. For example, if repairs are needed, we will suggest a partner repair company, and if we propose a review of the rent, we will present the new rent amount.

[0517] 5. The server performs the action.

[0518] If the user approves the proposal, the server automatically sends a repair request to the contractor or renews the lease. The user is also notified of the progress.

[0519] Specific example

[0520] Automation of repair requests

[0521] This explains a scenario where a user enters "The toilet in room 203 is broken" into the terminal.

[0522] 1. User inputs into terminal: The user inputs "The toilet in room 203 is broken" into the terminal.

[0523] 2. Server receives: The server receives this information and retrieves the property data for Room 203 from the database.

[0524] 3. Server analysis: The server analyzes the acquired data to identify the cause of the failure.

[0525] 4. Server Proposal: A server that determines it needs repair will propose to the user, "Repair is needed. Shall we request a repair service?"

[0526] 5. Server takes action: If the user approves, the server automatically sends a repair request to the affiliated repair company and notifies the user of the progress.

[0527] Proposal to review rent

[0528] This explains a scenario where a user enters "I want to review the rent for room 301" into their device.

[0529] 1. User inputs into terminal: The user inputs "I would like to review the rent for room 301" into the terminal.

[0530] 2. Server receives: The server receives this information and retrieves property data for Room 301 and rental data for the surrounding area from its database.

[0531] 3. Server analysis: The server analyzes the acquired data and calculates new rental rates based on market trends.

[0532] 4. The server makes a suggestion: It presents to the user, "We suggest changing the rent for room 301 from XX yen to YY yen."

[0533] 5. Server execution: If the user approves, the new rental fee is updated in the database, and the related contract information is also automatically updated.

[0534] Thus, the present invention can automate the specialized and complex tasks of real estate management, enabling efficient and accurate handling. This dramatically improves the efficiency of management operations and contributes to increased user satisfaction.

[0535] The following describes the processing flow.

[0536] Automation of repair requests

[0537] Step 1:

[0538] The user enters "The toilet in room 203 is broken" into the terminal. This is done through the terminal's input form, and the server receives the information.

[0539] Step 2:

[0540] Based on the information received by the server, property data for Room 203 is retrieved from the database. This includes detailed information about the property's repair history and current condition.

[0541] Step 3:

[0542] The system analyzes the data acquired by the server to identify the cause of the failure. For example, it refers to past repair history for similar incidents to determine if the current failure is due to the same cause.

[0543] Step 4:

[0544] Based on the analysis results, the server sends a suggestion to the user: "Repair is needed. Would you like to request a repair service?" This suggestion is displayed on the terminal.

[0545] Step 5:

[0546] If the user approves the proposal, select "Yes" and send the decision to the server.

[0547] Step 6:

[0548] The server retrieves information on affiliated repair companies from a database and automatically generates and sends repair requests. Specifically, it sends a message to the repair company containing information about the necessary repairs and their locations.

[0549] Step 7:

[0550] The server monitors the status of repair requests and receives progress reports from repair companies.

[0551] Step 8:

[0552] The server notifies the user of the progress of the repairs. For example, it displays messages on the user's terminal such as "The repair company has arrived on site" or "The repair work has been completed."

[0553] Proposal to review rent

[0554] Step 1:

[0555] The user enters "I would like to review the rent for room 301" into the terminal. The input is made through a form on the terminal, and the server receives the information.

[0556] Step 2:

[0557] The server retrieves property data for Room 301 and rental information for surrounding properties from a database and external real estate data APIs. This includes current rental rates, past rental history, and market prices.

[0558] Step 3:

[0559] The server analyzes the collected data to calculate the optimal rental price. The analysis takes into account local real estate market trends and the rental rates of competing properties.

[0560] Step 4:

[0561] Based on the analysis results, the server notifies the user, "We propose changing the rent for room 301 from XX yen to YY yen." This proposal is displayed on the terminal.

[0562] Step 5:

[0563] If the user approves the proposal, select "Yes" and send the decision to the server.

[0564] Step 6:

[0565] The server updates the database with new rental rates. This includes automatic updates of rental contract information and advertising data.

[0566] Step 7:

[0567] The server notifies the user of the update status. A message such as "Rental fee review is complete. The new rent is YY yen." is displayed on the user's terminal.

[0568] (Example 1)

[0569] 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".

[0570] Current real estate management operations are largely manual, and important tasks such as repair requests and rent reviews are not processed quickly and efficiently. This leads to decreased user satisfaction and undermines the efficiency of management operations. Furthermore, traditional systems lack sufficient automation for data analysis, proposals, and implementation, resulting in a high reliance on human resources.

[0571] 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.

[0572] In this invention, the server includes means for receiving user input information as an HTTP request, means for obtaining detailed property information from a database, means for analyzing the data using Python to determine how to handle repairs and rent adjustments, and means for sending requests to the APIs of partner companies. This enables the automation of real estate management operations, allowing for the rapid and efficient handling of repair requests and proposals and implementation of rent adjustments.

[0573] A "user" refers to an individual or company that uses a real estate management system to request repairs, review rental rates, and perform other similar actions.

[0574] "Terminal" refers to devices such as computers, smartphones, and tablets that users use to input and transmit information.

[0575] "Information" refers to data entered by users via their devices, such as repair requests and rent review data.

[0576] A "server" refers to a computer system that receives information from users, analyzes the data, and makes and implements suggestions.

[0577] A "database" refers to a data management system used to store property information, repair history, rental history, and so on.

[0578] "Data analysis" refers to the process of identifying the cause of a problem and determining appropriate countermeasures based on data acquired by the server.

[0579] "Countermeasures" refer to actions such as repairs or rent adjustments that the server proposes to the user based on the results of data analysis.

[0580] An "HTTP request" is a type of communication protocol used by a device to send information to a server.

[0581] "Python" refers to a high-level programming language used for data analysis, machine learning, and other applications.

[0582] An "API" refers to an interface for exchanging information between different software systems.

[0583] "Suggestion" refers to the recommended course of action that the server provides to the user based on the analysis results.

[0584] "Execution" refers to the server taking action based on a proposal after the user has approved it.

[0585] One embodiment of the present invention specifically describes a system for automating real estate management operations. In this system, the user inputs information via a terminal, which is received and processed by a server, which then proposes and implements appropriate countermeasures. The aim is to automate tasks such as repair requests and rent reviews, and to respond efficiently and accurately.

[0586] System Configuration

[0587] Hardware and software

[0588] Server: A central computer that performs data processing and analysis. Python is used for analysis, and a MySQL database is used for data management.

[0589] Terminal: A device used by a user to input information. This includes PCs, smartphones, and tablets.

[0590] software:

[0591] Frontend: Uses React.js.

[0592] Backend: Django is used.

[0593] Data analysis: Python will be used, with libraries such as Pandas and Scikit-learn.

[0594] Data processing and calculation

[0595] Receiving user input information

[0596] The server receives user input information sent from the terminal as an HTTP request.

[0597] Data acquisition

[0598] The server retrieves detailed property information from the database based on the information received from the user. This detailed information includes the property's repair history, rental history, and the condition of its facilities.

[0599] Data Analysis

[0600] The acquired information is analyzed using Python libraries to determine necessary repairs and rent adjustments. Data cleansing, statistical analysis, and machine learning techniques are used in the analysis.

[0601] Proposal generation

[0602] Based on the analysis results, the server determines whether repairs are needed or if the rental fee needs to be reviewed, and proposes appropriate solutions to the user. For example, these suggestions may include: "Repairs are needed. Shall we request a repair company?" or "We propose changing the rental fee from xx yen to yy yen."

[0603] Execute action

[0604] If the user approves the proposal, repair requests or rent updates will be automatically initiated. In the case of repair requests, an automatic request will be sent to the partner company's API, and if the user approves a rent revision, the rent information in the database will be updated. As a result, the user will be notified of the execution status.

[0605] Specific example

[0606] Automation of repair requests

[0607] As an example, let's explain a scenario where the user enters "The toilet in room 203 is broken."

[0608] Example of a prompt

[0609] User: The toilet in room 203 is out of order.

[0610] 1. User inputs into the terminal: The user inputs "The toilet in room 203 is broken" into the terminal and clicks the send button.

[0611] 2. Server receives: The server receives this information and retrieves the property data for Room 203 from the database.

[0612] 3. Server retrieves data: The server retrieves the repair history and current status of room 203 from the database.

[0613] 4. Server analyzes data: The server analyzes the repair history and determines whether repairs are necessary.

[0614] 5. Server generates a suggestion: The server suggests to the user, "Repair is needed. Would you like to contact a repair company?"

[0615] 6. Server takes action: If the user approves the proposal, the server automatically sends a repair request to the partner repair company and notifies the user of the progress.

[0616] Proposal to review rent

[0617] As an example, let's explain a scenario where a user enters "I want to review the rent for room 301."

[0618] Example of a prompt

[0619] User: I would like to review the rent for room 301.

[0620] 1. User inputs into the terminal: The user inputs "I would like to review the rent for room 301" into the terminal and clicks the send button.

[0621] 2. Server receives: The server receives this information and retrieves property data for Room 301 and rental data for the surrounding area from its database.

[0622] 3. Server retrieves data: The server retrieves the current rent for Room 301 and surrounding market trends from the database.

[0623] 4. Server analyzes data: The server analyzes market trends and calculates new rental rates.

[0624] 5. Server generates proposal: The server presents the user with the following proposal: "We propose changing the rent for room 301 from xx yen to yy yen."

[0625] 6. The server takes action: If the user approves the proposal, the server updates the database with the new rent and automatically updates the related contract information.

[0626] Thus, the present invention automates real estate management operations and enables the provision of fast and accurate services to users. This dramatically improves the efficiency of management operations and contributes to increased user satisfaction.

[0627] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0628] Step 1:

[0629] User enters information

[0630] Users use their devices to enter information such as repair requests or rent review requests, and then click the submit button.

[0631] Specific actions

[0632] The user enters "The toilet in room 203 is broken" into a web form on their device and presses the submit button. This sends the entered information to the server as an HTTP request.

[0633] Input: Repair request information stating "The toilet in room 203 is out of order."

[0634] Output: Information sent to the server as an HTTP request

[0635] Step 2:

[0636] The server receives the information.

[0637] The server receives HTTP requests from users, extracts information from the request body, and stores it in the database.

[0638] Specific actions

[0639] The server receives an HTTP request and extracts information from the request body (for example, in JSON format) stating that "the toilet in room 203 is out of order." This information is then stored in the database.

[0640] Input: User input information as an HTTP request

[0641] Output: Repair request information stored in the database

[0642] Step 3:

[0643] The server retrieves the data.

[0644] The server retrieves detailed information about the target property (Room 203) from the database.

[0645] Specific actions

[0646] The server executes a database query to retrieve detailed information about "Room 203," such as its repair history and the condition of its facilities.

[0647] Input: Property ID based on user repair request information

[0648] Output: Property details retrieved from the database

[0649] Step 4:

[0650] The server analyzes the data.

[0651] The server analyzes the acquired data to determine whether repairs are necessary.

[0652] Specific actions

[0653] The server uses Python libraries (such as Pandas and scikit-learn) to analyze repair history and equipment status. For example, it determines whether repairs are necessary based on past repair frequency and the current condition of the toilets.

[0654] Input: Property details

[0655] Output: Result of the determination of whether repairs are needed.

[0656] Step 5:

[0657] The server generates suggestions.

[0658] Based on the analysis results, the server will suggest appropriate countermeasures to the user.

[0659] Specific actions

[0660] The server generates a message saying, "Repair is needed. Do you want to contact a repair company?" and notifies the user.

[0661] Input: Result of the determination of whether repairs are needed.

[0662] Output: Suggestion message to the user

[0663] Step 6:

[0664] The server performs the action

[0665] If the user approves the proposal, the server automatically sends a repair request to the contractor and notifies the user of the progress.

[0666] Specific actions

[0667] Once the user approves the proposal, the server sends a repair request to the partner repair company's API. Furthermore, it tracks the progress and notifies the user.

[0668] Input: User authorization, API information of affiliated repair companies

[0669] Output: Sending a repair request to the repair company, user notification of progress.

[0670] Based on the steps described above, the system of the present invention can automate real estate management operations and provide users with fast and accurate services. This dramatically improves the efficiency of management operations and contributes to increased user satisfaction.

[0671] (Application Example 1)

[0672] 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."

[0673] Traditional real estate management and factory equipment management require numerous manual operations, making them inefficient and prone to errors. In particular, repair requests, rent reviews, and factory equipment maintenance are often handled independently, leading to a lack of coordination and making overall management difficult. This invention aims to solve these problems by providing a system that centrally and efficiently manages both real estate and factory equipment.

[0674] 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.

[0675] In this invention, the server includes means for receiving information entered by a user via a terminal, means for obtaining property data and equipment information related to the received information from a database, means for analyzing the obtained property data and equipment information to identify the cause of a problem, means for proposing appropriate countermeasures based on the analysis results, means for executing the proposed countermeasures, means for notifying the user of the status of the implementation of the countermeasures, and means for analyzing the maintenance status of equipment and automatically proposing and generating the next maintenance. This makes it possible to significantly improve the efficiency of real estate management and factory equipment management.

[0676] "User" refers to a person who operates a system.

[0677] A "terminal" refers to a device used by a user to input information.

[0678] "Information" refers to the data and instructions that users input through their devices.

[0679] A "server" refers to a central computer system that processes information received from users.

[0680] "Property data" refers to detailed information related to a real estate property.

[0681] "Equipment information" refers to detailed information related to factory equipment.

[0682] A "database" refers to a system that systematically stores and manages large amounts of data.

[0683] "Analysis" refers to the act of analyzing acquired data to identify the cause of a problem.

[0684] "Countermeasures" refer to the means of solving the problem proposed based on the analysis results.

[0685] "Maintenance" refers to the upkeep and inspection work of equipment and machinery.

[0686] "Proposal" refers to the countermeasures presented to the user based on the analysis results.

[0687] "Implementation" refers to actually carrying out the proposed countermeasures.

[0688] "Notification" refers to informing users about the status of the implementation of countermeasures.

[0689] "System" refers to the entire information processing structure that includes these means.

[0690] This invention relates to a system for automating real estate management operations and factory equipment maintenance management. The system functions by allowing users to input information via a terminal and transmit it to a server. The server retrieves necessary data from a database based on the input information, analyzes it, proposes appropriate countermeasures to the user, and implements them. Furthermore, it improves the efficiency of real estate management and factory equipment management by analyzing the equipment maintenance status and automatically proposing and generating the next maintenance schedule.

[0691] System Configuration

[0692] 1. User input

[0693] Users use terminals to input repair requests, rent reviews, and maintenance status of factory equipment into the system. These terminals include smartphones, smart glasses, and head-mounted displays.

[0694] 2. Receiving and acquiring information

[0695] The server retrieves property and equipment information from a database based on the information received from the user. This database includes property repair history, rental history, equipment status, and maintenance history. Possible databases used include SQLite and MySQL.

[0696] 3. Data Analysis

[0697] The server analyzes the acquired data to identify the root cause of the problem. This analysis considers historical data, market trends, and maintenance schedules. The software used includes analysis tools such as Python and R.

[0698] 4. Proposal and Implementation

[0699] The server proposes appropriate countermeasures to the user based on the analysis results. For example, if repairs are needed, it will suggest a partner repair company; if a rent review is proposed, it will present the new rent amount; and if equipment maintenance is needed, it will suggest the next maintenance date.

[0700] If the user approves a proposal, the server automatically sends a repair request or renews the lease. Similarly, for maintenance requests, the server automatically generates and executes the request. The user is notified of the execution status. Notifications are sent via email, push notifications, etc.

[0701] Specific example

[0702] Automation of repair requests

[0703] This scenario describes what happens when a user enters "The toilet in room 203 is broken" into their terminal. The server receives this information and retrieves the property data for room 203 from the database. The server analyzes the retrieved data to identify the cause of the malfunction. Based on the analysis, if the server determines that repairs are necessary, it will ask the user, "Repairs are needed. Shall we request a repair company?" If the user approves, the server will automatically send a repair request to a partner repair company and notify the user of the progress.

[0704] Proposal to review rent

[0705] This scenario shows what happens when a user enters "I want to review the rent for room 301" into their terminal. The server receives this information and retrieves property data for room 301 and rental data for the surrounding area from its database. The server analyzes the retrieved data and calculates a new rent based on market trends. It then presents to the user, "We propose changing the rent for room 301 from XX yen to YY yen." If the user approves, the new rent is updated in the database, and the related contract information is also automatically updated.

[0706] Equipment maintenance proposals

[0707] This system automatically suggests maintenance for equipment used in a factory. The server retrieves the last maintenance date and maintenance interval of the equipment from a database and calculates the next maintenance date. If necessary, it prompts the user with a message such as, "The next maintenance is approaching. Would you like to request maintenance?" If the user approves, a maintenance request is automatically generated.

[0708] Example of a prompt

[0709] Please enter the following information regarding the equipment within the factory. Example: Equipment ID, Equipment Name, Last Maintenance Date, Maintenance Interval.

[0710] Thus, the present invention provides a specific form for efficiently managing real estate and factory facilities.

[0711] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0712] Step 1:

[0713] Users input information regarding repair requests, rent reviews, and equipment maintenance via a terminal. The entered data includes specific details and information about the items involved. The terminal then transmits the entered data to the server.

[0714] Input: Repair requests, rent adjustments, or maintenance requests entered by the user.

[0715] Output: Request information sent to the server

[0716] Step 2:

[0717] Based on the information received from the user, the server retrieves relevant property and equipment data from the database. The database stores detailed information about properties and equipment, repair history, maintenance history, and more.

[0718] Input: Request information submitted by the user

[0719] Output: Property data and equipment information retrieved from the database.

[0720] Step 3:

[0721] The server analyzes the acquired data to identify the root cause of the problem related to the request and determine the optimal solution. The analysis uses a generative AI model, taking into account historical data, market trends, and maintenance schedules.

[0722] Input: Property data and equipment information obtained from the database

[0723] Output: Analysis results (causes of the problem and countermeasures)

[0724] Step 4:

[0725] The server proposes appropriate countermeasures to the user based on the analysis results. The proposal is notified to the user via the terminal, and the user reviews the proposal and approves it if necessary. Examples of prompt messages include, "The toilet in room 203 is out of order. Shall we contact a repair company?"

[0726] Input: Analysis results

[0727] Output: Suggestions notified to the user

[0728] Step 5:

[0729] If the user approves the proposal, the server automatically generates a repair or maintenance request and sends it to the relevant contractor. In the case of a rent review, the database is also updated. The user is notified of the execution status in real time.

[0730] Input: User approval

[0731] Output: Generated request information, database updates, and execution status notifications.

[0732] Step 6:

[0733] The server monitors the progress of the task and notifies the user in real time. The user can check the status through their terminal and give additional instructions as needed.

[0734] Input: Progress information of tasks

[0735] Output: Notification of progress to the user

[0736] Thus, this system starts with user input and proceeds through data acquisition, analysis, proposal, execution, and monitoring to streamline management tasks.

[0737] 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.

[0738] As a concrete example of this specification, we describe a form of system that automates real estate management tasks and combines them with an emotion engine that recognizes user emotions. In this system, the user inputs information via a terminal, which is received and processed by a server, which then proposes and implements appropriate countermeasures. Furthermore, it has a function to recognize the user's emotions and adjust the proposals and responses based on those emotions.

[0739] System Configuration

[0740] 1. User input

[0741] Users use a terminal to input information into the system, such as repair requests or rent review requests. This input information is received by the server and the process proceeds to the next step. The emotion engine recognizes the user's emotions from the input information.

[0742] 2. The server retrieves the information.

[0743] The server retrieves detailed information about the property from its database based on the information received from the user and the user's sentiment. This detailed information includes the property's repair history, rental history, and the condition of its facilities.

[0744] 3. The server analyzes the data.

[0745] The acquired information is analyzed to determine whether repairs are necessary or if the rent can be reviewed. This analysis also takes into account historical data and market trends.

[0746] 4. The server generates the proposal.

[0747] Based on the analysis results, appropriate countermeasures are proposed to the user. Here, the proposals are adjusted based on the user's emotions recognized by the emotion engine. For example, if it is determined that the user is experiencing high levels of stress, a more immediate countermeasure will be prioritized.

[0748] 5. The server performs the action.

[0749] If the user approves the proposal, the server automatically sends a repair request to the contractor or renews the lease. The user is also notified of the progress.

[0750] Specific example

[0751] Automated repair request and emotional support.

[0752] This explains a scenario where a user enters "The toilet in room 203 is broken" into the terminal, and the emotion engine detects stress.

[0753] 1. User inputs into terminal: The user inputs "The toilet in room 203 is broken" into the terminal.

[0754] 2. Server receives: The server receives this information and retrieves the property data for Room 203 from the database.

[0755] 3. Emotion engine recognizes emotions: The emotion engine detects high levels of stress from the user's input.

[0756] 4. Server analysis: The server analyzes the acquired data to identify the cause of the failure.

[0757] 5. The server adjusts the suggestion: Considering the high stress level, it sends the user a suggestion stating, "It has been determined that immediate repair is needed. Would you like to request a repair service immediately?"

[0758] 6. Server Execution: If the user approves, the server automatically sends a repair request to the affiliated repair company and notifies the user of the progress.

[0759] Proposal to review rent and emotional support.

[0760] This explains a scenario where a user enters "I want to review the rent for room 301" into the terminal, and the emotion engine detects anxiety.

[0761] 1. User inputs into terminal: The user inputs "I would like to review the rent for room 301" into the terminal.

[0762] 2. Server receives: The server receives this information and retrieves property data for Room 301 and rental data for the surrounding area from its database or an external real estate data API.

[0763] 3. Emotion engine recognizes emotions: The emotion engine detects anxiety from the user's input.

[0764] 4. Server analysis: The server analyzes the acquired data and calculates new rental rates based on market trends.

[0765] 5. The server adjusts the proposal: Taking into consideration the user's concerns, the server notifies the user of a proposal such as, "Based on market trends, we propose changing the rent from XX yen to YY yen. We will also explain the specific reasons, so please rest assured."

[0766] 6. Server execution: If the user approves, the new rental fee is updated in the database, and the related contract information is also automatically updated.

[0767] Thus, this invention, which combines an emotion engine, not only automates the specialized and complex tasks of real estate management, but also enables responses that take into account the user's emotions, thereby achieving a higher level of user satisfaction.

[0768] The following describes the processing flow.

[0769] Automated repair request and emotional support.

[0770] Step 1:

[0771] The user enters "The toilet in room 203 is broken" into the terminal. This is done through the terminal's input form, and the server receives the information.

[0772] Step 2:

[0773] The server passes the received information to the emotion engine, which analyzes the user's emotions. The emotion engine detects what kind of emotions the user is experiencing (e.g., stress, anger) from the input text.

[0774] Step 3:

[0775] The server receives results from the emotion engine, and if high stress levels are detected, it will consider special action.

[0776] Step 4:

[0777] Based on the information received by the server and the user's sentiment information, property data for Room 203 is retrieved from the database. This includes information on the property's repair history and current equipment status.

[0778] Step 5:

[0779] The server analyzes the data it acquires to identify the cause of the failure. For example, past repair history is referenced to pinpoint the cause of the current failure.

[0780] Step 6:

[0781] Based on the analysis results, the server generates suggestions that take into account the user's emotions as recognized by the emotion engine. For example, if the user is experiencing high levels of stress, it might send the user a suggestion such as, "Prompt repair is needed. Shall we contact a repair company immediately?"

[0782] Step 7:

[0783] The user approves the proposal from their device. The user selects "Yes," and that information is sent to the server.

[0784] Step 8:

[0785] The server retrieves information on affiliated repair companies from its database and automatically sends repair requests. It automatically generates and sends a message to the repair company containing the necessary repair details and location information.

[0786] Step 9:

[0787] The server monitors the status of repair requests and receives progress reports from repair companies. These reports include the repair company's arrival time and completion of work.

[0788] Step 10:

[0789] The server notifies the user of the progress of the repairs. For example, it displays messages on the user's device such as "The repair company has arrived on site" or "The repair work is complete."

[0790] Proposal to review rent and emotional support.

[0791] Step 1:

[0792] The user enters "I would like to review the rent for room 301" into the terminal. The input is made through a form on the terminal, and the server receives the information.

[0793] Step 2:

[0794] The server passes the received information to the emotion engine, which analyzes the user's emotions. The emotion engine detects what kind of emotions the user is experiencing (e.g., anxiety, excitement) from the input text.

[0795] Step 3:

[0796] The server receives results from the emotion engine, and if anxiety is detected, it considers special considerations.

[0797] Step 4:

[0798] Based on the information received by the server and the user's sentiment information, property data for Room 301 and rental information for surrounding properties are retrieved from a database and external real estate data APIs. This includes current rental rates, past rental history, and market prices.

[0799] Step 5:

[0800] The server analyzes the collected data to calculate new rental rates. The analysis takes into account market trends and the rental rates of competing properties.

[0801] Step 6:

[0802] Based on the analysis results, the server generates suggestions that take into account the user's emotions as recognized by the emotion engine. For example, for a user who is feeling anxious, it might notify the user with a suggestion such as, "Based on market trends, we suggest changing the rent from XX yen to YY yen. We will also explain the specific reasons, so please rest assured."

[0803] Step 7:

[0804] The user approves the proposal from their device. The user selects "Yes," and that information is sent to the server.

[0805] Step 8:

[0806] The server updates the database with new rental rates. This includes automatic updates of rental contract information and advertising data.

[0807] Step 9:

[0808] The server notifies the user of the update status. A message such as "Rental fee review is complete. The new rent is YY yen." is displayed on the user's terminal.

[0809] (Example 2)

[0810] 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".

[0811] Traditional real estate management systems only receive user input and perform basic processing, lacking the ability to sense user emotions and optimize responses. Therefore, even when users are experiencing high levels of stress or anxiety, appropriate responses are difficult to provide, potentially leading to decreased customer satisfaction. Furthermore, the automation of crucial tasks such as repair requests and rent reviews is insufficient, highlighting the need for more efficient real estate management.

[0812] The identification processing performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for receiving information entered by the user via a terminal, means for recognizing the user's emotions from the received information, and means for obtaining the recognized emotion data and property data related to the information from a database. This makes it possible to propose appropriate countermeasures that take the user's emotions into consideration.

[0813] "A means of receiving information entered by a user via a device" refers to a function that allows a server to receive data sent from a device such as a computer or smartphone operated by a user.

[0814] "Means of recognizing user emotions from received information" refers to a function that analyzes text data entered by the user and uses natural language processing and machine learning algorithms to identify the user's emotions (e.g., stress, anxiety, joy, etc.).

[0815] "Means for retrieving recognized emotion data and related property data from a database" refers to a function that retrieves data, including related property information, from a database based on the user's emotions and input requests.

[0816] "A means of identifying the cause of a problem by analyzing acquired property data and sentiment data" refers to a function that uses the acquired property history and condition information, as well as user sentiment data, to determine whether repairs are needed or if the rent needs to be reviewed.

[0817] "A means of proposing appropriate countermeasures based on analysis results and user sentiment" refers to a function that considers the results of data analysis and the emotions expressed by the user, generates specific countermeasures (for example, repair methods or proposed revisions to rent) and presents them to the user.

[0818] "Means for implementing proposed countermeasures" refers to a function that allows users to implement proposed countermeasures (for example, sending a repair request or renewing their lease) once they have approved them.

[0819] "Means for notifying users of the status of countermeasures" refers to a function that informs users of the progress and completion status of the above-mentioned countermeasures once they have been implemented.

[0820] This invention is an automated system for real estate management that takes user emotions into consideration and can respond appropriately. The system works by having the user input information via a terminal, which is then received and processed by a server, which proposes and implements appropriate countermeasures. Furthermore, it has a function to recognize user emotions and adjust proposals and responses accordingly.

[0821] System Configuration

[0822] 1. Receiving user input

[0823] Users input information such as repair requests or rent review requests using devices like computers or smartphones. The device sends the user's input to the server. The server receives this input information, performs appropriate verification, and proceeds to the next step.

[0824] 2. Emotion recognition by an emotion engine

[0825] The server analyzes the input data received from the user and uses an emotion engine to identify the user's emotions. This emotion engine can determine what emotions the user is experiencing using natural language processing and machine learning algorithms.

[0826] 3. Obtaining detailed property information

[0827] The server retrieves relevant property data from the database based on the user's sentiment and input information. This detailed information includes the property's repair history, rental history, and information about the condition of its facilities. The server then prepares this information for analysis.

[0828] 4. Data Analysis and Proposal Generation

[0829] The server analyzes acquired property and sentiment data to generate appropriate countermeasures. The analysis considers historical data and market trends, and uses machine learning models to determine the optimal action. The server then incorporates the results of the sentiment engine to generate the most suitable suggestions for the user.

[0830] 5. Approval of proposals and implementation of actions

[0831] Once the user reviews and approves the proposal, the server automatically implements the suggested actions. This includes submitting repair requests and renewing lease payments. The server notifies the user of the implementation status and reports progress in a timely manner.

[0832] Hardware and software to be used

[0833] Server: A server with high-performance data processing capabilities. Specifically, a web server and a database server that provide a REST API.

[0834] Device: A device that can connect to the internet, such as a computer, smartphone, or tablet.

[0835] Emotion engine: Natural language processing libraries (NLTK, Transformers, etc.) and machine learning algorithms (regression models, classification models).

[0836] Specific example

[0837] Automated repair request and emotional support.

[0838] This explains a scenario where a user enters "The toilet in room 203 is broken" into the terminal, and the emotion engine detects stress.

[0839] 1. User inputs into terminal: The user inputs "The toilet in room 203 is broken" into the terminal.

[0840] 2. Server receives: The server receives this information and retrieves the property data for Room 203 from the database.

[0841] 3. Emotion engine recognizes emotions: The emotion engine detects high levels of stress from the user's input.

[0842] 4. Server analysis: The server analyzes the acquired data to identify the cause of the failure.

[0843] 5. The server adjusts the suggestion: Considering the high stress level, it sends the user a suggestion stating, "It has been determined that immediate repair is needed. Would you like to request a repair service immediately?"

[0844] 6. Server takes action: If the user approves, the server automatically sends a repair request to the service provider and notifies the user of the progress.

[0845] Proposal to review rent and emotional support.

[0846] This explains a scenario where a user enters "I want to review the rent for room 301" into the terminal, and the emotion engine detects anxiety.

[0847] 1. User inputs into terminal: The user inputs "I would like to review the rent for room 301" into the terminal.

[0848] 2. Server receives: The server receives this information and retrieves property data for Room 301 and rental data for the surrounding area from its database or an external real estate data API.

[0849] 3. Emotion engine recognizes emotions: The emotion engine detects anxiety from the user's input.

[0850] 4. Server analysis: The server analyzes the acquired data and calculates new rental rates based on market trends.

[0851] 5. The server adjusts the proposal: Taking into consideration the user's concerns, the server notifies the user of a proposal such as, "Based on market trends, we propose changing the rent from XX yen to YY yen. We will also explain the specific reasons, so please rest assured."

[0852] 6. Server execution: If the user approves, the new rental fee is updated in the database, and the related contract information is also automatically updated.

[0853] Example of a prompt

[0854] "Please generate a proposal document that takes into consideration user stress regarding prompt repairs in the event of a toilet malfunction in Room 203."

[0855] "Please generate a proposal document for revising the rent for Room 301 that takes into account the user's concerns."

[0856] Thus, the system of the present invention not only automates the specialized and complex tasks of real estate management, but also aims to improve user satisfaction by providing responses based on the user's emotions.

[0857] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0858] Step 1:

[0859] Input: The user enters information into the terminal.

[0860] Processing: The user operates the terminal, enters information such as repair requests or rent review into a form, and clicks the submit button.

[0861] Output: The information entered by the user is sent from the terminal to the server.

[0862] Specific operation: When the user enters "The toilet in room 203 is broken" and presses the submit button, the terminal sends this information to the server as an HTTP request in JSON format.

[0863] Step 2:

[0864] Input: HTTP request sent from the terminal.

[0865] Processing: The server verifies the information received from the terminal and prepares it for storage in the database.

[0866] Output: The verified information is saved to the server's database.

[0867] Specific operation: The server validates the schema of the received JSON data and checks for invalid data. If there are no problems, it saves it to the database.

[0868] Step 3:

[0869] Input: Information entered by the user.

[0870] Processing: The server activates the emotion engine and sends the user's input data for emotion analysis.

[0871] Output: Sentimental information contained in the user's input data.

[0872] Specific operation: The server uses natural language processing libraries (NLTK, Transformers) to analyze the user's input text. The analysis results are output as emotion scores such as "high stress" or "anxiety."

[0873] Step 4:

[0874] Input: Verified user information and sentiment data.

[0875] Processing: The server retrieves relevant property data from the database based on the user's emotions and input information.

[0876] Output: Detailed information about the retrieved property.

[0877] Specific operation: The server executes an SQL query to retrieve information such as the repair history, rental history, and equipment status of the relevant property from the database.

[0878] Step 5:

[0879] Input: Acquired property data and sentiment data.

[0880] Processing: The server analyzes this data, identifies the cause of the problem, and generates appropriate countermeasures.

[0881] Output: Proposed solutions to present to the user.

[0882] Specific operation: The server uses machine learning models (regression models, classification models) to analyze data. For example, along with the judgment that "prompt repair is needed," it generates a proposal statement: "Prompt repair is needed. Shall we contact a repair company immediately?"

[0883] Step 6:

[0884] Input: Server-generated suggestions.

[0885] Processing: The user reviews the proposal via their device and takes action to approve or reject it.

[0886] Output: User's approval or rejection response.

[0887] Specific action: The user reads the proposal displayed on the device and clicks the accept button.

[0888] Step 7:

[0889] Input: User's approval response.

[0890] Processing: The server will execute the proposed solution after receiving user approval.

[0891] Output: Notification of the countermeasures taken.

[0892] Specific operation: The server sends requests to repair contractors or updates the rental fee in the database via API calls. The execution status is reported to the user via email or system notification.

[0893] (Application Example 2)

[0894] 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."

[0895] Existing real estate management systems and customer service systems in stores struggle to respond flexibly to user emotions and specific situations. Furthermore, faster and more appropriate responses are required to improve customer satisfaction and convenience. To address this, a system is needed that recognizes user emotions in real time and adjusts responses accordingly.

[0896] 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. In this invention, the server includes means for receiving information entered by the user via a terminal, means for obtaining item data related to the received information from a database, and means for analyzing the obtained item data to generate optimal suggestions based on the user's emotions and usage. This makes it possible to provide optimal suggestions and responses that take the user's emotions into consideration.

[0897] "Means of receiving information entered by users via their devices" refers to a function that allows a server to receive text or audio data entered by users via devices such as smartphones, tablets, or computers.

[0898] "Means for obtaining item data related to received information from a database" refers to a function that allows the server to search and retrieve corresponding item and customer information from a database based on the information received from the user.

[0899] "A means of analyzing acquired item data to generate optimal suggestions based on user emotions and usage patterns" refers to a function in which the analysis engine generates optimal suggestions based on information acquired from the database, taking into account the user's emotions and past usage patterns.

[0900] "Means of providing proposed countermeasures to users via smart devices" refers to a function that displays countermeasures generated by the server to the user via smart devices such as smart glasses or head-mounted displays.

[0901] "Means for implementing proposed countermeasures" refers to a function that automatically implements countermeasures based on proposals approved by the user, such as requesting repairs or presenting product suggestions.

[0902] "Means of notifying users of the status of countermeasures" refers to a function that allows the server to inform users in real time about the progress and completion status of countermeasures.

[0903] This invention relates to a customer service system for physical stores, which uses smart devices to recognize user emotions in real time and provide appropriate responses. Specific embodiments for carrying out this invention are described below.

[0904] System Configuration

[0905] The system includes the following main components:

[0906] 1. Terminal: A device used by the user to input information, such as smart glasses or head-mounted displays (HMDs).

[0907] 2. Server: A device that receives and processes input information. It also interacts with databases.

[0908] 3. Emotion Recognition Engine: This is a software module for analyzing the user's emotions from the input text.

[0909] 4. Database: A system for storing item data and user information, such as SQLite.

[0910] 5. Recommendation Engine: A software module for generating optimal suggestions based on emotion recognition results.

[0911] Program processing

[0912] Terminal: Users input information through smart glasses or HMDs. For example, they might ask questions like, "Are there other colors of this product available?" via voice or text.

[0913] Server: The server performs the following processing based on the information received from the terminal.

[0914] The input information is analyzed and sent to the emotion recognition engine. The emotion recognition engine identifies the user's emotions from the input information.

[0915] Based on the emotion recognition results, information about related products (e.g., stock status, different color variations, etc.) is retrieved from the database.

[0916] Using a recommendation engine, the system generates optimal suggestions based on acquired product information and sentiment data.

[0917] Emotion recognition engine: This engine analyzes user input information and processes it to identify emotions (e.g., confusion, excitement, stress). The model used is typically a generative AI model based on natural language processing.

[0918] Database: A database system such as SQLite is used to store and manage item information and user data. The server executes SQL queries to retrieve the necessary information.

[0919] Recommendation Engine: Generates optimal suggestions based on acquired information and sentiment data. For example, if a user is confused, it will immediately suggest a different color of the product that is in stock.

[0920] Displaying results: Generated suggestions are displayed to the user in real time via a smart device. This allows store staff to respond quickly and appropriately.

[0921] Specific example

[0922] For example, if a customer asks, "Do you have this product in a different color?", the system will work as follows:

[0923] 1. Receiving user questions: The store clerk inputs the customer's questions through smart glasses.

[0924] 2. Emotion recognition: The system recognizes from the customer's questions that the customer is confused.

[0925] 3. Retrieve product information: Retrieve detailed information and inventory information for the relevant product from the database.

[0926] 4. Suggestion Generation: To alleviate customer confusion, generate suggestions that immediately introduce different color variations of the product if they are in stock.

[0927] 5. Display: The suggested content will be displayed on the smart glasses.

[0928] Example of a prompt

[0929] Question: Is this product available in other colors?

[0930] Emotion recognition: perplexed

[0931] Applicable product: blue shirt

[0932] in stock

[0933] Suggestion: To alleviate the confusion, how about introducing a different colored green shirt right away?

[0934] As described above, the system can significantly improve customer satisfaction by understanding user emotions in real time and providing optimal responses.

[0935] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0936] Step 1:

[0937] The user inputs information into the device. Smart glasses or HMDs are used as devices for users to input questions and requests. Specifically, the user inputs information such as "Are there other colors of this product available?" via voice or text. This input information becomes the input data for the system.

[0938] Step 2:

[0939] The terminal sends information to the server. Information entered by the user is sent from the terminal to the server. During this process, the input information is converted into text data and reaches the server via the communication network. The server receives this information and prepares it for analysis.

[0940] Step 3:

[0941] The server performs analysis using an emotion recognition engine. The server passes the received input information to the emotion recognition engine, and uses an emotion recognition model (generative AI model) to determine the user's emotions. For example, emotions such as "confused" or "excited" may be output. The input for this step is the user's text information, and the output is identified emotion data.

[0942] Step 4:

[0943] The server retrieves relevant information from the item database. The server issues SQL queries to the database and retrieves product data related to the input information. Specifically, product inventory status and detailed information are retrieved from the database. The input for this step is user text information, and the output is product data.

[0944] Step 5:

[0945] The server generates suggestions based on the data it acquires. The server combines the emotion recognition results with product data retrieved from the database to generate the most suitable suggestions based on the recommendation engine. For example, in the case of "confusion," a suggestion such as "immediately introduce products in different colors" is generated. The input for this step is emotion data and product data, and the output is the suggested content.

[0946] Step 6:

[0947] The server sends the proposal content to the terminal. The generated proposal content is sent from the server to the smart glasses or HMD. The input for this step is the proposal content, and the output is the proposal information displayed on the terminal.

[0948] Step 7:

[0949] The terminal displays the suggestion to the user. The user checks the suggestion sent from the server in real time via their smart device. For example, the suggestion might be, "To reduce confusion, would you like us to immediately introduce a different colored green shirt?" The input for this step is the suggestion information, and the output is a notification to the user.

[0950] Step 8:

[0951] The user provides feedback on the proposal. The user uses a smart device to provide feedback on the proposal. For example, they might respond with "Yes, please introduce it." This feedback information becomes new input data for the system.

[0952] Step 9:

[0953] The server takes action based on the feedback. Upon receiving user feedback, the server implements the suggested countermeasures. For example, it might display the details of a suggested product or re-check the inventory. The input for this step is the user's feedback, and the output is the specific action taken.

[0954] Example of a prompt

[0955] Question: Is this product available in other colors?

[0956] Emotion recognition: perplexed

[0957] Applicable product: blue shirt

[0958] in stock

[0959] Suggestion: To alleviate the confusion, how about introducing a different colored green shirt right away?

[0960] The above series of processing steps enables optimal product recommendations and responses based on user emotions, resulting in a system that improves customer satisfaction.

[0961] 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.

[0962] 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.

[0963] 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.

[0964] [Third Embodiment]

[0965] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0966] 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.

[0967] 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).

[0968] 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.

[0969] 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.

[0970] 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).

[0971] 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.

[0972] 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.

[0973] 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.

[0974] 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.

[0975] 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.

[0976] 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".

[0977] As a concrete example of this specification, a form of a system for automating real estate management operations will be described. In this system, the user inputs information via a terminal, which is received and processed by a server, which then proposes and implements appropriate countermeasures. The present invention can effectively automate tasks such as repair requests and rent reviews.

[0978] System Configuration

[0979] 1. User input

[0980] Users use a terminal to input information into the system, such as repair requests or rent review requests. This input information is received by the server, which then proceeds to the next step.

[0981] 2. The server retrieves the information.

[0982] The server retrieves detailed information about the target property from the database based on the information received from the user. This detailed information includes the property's repair history, rental history, and the condition of its facilities.

[0983] 3. The server analyzes the data.

[0984] The acquired information is analyzed to determine whether repairs are necessary or if the rent can be reviewed. This analysis also takes into account historical data and market trends.

[0985] 4. The server generates the proposal.

[0986] Based on the analysis results, we propose appropriate countermeasures to the user. For example, if repairs are needed, we will suggest a partner repair company, and if we propose a review of the rent, we will present the new rent amount.

[0987] 5. The server performs the action.

[0988] If the user approves the proposal, the server automatically sends a repair request to the contractor or renews the lease. The user is also notified of the progress.

[0989] Specific example

[0990] Automation of repair requests

[0991] This explains a scenario where a user enters "The toilet in room 203 is broken" into the terminal.

[0992] 1. User inputs into terminal: The user inputs "The toilet in room 203 is broken" into the terminal.

[0993] 2. Server receives: The server receives this information and retrieves the property data for Room 203 from the database.

[0994] 3. Server analysis: The server analyzes the acquired data to identify the cause of the failure.

[0995] 4. Server Proposal: A server that determines it needs repair will propose to the user, "Repair is needed. Shall we request a repair service?"

[0996] 5. Server takes action: If the user approves, the server automatically sends a repair request to the affiliated repair company and notifies the user of the progress.

[0997] Proposal to review rent

[0998] This explains a scenario where a user enters "I want to review the rent for room 301" into their device.

[0999] 1. User inputs into terminal: The user inputs "I would like to review the rent for room 301" into the terminal.

[1000] 2. Server receives: The server receives this information and retrieves property data for Room 301 and rental data for the surrounding area from its database.

[1001] 3. Server analysis: The server analyzes the acquired data and calculates new rental rates based on market trends.

[1002] 4. The server makes a suggestion: It presents to the user, "We suggest changing the rent for room 301 from XX yen to YY yen."

[1003] 5. Server execution: If the user approves, the new rental fee is updated in the database, and the related contract information is also automatically updated.

[1004] Thus, the present invention can automate the specialized and complex tasks of real estate management, enabling efficient and accurate handling. This dramatically improves the efficiency of management operations and contributes to increased user satisfaction.

[1005] The following describes the processing flow.

[1006] Automation of repair requests

[1007] Step 1:

[1008] The user enters "The toilet in room 203 is broken" into the terminal. This is done through the terminal's input form, and the server receives the information.

[1009] Step 2:

[1010] Based on the information received by the server, property data for Room 203 is retrieved from the database. This includes detailed information about the property's repair history and current condition.

[1011] Step 3:

[1012] The system analyzes the data acquired by the server to identify the cause of the failure. For example, it refers to past repair history for similar incidents to determine if the current failure is due to the same cause.

[1013] Step 4:

[1014] Based on the analysis results, the server sends a suggestion to the user: "Repair is needed. Would you like to request a repair service?" This suggestion is displayed on the terminal.

[1015] Step 5:

[1016] If the user approves the proposal, select "Yes" and send the decision to the server.

[1017] Step 6:

[1018] The server retrieves information on affiliated repair companies from a database and automatically generates and sends repair requests. Specifically, it sends a message to the repair company containing information about the necessary repairs and their locations.

[1019] Step 7:

[1020] The server monitors the status of repair requests and receives progress reports from repair companies.

[1021] Step 8:

[1022] The server notifies the user of the progress of the repairs. For example, it displays messages on the user's terminal such as "The repair company has arrived on site" or "The repair work has been completed."

[1023] Proposal to review rent

[1024] Step 1:

[1025] The user enters "I would like to review the rent for room 301" into the terminal. The input is made through a form on the terminal, and the server receives the information.

[1026] Step 2:

[1027] The server retrieves property data for Room 301 and rental information for surrounding properties from a database and external real estate data APIs. This includes current rental rates, past rental history, and market prices.

[1028] Step 3:

[1029] The server analyzes the collected data to calculate the optimal rental price. The analysis takes into account local real estate market trends and the rental rates of competing properties.

[1030] Step 4:

[1031] Based on the analysis results, the server notifies the user, "We propose changing the rent for room 301 from XX yen to YY yen." This proposal is displayed on the terminal.

[1032] Step 5:

[1033] If the user approves the proposal, select "Yes" and send the decision to the server.

[1034] Step 6:

[1035] The server updates the database with new rental rates. This includes automatic updates of rental contract information and advertising data.

[1036] Step 7:

[1037] The server notifies the user of the update status. A message such as "Rental fee review is complete. The new rent is YY yen." is displayed on the user's terminal.

[1038] (Example 1)

[1039] 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."

[1040] Current real estate management operations are largely manual, and important tasks such as repair requests and rent reviews are not processed quickly and efficiently. This leads to decreased user satisfaction and undermines the efficiency of management operations. Furthermore, traditional systems lack sufficient automation for data analysis, proposals, and implementation, resulting in a high reliance on human resources.

[1041] 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.

[1042] In this invention, the server includes means for receiving user input information as an HTTP request, means for obtaining detailed property information from a database, means for analyzing the data using Python to determine how to handle repairs and rent adjustments, and means for sending requests to the APIs of partner companies. This enables the automation of real estate management operations, allowing for the rapid and efficient handling of repair requests and proposals and implementation of rent adjustments.

[1043] A "user" refers to an individual or company that uses a real estate management system to request repairs, review rental rates, and perform other similar actions.

[1044] "Terminal" refers to devices such as computers, smartphones, and tablets that users use to input and transmit information.

[1045] "Information" refers to data entered by users via their devices, such as repair requests and rent review data.

[1046] A "server" refers to a computer system that receives information from users, analyzes the data, and makes and implements suggestions.

[1047] A "database" refers to a data management system used to store property information, repair history, rental history, and so on.

[1048] "Data analysis" refers to the process of identifying the cause of a problem and determining appropriate countermeasures based on data acquired by the server.

[1049] "Countermeasures" refer to actions such as repairs or rent adjustments that the server proposes to the user based on the results of data analysis.

[1050] An "HTTP request" is a type of communication protocol used by a device to send information to a server.

[1051] "Python" refers to a high-level programming language used for data analysis, machine learning, and other applications.

[1052] An "API" refers to an interface for exchanging information between different software systems.

[1053] "Suggestion" refers to the recommended course of action that the server provides to the user based on the analysis results.

[1054] "Execution" refers to the server taking action based on a proposal after the user has approved it.

[1055] One embodiment of the present invention specifically describes a system for automating real estate management operations. In this system, the user inputs information via a terminal, which is received and processed by a server, which then proposes and implements appropriate countermeasures. The aim is to automate tasks such as repair requests and rent reviews, and to respond efficiently and accurately.

[1056] System Configuration

[1057] Hardware and software

[1058] Server: A central computer that performs data processing and analysis. Python is used for analysis, and a MySQL database is used for data management.

[1059] Terminal: A device used by a user to input information. This includes PCs, smartphones, and tablets.

[1060] software:

[1061] Frontend: Uses React.js.

[1062] Backend: Django is used.

[1063] Data analysis: Python will be used, with libraries such as Pandas and Scikit-learn.

[1064] Data processing and calculation

[1065] Receiving user input information

[1066] The server receives user input information sent from the terminal as an HTTP request.

[1067] Data acquisition

[1068] The server retrieves detailed property information from the database based on the information received from the user. This detailed information includes the property's repair history, rental history, and the condition of its facilities.

[1069] Data Analysis

[1070] The acquired information is analyzed using Python libraries to determine necessary repairs and rent adjustments. Data cleansing, statistical analysis, and machine learning techniques are used in the analysis.

[1071] Proposal generation

[1072] Based on the analysis results, the server determines whether repairs are needed or if the rental fee needs to be reviewed, and proposes appropriate solutions to the user. For example, these suggestions may include: "Repairs are needed. Shall we request a repair company?" or "We propose changing the rental fee from xx yen to yy yen."

[1073] Execute action

[1074] If the user approves the proposal, repair requests or rent updates will be automatically initiated. In the case of repair requests, an automatic request will be sent to the partner company's API, and if the user approves a rent revision, the rent information in the database will be updated. As a result, the user will be notified of the execution status.

[1075] Specific example

[1076] Automation of repair requests

[1077] As an example, let's explain a scenario where the user enters "The toilet in room 203 is broken."

[1078] Example of a prompt

[1079] User: The toilet in room 203 is out of order.

[1080] 1. User inputs into the terminal: The user inputs "The toilet in room 203 is broken" into the terminal and clicks the send button.

[1081] 2. Server receives: The server receives this information and retrieves the property data for Room 203 from the database.

[1082] 3. Server retrieves data: The server retrieves the repair history and current status of room 203 from the database.

[1083] 4. Server analyzes data: The server analyzes the repair history and determines whether repairs are necessary.

[1084] 5. Server generates a suggestion: The server suggests to the user, "Repair is needed. Would you like to contact a repair company?"

[1085] 6. Server takes action: If the user approves the proposal, the server automatically sends a repair request to the partner repair company and notifies the user of the progress.

[1086] Proposal to review rent

[1087] As an example, let's explain a scenario where a user enters "I want to review the rent for room 301."

[1088] Example of a prompt

[1089] User: I would like to review the rent for room 301.

[1090] 1. User inputs into the terminal: The user inputs "I would like to review the rent for room 301" into the terminal and clicks the send button.

[1091] 2. Server receives: The server receives this information and retrieves property data for Room 301 and rental data for the surrounding area from its database.

[1092] 3. Server retrieves data: The server retrieves the current rent for Room 301 and surrounding market trends from the database.

[1093] 4. Server analyzes data: The server analyzes market trends and calculates new rental rates.

[1094] 5. Server generates proposal: The server presents the user with the following proposal: "We propose changing the rent for room 301 from xx yen to yy yen."

[1095] 6. The server takes action: If the user approves the proposal, the server updates the database with the new rent and automatically updates the related contract information.

[1096] Thus, the present invention automates real estate management operations and enables the provision of fast and accurate services to users. This dramatically improves the efficiency of management operations and contributes to increased user satisfaction.

[1097] The flow of the specific processing in Example 1 will be explained using Figure 11.

[1098] Step 1:

[1099] User enters information

[1100] Users use their devices to enter information such as repair requests or rent review requests, and then click the submit button.

[1101] Specific actions

[1102] The user enters "The toilet in room 203 is broken" into a web form on their device and presses the submit button. This sends the entered information to the server as an HTTP request.

[1103] Input: Repair request information stating "The toilet in room 203 is out of order."

[1104] Output: Information sent to the server as an HTTP request

[1105] Step 2:

[1106] The server receives the information.

[1107] The server receives HTTP requests from users, extracts information from the request body, and stores it in the database.

[1108] Specific actions

[1109] The server receives an HTTP request and extracts information from the request body (for example, in JSON format) stating that "the toilet in room 203 is out of order." This information is then stored in the database.

[1110] Input: User input information as an HTTP request

[1111] Output: Repair request information stored in the database

[1112] Step 3:

[1113] The server retrieves the data.

[1114] The server retrieves detailed information about the target property (Room 203) from the database.

[1115] Specific actions

[1116] The server executes a database query to retrieve detailed information about "Room 203," such as its repair history and the condition of its facilities.

[1117] Input: Property ID based on user repair request information

[1118] Output: Property details retrieved from the database

[1119] Step 4:

[1120] The server analyzes the data.

[1121] The server analyzes the acquired data to determine whether repairs are necessary.

[1122] Specific actions

[1123] The server uses Python libraries (such as Pandas and scikit-learn) to analyze repair history and equipment status. For example, it determines whether repairs are necessary based on past repair frequency and the current condition of the toilets.

[1124] Input: Property details

[1125] Output: Result of the determination of whether repairs are needed.

[1126] Step 5:

[1127] The server generates suggestions.

[1128] Based on the analysis results, the server will suggest appropriate countermeasures to the user.

[1129] Specific actions

[1130] The server generates a message saying, "Repair is needed. Do you want to contact a repair company?" and notifies the user.

[1131] Input: Result of the determination of whether repairs are needed.

[1132] Output: Suggestion message to the user

[1133] Step 6:

[1134] The server performs the action

[1135] If the user approves the proposal, the server automatically sends a repair request to the contractor and notifies the user of the progress.

[1136] Specific actions

[1137] Once the user approves the proposal, the server sends a repair request to the partner repair company's API. Furthermore, it tracks the progress and notifies the user.

[1138] Input: User authorization, API information of affiliated repair companies

[1139] Output: Sending a repair request to the repair company, user notification of progress.

[1140] Based on the steps described above, the system of the present invention can automate real estate management operations and provide users with fast and accurate services. This dramatically improves the efficiency of management operations and contributes to increased user satisfaction.

[1141] (Application Example 1)

[1142] 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."

[1143] Traditional real estate management and factory equipment management require numerous manual operations, making them inefficient and prone to errors. In particular, repair requests, rent reviews, and factory equipment maintenance are often handled independently, leading to a lack of coordination and making overall management difficult. This invention aims to solve these problems by providing a system that centrally and efficiently manages both real estate and factory equipment.

[1144] 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.

[1145] In this invention, the server includes means for receiving information entered by a user via a terminal, means for obtaining property data and equipment information related to the received information from a database, means for analyzing the obtained property data and equipment information to identify the cause of a problem, means for proposing appropriate countermeasures based on the analysis results, means for executing the proposed countermeasures, means for notifying the user of the status of the implementation of the countermeasures, and means for analyzing the maintenance status of equipment and automatically proposing and generating the next maintenance. This makes it possible to significantly improve the efficiency of real estate management and factory equipment management.

[1146] "User" refers to a person who operates a system.

[1147] A "terminal" refers to a device used by a user to input information.

[1148] "Information" refers to the data and instructions that users input through their devices.

[1149] A "server" refers to a central computer system that processes information received from users.

[1150] "Property data" refers to detailed information related to a real estate property.

[1151] "Equipment information" refers to detailed information related to factory equipment.

[1152] A "database" refers to a system that systematically stores and manages large amounts of data.

[1153] "Analysis" refers to the act of analyzing acquired data to identify the cause of a problem.

[1154] "Countermeasures" refer to the means of solving the problem proposed based on the analysis results.

[1155] "Maintenance" refers to the upkeep and inspection work of equipment and machinery.

[1156] "Proposal" refers to the countermeasures presented to the user based on the analysis results.

[1157] "Implementation" refers to actually carrying out the proposed countermeasures.

[1158] "Notification" refers to informing users about the status of the implementation of countermeasures.

[1159] "System" refers to the entire information processing structure that includes these means.

[1160] This invention relates to a system for automating real estate management operations and factory equipment maintenance management. The system functions by allowing users to input information via a terminal and transmit it to a server. The server retrieves necessary data from a database based on the input information, analyzes it, proposes appropriate countermeasures to the user, and implements them. Furthermore, it improves the efficiency of real estate management and factory equipment management by analyzing the equipment maintenance status and automatically proposing and generating the next maintenance schedule.

[1161] System Configuration

[1162] 1. User input

[1163] Users use terminals to input repair requests, rent reviews, and maintenance status of factory equipment into the system. These terminals include smartphones, smart glasses, and head-mounted displays.

[1164] 2. Receiving and acquiring information

[1165] The server retrieves property and equipment information from a database based on the information received from the user. This database includes property repair history, rental history, equipment status, and maintenance history. Possible databases used include SQLite and MySQL.

[1166] 3. Data Analysis

[1167] The server analyzes the acquired data to identify the root cause of the problem. This analysis considers historical data, market trends, and maintenance schedules. The software used includes analysis tools such as Python and R.

[1168] 4. Proposal and Implementation

[1169] The server proposes appropriate countermeasures to the user based on the analysis results. For example, if repairs are needed, it will suggest a partner repair company; if a rent review is proposed, it will present the new rent amount; and if equipment maintenance is needed, it will suggest the next maintenance date.

[1170] If the user approves a proposal, the server automatically sends a repair request or renews the lease. Similarly, for maintenance requests, the server automatically generates and executes the request. The user is notified of the execution status. Notifications are sent via email, push notifications, etc.

[1171] Specific example

[1172] Automation of repair requests

[1173] This scenario describes what happens when a user enters "The toilet in room 203 is broken" into their terminal. The server receives this information and retrieves the property data for room 203 from the database. The server analyzes the retrieved data to identify the cause of the malfunction. Based on the analysis, if the server determines that repairs are necessary, it will ask the user, "Repairs are needed. Shall we request a repair company?" If the user approves, the server will automatically send a repair request to a partner repair company and notify the user of the progress.

[1174] Proposal to review rent

[1175] This scenario shows what happens when a user enters "I want to review the rent for room 301" into their terminal. The server receives this information and retrieves property data for room 301 and rental data for the surrounding area from its database. The server analyzes the retrieved data and calculates a new rent based on market trends. It then presents to the user, "We propose changing the rent for room 301 from XX yen to YY yen." If the user approves, the new rent is updated in the database, and the related contract information is also automatically updated.

[1176] Equipment maintenance proposals

[1177] This system automatically suggests maintenance for equipment used in a factory. The server retrieves the last maintenance date and maintenance interval of the equipment from a database and calculates the next maintenance date. If necessary, it prompts the user with a message such as, "The next maintenance is approaching. Would you like to request maintenance?" If the user approves, a maintenance request is automatically generated.

[1178] Example of a prompt

[1179] Please enter the following information regarding the equipment within the factory. Example: Equipment ID, Equipment Name, Last Maintenance Date, Maintenance Interval.

[1180] Thus, the present invention provides a specific form for efficiently managing real estate and factory facilities.

[1181] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[1182] Step 1:

[1183] Users input information regarding repair requests, rent reviews, and equipment maintenance via a terminal. The entered data includes specific details and information about the items involved. The terminal then transmits the entered data to the server.

[1184] Input: Repair requests, rent adjustments, or maintenance requests entered by the user.

[1185] Output: Request information sent to the server

[1186] Step 2:

[1187] Based on the information received from the user, the server retrieves relevant property and equipment data from the database. The database stores detailed information about properties and equipment, repair history, maintenance history, and more.

[1188] Input: Request information submitted by the user

[1189] Output: Property data and equipment information retrieved from the database.

[1190] Step 3:

[1191] The server analyzes the acquired data to identify the root cause of the problem related to the request and determine the optimal solution. The analysis uses a generative AI model, taking into account historical data, market trends, and maintenance schedules.

[1192] Input: Property data and equipment information obtained from the database

[1193] Output: Analysis results (causes of the problem and countermeasures)

[1194] Step 4:

[1195] The server proposes appropriate countermeasures to the user based on the analysis results. The proposal is notified to the user via the terminal, and the user reviews the proposal and approves it if necessary. Examples of prompt messages include, "The toilet in room 203 is out of order. Shall we contact a repair company?"

[1196] Input: Analysis results

[1197] Output: Suggestions notified to the user

[1198] Step 5:

[1199] If the user approves the proposal, the server automatically generates a repair or maintenance request and sends it to the relevant contractor. In the case of a rent review, the database is also updated. The user is notified of the execution status in real time.

[1200] Input: User approval

[1201] Output: Generated request information, database updates, and execution status notifications.

[1202] Step 6:

[1203] The server monitors the progress of the task and notifies the user in real time. The user can check the status through their terminal and give additional instructions as needed.

[1204] Input: Progress information of tasks

[1205] Output: Notification of progress to the user

[1206] Thus, this system starts with user input and proceeds through data acquisition, analysis, proposal, execution, and monitoring to streamline management tasks.

[1207] 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.

[1208] As a concrete example of this specification, we describe a form of system that automates real estate management tasks and combines them with an emotion engine that recognizes user emotions. In this system, the user inputs information via a terminal, which is received and processed by a server, which then proposes and implements appropriate countermeasures. Furthermore, it has a function to recognize the user's emotions and adjust the proposals and responses based on those emotions.

[1209] System Configuration

[1210] 1. User input

[1211] Users use a terminal to input information into the system, such as repair requests or rent review requests. This input information is received by the server and the process proceeds to the next step. The emotion engine recognizes the user's emotions from the input information.

[1212] 2. The server retrieves the information.

[1213] The server retrieves detailed information about the property from its database based on the information received from the user and the user's sentiment. This detailed information includes the property's repair history, rental history, and the condition of its facilities.

[1214] 3. The server analyzes the data.

[1215] The acquired information is analyzed to determine whether repairs are necessary or if the rent can be reviewed. This analysis also takes into account historical data and market trends.

[1216] 4. The server generates the proposal.

[1217] Based on the analysis results, appropriate countermeasures are proposed to the user. Here, the proposals are adjusted based on the user's emotions recognized by the emotion engine. For example, if it is determined that the user is experiencing high levels of stress, a more immediate countermeasure will be prioritized.

[1218] 5. The server performs the action.

[1219] If the user approves the proposal, the server automatically sends a repair request to the contractor or renews the lease. The user is also notified of the progress.

[1220] Specific example

[1221] Automated repair request and emotional support.

[1222] This explains a scenario where a user enters "The toilet in room 203 is broken" into the terminal, and the emotion engine detects stress.

[1223] 1. User inputs into terminal: The user inputs "The toilet in room 203 is broken" into the terminal.

[1224] 2. Server receives: The server receives this information and retrieves the property data for Room 203 from the database.

[1225] 3. Emotion engine recognizes emotions: The emotion engine detects high levels of stress from the user's input.

[1226] 4. Server analysis: The server analyzes the acquired data to identify the cause of the failure.

[1227] 5. The server adjusts the suggestion: Considering the high stress level, it sends the user a suggestion stating, "It has been determined that immediate repair is needed. Would you like to request a repair service immediately?"

[1228] 6. Server Execution: If the user approves, the server automatically sends a repair request to the affiliated repair company and notifies the user of the progress.

[1229] Proposal to review rent and emotional support.

[1230] This explains a scenario where a user enters "I want to review the rent for room 301" into the terminal, and the emotion engine detects anxiety.

[1231] 1. User inputs into terminal: The user inputs "I would like to review the rent for room 301" into the terminal.

[1232] 2. Server receives: The server receives this information and retrieves property data for Room 301 and rental data for the surrounding area from its database or an external real estate data API.

[1233] 3. Emotion engine recognizes emotions: The emotion engine detects anxiety from the user's input.

[1234] 4. Server analysis: The server analyzes the acquired data and calculates new rental rates based on market trends.

[1235] 5. The server adjusts the proposal: Taking into consideration the user's concerns, the server notifies the user of a proposal such as, "Based on market trends, we propose changing the rent from XX yen to YY yen. We will also explain the specific reasons, so please rest assured."

[1236] 6. Server execution: If the user approves, the new rental fee is updated in the database, and the related contract information is also automatically updated.

[1237] Thus, this invention, which combines an emotion engine, not only automates the specialized and complex tasks of real estate management, but also enables responses that take into account the user's emotions, thereby achieving a higher level of user satisfaction.

[1238] The following describes the processing flow.

[1239] Automated repair request and emotional support.

[1240] Step 1:

[1241] The user enters "The toilet in room 203 is broken" into the terminal. This is done through the terminal's input form, and the server receives the information.

[1242] Step 2:

[1243] The server passes the received information to the emotion engine, which analyzes the user's emotions. The emotion engine detects what kind of emotions the user is experiencing (e.g., stress, anger) from the input text.

[1244] Step 3:

[1245] The server receives results from the emotion engine, and if high stress levels are detected, it will consider special action.

[1246] Step 4:

[1247] Based on the information received by the server and the user's sentiment information, property data for Room 203 is retrieved from the database. This includes information on the property's repair history and current equipment status.

[1248] Step 5:

[1249] The server analyzes the data it acquires to identify the cause of the failure. For example, past repair history is referenced to pinpoint the cause of the current failure.

[1250] Step 6:

[1251] Based on the analysis results, the server generates suggestions that take into account the user's emotions as recognized by the emotion engine. For example, if the user is experiencing high levels of stress, it might send the user a suggestion such as, "Prompt repair is needed. Shall we contact a repair company immediately?"

[1252] Step 7:

[1253] The user approves the proposal from their device. The user selects "Yes," and that information is sent to the server.

[1254] Step 8:

[1255] The server retrieves information on affiliated repair companies from its database and automatically sends repair requests. It automatically generates and sends a message to the repair company containing the necessary repair details and location information.

[1256] Step 9:

[1257] The server monitors the status of repair requests and receives progress reports from repair companies. These reports include the repair company's arrival time and completion of work.

[1258] Step 10:

[1259] The server notifies the user of the progress of the repairs. For example, it displays messages on the user's device such as "The repair company has arrived on site" or "The repair work is complete."

[1260] Proposal to review rent and emotional support.

[1261] Step 1:

[1262] The user enters "I would like to review the rent for room 301" into the terminal. The input is made through a form on the terminal, and the server receives the information.

[1263] Step 2:

[1264] The server passes the received information to the emotion engine, which analyzes the user's emotions. The emotion engine detects what kind of emotions the user is experiencing (e.g., anxiety, excitement) from the input text.

[1265] Step 3:

[1266] The server receives results from the emotion engine, and if anxiety is detected, it considers special considerations.

[1267] Step 4:

[1268] Based on the information received by the server and the user's sentiment information, property data for Room 301 and rental information for surrounding properties are retrieved from a database and external real estate data APIs. This includes current rental rates, past rental history, and market prices.

[1269] Step 5:

[1270] The server analyzes the collected data to calculate new rental rates. The analysis takes into account market trends and the rental rates of competing properties.

[1271] Step 6:

[1272] Based on the analysis results, the server generates suggestions that take into account the user's emotions as recognized by the emotion engine. For example, for a user who is feeling anxious, it might notify the user with a suggestion such as, "Based on market trends, we suggest changing the rent from XX yen to YY yen. We will also explain the specific reasons, so please rest assured."

[1273] Step 7:

[1274] The user approves the proposal from their device. The user selects "Yes," and that information is sent to the server.

[1275] Step 8:

[1276] The server updates the database with new rental rates. This includes automatic updates of rental contract information and advertising data.

[1277] Step 9:

[1278] The server notifies the user of the update status. A message such as "Rental fee review is complete. The new rent is YY yen." is displayed on the user's terminal.

[1279] (Example 2)

[1280] 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."

[1281] Traditional real estate management systems only receive user input and perform basic processing, lacking the ability to sense user emotions and optimize responses. Therefore, even when users are experiencing high levels of stress or anxiety, appropriate responses are difficult to provide, potentially leading to decreased customer satisfaction. Furthermore, the automation of crucial tasks such as repair requests and rent reviews is insufficient, highlighting the need for more efficient real estate management.

[1282] The identification processing performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for receiving information entered by the user via a terminal, means for recognizing the user's emotions from the received information, and means for obtaining the recognized emotion data and property data related to the information from a database. This makes it possible to propose appropriate countermeasures that take the user's emotions into consideration.

[1283] "A means of receiving information entered by a user via a device" refers to a function that allows a server to receive data sent from a device such as a computer or smartphone operated by a user.

[1284] "Means of recognizing user emotions from received information" refers to a function that analyzes text data entered by the user and uses natural language processing and machine learning algorithms to identify the user's emotions (e.g., stress, anxiety, joy, etc.).

[1285] "Means for retrieving recognized emotion data and related property data from a database" refers to a function that retrieves data, including related property information, from a database based on the user's emotions and input requests.

[1286] "A means of identifying the cause of a problem by analyzing acquired property data and sentiment data" refers to a function that uses the acquired property history and condition information, as well as user sentiment data, to determine whether repairs are needed or if the rent needs to be reviewed.

[1287] "A means of proposing appropriate countermeasures based on analysis results and user sentiment" refers to a function that considers the results of data analysis and the emotions expressed by the user, generates specific countermeasures (for example, repair methods or proposed revisions to rent) and presents them to the user.

[1288] "Means for implementing proposed countermeasures" refers to a function that allows users to implement proposed countermeasures (for example, sending a repair request or renewing their lease) once they have approved them.

[1289] "Means for notifying users of the status of countermeasures" refers to a function that informs users of the progress and completion status of the above-mentioned countermeasures once they have been implemented.

[1290] This invention is an automated system for real estate management that takes user emotions into consideration and can respond appropriately. The system works by having the user input information via a terminal, which is then received and processed by a server, which proposes and implements appropriate countermeasures. Furthermore, it has a function to recognize user emotions and adjust proposals and responses accordingly.

[1291] System Configuration

[1292] 1. Receiving user input

[1293] Users input information such as repair requests or rent review requests using devices like computers or smartphones. The device sends the user's input to the server. The server receives this input information, performs appropriate verification, and proceeds to the next step.

[1294] 2. Emotion recognition by an emotion engine

[1295] The server analyzes the input data received from the user and uses an emotion engine to identify the user's emotions. This emotion engine can determine what emotions the user is experiencing using natural language processing and machine learning algorithms.

[1296] 3. Obtaining detailed property information

[1297] The server retrieves relevant property data from the database based on the user's sentiment and input information. This detailed information includes the property's repair history, rental history, and information about the condition of its facilities. The server then prepares this information for analysis.

[1298] 4. Data Analysis and Proposal Generation

[1299] The server analyzes acquired property and sentiment data to generate appropriate countermeasures. The analysis considers historical data and market trends, and uses machine learning models to determine the optimal action. The server then incorporates the results of the sentiment engine to generate the most suitable suggestions for the user.

[1300] 5. Approval of proposals and implementation of actions

[1301] Once the user reviews and approves the proposal, the server automatically implements the suggested actions. This includes submitting repair requests and renewing lease payments. The server notifies the user of the implementation status and reports progress in a timely manner.

[1302] Hardware and software to be used

[1303] Server: A server with high-performance data processing capabilities. Specifically, a web server and a database server that provide a REST API.

[1304] Device: A device that can connect to the internet, such as a computer, smartphone, or tablet.

[1305] Emotion engine: Natural language processing libraries (NLTK, Transformers, etc.) and machine learning algorithms (regression models, classification models).

[1306] Specific example

[1307] Automated repair request and emotional support.

[1308] This explains a scenario where a user enters "The toilet in room 203 is broken" into the terminal, and the emotion engine detects stress.

[1309] 1. User inputs into terminal: The user inputs "The toilet in room 203 is broken" into the terminal.

[1310] 2. Server receives: The server receives this information and retrieves the property data for Room 203 from the database.

[1311] 3. Emotion engine recognizes emotions: The emotion engine detects high levels of stress from the user's input.

[1312] 4. Server analysis: The server analyzes the acquired data to identify the cause of the failure.

[1313] 5. The server adjusts the suggestion: Considering the high stress level, it sends the user a suggestion stating, "It has been determined that immediate repair is needed. Would you like to request a repair service immediately?"

[1314] 6. Server takes action: If the user approves, the server automatically sends a repair request to the service provider and notifies the user of the progress.

[1315] Proposal to review rent and emotional support.

[1316] This explains a scenario where a user enters "I want to review the rent for room 301" into the terminal, and the emotion engine detects anxiety.

[1317] 1. User inputs into terminal: The user inputs "I would like to review the rent for room 301" into the terminal.

[1318] 2. Server receives: The server receives this information and retrieves property data for Room 301 and rental data for the surrounding area from its database or an external real estate data API.

[1319] 3. Emotion engine recognizes emotions: The emotion engine detects anxiety from the user's input.

[1320] 4. Server analysis: The server analyzes the acquired data and calculates new rental rates based on market trends.

[1321] 5. The server adjusts the proposal: Taking into consideration the user's concerns, the server notifies the user of a proposal such as, "Based on market trends, we propose changing the rent from XX yen to YY yen. We will also explain the specific reasons, so please rest assured."

[1322] 6. Server execution: If the user approves, the new rental fee is updated in the database, and the related contract information is also automatically updated.

[1323] Example of a prompt

[1324] "Please generate a proposal document that takes into consideration user stress regarding prompt repairs in the event of a toilet malfunction in Room 203."

[1325] "Please generate a proposal document for revising the rent for Room 301 that takes into account the user's concerns."

[1326] Thus, the system of the present invention not only automates the specialized and complex tasks of real estate management, but also aims to improve user satisfaction by providing responses based on the user's emotions.

[1327] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1328] Step 1:

[1329] Input: The user enters information into the terminal.

[1330] Processing: The user operates the terminal, enters information such as repair requests or rent review into a form, and clicks the submit button.

[1331] Output: The information entered by the user is sent from the terminal to the server.

[1332] Specific operation: When the user enters "The toilet in room 203 is broken" and presses the submit button, the terminal sends this information to the server as an HTTP request in JSON format.

[1333] Step 2:

[1334] Input: HTTP request sent from the terminal.

[1335] Processing: The server verifies the information received from the terminal and prepares it for storage in the database.

[1336] Output: The verified information is saved to the server's database.

[1337] Specific operation: The server validates the schema of the received JSON data and checks for invalid data. If there are no problems, it saves it to the database.

[1338] Step 3:

[1339] Input: Information entered by the user.

[1340] Processing: The server activates the emotion engine and sends the user's input data for emotion analysis.

[1341] Output: Sentimental information contained in the user's input data.

[1342] Specific operation: The server uses natural language processing libraries (NLTK, Transformers) to analyze the user's input text. The analysis results are output as emotion scores such as "high stress" or "anxiety."

[1343] Step 4:

[1344] Input: Verified user information and sentiment data.

[1345] Processing: The server retrieves relevant property data from the database based on the user's emotions and input information.

[1346] Output: Detailed information about the retrieved property.

[1347] Specific operation: The server executes an SQL query to retrieve information such as the repair history, rental history, and equipment status of the relevant property from the database.

[1348] Step 5:

[1349] Input: Acquired property data and sentiment data.

[1350] Processing: The server analyzes this data, identifies the cause of the problem, and generates appropriate countermeasures.

[1351] Output: Proposed solutions to present to the user.

[1352] Specific operation: The server uses machine learning models (regression models, classification models) to analyze data. For example, along with the judgment that "prompt repair is needed," it generates a proposal statement: "Prompt repair is needed. Shall we contact a repair company immediately?"

[1353] Step 6:

[1354] Input: Server-generated suggestions.

[1355] Processing: The user reviews the proposal via their device and takes action to approve or reject it.

[1356] Output: User's approval or rejection response.

[1357] Specific action: The user reads the proposal displayed on the device and clicks the accept button.

[1358] Step 7:

[1359] Input: User's approval response.

[1360] Processing: The server will execute the proposed solution after receiving user approval.

[1361] Output: Notification of the countermeasures taken.

[1362] Specific operation: The server sends requests to repair contractors or updates the rental fee in the database via API calls. The execution status is reported to the user via email or system notification.

[1363] (Application Example 2)

[1364] 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."

[1365] Existing real estate management systems and customer service systems in stores struggle to respond flexibly to user emotions and specific situations. Furthermore, faster and more appropriate responses are required to improve customer satisfaction and convenience. To address this, a system is needed that recognizes user emotions in real time and adjusts responses accordingly.

[1366] 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. In this invention, the server includes means for receiving information entered by the user via a terminal, means for obtaining item data related to the received information from a database, and means for analyzing the obtained item data to generate optimal suggestions based on the user's emotions and usage. This makes it possible to provide optimal suggestions and responses that take the user's emotions into consideration.

[1367] "Means of receiving information entered by users via their devices" refers to a function that allows a server to receive text or audio data entered by users via devices such as smartphones, tablets, or computers.

[1368] "Means for obtaining item data related to received information from a database" refers to a function that allows the server to search and retrieve corresponding item and customer information from a database based on the information received from the user.

[1369] "A means of analyzing acquired item data to generate optimal suggestions based on user emotions and usage patterns" refers to a function in which the analysis engine generates optimal suggestions based on information acquired from the database, taking into account the user's emotions and past usage patterns.

[1370] "Means of providing proposed countermeasures to users via smart devices" refers to a function that displays countermeasures generated by the server to the user via smart devices such as smart glasses or head-mounted displays.

[1371] "Means for implementing proposed countermeasures" refers to a function that automatically implements countermeasures based on proposals approved by the user, such as requesting repairs or presenting product suggestions.

[1372] "Means of notifying users of the status of countermeasures" refers to a function that allows the server to inform users in real time about the progress and completion status of countermeasures.

[1373] This invention relates to a customer service system for physical stores, which uses smart devices to recognize user emotions in real time and provide appropriate responses. Specific embodiments for carrying out this invention are described below.

[1374] System Configuration

[1375] The system includes the following main components:

[1376] 1. Terminal: A device used by the user to input information, such as smart glasses or head-mounted displays (HMDs).

[1377] 2. Server: A device that receives and processes input information. It also interacts with databases.

[1378] 3. Emotion Recognition Engine: This is a software module for analyzing the user's emotions from the input text.

[1379] 4. Database: A system for storing item data and user information, such as SQLite.

[1380] 5. Recommendation Engine: A software module for generating optimal suggestions based on emotion recognition results.

[1381] Program processing

[1382] Terminal: Users input information through smart glasses or HMDs. For example, they might ask questions like, "Are there other colors of this product available?" via voice or text.

[1383] Server: The server performs the following processing based on the information received from the terminal.

[1384] The input information is analyzed and sent to the emotion recognition engine. The emotion recognition engine identifies the user's emotions from the input information.

[1385] Based on the emotion recognition results, information about related products (e.g., stock status, different color variations, etc.) is retrieved from the database.

[1386] Using a recommendation engine, the system generates optimal suggestions based on acquired product information and sentiment data.

[1387] Emotion recognition engine: This engine analyzes user input information and processes it to identify emotions (e.g., confusion, excitement, stress). The model used is typically a generative AI model based on natural language processing.

[1388] Database: A database system such as SQLite is used to store and manage item information and user data. The server executes SQL queries to retrieve the necessary information.

[1389] Recommendation Engine: Generates optimal suggestions based on acquired information and sentiment data. For example, if a user is confused, it will immediately suggest a different color of the product that is in stock.

[1390] Displaying results: Generated suggestions are displayed to the user in real time via a smart device. This allows store staff to respond quickly and appropriately.

[1391] Specific example

[1392] For example, if a customer asks, "Do you have this product in a different color?", the system will work as follows:

[1393] 1. Receiving user questions: The store clerk inputs the customer's questions through smart glasses.

[1394] 2. Emotion recognition: The system recognizes from the customer's questions that the customer is confused.

[1395] 3. Retrieve product information: Retrieve detailed information and inventory information for the relevant product from the database.

[1396] 4. Suggestion Generation: To alleviate customer confusion, generate suggestions that immediately introduce different color variations of the product if they are in stock.

[1397] 5. Display: The suggested content will be displayed on the smart glasses.

[1398] Example of a prompt

[1399] Question: Is this product available in other colors?

[1400] Emotion recognition: perplexed

[1401] Applicable product: blue shirt

[1402] in stock

[1403] Suggestion: To alleviate the confusion, how about introducing a different colored green shirt right away?

[1404] As described above, the system can significantly improve customer satisfaction by understanding user emotions in real time and providing optimal responses.

[1405] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1406] Step 1:

[1407] The user inputs information into the device. Smart glasses or HMDs are used as devices for users to input questions and requests. Specifically, the user inputs information such as "Are there other colors of this product available?" via voice or text. This input information becomes the input data for the system.

[1408] Step 2:

[1409] The terminal sends information to the server. Information entered by the user is sent from the terminal to the server. During this process, the input information is converted into text data and reaches the server via the communication network. The server receives this information and prepares it for analysis.

[1410] Step 3:

[1411] The server performs analysis using an emotion recognition engine. The server passes the received input information to the emotion recognition engine, and uses an emotion recognition model (generative AI model) to determine the user's emotions. For example, emotions such as "confused" or "excited" may be output. The input for this step is the user's text information, and the output is identified emotion data.

[1412] Step 4:

[1413] The server retrieves relevant information from the item database. The server issues SQL queries to the database and retrieves product data related to the input information. Specifically, product inventory status and detailed information are retrieved from the database. The input for this step is user text information, and the output is product data.

[1414] Step 5:

[1415] The server generates suggestions based on the data it acquires. The server combines the emotion recognition results with product data retrieved from the database to generate the most suitable suggestions based on the recommendation engine. For example, in the case of "confusion," a suggestion such as "immediately introduce products in different colors" is generated. The input for this step is emotion data and product data, and the output is the suggested content.

[1416] Step 6:

[1417] The server sends the proposal content to the terminal. The generated proposal content is sent from the server to the smart glasses or HMD. The input for this step is the proposal content, and the output is the proposal information displayed on the terminal.

[1418] Step 7:

[1419] The terminal displays the suggestion to the user. The user checks the suggestion sent from the server in real time via their smart device. For example, the suggestion might be, "To reduce confusion, would you like us to immediately introduce a different colored green shirt?" The input for this step is the suggestion information, and the output is a notification to the user.

[1420] Step 8:

[1421] The user provides feedback on the proposal. The user uses a smart device to provide feedback on the proposal. For example, they might respond with "Yes, please introduce it." This feedback information becomes new input data for the system.

[1422] Step 9:

[1423] The server takes action based on the feedback. Upon receiving user feedback, the server implements the suggested countermeasures. For example, it might display the details of a suggested product or re-check the inventory. The input for this step is the user's feedback, and the output is the specific action taken.

[1424] Example of a prompt

[1425] Question: Is this product available in other colors?

[1426] Emotion recognition: perplexed

[1427] Applicable product: blue shirt

[1428] in stock

[1429] Suggestion: To alleviate the confusion, how about introducing a different colored green shirt right away?

[1430] The above series of processing steps enables optimal product recommendations and responses based on user emotions, resulting in a system that improves customer satisfaction.

[1431] 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.

[1432] 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.

[1433] 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.

[1434] [Fourth Embodiment]

[1435] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[1436] 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.

[1437] 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).

[1438] 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.

[1439] 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.

[1440] 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).

[1441] 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.

[1442] 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.

[1443] 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.

[1444] 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.

[1445] 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.

[1446] 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.

[1447] 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".

[1448] As a concrete example of this specification, a form of a system for automating real estate management operations will be described. In this system, the user inputs information via a terminal, which is received and processed by a server, which then proposes and implements appropriate countermeasures. The present invention can effectively automate tasks such as repair requests and rent reviews.

[1449] System Configuration

[1450] 1. User input

[1451] Users use a terminal to input information into the system, such as repair requests or rent review requests. This input information is received by the server, which then proceeds to the next step.

[1452] 2. The server retrieves the information.

[1453] The server retrieves detailed information about the target property from the database based on the information received from the user. This detailed information includes the property's repair history, rental history, and the condition of its facilities.

[1454] 3. The server analyzes the data.

[1455] The acquired information is analyzed to determine whether repairs are necessary or if the rent can be reviewed. This analysis also takes into account historical data and market trends.

[1456] 4. The server generates the proposal.

[1457] Based on the analysis results, we propose appropriate countermeasures to the user. For example, if repairs are needed, we will suggest a partner repair company, and if we propose a review of the rent, we will present the new rent amount.

[1458] 5. The server performs the action.

[1459] If the user approves the proposal, the server automatically sends a repair request to the contractor or renews the lease. The user is also notified of the progress.

[1460] Specific example

[1461] Automation of repair requests

[1462] This explains a scenario where a user enters "The toilet in room 203 is broken" into the terminal.

[1463] 1. User inputs into terminal: The user inputs "The toilet in room 203 is broken" into the terminal.

[1464] 2. Server receives: The server receives this information and retrieves the property data for Room 203 from the database.

[1465] 3. Server analysis: The server analyzes the acquired data to identify the cause of the failure.

[1466] 4. Server Proposal: A server that determines it needs repair will propose to the user, "Repair is needed. Shall we request a repair service?"

[1467] 5. Server takes action: If the user approves, the server automatically sends a repair request to the affiliated repair company and notifies the user of the progress.

[1468] Proposal to review rent

[1469] This explains a scenario where a user enters "I want to review the rent for room 301" into their device.

[1470] 1. User inputs into terminal: The user inputs "I would like to review the rent for room 301" into the terminal.

[1471] 2. Server receives: The server receives this information and retrieves property data for Room 301 and rental data for the surrounding area from its database.

[1472] 3. Server analysis: The server analyzes the acquired data and calculates new rental rates based on market trends.

[1473] 4. The server makes a suggestion: It presents to the user, "We suggest changing the rent for room 301 from XX yen to YY yen."

[1474] 5. Server execution: If the user approves, the new rental fee is updated in the database, and the related contract information is also automatically updated.

[1475] Thus, the present invention can automate the specialized and complex tasks of real estate management, enabling efficient and accurate handling. This dramatically improves the efficiency of management operations and contributes to increased user satisfaction.

[1476] The following describes the processing flow.

[1477] Automation of repair requests

[1478] Step 1:

[1479] The user enters "The toilet in room 203 is broken" into the terminal. This is done through the terminal's input form, and the server receives the information.

[1480] Step 2:

[1481] Based on the information received by the server, property data for Room 203 is retrieved from the database. This includes detailed information about the property's repair history and current condition.

[1482] Step 3:

[1483] The system analyzes the data acquired by the server to identify the cause of the failure. For example, it refers to past repair history for similar incidents to determine if the current failure is due to the same cause.

[1484] Step 4:

[1485] Based on the analysis results, the server sends a suggestion to the user: "Repair is needed. Would you like to request a repair service?" This suggestion is displayed on the terminal.

[1486] Step 5:

[1487] If the user approves the proposal, select "Yes" and send the decision to the server.

[1488] Step 6:

[1489] The server retrieves information on affiliated repair companies from a database and automatically generates and sends repair requests. Specifically, it sends a message to the repair company containing information about the necessary repairs and their locations.

[1490] Step 7:

[1491] The server monitors the status of repair requests and receives progress reports from repair companies.

[1492] Step 8:

[1493] The server notifies the user of the progress of the repairs. For example, it displays messages on the user's terminal such as "The repair company has arrived on site" or "The repair work has been completed."

[1494] Proposal to review rent

[1495] Step 1:

[1496] The user enters "I would like to review the rent for room 301" into the terminal. The input is made through a form on the terminal, and the server receives the information.

[1497] Step 2:

[1498] The server retrieves property data for Room 301 and rental information for surrounding properties from a database and external real estate data APIs. This includes current rental rates, past rental history, and market prices.

[1499] Step 3:

[1500] The server analyzes the collected data to calculate the optimal rental price. The analysis takes into account local real estate market trends and the rental rates of competing properties.

[1501] Step 4:

[1502] Based on the analysis results, the server notifies the user, "We propose changing the rent for room 301 from XX yen to YY yen." This proposal is displayed on the terminal.

[1503] Step 5:

[1504] If the user approves the proposal, select "Yes" and send the decision to the server.

[1505] Step 6:

[1506] The server updates the database with new rental rates. This includes automatic updates of rental contract information and advertising data.

[1507] Step 7:

[1508] The server notifies the user of the update status. A message such as "Rental fee review is complete. The new rent is YY yen." is displayed on the user's terminal.

[1509] (Example 1)

[1510] 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".

[1511] Current real estate management operations are largely manual, and important tasks such as repair requests and rent reviews are not processed quickly and efficiently. This leads to decreased user satisfaction and undermines the efficiency of management operations. Furthermore, traditional systems lack sufficient automation for data analysis, proposals, and implementation, resulting in a high reliance on human resources.

[1512] 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.

[1513] In this invention, the server includes means for receiving user input information as an HTTP request, means for obtaining detailed property information from a database, means for analyzing the data using Python to determine how to handle repairs and rent adjustments, and means for sending requests to the APIs of partner companies. This enables the automation of real estate management operations, allowing for the rapid and efficient handling of repair requests and proposals and implementation of rent adjustments.

[1514] A "user" refers to an individual or company that uses a real estate management system to request repairs, review rental rates, and perform other similar actions.

[1515] "Terminal" refers to devices such as computers, smartphones, and tablets that users use to input and transmit information.

[1516] "Information" refers to data entered by users via their devices, such as repair requests and rent review data.

[1517] A "server" refers to a computer system that receives information from users, analyzes the data, and makes and implements suggestions.

[1518] A "database" refers to a data management system used to store property information, repair history, rental history, and so on.

[1519] "Data analysis" refers to the process of identifying the cause of a problem and determining appropriate countermeasures based on data acquired by the server.

[1520] "Countermeasures" refer to actions such as repairs or rent adjustments that the server proposes to the user based on the results of data analysis.

[1521] An "HTTP request" is a type of communication protocol used by a device to send information to a server.

[1522] "Python" refers to a high-level programming language used for data analysis, machine learning, and other applications.

[1523] An "API" refers to an interface for exchanging information between different software systems.

[1524] "Suggestion" refers to the recommended course of action that the server provides to the user based on the analysis results.

[1525] "Execution" refers to the server taking action based on a proposal after the user has approved it.

[1526] One embodiment of the present invention specifically describes a system for automating real estate management operations. In this system, the user inputs information via a terminal, which is received and processed by a server, which then proposes and implements appropriate countermeasures. The aim is to automate tasks such as repair requests and rent reviews, and to respond efficiently and accurately.

[1527] System Configuration

[1528] Hardware and software

[1529] Server: A central computer that performs data processing and analysis. Python is used for analysis, and a MySQL database is used for data management.

[1530] Terminal: A device used by a user to input information. This includes PCs, smartphones, and tablets.

[1531] software:

[1532] Frontend: Uses React.js.

[1533] Backend: Django is used.

[1534] Data analysis: Python will be used, with libraries such as Pandas and Scikit-learn.

[1535] Data processing and calculation

[1536] Receiving user input information

[1537] The server receives user input information sent from the terminal as an HTTP request.

[1538] Data acquisition

[1539] The server retrieves detailed property information from the database based on the information received from the user. This detailed information includes the property's repair history, rental history, and the condition of its facilities.

[1540] Data Analysis

[1541] The acquired information is analyzed using Python libraries to determine necessary repairs and rent adjustments. Data cleansing, statistical analysis, and machine learning techniques are used in the analysis.

[1542] Proposal generation

[1543] Based on the analysis results, the server determines whether repairs are needed or if the rental fee needs to be reviewed, and proposes appropriate solutions to the user. For example, these suggestions may include: "Repairs are needed. Shall we request a repair company?" or "We propose changing the rental fee from xx yen to yy yen."

[1544] Execute action

[1545] If the user approves the proposal, repair requests or rent updates will be automatically initiated. In the case of repair requests, an automatic request will be sent to the partner company's API, and if the user approves a rent revision, the rent information in the database will be updated. As a result, the user will be notified of the execution status.

[1546] Specific example

[1547] Automation of repair requests

[1548] As an example, let's explain a scenario where the user enters "The toilet in room 203 is broken."

[1549] Example of a prompt

[1550] User: The toilet in room 203 is out of order.

[1551] 1. User inputs into the terminal: The user inputs "The toilet in room 203 is broken" into the terminal and clicks the send button.

[1552] 2. Server receives: The server receives this information and retrieves the property data for Room 203 from the database.

[1553] 3. Server retrieves data: The server retrieves the repair history and current status of room 203 from the database.

[1554] 4. Server analyzes data: The server analyzes the repair history and determines whether repairs are necessary.

[1555] 5. Server generates a suggestion: The server suggests to the user, "Repair is needed. Would you like to contact a repair company?"

[1556] 6. Server takes action: If the user approves the proposal, the server automatically sends a repair request to the partner repair company and notifies the user of the progress.

[1557] Proposal to review rent

[1558] As an example, let's explain a scenario where a user enters "I want to review the rent for room 301."

[1559] Example of a prompt

[1560] User: I would like to review the rent for room 301.

[1561] 1. User inputs into the terminal: The user inputs "I would like to review the rent for room 301" into the terminal and clicks the send button.

[1562] 2. Server receives: The server receives this information and retrieves property data for Room 301 and rental data for the surrounding area from its database.

[1563] 3. Server retrieves data: The server retrieves the current rent for Room 301 and surrounding market trends from the database.

[1564] 4. Server analyzes data: The server analyzes market trends and calculates new rental rates.

[1565] 5. Server generates proposal: The server presents the user with the following proposal: "We propose changing the rent for room 301 from xx yen to yy yen."

[1566] 6. The server takes action: If the user approves the proposal, the server updates the database with the new rent and automatically updates the related contract information.

[1567] Thus, the present invention automates real estate management operations and enables the provision of fast and accurate services to users. This dramatically improves the efficiency of management operations and contributes to increased user satisfaction.

[1568] The flow of the specific processing in Example 1 will be explained using Figure 11.

[1569] Step 1:

[1570] User enters information

[1571] Users use their devices to enter information such as repair requests or rent review requests, and then click the submit button.

[1572] Specific actions

[1573] The user enters "The toilet in room 203 is broken" into a web form on their device and presses the submit button. This sends the entered information to the server as an HTTP request.

[1574] Input: Repair request information stating "The toilet in room 203 is out of order."

[1575] Output: Information sent to the server as an HTTP request

[1576] Step 2:

[1577] The server receives the information.

[1578] The server receives HTTP requests from users, extracts information from the request body, and stores it in the database.

[1579] Specific actions

[1580] The server receives an HTTP request and extracts information from the request body (for example, in JSON format) stating that "the toilet in room 203 is out of order." This information is then stored in the database.

[1581] Input: User input information as an HTTP request

[1582] Output: Repair request information stored in the database

[1583] Step 3:

[1584] The server retrieves the data.

[1585] The server retrieves detailed information about the target property (Room 203) from the database.

[1586] Specific actions

[1587] The server executes a database query to retrieve detailed information about "Room 203," such as its repair history and the condition of its facilities.

[1588] Input: Property ID based on user repair request information

[1589] Output: Property details retrieved from the database

[1590] Step 4:

[1591] The server analyzes the data.

[1592] The server analyzes the acquired data to determine whether repairs are necessary.

[1593] Specific actions

[1594] The server uses Python libraries (such as Pandas and scikit-learn) to analyze repair history and equipment status. For example, it determines whether repairs are necessary based on past repair frequency and the current condition of the toilets.

[1595] Input: Property details

[1596] Output: Result of the determination of whether repairs are needed.

[1597] Step 5:

[1598] The server generates suggestions.

[1599] Based on the analysis results, the server will suggest appropriate countermeasures to the user.

[1600] Specific actions

[1601] The server generates a message saying, "Repair is needed. Do you want to contact a repair company?" and notifies the user.

[1602] Input: Result of the determination of whether repairs are needed.

[1603] Output: Suggestion message to the user

[1604] Step 6:

[1605] The server performs the action

[1606] If the user approves the proposal, the server automatically sends a repair request to the contractor and notifies the user of the progress.

[1607] Specific actions

[1608] Once the user approves the proposal, the server sends a repair request to the partner repair company's API. Furthermore, it tracks the progress and notifies the user.

[1609] Input: User authorization, API information of affiliated repair companies

[1610] Output: Sending a repair request to the repair company, user notification of progress.

[1611] Based on the steps described above, the system of the present invention can automate real estate management operations and provide users with fast and accurate services. This dramatically improves the efficiency of management operations and contributes to increased user satisfaction.

[1612] (Application Example 1)

[1613] 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".

[1614] Traditional real estate management and factory equipment management require numerous manual operations, making them inefficient and prone to errors. In particular, repair requests, rent reviews, and factory equipment maintenance are often handled independently, leading to a lack of coordination and making overall management difficult. This invention aims to solve these problems by providing a system that centrally and efficiently manages both real estate and factory equipment.

[1615] 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.

[1616] In this invention, the server includes means for receiving information entered by a user via a terminal, means for obtaining property data and equipment information related to the received information from a database, means for analyzing the obtained property data and equipment information to identify the cause of a problem, means for proposing appropriate countermeasures based on the analysis results, means for executing the proposed countermeasures, means for notifying the user of the status of the implementation of the countermeasures, and means for analyzing the maintenance status of equipment and automatically proposing and generating the next maintenance. This makes it possible to significantly improve the efficiency of real estate management and factory equipment management.

[1617] "User" refers to a person who operates a system.

[1618] A "terminal" refers to a device used by a user to input information.

[1619] "Information" refers to the data and instructions that users input through their devices.

[1620] A "server" refers to a central computer system that processes information received from users.

[1621] "Property data" refers to detailed information related to a real estate property.

[1622] "Equipment information" refers to detailed information related to factory equipment.

[1623] A "database" refers to a system that systematically stores and manages large amounts of data.

[1624] "Analysis" refers to the act of analyzing acquired data to identify the cause of a problem.

[1625] "Countermeasures" refer to the means of solving the problem proposed based on the analysis results.

[1626] "Maintenance" refers to the upkeep and inspection work of equipment and machinery.

[1627] "Proposal" refers to the countermeasures presented to the user based on the analysis results.

[1628] "Implementation" refers to actually carrying out the proposed countermeasures.

[1629] "Notification" refers to informing users about the status of the implementation of countermeasures.

[1630] "System" refers to the entire information processing structure that includes these means.

[1631] This invention relates to a system for automating real estate management operations and factory equipment maintenance management. The system functions by allowing users to input information via a terminal and transmit it to a server. The server retrieves necessary data from a database based on the input information, analyzes it, proposes appropriate countermeasures to the user, and implements them. Furthermore, it improves the efficiency of real estate management and factory equipment management by analyzing the equipment maintenance status and automatically proposing and generating the next maintenance schedule.

[1632] System Configuration

[1633] 1. User input

[1634] Users use terminals to input repair requests, rent reviews, and maintenance status of factory equipment into the system. These terminals include smartphones, smart glasses, and head-mounted displays.

[1635] 2. Receiving and acquiring information

[1636] The server retrieves property and equipment information from a database based on the information received from the user. This database includes property repair history, rental history, equipment status, and maintenance history. Possible databases used include SQLite and MySQL.

[1637] 3. Data Analysis

[1638] The server analyzes the acquired data to identify the root cause of the problem. This analysis considers historical data, market trends, and maintenance schedules. The software used includes analysis tools such as Python and R.

[1639] 4. Proposal and Implementation

[1640] The server proposes appropriate countermeasures to the user based on the analysis results. For example, if repairs are needed, it will suggest a partner repair company; if a rent review is proposed, it will present the new rent amount; and if equipment maintenance is needed, it will suggest the next maintenance date.

[1641] If the user approves a proposal, the server automatically sends a repair request or renews the lease. Similarly, for maintenance requests, the server automatically generates and executes the request. The user is notified of the execution status. Notifications are sent via email, push notifications, etc.

[1642] Specific example

[1643] Automation of repair requests

[1644] This scenario describes what happens when a user enters "The toilet in room 203 is broken" into their terminal. The server receives this information and retrieves the property data for room 203 from the database. The server analyzes the retrieved data to identify the cause of the malfunction. Based on the analysis, if the server determines that repairs are necessary, it will ask the user, "Repairs are needed. Shall we request a repair company?" If the user approves, the server will automatically send a repair request to a partner repair company and notify the user of the progress.

[1645] Proposal to review rent

[1646] This scenario shows what happens when a user enters "I want to review the rent for room 301" into their terminal. The server receives this information and retrieves property data for room 301 and rental data for the surrounding area from its database. The server analyzes the retrieved data and calculates a new rent based on market trends. It then presents to the user, "We propose changing the rent for room 301 from XX yen to YY yen." If the user approves, the new rent is updated in the database, and the related contract information is also automatically updated.

[1647] Equipment maintenance proposals

[1648] This system automatically suggests maintenance for equipment used in a factory. The server retrieves the last maintenance date and maintenance interval of the equipment from a database and calculates the next maintenance date. If necessary, it prompts the user with a message such as, "The next maintenance is approaching. Would you like to request maintenance?" If the user approves, a maintenance request is automatically generated.

[1649] Example of a prompt

[1650] Please enter the following information regarding the equipment within the factory. Example: Equipment ID, Equipment Name, Last Maintenance Date, Maintenance Interval.

[1651] Thus, the present invention provides a specific form for efficiently managing real estate and factory facilities.

[1652] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[1653] Step 1:

[1654] Users input information regarding repair requests, rent reviews, and equipment maintenance via a terminal. The entered data includes specific details and information about the items involved. The terminal then transmits the entered data to the server.

[1655] Input: Repair requests, rent adjustments, or maintenance requests entered by the user.

[1656] Output: Request information sent to the server

[1657] Step 2:

[1658] Based on the information received from the user, the server retrieves relevant property and equipment data from the database. The database stores detailed information about properties and equipment, repair history, maintenance history, and more.

[1659] Input: Request information submitted by the user

[1660] Output: Property data and equipment information retrieved from the database.

[1661] Step 3:

[1662] The server analyzes the acquired data to identify the root cause of the problem related to the request and determine the optimal solution. The analysis uses a generative AI model, taking into account historical data, market trends, and maintenance schedules.

[1663] Input: Property data and equipment information obtained from the database

[1664] Output: Analysis results (causes of the problem and countermeasures)

[1665] Step 4:

[1666] The server proposes appropriate countermeasures to the user based on the analysis results. The proposal is notified to the user via the terminal, and the user reviews the proposal and approves it if necessary. Examples of prompt messages include, "The toilet in room 203 is out of order. Shall we contact a repair company?"

[1667] Input: Analysis results

[1668] Output: Suggestions notified to the user

[1669] Step 5:

[1670] If the user approves the proposal, the server automatically generates a repair or maintenance request and sends it to the relevant contractor. In the case of a rent review, the database is also updated. The user is notified of the execution status in real time.

[1671] Input: User approval

[1672] Output: Generated request information, database updates, and execution status notifications.

[1673] Step 6:

[1674] The server monitors the progress of the task and notifies the user in real time. The user can check the status through their terminal and give additional instructions as needed.

[1675] Input: Progress information of tasks

[1676] Output: Notification of progress to the user

[1677] Thus, this system starts with user input and proceeds through data acquisition, analysis, proposal, execution, and monitoring to streamline management tasks.

[1678] 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.

[1679] As a concrete example of this specification, we describe a form of system that automates real estate management tasks and combines them with an emotion engine that recognizes user emotions. In this system, the user inputs information via a terminal, which is received and processed by a server, which then proposes and implements appropriate countermeasures. Furthermore, it has a function to recognize the user's emotions and adjust the proposals and responses based on those emotions.

[1680] System Configuration

[1681] 1. User input

[1682] Users use a terminal to input information into the system, such as repair requests or rent review requests. This input information is received by the server and the process proceeds to the next step. The emotion engine recognizes the user's emotions from the input information.

[1683] 2. The server retrieves the information.

[1684] The server retrieves detailed information about the property from its database based on the information received from the user and the user's sentiment. This detailed information includes the property's repair history, rental history, and the condition of its facilities.

[1685] 3. The server analyzes the data.

[1686] The acquired information is analyzed to determine whether repairs are necessary or if the rent can be reviewed. This analysis also takes into account historical data and market trends.

[1687] 4. The server generates the proposal.

[1688] Based on the analysis results, appropriate countermeasures are proposed to the user. Here, the proposals are adjusted based on the user's emotions recognized by the emotion engine. For example, if it is determined that the user is experiencing high levels of stress, a more immediate countermeasure will be prioritized.

[1689] 5. The server performs the action.

[1690] If the user approves the proposal, the server automatically sends a repair request to the contractor or renews the lease. The user is also notified of the progress.

[1691] Specific example

[1692] Automated repair request and emotional support.

[1693] This explains a scenario where a user enters "The toilet in room 203 is broken" into the terminal, and the emotion engine detects stress.

[1694] 1. User inputs into terminal: The user inputs "The toilet in room 203 is broken" into the terminal.

[1695] 2. Server receives: The server receives this information and retrieves the property data for Room 203 from the database.

[1696] 3. Emotion engine recognizes emotions: The emotion engine detects high levels of stress from the user's input.

[1697] 4. Server analysis: The server analyzes the acquired data to identify the cause of the failure.

[1698] 5. The server adjusts the suggestion: Considering the high stress level, it sends the user a suggestion stating, "It has been determined that immediate repair is needed. Would you like to request a repair service immediately?"

[1699] 6. Server Execution: If the user approves, the server automatically sends a repair request to the affiliated repair company and notifies the user of the progress.

[1700] Proposal to review rent and emotional support.

[1701] This explains a scenario where a user enters "I want to review the rent for room 301" into the terminal, and the emotion engine detects anxiety.

[1702] 1. User inputs into terminal: The user inputs "I would like to review the rent for room 301" into the terminal.

[1703] 2. Server receives: The server receives this information and retrieves property data for Room 301 and rental data for the surrounding area from its database or an external real estate data API.

[1704] 3. Emotion engine recognizes emotions: The emotion engine detects anxiety from the user's input.

[1705] 4. Server analysis: The server analyzes the acquired data and calculates new rental rates based on market trends.

[1706] 5. The server adjusts the proposal: Taking into consideration the user's concerns, the server notifies the user of a proposal such as, "Based on market trends, we propose changing the rent from XX yen to YY yen. We will also explain the specific reasons, so please rest assured."

[1707] 6. Server execution: If the user approves, the new rental fee is updated in the database, and the related contract information is also automatically updated.

[1708] Thus, this invention, which combines an emotion engine, not only automates the specialized and complex tasks of real estate management, but also enables responses that take into account the user's emotions, thereby achieving a higher level of user satisfaction.

[1709] The following describes the processing flow.

[1710] Automated repair request and emotional support.

[1711] Step 1:

[1712] The user enters "The toilet in room 203 is broken" into the terminal. This is done through the terminal's input form, and the server receives the information.

[1713] Step 2:

[1714] The server passes the received information to the emotion engine, which analyzes the user's emotions. The emotion engine detects what kind of emotions the user is experiencing (e.g., stress, anger) from the input text.

[1715] Step 3:

[1716] The server receives results from the emotion engine, and if high stress levels are detected, it will consider special action.

[1717] Step 4:

[1718] Based on the information received by the server and the user's sentiment information, property data for Room 203 is retrieved from the database. This includes information on the property's repair history and current equipment status.

[1719] Step 5:

[1720] The server analyzes the data it acquires to identify the cause of the failure. For example, past repair history is referenced to pinpoint the cause of the current failure.

[1721] Step 6:

[1722] Based on the analysis results, the server generates suggestions that take into account the user's emotions as recognized by the emotion engine. For example, if the user is experiencing high levels of stress, it might send the user a suggestion such as, "Prompt repair is needed. Shall we contact a repair company immediately?"

[1723] Step 7:

[1724] The user approves the proposal from their device. The user selects "Yes," and that information is sent to the server.

[1725] Step 8:

[1726] The server retrieves information on affiliated repair companies from its database and automatically sends repair requests. It automatically generates and sends a message to the repair company containing the necessary repair details and location information.

[1727] Step 9:

[1728] The server monitors the status of repair requests and receives progress reports from repair companies. These reports include the repair company's arrival time and completion of work.

[1729] Step 10:

[1730] The server notifies the user of the progress of the repairs. For example, it displays messages on the user's device such as "The repair company has arrived on site" or "The repair work is complete."

[1731] Proposal to review rent and emotional support.

[1732] Step 1:

[1733] The user enters "I would like to review the rent for room 301" into the terminal. The input is made through a form on the terminal, and the server receives the information.

[1734] Step 2:

[1735] The server passes the received information to the emotion engine, which analyzes the user's emotions. The emotion engine detects what kind of emotions the user is experiencing (e.g., anxiety, excitement) from the input text.

[1736] Step 3:

[1737] The server receives results from the emotion engine, and if anxiety is detected, it considers special considerations.

[1738] Step 4:

[1739] Based on the information received by the server and the user's sentiment information, property data for Room 301 and rental information for surrounding properties are retrieved from a database and external real estate data APIs. This includes current rental rates, past rental history, and market prices.

[1740] Step 5:

[1741] The server analyzes the collected data to calculate new rental rates. The analysis takes into account market trends and the rental rates of competing properties.

[1742] Step 6:

[1743] Based on the analysis results, the server generates suggestions that take into account the user's emotions as recognized by the emotion engine. For example, for a user who is feeling anxious, it might notify the user with a suggestion such as, "Based on market trends, we suggest changing the rent from XX yen to YY yen. We will also explain the specific reasons, so please rest assured."

[1744] Step 7:

[1745] The user approves the proposal from their device. The user selects "Yes," and that information is sent to the server.

[1746] Step 8:

[1747] The server updates the database with new rental rates. This includes automatic updates of rental contract information and advertising data.

[1748] Step 9:

[1749] The server notifies the user of the update status. A message such as "Rental fee review is complete. The new rent is YY yen." is displayed on the user's terminal.

[1750] (Example 2)

[1751] 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".

[1752] Traditional real estate management systems only receive user input and perform basic processing, lacking the ability to sense user emotions and optimize responses. Therefore, even when users are experiencing high levels of stress or anxiety, appropriate responses are difficult to provide, potentially leading to decreased customer satisfaction. Furthermore, the automation of crucial tasks such as repair requests and rent reviews is insufficient, highlighting the need for more efficient real estate management.

[1753] The identification processing performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for receiving information entered by the user via a terminal, means for recognizing the user's emotions from the received information, and means for obtaining the recognized emotion data and property data related to the information from a database. This makes it possible to propose appropriate countermeasures that take the user's emotions into consideration.

[1754] "A means of receiving information entered by a user via a device" refers to a function that allows a server to receive data sent from a device such as a computer or smartphone operated by a user.

[1755] "Means of recognizing user emotions from received information" refers to a function that analyzes text data entered by the user and uses natural language processing and machine learning algorithms to identify the user's emotions (e.g., stress, anxiety, joy, etc.).

[1756] "Means for retrieving recognized emotion data and related property data from a database" refers to a function that retrieves data, including related property information, from a database based on the user's emotions and input requests.

[1757] "A means of identifying the cause of a problem by analyzing acquired property data and sentiment data" refers to a function that uses the acquired property history and condition information, as well as user sentiment data, to determine whether repairs are needed or if the rent needs to be reviewed.

[1758] "A means of proposing appropriate countermeasures based on analysis results and user sentiment" refers to a function that considers the results of data analysis and the emotions expressed by the user, generates specific countermeasures (for example, repair methods or proposed revisions to rent) and presents them to the user.

[1759] "Means for implementing proposed countermeasures" refers to a function that allows users to implement proposed countermeasures (for example, sending a repair request or renewing their lease) once they have approved them.

[1760] "Means for notifying users of the status of countermeasures" refers to a function that informs users of the progress and completion status of the above-mentioned countermeasures once they have been implemented.

[1761] This invention is an automated system for real estate management that takes user emotions into consideration and can respond appropriately. The system works by having the user input information via a terminal, which is then received and processed by a server, which proposes and implements appropriate countermeasures. Furthermore, it has a function to recognize user emotions and adjust proposals and responses accordingly.

[1762] System Configuration

[1763] 1. Receiving user input

[1764] Users input information such as repair requests or rent review requests using devices like computers or smartphones. The device sends the user's input to the server. The server receives this input information, performs appropriate verification, and proceeds to the next step.

[1765] 2. Emotion recognition by an emotion engine

[1766] The server analyzes the input data received from the user and uses an emotion engine to identify the user's emotions. This emotion engine can determine what emotions the user is experiencing using natural language processing and machine learning algorithms.

[1767] 3. Obtaining detailed property information

[1768] The server retrieves relevant property data from the database based on the user's sentiment and input information. This detailed information includes the property's repair history, rental history, and information about the condition of its facilities. The server then prepares this information for analysis.

[1769] 4. Data Analysis and Proposal Generation

[1770] The server analyzes acquired property and sentiment data to generate appropriate countermeasures. The analysis considers historical data and market trends, and uses machine learning models to determine the optimal action. The server then incorporates the results of the sentiment engine to generate the most suitable suggestions for the user.

[1771] 5. Approval of proposals and implementation of actions

[1772] Once the user reviews and approves the proposal, the server automatically implements the suggested actions. This includes submitting repair requests and renewing lease payments. The server notifies the user of the implementation status and reports progress in a timely manner.

[1773] Hardware and software to be used

[1774] Server: A server with high-performance data processing capabilities. Specifically, a web server and a database server that provide a REST API.

[1775] Device: A device that can connect to the internet, such as a computer, smartphone, or tablet.

[1776] Emotion engine: Natural language processing libraries (NLTK, Transformers, etc.) and machine learning algorithms (regression models, classification models).

[1777] Specific example

[1778] Automated repair request and emotional support.

[1779] This explains a scenario where a user enters "The toilet in room 203 is broken" into the terminal, and the emotion engine detects stress.

[1780] 1. User inputs into terminal: The user inputs "The toilet in room 203 is broken" into the terminal.

[1781] 2. Server receives: The server receives this information and retrieves the property data for Room 203 from the database.

[1782] 3. Emotion engine recognizes emotions: The emotion engine detects high levels of stress from the user's input.

[1783] 4. Server analysis: The server analyzes the acquired data to identify the cause of the failure.

[1784] 5. The server adjusts the suggestion: Considering the high stress level, it sends the user a suggestion stating, "It has been determined that immediate repair is needed. Would you like to request a repair service immediately?"

[1785] 6. Server takes action: If the user approves, the server automatically sends a repair request to the service provider and notifies the user of the progress.

[1786] Proposal to review rent and emotional support.

[1787] This explains a scenario where a user enters "I want to review the rent for room 301" into the terminal, and the emotion engine detects anxiety.

[1788] 1. User inputs into terminal: The user inputs "I would like to review the rent for room 301" into the terminal.

[1789] 2. Server receives: The server receives this information and retrieves property data for Room 301 and rental data for the surrounding area from its database or an external real estate data API.

[1790] 3. Emotion engine recognizes emotions: The emotion engine detects anxiety from the user's input.

[1791] 4. Server analysis: The server analyzes the acquired data and calculates new rental rates based on market trends.

[1792] 5. The server adjusts the proposal: Taking into consideration the user's concerns, the server notifies the user of a proposal such as, "Based on market trends, we propose changing the rent from XX yen to YY yen. We will also explain the specific reasons, so please rest assured."

[1793] 6. Server execution: If the user approves, the new rental fee is updated in the database, and the related contract information is also automatically updated.

[1794] Example of a prompt

[1795] "Please generate a proposal document that takes into consideration user stress regarding prompt repairs in the event of a toilet malfunction in Room 203."

[1796] "Please generate a proposal document for revising the rent for Room 301 that takes into account the user's concerns."

[1797] Thus, the system of the present invention not only automates the specialized and complex tasks of real estate management, but also aims to improve user satisfaction by providing responses based on the user's emotions.

[1798] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1799] Step 1:

[1800] Input: The user enters information into the terminal.

[1801] Processing: The user operates the terminal, enters information such as repair requests or rent review into a form, and clicks the submit button.

[1802] Output: The information entered by the user is sent from the terminal to the server.

[1803] Specific operation: When the user enters "The toilet in room 203 is broken" and presses the submit button, the terminal sends this information to the server as an HTTP request in JSON format.

[1804] Step 2:

[1805] Input: HTTP request sent from the terminal.

[1806] Processing: The server verifies the information received from the terminal and prepares it for storage in the database.

[1807] Output: The verified information is saved to the server's database.

[1808] Specific operation: The server validates the schema of the received JSON data and checks for invalid data. If there are no problems, it saves it to the database.

[1809] Step 3:

[1810] Input: Information entered by the user.

[1811] Processing: The server activates the emotion engine and sends the user's input data for emotion analysis.

[1812] Output: Sentimental information contained in the user's input data.

[1813] Specific operation: The server uses natural language processing libraries (NLTK, Transformers) to analyze the user's input text. The analysis results are output as emotion scores such as "high stress" or "anxiety."

[1814] Step 4:

[1815] Input: Verified user information and sentiment data.

[1816] Processing: The server retrieves relevant property data from the database based on the user's emotions and input information.

[1817] Output: Detailed information about the retrieved property.

[1818] Specific operation: The server executes an SQL query to retrieve information such as the repair history, rental history, and equipment status of the relevant property from the database.

[1819] Step 5:

[1820] Input: Acquired property data and sentiment data.

[1821] Processing: The server analyzes this data, identifies the cause of the problem, and generates appropriate countermeasures.

[1822] Output: Proposed solutions to present to the user.

[1823] Specific operation: The server uses machine learning models (regression models, classification models) to analyze data. For example, along with the judgment that "prompt repair is needed," it generates a proposal statement: "Prompt repair is needed. Shall we contact a repair company immediately?"

[1824] Step 6:

[1825] Input: Server-generated suggestions.

[1826] Processing: The user reviews the proposal via their device and takes action to approve or reject it.

[1827] Output: User's approval or rejection response.

[1828] Specific action: The user reads the proposal displayed on the device and clicks the accept button.

[1829] Step 7:

[1830] Input: User's approval response.

[1831] Processing: The server will execute the proposed solution after receiving user approval.

[1832] Output: Notification of the countermeasures taken.

[1833] Specific operation: The server sends requests to repair contractors or updates the rental fee in the database via API calls. The execution status is reported to the user via email or system notification.

[1834] (Application Example 2)

[1835] 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".

[1836] Existing real estate management systems and customer service systems in stores struggle to respond flexibly to user emotions and specific situations. Furthermore, faster and more appropriate responses are required to improve customer satisfaction and convenience. To address this, a system is needed that recognizes user emotions in real time and adjusts responses accordingly.

[1837] 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. In this invention, the server includes means for receiving information entered by the user via a terminal, means for obtaining item data related to the received information from a database, and means for analyzing the obtained item data to generate optimal suggestions based on the user's emotions and usage. This makes it possible to provide optimal suggestions and responses that take the user's emotions into consideration.

[1838] "Means of receiving information entered by users via their devices" refers to a function that allows a server to receive text or audio data entered by users via devices such as smartphones, tablets, or computers.

[1839] "Means for obtaining item data related to received information from a database" refers to a function that allows the server to search and retrieve corresponding item and customer information from a database based on the information received from the user.

[1840] "A means of analyzing acquired item data to generate optimal suggestions based on user emotions and usage patterns" refers to a function in which the analysis engine generates optimal suggestions based on information acquired from the database, taking into account the user's emotions and past usage patterns.

[1841] "Means of providing proposed countermeasures to users via smart devices" refers to a function that displays countermeasures generated by the server to the user via smart devices such as smart glasses or head-mounted displays.

[1842] "Means for implementing proposed countermeasures" refers to a function that automatically implements countermeasures based on proposals approved by the user, such as requesting repairs or presenting product suggestions.

[1843] "Means of notifying users of the status of countermeasures" refers to a function that allows the server to inform users in real time about the progress and completion status of countermeasures.

[1844] This invention relates to a customer service system for physical stores, which uses smart devices to recognize user emotions in real time and provide appropriate responses. Specific embodiments for carrying out this invention are described below.

[1845] System Configuration

[1846] The system includes the following main components:

[1847] 1. Terminal: A device used by the user to input information, such as smart glasses or head-mounted displays (HMDs).

[1848] 2. Server: A device that receives and processes input information. It also interacts with databases.

[1849] 3. Emotion Recognition Engine: This is a software module for analyzing the user's emotions from the input text.

[1850] 4. Database: A system for storing item data and user information, such as SQLite.

[1851] 5. Recommendation Engine: A software module for generating optimal suggestions based on emotion recognition results.

[1852] Program processing

[1853] Terminal: Users input information through smart glasses or HMDs. For example, they might ask questions like, "Are there other colors of this product available?" via voice or text.

[1854] Server: The server performs the following processing based on the information received from the terminal.

[1855] The input information is analyzed and sent to the emotion recognition engine. The emotion recognition engine identifies the user's emotions from the input information.

[1856] Based on the emotion recognition results, information about related products (e.g., stock status, different color variations, etc.) is retrieved from the database.

[1857] Using a recommendation engine, the system generates optimal suggestions based on acquired product information and sentiment data.

[1858] Emotion recognition engine: This engine analyzes user input information and processes it to identify emotions (e.g., confusion, excitement, stress). The model used is typically a generative AI model based on natural language processing.

[1859] Database: A database system such as SQLite is used to store and manage item information and user data. The server executes SQL queries to retrieve the necessary information.

[1860] Recommendation Engine: Generates optimal suggestions based on acquired information and sentiment data. For example, if a user is confused, it will immediately suggest a different color of the product that is in stock.

[1861] Displaying results: Generated suggestions are displayed to the user in real time via a smart device. This allows store staff to respond quickly and appropriately.

[1862] Specific example

[1863] For example, if a customer asks, "Do you have this product in a different color?", the system will work as follows:

[1864] 1. Receiving user questions: The store clerk inputs the customer's questions through smart glasses.

[1865] 2. Emotion recognition: The system recognizes from the customer's questions that the customer is confused.

[1866] 3. Retrieve product information: Retrieve detailed information and inventory information for the relevant product from the database.

[1867] 4. Suggestion Generation: To alleviate customer confusion, generate suggestions that immediately introduce different color variations of the product if they are in stock.

[1868] 5. Display: The suggested content will be displayed on the smart glasses.

[1869] Example of a prompt

[1870] Question: Is this product available in other colors?

[1871] Emotion recognition: perplexed

[1872] Applicable product: blue shirt

[1873] in stock

[1874] Suggestion: To alleviate the confusion, how about introducing a different colored green shirt right away?

[1875] As described above, the system can significantly improve customer satisfaction by understanding user emotions in real time and providing optimal responses.

[1876] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1877] Step 1:

[1878] The user inputs information into the device. Smart glasses or HMDs are used as devices for users to input questions and requests. Specifically, the user inputs information such as "Are there other colors of this product available?" via voice or text. This input information becomes the input data for the system.

[1879] Step 2:

[1880] The terminal sends information to the server. Information entered by the user is sent from the terminal to the server. During this process, the input information is converted into text data and reaches the server via the communication network. The server receives this information and prepares it for analysis.

[1881] Step 3:

[1882] The server performs analysis using an emotion recognition engine. The server passes the received input information to the emotion recognition engine, and uses an emotion recognition model (generative AI model) to determine the user's emotions. For example, emotions such as "confused" or "excited" may be output. The input for this step is the user's text information, and the output is identified emotion data.

[1883] Step 4:

[1884] The server retrieves relevant information from the item database. The server issues SQL queries to the database and retrieves product data related to the input information. Specifically, product inventory status and detailed information are retrieved from the database. The input for this step is user text information, and the output is product data.

[1885] Step 5:

[1886] The server generates suggestions based on the data it acquires. The server combines the emotion recognition results with product data retrieved from the database to generate the most suitable suggestions based on the recommendation engine. For example, in the case of "confusion," a suggestion such as "immediately introduce products in different colors" is generated. The input for this step is emotion data and product data, and the output is the suggested content.

[1887] Step 6:

[1888] The server sends the proposal content to the terminal. The generated proposal content is sent from the server to the smart glasses or HMD. The input for this step is the proposal content, and the output is the proposal information displayed on the terminal.

[1889] Step 7:

[1890] The terminal displays the suggestion to the user. The user checks the suggestion sent from the server in real time via their smart device. For example, the suggestion might be, "To reduce confusion, would you like us to immediately introduce a different colored green shirt?" The input for this step is the suggestion information, and the output is a notification to the user.

[1891] Step 8:

[1892] The user provides feedback on the proposal. The user uses a smart device to provide feedback on the proposal. For example, they might respond with "Yes, please introduce it." This feedback information becomes new input data for the system.

[1893] Step 9:

[1894] The server takes action based on the feedback. Upon receiving user feedback, the server implements the suggested countermeasures. For example, it might display the details of a suggested product or re-check the inventory. The input for this step is the user's feedback, and the output is the specific action taken.

[1895] Example of a prompt

[1896] Question: Is this product available in other colors?

[1897] Emotion recognition: perplexed

[1898] Applicable product: blue shirt

[1899] in stock

[1900] Suggestion: To alleviate the confusion, how about introducing a different colored green shirt right away?

[1901] The above series of processing steps enables optimal product recommendations and responses based on user emotions, resulting in a system that improves customer satisfaction.

[1902] 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.

[1903] 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.

[1904] 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.

[1905] 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.

[1906] 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.

[1907] 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.

[1908] 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.

[1909] 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.

[1910] 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."

[1911] 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.

[1912] 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.

[1913] 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.

[1914] 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.

[1915] 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.

[1916] 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.

[1917] 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.

[1918] 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.

[1919] 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.

[1920] 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.

[1921] 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.

[1922] 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.

[1923] The following is further disclosed regarding the embodiments described above.

[1924] (Claim 1)

[1925] An automated system for real estate management,

[1926] A means of receiving information entered by the user via the device,

[1927] A means of obtaining property data related to the received information from a database,

[1928] A means of analyzing acquired property data to identify the cause of the problem,

[1929] A means of proposing appropriate countermeasures based on the analysis results,

[1930] The means to implement the proposed countermeasures,

[1931] A means of notifying users of the status of the implementation of countermeasures.

[1932] A system that includes this.

[1933] (Claim 2)

[1934] A system according to claim 1 for automating the processing of repair requests.

[1935] (Claim 3)

[1936] A system according to claim 1 for proposing a review of rent.

[1937] "Example 1"

[1938] (Claim 1)

[1939] A means of receiving information entered by the user via the device,

[1940] A means of obtaining property data related to the received information from a database,

[1941] A means of analyzing acquired property data to identify the cause of the problem,

[1942] A means of proposing appropriate countermeasures based on the analysis results,

[1943] The means to implement the proposed countermeasures,

[1944] A means of notifying users of the status of the implementation of countermeasures,

[1945] A means of receiving user input information as an HTTP request,

[1946] Methods for obtaining detailed property information from a database,

[1947] A method for analyzing data using Python to determine how to handle repairs and rental fees,

[1948] A means of sending requests to the API of a partner company,

[1949] A system that includes this.

[1950] (Claim 2)

[1951] A system according to claim 1 for automating the processing of repair requests.

[1952] (Claim 3)

[1953] A system according to claim 1 for proposing a review of rent.

[1954] "Application Example 1"

[1955] (Claim 1)

[1956] A means of receiving information entered by the user via the device,

[1957] A means of obtaining property data and equipment information related to the received information from a database,

[1958] A means of identifying the cause of the problem by analyzing acquired property data and equipment information,

[1959] A means of proposing appropriate countermeasures based on the analysis results,

[1960] The means to implement the proposed countermeasures,

[1961] A means of notifying users of the status of the implementation of countermeasures,

[1962] A means to analyze the maintenance status of equipment and automatically propose and generate the next maintenance schedule,

[1963] A system that includes this.

[1964] (Claim 2)

[1965] A system according to claim 1 for automating the processing of repair requests.

[1966] (Claim 3)

[1967] A system according to claim 1 for proposing a review of rent.

[1968] (Claim 4)

[1969] A system according to claim 1 for automatically generating and proposing equipment maintenance schedules.

[1970] "Example 2 of combining an emotion engine"

[1971] (Claim 1)

[1972] A means of receiving information entered by the user via the device,

[1973] A means of recognizing the user's emotions from the information received,

[1974] A means of obtaining recognized emotion data and property data related to that information from a database,

[1975] A method for identifying the cause of a problem by analyzing acquired property data and sentiment data,

[1976] A means of proposing appropriate countermeasures based on analysis results and user sentiment,

[1977] The means to implement the proposed countermeasures,

[1978] A means of notifying users of the status of the implementation of countermeasures.

[1979] A system that includes this.

[1980] (Claim 2)

[1981] A system according to claim 1 for automating the processing of repair requests.

[1982] (Claim 3)

[1983] A system according to claim 1 for proposing a review of rent.

[1984] "Application example 2 of combining emotional engines"

[1985] (Claim 1)

[1986] A means of receiving information entered by the user via the device,

[1987] A means of obtaining item data related to the received information from a database,

[1988] A means for analyzing acquired item data to generate optimal suggestions based on the user's emotions and usage,

[1989] A means of providing the proposed countermeasures to users via smart devices,

[1990] The means to implement the proposed countermeasures,

[1991] A means of notifying users of the status of the implementation of countermeasures.

[1992] A system that includes this.

[1993] (Claim 2)

[1994] The system according to claim 1 for supporting customer service in a store via a smart device.

[1995] (Claim 3)

[1996] The system according to claim 1 for making optimal product suggestions based on the recognition of customer emotions. [Explanation of symbols]

[1997] 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. An automated system for real estate management, A means of receiving information entered by the user via the device, A means of obtaining property data related to the received information from a database, A means of analyzing acquired property data to identify the cause of the problem, A means of proposing appropriate countermeasures based on the analysis results, The means to implement the proposed countermeasures, A means of notifying users of the status of the implementation of countermeasures. A system that includes this.

2. A system according to claim 1 for automating the processing of repair requests.

3. A system according to claim 1 for proposing a review of rent.

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