system

Abstract

We provide the system. [Solution] A means for receiving inquiry messages and location information from the user, A means for analyzing the received inquiry message and identifying the user's request, A means of obtaining the nearest destination information from a database based on location information, A system including means for generating the acquired destination information in text format and returning it to the user.

Description

Technical Field

[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, the method including: receiving a user utterance; adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot; 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] Modern users generally use mobile terminals such as smartphones and tablets to obtain information in real time. However, conventional navigation systems only provide static information based on maps and GPS information, lacking the flexibility to immediately respond to various requirements and situations of users. In particular, when a user searches for the nearest restaurant or destination, there is no system that not only searches for locations but also provides reservations and detailed information in an interactive manner, so there is a need to improve the user experience. Based on such a situation, it is an issue to provide a system that provides optimal information in a natural interactive manner based on the user's current location and inquiry content, and further enables reservation procedures.

Means for Solving the Problems

[0005] To solve the above problems, the present invention provides the following means: a means for receiving inquiry messages and location information from a user, combined with a means for analyzing the received inquiry messages to identify the user's request. The system also includes a means for obtaining the nearest destination information from a database based on the location information, and further includes a means for generating the obtained destination information in text format and returning it to the user. In particular, if the obtained destination information includes restaurant information, the system provides detailed information including a reservation link, allowing the user to easily complete the reservation process. With this system, the user can obtain the necessary information in an interactive format and enjoy the convenience of being able to respond flexibly in real time.

[0006] "User" refers to a person who uses a system, and in particular, a person who seeks information via a mobile device.

[0007] An "inquiry message" refers to a text-based question or request that a user enters and sends to the system.

[0008] "Location information" refers to the user's current geographical coordinates (latitude and longitude) obtained through a mobile device or GPS system.

[0009] "Means of receiving" refers to the function or device that a server or system uses to receive inquiry messages and location information sent by a user.

[0010] "Means of analysis" refers to a function or process that analyzes an incoming query message and identifies what it specifically requests.

[0011] A "database" refers to a systematic collection of information that stores destination information, such as restaurant details, and allows users to search and retrieve information as needed.

[0012] "Destination information" refers to detailed information about places of interest to users, such as restaurants (name, address, distance, reservation URL, etc.).

[0013] "Means of acquisition" refers to a function or process for searching and retrieving destination information from a database based on specific conditions (e.g., current location).

[0014] "Means of generating in text format" refers to a function or process that organizes acquired destination information into a text format that is easy for the user to understand.

[0015] "Means of responding" refers to a function or process that sends and displays generated text-based information to the user.

[0016] "System" refers to the entire process that combines all of the above methods to respond to user inquiries and provide information.

[0017] A "reservation link" refers to a URL or hyperlink used to make online reservations for destinations such as restaurants. [Brief explanation of the drawing]

[0018] [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]It is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] It shows an emotion map to which a plurality of emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined.

Embodiments for Carrying Out the Invention

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

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

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

[0022] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

[0024] 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).

[0025] 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."

[0026] [First Embodiment]

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

[0028] 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.

[0029] 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).

[0030] 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.

[0031] 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.

[0032] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form 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.

[0033] 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.

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

[0035] 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.

[0036] 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.

[0037] 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.

[0038] 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".

[0039] This invention provides a system that allows users to easily obtain information about their nearest destination using a mobile device, and to access detailed information and even make reservations. The system receives inquiry messages from users, analyzes them, and provides appropriate information. In particular, it provides high convenience to users by generating responses that include detailed information about places such as restaurants and reservation links.

[0040] Description of the system's programs and processes

[0041] Main components of the program

[0042] The system includes the following main features:

[0043] 1. Message receiving function

[0044] The server receives inquiry messages and current location information from the user.

[0045] 2. Message analysis function

[0046] The server analyzes the received message and identifies the user's request from its content.

[0047] 3. Database search function

[0048] The server accesses the database and retrieves the nearest destination information based on the user's current location.

[0049] 4. Response generation function

[0050] The server formats the acquired destination information into text format and returns it to the user.

[0051] Processing flow

[0052] The following is a specific example of the system's processing.

[0053] 1. User requests are submitted.

[0054] The user types "Please tell me the nearest restaurant" on their mobile device.

[0055] The device obtains the user's current location information (latitude and longitude) and sends it to the server.

[0056] 2. Receiving and parsing server requests

[0057] The server receives requests from users.

[0058] Identify the user's request (in this case, "nearest restaurant") from the received message.

[0059] 3. Retrieving information from the database

[0060] The server retrieves restaurant information from a dummy database.

[0061] The system will select the nearest restaurant based on your current location.

[0062] 4. Generating responses to users

[0063] The server formats the information of the selected restaurants into text format.

[0064] As a concrete example, it generates a response message such as, "The nearest restaurant is Restaurant A. Address: Example Town 1-1. Make a reservation here: https: / / example.com / reserve / a".

[0065] 5. Sending responses to users

[0066] The server sends the generated message to the terminal, and the message is displayed on the terminal.

[0067] Users can view this information on their devices and make reservations as needed.

[0068] This system configuration allows users to obtain necessary information in real time through an interactive format and even make reservations on the spot. This process provides a far more natural and flexible user experience compared to maps or static information.

[0069] The following describes the processing flow.

[0070] Step 1:

[0071] The user types "Please tell me the nearest restaurant" on their mobile device. The device retrieves the user's current location information (e.g., latitude and longitude).

[0072] Step 2:

[0073] The device sends the user's inquiry message and current location information to the server. Specifically, it sends a data packet containing the inquiry message and location information as an HTTP request.

[0074] Step 3:

[0075] The server parses the received request data. The query message and location information are separated into data structures to prepare for the next processing step.

[0076] Step 4:

[0077] The server performs natural language analysis on the query message to identify the user's request. In this example, it extracts the request "Please tell me the nearest restaurant" from the query message.

[0078] Step 5:

[0079] The server accesses the database and searches for the nearest restaurant based on location information. Using database queries, it calculates the distance from the current location and identifies the closest restaurant.

[0080] Step 6:

[0081] The system formats the restaurant information (name, address, reservation URL, etc.) obtained by the server into text format. For example, it generates a response message such as, "The nearest restaurant is Restaurant A. Address: Example Town 1-1. Make a reservation here: https: / / example.com / reserve / a".

[0082] Step 7:

[0083] The server sends the generated response message back to the user's device. It is sent as an HTTP response.

[0084] Step 8:

[0085] The terminal displays received messages on the user interface. Users can review the information displayed on the screen and complete their online reservation by clicking the reservation link as needed.

[0086] (Example 1)

[0087] 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."

[0088] Traditional location-based information systems have suffered from a poor user experience due to the cumbersome process required to obtain detailed information about the user's current location. Furthermore, the need for users to utilize multiple platforms when making reservations at restaurants and other establishments, resulting in a lack of convenience, is also a challenge.

[0089] 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.

[0090] In this invention, the server includes means for receiving inquiry messages and location information from a user, means for analyzing the received inquiry messages and identifying the user's request, means for obtaining the nearest destination information from a database based on the location information, means for obtaining the user's current location using GPS functionality, and means for generating the obtained destination information in text format and responding to the user. This allows the user to easily obtain information on the nearest restaurant and proceed directly to making a reservation.

[0091] An "inquiry message" is a message, such as text, that a user enters and sends to a server in order to obtain information.

[0092] "Location information" refers to data indicating the user's current latitude and longitude, obtained using methods such as GPS.

[0093] The "GPS function" is a function that uses a global positioning system to obtain geographical location information.

[0094] "Means of receiving" refers to hardware or software mechanisms that can receive data transmitted by a user.

[0095] "Means of analysis" refers to a mechanism that includes algorithms and programs for analyzing received data.

[0096] "Means for identifying user requirements" refer to processes and mechanisms for clarifying the information and actions that users desire based on analyzed data.

[0097] A "database" is a management system that systematically stores information and allows it to be searched and retrieved as needed.

[0098] "Nearest destination information" refers to data that indicates the location closest to the user's current location.

[0099] "Means of generating in text format" refers to a mechanism for formatting acquired information into text that is easy for the user to understand.

[0100] "Means of responding" refers to the process or device that sends the generated text message to the user.

[0101] "Restaurant information" refers to data such as the name, address, contact information, and business hours of restaurants and cafes.

[0102] A "reservation link" is a URL that allows direct access to the reservation page of a specific restaurant.

[0103] This invention provides a system that allows users to easily obtain information on nearby restaurants using their mobile devices, and to access detailed information and even make reservations. The system receives inquiry messages from users, analyzes them, and provides appropriate information. In particular, it provides high convenience to users by generating responses that include detailed information about restaurants and other locations, as well as reservation links.

[0104] This system primarily includes the following key features:

[0105] 1. Message receiving function

[0106] The server receives inquiry messages and current location information from the user. The message entered by the user on their mobile device and the current latitude and longitude information obtained using the device's GPS function are sent to the server.

[0107] 2. Message analysis function

[0108] The server analyzes the received message using a natural language processing engine (e.g., NLTK or spaCy) and identifies the user's request from its content.

[0109] 3. Database search function

[0110] The server accesses a database (e.g., MySQL®) to retrieve information about the nearest restaurant based on the user's current location. The database stores information such as the restaurant's name, address, contact information, and business hours.

[0111] 4. Response generation function

[0112] The server formats the retrieved restaurant information into text format. For example, it generates a response message such as, "The nearest restaurant is Restaurant A. Address: Example Town 1-1. Make a reservation here: https: / / example.com / reserve / a".

[0113] 5. Sending responses to users

[0114] The server sends the generated message to the terminal, and the message is displayed on the terminal. The user checks this information on the terminal and makes a reservation if necessary.

[0115] The following is an example of the system's specific operation:

[0116] When a user types "Tell me the nearest restaurant" using their mobile device, the device uses its GPS function to obtain its current location and sends it to the server along with the inquiry message. The server parses the received message to understand that the user is looking for a restaurant and retrieves information about the nearest restaurant from its database. The retrieved information is formatted into a text message and sent to the user's device as a reply. Upon receiving this message on their device, the user can review the displayed restaurant information and click the link to proceed with the reservation process.

[0117] The following is an example of a specific prompt statement:

[0118] Prompt message:

[0119] This document describes the processing of a location-based restaurant search system using mobile devices. It details how the process works, what data is used, and includes user-side operating procedures.

[0120] This provides detailed clues to gain a concrete understanding of the system's operation and processing flow.

[0121] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0122] Step 1: User submits request

[0123] Input: The user launches the application on their mobile device and enters "Please tell me the nearest restaurant" in the text input field.

[0124] Specific operation: The user opens the app on their smartphone, types "What are some restaurants near me?" into the search bar, and taps the send button. The device uses its GPS function to obtain the user's current location (latitude and longitude) and sends it to the server along with the text input.

[0125] Output: The user's current location information and inquiry message are sent to the server.

[0126] Step 2: Server receives request

[0127] Input: Inquiry message and current location information sent from the user's mobile device.

[0128] Specific operation: The server-side API endpoint receives the HTTP request and extracts the message content and location information.

[0129] Output: The received message and location information are passed to the analysis module.

[0130] Step 3: Message Analysis

[0131] Input: Received inquiry message.

[0132] Specific operation: The server uses a natural language processing engine (e.g., NLTK or spaCy) to analyze the user's input text, "What are some nearby restaurants?", and extracts the keywords "restaurants" and the information "nearest". This identifies the user's request.

[0133] Output: The information the user is looking for (nearest restaurants) is identified.

[0134] Step 4: Database Search

[0135] Input: User's current location and specified request (nearest restaurant).

[0136] Specific operation: The server executes a query like "SELECT FROM restaurants WHERE ST_Distance(location, ST_Point(user's latitude, user's longitude)) ORDER BY distance LIMIT 1" on the database (e.g., MySQL). This retrieves the record of the nearest restaurant.

[0137] Output: Information about the nearest restaurant (name, address, reservation link, etc.) is retrieved.

[0138] Step 5: Generate response

[0139] Input: Information about the nearest restaurant obtained.

[0140] Specific operation: The server uses a template engine (e.g., Jinja) to format the retrieved restaurant information into text format and generate a text message with the following content: "The nearest restaurant is Restaurant A. Address: Example Town 1-1. Make a reservation here: https: / / example.com / reserve / a"

[0141] Output: The generated text message is prepared as a reply message to the user.

[0142] Step 6: Sending responses to users

[0143] Input: The generated text message.

[0144] Specific operation: The server converts the generated text message into JSON format and sends it to the user's terminal as an HTTP response. The terminal displays the received message in its user interface.

[0145] Output: The response message displayed on the user's device. The user reviews the displayed information, and if necessary, taps the displayed reservation link to open a browser and proceed with the reservation process.

[0146] (Application Example 1)

[0147] 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."

[0148] Currently, when users search for restaurant information, view details, make reservations, and place orders, they have to go through multiple applications and websites. This process is time-consuming and degrades the user experience. In particular, when using food delivery services, a smooth information retrieval and ordering process is required, but the current system does not adequately meet this need.

[0149] 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.

[0150] In this invention, the server includes means for receiving inquiry messages and location information from a user, means for analyzing the received inquiry messages and identifying the user's request, means for obtaining the nearest destination information from a database based on the location information, means for generating the obtained destination information in text format and responding to the user, and means for generating a link that allows the user to complete an order based on the obtained information. This enables the user to smoothly complete everything from searching for restaurant information to making reservations and placing orders within a single application.

[0151] "User inquiry messages" refer to questions or requests about locations or services sent by users of the system.

[0152] "Location information" refers to data on the user's current location, identified by latitude and longitude.

[0153] "Means of receiving" refers to functions and devices for acquiring messages and location information from users.

[0154] "Means of analysis and identification of user requests" refers to functions or software that analyze the content of received messages and identify the information or services that the user is seeking.

[0155] "Means of obtaining the nearest destination information from a database based on location information" refers to functions or systems that retrieve data on the nearest destination (e.g., a restaurant) from a database based on the user's location information.

[0156] "Means of generating and responding in text format" refers to functions or software that convert acquired destination information into a text message and send it to the user.

[0157] "Means of generating a link that allows an order to be completed based on acquired information" refers to a function or system that creates a web link to complete an order based on information specified by the user and provides that link to the user.

[0158] This section describes the system for realizing this invention. The overall system configuration and processing details are described in detail below.

[0159] In this invention, the server includes the following means:

[0160] 1. Means for receiving user inquiry messages and location information

[0161] Users use a mobile device, such as a smartphone, to input and send inquiry messages about restaurants. During this process, the mobile device uses its GPS function to obtain its current location information (latitude and longitude) and sends it to the server along with the message.

[0162] 2. Means for analyzing received inquiry messages and identifying user requests.

[0163] The server analyzes the received message. This analysis uses natural language processing to identify the user's specific request from the message content. For example, if the message "Tell me the nearest restaurant that can deliver quickly" is entered, the server will identify "restaurants that can deliver quickly."

[0164] 3. Means for obtaining the nearest destination information from a database based on location information.

[0165] The server accesses its database and searches for information on the nearest restaurants based on the user's current location. This database includes restaurant locations, names, addresses, delivery times, and menu links.

[0166] 4. A means of generating acquired destination information in text format and returning it to the user.

[0167] The server generates a text message to respond to the user based on the acquired restaurant information. For example, it might be in the format of: "The nearest restaurant is Restaurant A. Address: Example Town 1-1. Delivery time: 30 minutes. Menu available here: https: / / example.com / menu / a".

[0168] 5. Means for generating a link that allows the customer to complete an order based on the information obtained.

[0169] The server generates a web link to complete the order based on the restaurant information specified by the user and provides it to the user. This link allows the user to complete the order with a single tap from within the application.

[0170] Hardware and software to be used

[0171] Hardware: Smartphone (iOS / ANDROID®)

[0172] Software: Python (programming language), Geopy (Python location acquisition library), requests (HTTP request library)

[0173] Specific example

[0174] Let's say a user opens a food delivery app and enters a request saying, "Tell me the nearest restaurant in Shinjuku Ward that can deliver quickly." The app obtains the user's current location information and sends it to the server. The server analyzes the received message and identifies the user's request. Next, it searches its database for information on the nearest restaurants that can deliver quickly and generates the details of the relevant restaurants in text format. This is sent back to the user, who can complete the order by tapping the link.

[0175] Example of a prompt

[0176] If a user enters "Tell me the nearest restaurant in Shinjuku Ward that offers fast delivery," the AI ​​model will input the following prompt:

[0177] Design a Python program that, when a user enters "Tell me the nearest restaurant in Shinjuku Ward that offers fast delivery," retrieves the user's location, searches for nearby delivery restaurants, and provides detailed information (restaurant name, address, delivery time, menu link, etc.).

[0178] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0179] Step 1:

[0180] A user types "Tell me the nearest restaurant in Shinjuku Ward that offers fast delivery" on their mobile device. The device retrieves this inquiry message and location information (latitude and longitude) and sends it to the server. The input is the user's request and current location, and the output is the data transmission to the server.

[0181] Step 2:

[0182] The server receives an inquiry message sent by the user. The input data consists of the user's inquiry message and location information. The server analyzes this data to identify the information the user is looking for (in this case, the nearest restaurant with fast delivery). Natural language processing is used for the analysis, and the output is the identification of the necessary information.

[0183] Step 3:

[0184] The server queries the database based on the analysis results. The database stores information such as the location, name, address, delivery time, and menu links of restaurants. The input is the user's request and location information, and the output retrieved from the database is information about the most suitable restaurant. The server retrieves the restaurant information closest to the user's current location.

[0185] Step 4:

[0186] The server formats the retrieved restaurant information into text format. Specifically, it generates a reply message containing information such as the restaurant name, address, delivery time, and menu links. The input is restaurant information retrieved from the database, and the output is a text message to be sent to the user. The server prepares this for replying to the user's mobile device.

[0187] Step 5:

[0188] The server sends a final response message to the user's mobile device. The user's device receives this message and displays its contents. The input is the text message sent from the server, and the output is the display on the user's device screen. This allows the user to check information about nearby restaurants and complete their order by clicking on menu links.

[0189] Step 6:

[0190] The server then generates a link that allows the user to complete their order based on the restaurant information they specify. The input is the restaurant details, and the output is a link to the order page. The server also provides this link to the user. For example, there is a specific example: "The nearest restaurant is Restaurant A. Address: Example Town 1-1. Delivery time: 30 minutes. Menu available here: https: / / example.com / menu / a".

[0191] 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.

[0192] This invention provides a system that allows users to interactively obtain information about their nearest destination using a mobile device and also make reservations for that location. This system differs from conventional systems in that it analyzes user inquiry messages using an emotion engine and provides appropriate information. In particular, it generates responses including detailed information about places such as restaurants and reservation links, while simultaneously adjusting the way information is provided based on the user's emotions, thereby providing a better user experience.

[0193] Description of the system's programs and processes

[0194] Main components of the program

[0195] The system includes the following main features:

[0196] 1. Message receiving function

[0197] The server receives inquiry messages and current location information from the user.

[0198] 2. Emotional Engine

[0199] The server analyzes the received inquiry message and recognizes the user's emotions.

[0200] 3. Message analysis function

[0201] The server further analyzes the message, which includes emotional data added by the emotion engine, to identify the user's request.

[0202] 4. Database search function

[0203] The server accesses the database and retrieves the nearest destination information based on the user's current location. It also uses an emotion engine to tailor the suggested information.

[0204] 5. Response generation function

[0205] The server formats the acquired destination information into text format and sends it back to the user. It also adjusts the content and tone of the response message according to the user's mood.

[0206] Processing flow

[0207] The following is a specific example of the system's processing.

[0208] 1. User requests are submitted.

[0209] The user types on their mobile device, "I'm really hungry, please tell me a restaurant I can go to right away."

[0210] The device obtains the user's current location information (e.g., latitude and longitude) and sends it to the server.

[0211] 2. Receiving and parsing server requests

[0212] The server receives requests from users.

[0213] The emotion engine analyzes the user's emotions (in this example, "very hungry") from the received message.

[0214] 3. Message analysis and request identification

[0215] The server analyzes the message based on the results of the emotion engine and identifies the user's specific request (in this case, "a restaurant I can go to right away").

[0216] 4. Retrieving information from the database

[0217] The server retrieves restaurant information from the database. Based on the current location, it selects the nearest restaurant.

[0218] The restaurant information provided will be adjusted based on the user's preferences. For example, restaurants that are easily accessible will be prioritized.

[0219] 5. Generating responses to users

[0220] The server formats the information of the selected restaurant into text format. For example, it generates a response message such as, "The nearest restaurant is Restaurant A. Address: Example Town 1-1. Make a reservation here: https: / / example.com / reserve / a".

[0221] It also generates responses in a tone that matches the user's mood, such as, "You seem hungry! I'll recommend restaurant A, which we can go to right away."

[0222] 6. Sending responses to users

[0223] The server sends the generated message back to the user's terminal. It sends it as an HTTP response.

[0224] 7. Display by device

[0225] The terminal displays received messages on the user interface. Users can review the information displayed on the screen and complete their online reservation by clicking the reservation link as needed.

[0226] Thus, the system of the present invention receives user inquiry messages and location information, recognizes and analyzes the user's emotions using an emotion engine, and provides optimal information. As a result, users can obtain the necessary information in a dialogue format and receive detailed responses tailored to their emotions.

[0227] The following describes the processing flow.

[0228] Step 1:

[0229] The user types "I'm really hungry, please tell me a restaurant I can go to right away" on their mobile device. The device then retrieves the user's current location information (e.g., latitude and longitude).

[0230] Step 2:

[0231] The device sends the user's inquiry message and current location information to the server. Specifically, it sends a data packet containing the inquiry message and location information to the server as an HTTP request.

[0232] Step 3:

[0233] The server parses the received request. First, the inquiry message and location information are separated into data structures, and then the message is passed to the sentiment engine to analyze the user's sentiment.

[0234] Step 4:

[0235] The emotion engine recognizes emotions from the received query message. For example, it identifies the emotion "I'm hungry" as "very hungry" and returns that emotion data to the server.

[0236] Step 5:

[0237] The server identifies the user's request based on the sentiment data returned from the sentiment engine. In this case, it determines that the user is looking for a "restaurant they can go to right away."

[0238] Step 6:

[0239] The server accesses the database and searches for the nearest restaurant based on the user's current location. At the same time, it filters the results based on the user's sentiment data, prioritizing restaurants suitable for those in a hurry.

[0240] Step 7:

[0241] The server formats the acquired restaurant information into text format. For example, it generates a response message such as, "Looks like you're hungry! The nearest restaurant is Restaurant A. Address: Example Town 1-1. Make a reservation here: https: / / example.com / reserve / a".

[0242] Step 8:

[0243] The server sends the generated response message back to the user's terminal. It is sent as an HTTP response, preparing the terminal to display the received data.

[0244] Step 9:

[0245] The terminal displays received messages on the user interface. Users can review the information displayed on the screen and complete their online reservation by clicking the reservation link as needed.

[0246] In this way, the system improves the user experience and enables flexible, real-time information delivery by providing optimal information while taking user emotions into consideration.

[0247] (Example 2)

[0248] 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".

[0249] Traditional information systems simply provided information in response to user inquiries, lacking the flexibility to adapt to users' emotions and circumstances. As a result, users often felt that the information they received didn't match their feelings or urgency, leading to decreased satisfaction. For example, even if someone was extremely hungry and urgently needed a meal, they might only be provided with information about nearby restaurants, which was insufficient in situations requiring immediate assistance.

[0250] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0251] In this invention, the server includes means for receiving inquiry messages and location information from a user; means for analyzing the sentiment of the received inquiry message using an emotion engine; means for analyzing the message based on the analyzed sentiment data and identifying the user's request; means for obtaining the nearest destination information from a database based on location information and adjusting the information based on sentiment data; means for generating the obtained destination information in text format and adjusting the content of the response message according to the user's sentiment; means for sending the generated response message to the user; and means for displaying the received response message on a user interface and making it operable. This enables the provision of optimal information according to the user's sentiment and urgency, thereby improving the user experience.

[0252] An "inquiry message" is text or audio data that a user enters when asking a question or requesting information from the system.

[0253] "Location information" refers to latitude and longitude data that indicates the user's current location.

[0254] An "emotion engine" is an algorithm or software that analyzes the content of inquiry messages received from users and identifies and classifies the user's emotional state.

[0255] "Target destination information" refers to information about a specific destination that the user is looking for, and includes data such as detailed information about restaurants, shops, and facilities.

[0256] "Text format" refers to a format in which information is represented as a string of characters, providing data in a way that is easy for users to read.

[0257] A "response message" is text data containing answers or suggestions that the system generates and provides in response to a user's inquiry.

[0258] A "user interface" refers to the screens and control elements that allow a user to interact with a system, and are the elements that display information to the user and accept input.

[0259] "Dining establishment information" refers to information containing detailed data about restaurants, cafes, and other food establishments, including location, business hours, and menus.

[0260] A "reservation link" is a URL or web address that allows a user to make a reservation for a specific service or facility.

[0261] This invention provides a system that allows users to interactively obtain information about their nearest destination using a mobile device and also make reservations for that location. This system differs from conventional systems in that it analyzes user inquiry messages using an emotion engine and provides appropriate information. In particular, it generates responses including detailed information about places such as restaurants and reservation links, while simultaneously adjusting the way information is provided based on the user's emotions, thereby providing a better user experience.

[0262] The system configuration includes the following main functions:

[0263] 1. Message receiving function

[0264] The server receives inquiry messages and current location information from the user. To implement this functionality, an HTTP server is used to receive data. Specifically, the API can be built using programming languages ​​such as Python or Node.js.

[0265] 2. Emotional Engine

[0266] The server analyzes the received inquiry message and recognizes the user's emotions. The emotion engine uses natural language processing libraries such as Google® NLP API and IBM Watson®. Based on this analysis, the server can determine the user's emotions, such as "very hungry."

[0267] 3. Message analysis function

[0268] The server further analyzes the message, which includes sentiment data added by the sentiment engine, to identify the user's request. In this step, the server clarifies the user's specific request by referring to the sentiment data.

[0269] 4. Database search function

[0270] The server accesses the database and retrieves the nearest destination information based on the user's current location. It also uses an emotion engine to refine the suggested information. MySQL and MongoDB are suitable databases for this purpose. Specifically, SQL queries are used to retrieve the necessary information.

[0271] 5. Response generation function

[0272] The server formats the acquired destination information into text format and sends it back to the user. Here, the content and tone of the response message are adjusted according to the user's mood. For example, it might generate a response with a tone that matches the user's mood, such as, "You seem hungry! I'll recommend restaurant A, which we can go to right away."

[0273] 6. Function to send responses to users

[0274] The server sends the generated response message back to the user's terminal. Specifically, it sends it as an HTTP response.

[0275] 7. Display function on the device

[0276] The terminal displays the received message on the user interface. The user can complete an online reservation by checking the information displayed on the screen and clicking the reservation link if necessary.

[0277] Specific Example

[0278] The following shows a specific example of the system's processing:

[0279] The user inputs "I'm really hungry. Please tell me a restaurant that I can go to right away." on the mobile terminal.

[0280] The terminal obtains the user's current location information (e.g., latitude and longitude) and sends it to the server.

[0281] The server analyzes the user's sentiment (in this example, "very hungry") from the received request message using the sentiment engine.

[0282] The server analyzes the message based on the result of the sentiment engine and identifies the user's specific request (in this case, "a restaurant that I can go to right away").

[0283] The server obtains restaurant information from the database and selects the nearest restaurant based on the current location.

[0284] The server formats the restaurant information in text form and generates a response that suits the sentiment, such as "You seem to be hungry! I would like to introduce Restaurant A that you can go to right away. Address: Example Town 1-1. Reservations can be made from here: https: / / example.com / reserve / a".

[0285] The server sends the generated message back to the user's terminal.

[0286] The terminal displays the received message on the user interface, and the user checks the displayed information. If necessary, the user can click on the reservation link to complete an online reservation.

[0287] Examples of prompt sentences:

[0288] "I'm really hungry. Please tell me a restaurant that I can go to right away."

[0289] "Please tell me a café that I can go to quickly."

[0290] Thus, the system of the present invention receives the user's inquiry message and location information, recognizes and analyzes the user's emotion with an emotion engine, and provides optimal information. As a result, the user can obtain the necessary information in an interactive manner and receive a detailed response according to the emotion.

[0291] The flow of specific processing in Example 2 will be described using FIG. 13.

[0292] Step 1: Sending a user request

[0293] The user opens the mobile terminal and launches the message application. The user enters "I'm really hungry. Please tell me a restaurant that I can go to right away." The terminal uses the built-in GPS to obtain the current latitude (35.6895) and longitude (139.6917). This input data (the user's text and location information) is sent to the server as an HTTP POST request.

[0294] Step 2: Receiving and analyzing the server request

[0295] The server receives an HTTP POST request at the API endpoint. The server extracts the inquiry content and location information from the received message. This data is passed to the sentiment engine for analysis. The sentiment engine identifies the emotion as "very hungry." The input data consists of the user's inquiry message and location information, and the output is the sentiment data as a result of the analysis.

[0296] Step 3: Analyze the message and identify the request.

[0297] The server identifies the user's request specifically based on the analysis results of the emotion engine. It analyzes the received message and emotion data to identify a "restaurant you can go to right away." The input data is emotion data and the user's message, and the output is the identified request.

[0298] Step 4: Retrieve information from the database

[0299] The server accesses the database and retrieves information on the nearest restaurant based on the user's current location. Based on sentiment data, it prioritizes selecting restaurants that are easily accessible. Specifically, it executes an SQL query to search for restaurants within a 500m radius of the user's current location (35.6895,139.6917). The input data consists of location information and sentiment data, and the output is information on the selected restaurants.

[0300] Step 5: Generate responses for the user

[0301] The server generates a response message in text format based on the acquired restaurant information. It references sentiment data and makes adjustments according to the sentiment. For example, it might generate a message like, "Looks like you're hungry! Here's a restaurant A that's right away. Address: Example Town 1-1. Make a reservation here: https: / / example.com / reserve / a". The input data is restaurant information and sentiment data, and the output is the response message.

[0302] Step 6: Sending responses to users

[0303] The server sends the generated response message to the user's terminal as an HTTP response. Specifically, it returns a response with an HTTP status code of 200. The input data is the response message, and the output is the HTTP response.

[0304] Step 7: Display by the terminal

[0305] The terminal displays the received response message on the screen. The user can check the message "It seems you're hungry! I'd like to introduce Restaurant A, which is nearby. Address: 1-1, Example Town. Reservations can be made here: https: / / example.com / reserve / a" and complete an online reservation by clicking the reservation link. The input data is the received response, and the output is the displayed content.

[0306] (Application Example 2)

[0307] Next, Application Example 2 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart device 14 is referred to as the "terminal".

[0308] In the conventional user interface, there was a problem that the user experience deteriorated because information was provided mechanically without considering the user's emotions. Also, there was a lack of a mechanism to quickly and appropriately provide information on nearby destinations such as restaurants, and the user satisfaction could not be improved.

[0309] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0310] In this invention, the server includes means for receiving inquiry messages and location information from a user, means for analyzing the received inquiry messages and recognizing the user's emotions, means for identifying the user's requests based on the analyzed emotions, means for obtaining the nearest destination information from a database based on the current location information, and means for generating the obtained destination information in text format and responding to the user. This makes it possible to provide timely and appropriate information based on the user's emotions and improve the user experience.

[0311] A "user inquiry message" refers to an information request that a user sends to the system via a mobile device or other device.

[0312] "Location information" refers to data that indicates the user's current location, and is usually expressed as latitude and longitude.

[0313] "Means of receiving" refers to the mechanism by which the server obtains inquiry messages and location information sent by the user.

[0314] "Means of analysis and recognition of user emotions" refer to algorithms and models that process received inquiry messages and identify user emotions.

[0315] "Methods for identifying user requests based on analyzed emotions" refers to a process for identifying the information and services that users truly want, while taking their emotions into consideration.

[0316] A "database" is a repository of information that stores destination information and other data to be provided to users.

[0317] "Means for obtaining the nearest destination information based on current location information" refers to a function that searches for and obtains information about the nearest destination based on the user's current location.

[0318] "Destination information" refers to detailed information such as the name, address, and contact information of restaurants and other establishments.

[0319] "A means of generating and responding to the user in text format" refers to a mechanism for formatting destination information as a string and providing it to the user.

[0320] "Restaurant information" refers to information such as the location, menu, and business hours of restaurants and cafes.

[0321] A "reservation link" is a URL or web address that allows users to make reservations directly via the internet.

[0322] This invention provides a system that allows users to interactively obtain information about their nearest destination using a mobile device and also make reservations for that location. This system is characterized by its ability to analyze the user's emotions and provide optimal information.

[0323] Main components of the program

[0324] The system includes the following main features:

[0325] 1. Message receiving function

[0326] The server receives inquiry messages and current location information from the user. This allows the server to understand what the user is requesting.

[0327] 2. Emotional Engine

[0328] The server analyzes the received inquiry message and recognizes the user's emotions. This emotion analysis uses the "transformers" library from Hugging Face.

[0329] 3. Message analysis function

[0330] The server further analyzes the message, which includes emotional data added by the emotion engine, to identify the user's specific request.

[0331] 4. Database search function

[0332] The server accesses the database and retrieves information about the nearest destination based on the user's current location. The information retrieved includes restaurant information.

[0333] 5. Response generation function

[0334] The server formats the retrieved destination information into text format and sends it back to the user. It also adjusts the content and tone of the response message according to the user's mood. The response also includes a booking link.

[0335] System operation

[0336] The system starts working when a user sends a specific request. For example, a user might type, "I'm really hungry, please tell me a restaurant I can go to right away." Along with this input message, the user's current location is sent to the server.

[0337] The server analyzes the received request using an emotion engine to identify the user's emotion (in this case, "very hungry"). Next, the message analysis function extracts the user's specific request ("a restaurant I can go to right now").

[0338] Next, the database search function searches for the nearest restaurant based on the user's current location and retrieves that information. At this stage, the results of the sentiment engine are also considered to select the most appropriate restaurant.

[0339] Finally, the response generation function sends the acquired restaurant information back to the user in text format. The specific response message would look something like this: "Looks like you're hungry! Here's a recommendation for the nearest restaurant, Restaurant A. Make a reservation here: https: / / example.com / reserve / a".

[0340] System implementation example

[0341] The hardware used to implement this system includes a standard server, and the software includes Python, Flask, and the Hugging Face transformers library, while the database uses SQL, etc. This enables a series of processes that receive user input, perform sentiment analysis, and provide optimal information in text format.

[0342] Example of a prompt

[0343] "I'm really hungry, so please tell me about a restaurant we can go to right away."

[0344] When this prompt message is sent to the system, the emotion engine interprets it as "very hungry," and the user is then provided with restaurant information that can be used quickly.

[0345] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0346] Step 1:

[0347] The user enters an inquiry message and current location information on their mobile device.

[0348] Input: User's message "I'm really hungry, please tell me a restaurant I can go to right away," and current location information (e.g., latitude and longitude).

[0349] Processing: The mobile device retrieves the message and location information and sends it to the server.

[0350] Output: The message and location information are sent to the server.

[0351] Step 2:

[0352] The server receives the inquiry message and location information.

[0353] Input: Inquiry message and location information submitted by the user

[0354] Processing: The server receives these as HTTP requests.

[0355] Output: Received messages and location information

[0356] Step 3:

[0357] The server's emotion engine analyzes the received messages and recognizes the user's emotions.

[0358] Input: Received inquiry message

[0359] Processing: Use the "transformers" library from Hugging Face to analyze the sentiment of the message.

[0360] Output: User sentiment data (e.g., "Very hungry")

[0361] Step 4:

[0362] The server analyzes messages based on emotion to identify the user's specific requests.

[0363] Input: Message and sentiment data

[0364] Processing: Use message analysis to identify that the user is looking for "restaurants they can go to right away."

[0365] Output: Request from the identified user

[0366] Step 5:

[0367] The server accesses the database and retrieves the nearest destination information based on the user's current location.

[0368] Input: User's current location and specified request

[0369] Processing: Use SQL queries or similar methods to search and retrieve information about the nearest destination from the database.

[0370] Output: Information on the nearest restaurants

[0371] Step 6:

[0372] The server formats the destination information it has obtained into text format and returns it to the user.

[0373] Input: Nearest restaurant information

[0374] Process: Generate a message in text format that reads, "Looks like you're hungry! Here's a recommendation for the nearest restaurant, Restaurant A. Make a reservation here: https: / / example.com / reserve / a".

[0375] Output: Generated response message

[0376] Step 7:

[0377] The server generates a message and sends it to the user's mobile device.

[0378] Input: Generated response message

[0379] Processing: Send the response message as an HTTP response.

[0380] Output: Message sent to the user's mobile device

[0381] Step 8:

[0382] The system displays messages received by the user's mobile device in the user interface.

[0383] Input: Received message

[0384] Processing: Display the message as text in the user interface.

[0385] Output: The user confirms the information displayed on their mobile device screen.

[0386] 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.

[0387] 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.

[0388] 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.

[0389] [Second Embodiment]

[0390] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0391] 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.

[0392] 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).

[0393] 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.

[0394] 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.

[0395] 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).

[0396] 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.

[0397] 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.

[0398] 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.

[0399] 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.

[0400] 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.

[0401] 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".

[0402] This invention provides a system that allows users to easily obtain information about their nearest destination using a mobile device, and to access detailed information and even make reservations. The system receives inquiry messages from users, analyzes them, and provides appropriate information. In particular, it provides high convenience to users by generating responses that include detailed information about places such as restaurants and reservation links.

[0403] Description of the system's programs and processes

[0404] Main components of the program

[0405] The system includes the following main features:

[0406] 1. Message receiving function

[0407] The server receives inquiry messages and current location information from the user.

[0408] 2. Message analysis function

[0409] The server analyzes the received message and identifies the user's request from its content.

[0410] 3. Database search function

[0411] The server accesses the database and retrieves the nearest destination information based on the user's current location.

[0412] 4. Response generation function

[0413] The server formats the acquired destination information into text format and returns it to the user.

[0414] Processing flow

[0415] The following is a specific example of the system's processing.

[0416] 1. User requests are submitted.

[0417] The user types "Please tell me the nearest restaurant" on their mobile device.

[0418] The device obtains the user's current location information (latitude and longitude) and sends it to the server.

[0419] 2. Receiving and parsing server requests

[0420] The server receives requests from users.

[0421] Identify the user's request (in this case, "nearest restaurant") from the received message.

[0422] 3. Retrieving information from the database

[0423] The server retrieves restaurant information from a dummy database.

[0424] The system will select the nearest restaurant based on your current location.

[0425] 4. Generating responses to users

[0426] The server formats the information of the selected restaurants into text format.

[0427] As a concrete example, it generates a response message such as, "The nearest restaurant is Restaurant A. Address: Example Town 1-1. Make a reservation here: https: / / example.com / reserve / a".

[0428] 5. Sending responses to users

[0429] The server sends the generated message to the terminal, and the message is displayed on the terminal.

[0430] Users can view this information on their devices and make reservations as needed.

[0431] This system configuration allows users to obtain necessary information in real time through an interactive format and even make reservations on the spot. This process provides a far more natural and flexible user experience compared to maps or static information.

[0432] The following describes the processing flow.

[0433] Step 1:

[0434] The user types "Please tell me the nearest restaurant" on their mobile device. The device retrieves the user's current location information (e.g., latitude and longitude).

[0435] Step 2:

[0436] The device sends the user's inquiry message and current location information to the server. Specifically, it sends a data packet containing the inquiry message and location information as an HTTP request.

[0437] Step 3:

[0438] The server parses the received request data. The query message and location information are separated into data structures to prepare for the next processing step.

[0439] Step 4:

[0440] The server performs natural language analysis on the query message to identify the user's request. In this example, it extracts the request "Please tell me the nearest restaurant" from the query message.

[0441] Step 5:

[0442] The server accesses the database and searches for the nearest restaurant based on location information. Using database queries, it calculates the distance from the current location and identifies the closest restaurant.

[0443] Step 6:

[0444] The system formats the restaurant information (name, address, reservation URL, etc.) obtained by the server into text format. For example, it generates a response message such as, "The nearest restaurant is Restaurant A. Address: Example Town 1-1. Make a reservation here: https: / / example.com / reserve / a".

[0445] Step 7:

[0446] The server sends the generated response message back to the user's device. It is sent as an HTTP response.

[0447] Step 8:

[0448] The terminal displays received messages on the user interface. Users can review the information displayed on the screen and complete their online reservation by clicking the reservation link as needed.

[0449] (Example 1)

[0450] 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".

[0451] Traditional location-based information systems have suffered from a poor user experience due to the cumbersome process required to obtain detailed information about the user's current location. Furthermore, the need for users to utilize multiple platforms when making reservations at restaurants and other establishments, resulting in a lack of convenience, is also a challenge.

[0452] 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.

[0453] In this invention, the server includes means for receiving inquiry messages and location information from a user, means for analyzing the received inquiry messages and identifying the user's request, means for obtaining the nearest destination information from a database based on the location information, means for obtaining the user's current location using GPS functionality, and means for generating the obtained destination information in text format and responding to the user. This allows the user to easily obtain information on the nearest restaurant and proceed directly to making a reservation.

[0454] An "inquiry message" is a message, such as text, that a user enters and sends to a server in order to obtain information.

[0455] "Location information" refers to data indicating the user's current latitude and longitude, obtained using methods such as GPS.

[0456] The "GPS function" is a function that uses a global positioning system to obtain geographical location information.

[0457] "Means of receiving" refers to hardware or software mechanisms that can receive data transmitted by a user.

[0458] "Means of analysis" refers to a mechanism that includes algorithms and programs for analyzing received data.

[0459] "Means for identifying user requirements" refer to processes and mechanisms for clarifying the information and actions that users desire based on analyzed data.

[0460] A "database" is a management system that systematically stores information and allows it to be searched and retrieved as needed.

[0461] "Nearest destination information" refers to data that indicates the location closest to the user's current location.

[0462] "Means of generating in text format" refers to a mechanism for formatting acquired information into text that is easy for the user to understand.

[0463] "Means of responding" refers to the process or device that sends the generated text message to the user.

[0464] "Restaurant information" refers to data such as the name, address, contact information, and business hours of restaurants and cafes.

[0465] A "reservation link" is a URL that allows direct access to the reservation page of a specific restaurant.

[0466] This invention provides a system that allows users to easily obtain information on nearby restaurants using their mobile devices, and to access detailed information and even make reservations. The system receives inquiry messages from users, analyzes them, and provides appropriate information. In particular, it provides high convenience to users by generating responses that include detailed information about restaurants and other locations, as well as reservation links.

[0467] This system primarily includes the following key features:

[0468] 1. Message receiving function

[0469] The server receives inquiry messages and current location information from the user. The message entered by the user on their mobile device and the current latitude and longitude information obtained using the device's GPS function are sent to the server.

[0470] 2. Message analysis function

[0471] The server analyzes the received message using a natural language processing engine (e.g., NLTK or spaCy) and identifies the user's request from its content.

[0472] 3. Database search function

[0473] The server accesses a database (e.g., MySQL) to retrieve information about the nearest restaurant based on the user's current location. The database stores information such as the restaurant's name, address, contact information, and business hours.

[0474] 4. Response generation function

[0475] The server formats the retrieved restaurant information into text format. For example, it generates a response message such as, "The nearest restaurant is Restaurant A. Address: Example Town 1-1. Make a reservation here: https: / / example.com / reserve / a".

[0476] 5. Sending responses to users

[0477] The server sends the generated message to the terminal, and the message is displayed on the terminal. The user checks this information on the terminal and makes a reservation if necessary.

[0478] The following is an example of the system's specific operation:

[0479] When a user types "Tell me the nearest restaurant" using their mobile device, the device uses its GPS function to obtain its current location and sends it to the server along with the inquiry message. The server parses the received message to understand that the user is looking for a restaurant and retrieves information about the nearest restaurant from its database. The retrieved information is formatted into a text message and sent to the user's device as a reply. Upon receiving this message on their device, the user can review the displayed restaurant information and click the link to proceed with the reservation process.

[0480] The following is an example of a specific prompt statement:

[0481] Prompt message:

[0482] This document describes the processing of a location-based restaurant search system using mobile devices. It details how the process works, what data is used, and includes user-side operating procedures.

[0483] This provides detailed clues to gain a concrete understanding of the system's operation and processing flow.

[0484] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0485] Step 1: User submits request

[0486] Input: The user launches the application on their mobile device and enters "Please tell me the nearest restaurant" in the text input field.

[0487] Specific operation: The user opens the app on their smartphone, types "What are some restaurants near me?" into the search bar, and taps the send button. The device uses its GPS function to obtain the user's current location (latitude and longitude) and sends it to the server along with the text input.

[0488] Output: The user's current location information and inquiry message are sent to the server.

[0489] Step 2: Server receives request

[0490] Input: Inquiry message and current location information sent from the user's mobile device.

[0491] Specific operation: The server-side API endpoint receives the HTTP request and extracts the message content and location information.

[0492] Output: The received message and location information are passed to the analysis module.

[0493] Step 3: Message Analysis

[0494] Input: Received inquiry message.

[0495] Specific operation: The server uses a natural language processing engine (e.g., NLTK or spaCy) to analyze the user's input text, "What are some nearby restaurants?", and extracts the keywords "restaurants" and the information "nearest". This identifies the user's request.

[0496] Output: The information the user is looking for (nearest restaurants) is identified.

[0497] Step 4: Database Search

[0498] Input: User's current location and specified request (nearest restaurant).

[0499] Specific operation: The server executes a query like "SELECT FROM restaurants WHERE ST_Distance(location, ST_Point(user's latitude, user's longitude)) ORDER BY distance LIMIT 1" on the database (e.g., MySQL). This retrieves the record of the nearest restaurant.

[0500] Output: Information about the nearest restaurant (name, address, reservation link, etc.) is retrieved.

[0501] Step 5: Generate response

[0502] Input: Information about the nearest restaurant obtained.

[0503] Specific operation: The server uses a template engine (e.g., Jinja) to format the retrieved restaurant information into text format and generate a text message with the following content: "The nearest restaurant is Restaurant A. Address: Example Town 1-1. Make a reservation here: https: / / example.com / reserve / a"

[0504] Output: The generated text message is prepared as a reply message to the user.

[0505] Step 6: Sending responses to users

[0506] Input: The generated text message.

[0507] Specific operation: The server converts the generated text message into JSON format and sends it to the user's terminal as an HTTP response. The terminal displays the received message in its user interface.

[0508] Output: The response message displayed on the user's device. The user reviews the displayed information, and if necessary, taps the displayed reservation link to open a browser and proceed with the reservation process.

[0509] (Application Example 1)

[0510] 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."

[0511] Currently, when users search for restaurant information, view details, make reservations, and place orders, they have to go through multiple applications and websites. This process is time-consuming and degrades the user experience. In particular, when using food delivery services, a smooth information retrieval and ordering process is required, but the current system does not adequately meet this need.

[0512] 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.

[0513] In this invention, the server includes means for receiving inquiry messages and location information from a user, means for analyzing the received inquiry messages and identifying the user's request, means for obtaining the nearest destination information from a database based on the location information, means for generating the obtained destination information in text format and responding to the user, and means for generating a link that allows the user to complete an order based on the obtained information. This enables the user to smoothly complete everything from searching for restaurant information to making reservations and placing orders within a single application.

[0514] "User inquiry messages" refer to questions or requests about locations or services sent by users of the system.

[0515] "Location information" refers to data on the user's current location, identified by latitude and longitude.

[0516] "Means of receiving" refers to functions and devices for acquiring messages and location information from users.

[0517] "Means of analysis and identification of user requests" refers to functions or software that analyze the content of received messages and identify the information or services that the user is seeking.

[0518] "Means of obtaining the nearest destination information from a database based on location information" refers to functions or systems that retrieve data on the nearest destination (e.g., a restaurant) from a database based on the user's location information.

[0519] "Means of generating and responding in text format" refers to functions or software that convert acquired destination information into a text message and send it to the user.

[0520] "Means of generating a link that allows an order to be completed based on acquired information" refers to a function or system that creates a web link to complete an order based on information specified by the user and provides that link to the user.

[0521] This section describes the system for realizing this invention. The overall system configuration and processing details are described in detail below.

[0522] In this invention, the server includes the following means:

[0523] 1. Means for receiving user inquiry messages and location information

[0524] Users use a mobile device, such as a smartphone, to input and send inquiry messages about restaurants. During this process, the mobile device uses its GPS function to obtain its current location information (latitude and longitude) and sends it to the server along with the message.

[0525] 2. Means for analyzing received inquiry messages and identifying user requests.

[0526] The server analyzes the received message. This analysis uses natural language processing to identify the user's specific request from the message content. For example, if the message "Tell me the nearest restaurant that can deliver quickly" is entered, the server will identify "restaurants that can deliver quickly."

[0527] 3. Means for obtaining the nearest destination information from a database based on location information.

[0528] The server accesses its database and searches for information on the nearest restaurants based on the user's current location. This database includes restaurant locations, names, addresses, delivery times, and menu links.

[0529] 4. A means of generating acquired destination information in text format and returning it to the user.

[0530] The server generates a text message to respond to the user based on the acquired restaurant information. For example, it might be in the format of: "The nearest restaurant is Restaurant A. Address: Example Town 1-1. Delivery time: 30 minutes. Menu available here: https: / / example.com / menu / a".

[0531] 5. Means for generating a link that allows the customer to complete an order based on the information obtained.

[0532] The server generates a web link to complete the order based on the restaurant information specified by the user and provides it to the user. This link allows the user to complete the order with a single tap from within the application.

[0533] Hardware and software to be used

[0534] Hardware: Smartphone (iOS / Android)

[0535] Software: Python (programming language), Geopy (Python location acquisition library), requests (HTTP request library)

[0536] Specific example

[0537] Let's say a user opens a food delivery app and enters a request saying, "Tell me the nearest restaurant in Shinjuku Ward that can deliver quickly." The app obtains the user's current location information and sends it to the server. The server analyzes the received message and identifies the user's request. Next, it searches its database for information on the nearest restaurants that can deliver quickly and generates the details of the relevant restaurants in text format. This is sent back to the user, who can complete the order by tapping the link.

[0538] Example of a prompt

[0539] If a user enters "Tell me the nearest restaurant in Shinjuku Ward that offers fast delivery," the AI ​​model will input the following prompt:

[0540] Design a Python program that, when a user enters "Tell me the nearest restaurant in Shinjuku Ward that offers fast delivery," retrieves the user's location, searches for nearby delivery restaurants, and provides detailed information (restaurant name, address, delivery time, menu link, etc.).

[0541] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0542] Step 1:

[0543] A user types "Tell me the nearest restaurant in Shinjuku Ward that offers fast delivery" on their mobile device. The device retrieves this inquiry message and location information (latitude and longitude) and sends it to the server. The input is the user's request and current location, and the output is the data transmission to the server.

[0544] Step 2:

[0545] The server receives an inquiry message sent by the user. The input data consists of the user's inquiry message and location information. The server analyzes this data to identify the information the user is looking for (in this case, the nearest restaurant with fast delivery). Natural language processing is used for the analysis, and the output is the identification of the necessary information.

[0546] Step 3:

[0547] The server queries the database based on the analysis results. The database stores information such as the location, name, address, delivery time, and menu links of restaurants. The input is the user's request and location information, and the output retrieved from the database is information about the most suitable restaurant. The server retrieves the restaurant information closest to the user's current location.

[0548] Step 4:

[0549] The server formats the retrieved restaurant information into text format. Specifically, it generates a reply message containing information such as the restaurant name, address, delivery time, and menu links. The input is restaurant information retrieved from the database, and the output is a text message to be sent to the user. The server prepares this for replying to the user's mobile device.

[0550] Step 5:

[0551] The server sends a final response message to the user's mobile device. The user's device receives this message and displays its contents. The input is the text message sent from the server, and the output is the display on the user's device screen. This allows the user to check information about nearby restaurants and complete their order by clicking on menu links.

[0552] Step 6:

[0553] The server then generates a link that allows the user to complete their order based on the restaurant information they specify. The input is the restaurant details, and the output is a link to the order page. The server also provides this link to the user. For example, there is a specific example: "The nearest restaurant is Restaurant A. Address: Example Town 1-1. Delivery time: 30 minutes. Menu available here: https: / / example.com / menu / a".

[0554] 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.

[0555] This invention provides a system that allows users to interactively obtain information about their nearest destination using a mobile device and also make reservations for that location. This system differs from conventional systems in that it analyzes user inquiry messages using an emotion engine and provides appropriate information. In particular, it generates responses including detailed information about places such as restaurants and reservation links, while simultaneously adjusting the way information is provided based on the user's emotions, thereby providing a better user experience.

[0556] Description of the system's programs and processes

[0557] Main components of the program

[0558] The system includes the following main features:

[0559] 1. Message receiving function

[0560] The server receives inquiry messages and current location information from the user.

[0561] 2. Emotional Engine

[0562] The server analyzes the received inquiry message and recognizes the user's emotions.

[0563] 3. Message analysis function

[0564] The server further analyzes the message, which includes emotional data added by the emotion engine, to identify the user's request.

[0565] 4. Database search function

[0566] The server accesses the database and retrieves the nearest destination information based on the user's current location. It also uses an emotion engine to tailor the suggested information.

[0567] 5. Response generation function

[0568] The server formats the acquired destination information into text format and sends it back to the user. It also adjusts the content and tone of the response message according to the user's mood.

[0569] Processing flow

[0570] The following is a specific example of the system's processing.

[0571] 1. User requests are submitted.

[0572] The user types on their mobile device, "I'm really hungry, please tell me a restaurant I can go to right away."

[0573] The device obtains the user's current location information (e.g., latitude and longitude) and sends it to the server.

[0574] 2. Receiving and parsing server requests

[0575] The server receives requests from users.

[0576] The emotion engine analyzes the user's emotions (in this example, "very hungry") from the received message.

[0577] 3. Message analysis and request identification

[0578] The server analyzes the message based on the results of the emotion engine and identifies the user's specific request (in this case, "a restaurant I can go to right away").

[0579] 4. Retrieving information from the database

[0580] The server retrieves restaurant information from the database. Based on the current location, it selects the nearest restaurant.

[0581] The restaurant information provided will be adjusted based on the user's preferences. For example, restaurants that are easily accessible will be prioritized.

[0582] 5. Generating responses to users

[0583] The server formats the information of the selected restaurant into text format. For example, it generates a response message such as, "The nearest restaurant is Restaurant A. Address: Example Town 1-1. Make a reservation here: https: / / example.com / reserve / a".

[0584] It also generates responses in a tone that matches the user's mood, such as, "You seem hungry! I'll recommend restaurant A, which we can go to right away."

[0585] 6. Sending responses to users

[0586] The server sends the generated message back to the user's terminal. It sends it as an HTTP response.

[0587] 7. Display by device

[0588] The terminal displays received messages on the user interface. Users can review the information displayed on the screen and complete their online reservation by clicking the reservation link as needed.

[0589] Thus, the system of the present invention receives user inquiry messages and location information, recognizes and analyzes the user's emotions using an emotion engine, and provides optimal information. As a result, users can obtain the necessary information in a dialogue format and receive detailed responses tailored to their emotions.

[0590] The following describes the processing flow.

[0591] Step 1:

[0592] The user types "I'm really hungry, please tell me a restaurant I can go to right away" on their mobile device. The device then retrieves the user's current location information (e.g., latitude and longitude).

[0593] Step 2:

[0594] The device sends the user's inquiry message and current location information to the server. Specifically, it sends a data packet containing the inquiry message and location information to the server as an HTTP request.

[0595] Step 3:

[0596] The server parses the received request. First, the inquiry message and location information are separated into data structures, and then the message is passed to the sentiment engine to analyze the user's sentiment.

[0597] Step 4:

[0598] The emotion engine recognizes emotions from the received query message. For example, it identifies the emotion "I'm hungry" as "very hungry" and returns that emotion data to the server.

[0599] Step 5:

[0600] The server identifies the user's request based on the sentiment data returned from the sentiment engine. In this case, it determines that the user is looking for a "restaurant they can go to right away."

[0601] Step 6:

[0602] The server accesses the database and searches for the nearest restaurant based on the user's current location. At the same time, it filters the results based on the user's sentiment data, prioritizing restaurants suitable for those in a hurry.

[0603] Step 7:

[0604] The server formats the acquired restaurant information into text format. For example, it generates a response message such as, "Looks like you're hungry! The nearest restaurant is Restaurant A. Address: Example Town 1-1. Make a reservation here: https: / / example.com / reserve / a".

[0605] Step 8:

[0606] The server sends the generated response message back to the user's terminal. It is sent as an HTTP response, preparing the terminal to display the received data.

[0607] Step 9:

[0608] The terminal displays received messages on the user interface. Users can review the information displayed on the screen and complete their online reservation by clicking the reservation link as needed.

[0609] In this way, the system improves the user experience and enables flexible, real-time information delivery by providing optimal information while taking user emotions into consideration.

[0610] (Example 2)

[0611] 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".

[0612] Traditional information systems simply provided information in response to user inquiries, lacking the flexibility to adapt to users' emotions and circumstances. As a result, users often felt that the information they received didn't match their feelings or urgency, leading to decreased satisfaction. For example, even if someone was extremely hungry and urgently needed a meal, they might only be provided with information about nearby restaurants, which was insufficient in situations requiring immediate assistance.

[0613] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0614] In this invention, the server includes means for receiving inquiry messages and location information from a user; means for analyzing the sentiment of the received inquiry message using an emotion engine; means for analyzing the message based on the analyzed sentiment data and identifying the user's request; means for obtaining the nearest destination information from a database based on location information and adjusting the information based on sentiment data; means for generating the obtained destination information in text format and adjusting the content of the response message according to the user's sentiment; means for sending the generated response message to the user; and means for displaying the received response message on a user interface and making it operable. This enables the provision of optimal information according to the user's sentiment and urgency, thereby improving the user experience.

[0615] An "inquiry message" is text or audio data that a user enters when asking a question or requesting information from the system.

[0616] "Location information" refers to latitude and longitude data that indicates the user's current location.

[0617] An "emotion engine" is an algorithm or software that analyzes the content of inquiry messages received from users and identifies and classifies the user's emotional state.

[0618] "Target destination information" refers to information about a specific destination that the user is looking for, and includes data such as detailed information about restaurants, shops, and facilities.

[0619] "Text format" refers to a format in which information is represented as a string of characters, providing data in a way that is easy for users to read.

[0620] A "response message" is text data containing answers or suggestions that the system generates and provides in response to a user's inquiry.

[0621] A "user interface" refers to the screens and control elements that allow a user to interact with a system, and are the elements that display information to the user and accept input.

[0622] "Dining establishment information" refers to information containing detailed data about restaurants, cafes, and other food establishments, including location, business hours, and menus.

[0623] A "reservation link" is a URL or web address that allows a user to make a reservation for a specific service or facility.

[0624] This invention provides a system that allows users to interactively obtain information about their nearest destination using a mobile device and also make reservations for that location. This system differs from conventional systems in that it analyzes user inquiry messages using an emotion engine and provides appropriate information. In particular, it generates responses including detailed information about places such as restaurants and reservation links, while simultaneously adjusting the way information is provided based on the user's emotions, thereby providing a better user experience.

[0625] The system configuration includes the following main functions:

[0626] 1. Message receiving function

[0627] The server receives inquiry messages and current location information from the user. To implement this functionality, an HTTP server is used to receive data. Specifically, the API can be built using programming languages ​​such as Python or Node.js.

[0628] 2. Emotional Engine

[0629] The server analyzes the received inquiry message to recognize the user's emotions. The emotion engine uses natural language processing libraries such as the Google NLP API and IBM Watson. Based on this analysis, the server can determine the user's emotions, such as "very hungry."

[0630] 3. Message analysis function

[0631] The server further analyzes the message, which includes sentiment data added by the sentiment engine, to identify the user's request. In this step, the server clarifies the user's specific request by referring to the sentiment data.

[0632] 4. Database search function

[0633] The server accesses the database and retrieves the nearest destination information based on the user's current location. It also uses an emotion engine to refine the suggested information. MySQL and MongoDB are suitable databases for this purpose. Specifically, SQL queries are used to retrieve the necessary information.

[0634] 5. Response generation function

[0635] The server formats the acquired destination information into text format and sends it back to the user. Here, the content and tone of the response message are adjusted according to the user's mood. For example, it might generate a response with a tone that matches the user's mood, such as, "You seem hungry! I'll recommend restaurant A, which we can go to right away."

[0636] 6. Function to send responses to users

[0637] The server sends the generated response message back to the user's terminal. Specifically, it sends it as an HTTP response.

[0638] 7. Display function on the device

[0639] The terminal displays received messages on the user interface. Users can review the information displayed on the screen and complete their online reservation by clicking the reservation link as needed.

[0640] Specific example

[0641] The following is a concrete example of how the system processes:

[0642] The user types on their mobile device, "I'm really hungry, please tell me a restaurant I can go to right away."

[0643] The device obtains the user's current location information (e.g., latitude and longitude) and sends it to the server.

[0644] The server uses an emotion engine to analyze the user's emotions (in this example, "very hungry") from the received request message.

[0645] The server analyzes the message based on the results from the emotion engine and identifies the user's specific request (in this case, "a restaurant I can go to right away").

[0646] The server retrieves restaurant information from the database. Based on the current location, it selects the nearest restaurant.

[0647] The server formats the restaurant information into text format and generates a response that matches the user's mood, such as, "Looks like you're hungry! Here's a restaurant A that's right there. Address: Example-cho 1-1. Make a reservation here: https: / / example.com / reserve / a".

[0648] The server sends the generated message back to the user's terminal.

[0649] The terminal displays received messages on the user interface, and the user can review the displayed information. If necessary, they can click the reservation link to complete the online reservation.

[0650] Example of a prompt:

[0651] "I'm really hungry, so please tell me a restaurant we can go to right away."

[0652] "Can you recommend a cafe I can get to quickly?"

[0653] Thus, the system of the present invention receives user inquiry messages and location information, recognizes and analyzes the user's emotions using an emotion engine, and provides optimal information. As a result, users can obtain the necessary information in a dialogue format and receive detailed responses tailored to their emotions.

[0654] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0655] Step 1: User submits request

[0656] The user opens their mobile device and launches a messaging app. The user types, "I'm really hungry, please tell me a restaurant I can get to right away." The device uses its built-in GPS to obtain the current latitude (35.6895) and longitude (139.6917). This input data (user's text and location information) is sent to the server as an HTTP POST request.

[0657] Step 2: Server receives and parses requests

[0658] The server receives an HTTP POST request at the API endpoint. The server extracts the inquiry content and location information from the received message. This data is passed to the sentiment engine for analysis. The sentiment engine identifies the emotion as "very hungry." The input data consists of the user's inquiry message and location information, and the output is the sentiment data as a result of the analysis.

[0659] Step 3: Analyze the message and identify the request.

[0660] The server identifies the user's request specifically based on the analysis results of the emotion engine. It analyzes the received message and emotion data to identify a "restaurant you can go to right away." The input data is emotion data and the user's message, and the output is the identified request.

[0661] Step 4: Retrieve information from the database

[0662] The server accesses the database and retrieves information on the nearest restaurant based on the user's current location. Based on sentiment data, it prioritizes selecting restaurants that are easily accessible. Specifically, it executes an SQL query to search for restaurants within a 500m radius of the user's current location (35.6895,139.6917). The input data consists of location information and sentiment data, and the output is information on the selected restaurants.

[0663] Step 5: Generate responses for the user

[0664] The server generates a response message in text format based on the acquired restaurant information. It references sentiment data and makes adjustments according to the sentiment. For example, it might generate a message like, "Looks like you're hungry! Here's a restaurant A that's right away. Address: Example Town 1-1. Make a reservation here: https: / / example.com / reserve / a". The input data is restaurant information and sentiment data, and the output is the response message.

[0665] Step 6: Sending responses to users

[0666] The server sends the generated response message to the user's terminal as an HTTP response. Specifically, it returns a response with HTTP status code 200. The input data is the response message, and the output is the HTTP response.

[0667] Step 7: Display on the device

[0668] The terminal displays the received response message on the screen. The user can see the message, "Looks like you're hungry! Here's a restaurant A that's close by. Address: Example-cho 1-1. Make a reservation here: https: / / example.com / reserve / a", and click the reservation link to complete the online reservation. The input data is the received response, and the output is the displayed content.

[0669] (Application Example 2)

[0670] 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."

[0671] Traditional user interfaces suffered from a poor user experience because they mechanically provided information without considering the user's emotions. Furthermore, they lacked mechanisms to quickly and appropriately provide information on nearby destinations such as restaurants, which hindered user satisfaction.

[0672] 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.

[0673] In this invention, the server includes means for receiving inquiry messages and location information from a user, means for analyzing the received inquiry messages and recognizing the user's emotions, means for identifying the user's requests based on the analyzed emotions, means for obtaining the nearest destination information from a database based on the current location information, and means for generating the obtained destination information in text format and responding to the user. This makes it possible to provide timely and appropriate information based on the user's emotions and improve the user experience.

[0674] A "user inquiry message" refers to an information request that a user sends to the system via a mobile device or other device.

[0675] "Location information" refers to data that indicates the user's current location, and is usually expressed as latitude and longitude.

[0676] "Means of receiving" refers to the mechanism by which the server obtains inquiry messages and location information sent by the user.

[0677] "Means of analysis and recognition of user emotions" refer to algorithms and models that process received inquiry messages and identify user emotions.

[0678] "Methods for identifying user requests based on analyzed emotions" refers to a process for identifying the information and services that users truly want, while taking their emotions into consideration.

[0679] A "database" is a repository of information that stores destination information and other data to be provided to users.

[0680] "Means for obtaining the nearest destination information based on current location information" refers to a function that searches for and obtains information about the nearest destination based on the user's current location.

[0681] "Destination information" refers to detailed information such as the name, address, and contact information of restaurants and other establishments.

[0682] "A means of generating and responding to the user in text format" refers to a mechanism for formatting destination information as a string and providing it to the user.

[0683] "Restaurant information" refers to information such as the location, menu, and business hours of restaurants and cafes.

[0684] A "reservation link" is a URL or web address that allows users to make reservations directly via the internet.

[0685] This invention provides a system that allows users to interactively obtain information about their nearest destination using a mobile device and also make reservations for that location. This system is characterized by its ability to analyze the user's emotions and provide optimal information.

[0686] Main components of the program

[0687] The system includes the following main features:

[0688] 1. Message receiving function

[0689] The server receives inquiry messages and current location information from the user. This allows the server to understand what the user is requesting.

[0690] 2. Emotional Engine

[0691] The server analyzes the received inquiry message and recognizes the user's emotions. This emotion analysis uses the "transformers" library from Hugging Face.

[0692] 3. Message analysis function

[0693] The server further analyzes the message, which includes emotional data added by the emotion engine, to identify the user's specific request.

[0694] 4. Database search function

[0695] The server accesses the database and retrieves the nearest destination information based on the user's current location. The information retrieved includes restaurant information.

[0696] 5. Response generation function

[0697] The server formats the retrieved destination information into text format and sends it back to the user. It also adjusts the content and tone of the response message according to the user's mood. The response also includes a booking link.

[0698] System operation

[0699] The system starts working when a user sends a specific request. For example, a user might type, "I'm really hungry, please tell me a restaurant I can go to right away." Along with this input message, the user's current location is sent to the server.

[0700] The server analyzes the received request using an emotion engine to identify the user's emotion (in this case, "very hungry"). Next, the message analysis function extracts the user's specific request ("a restaurant I can go to right now").

[0701] Next, the database search function searches for the nearest restaurant based on the user's current location and retrieves that information. At this stage, the results of the sentiment engine are also considered to select the most appropriate restaurant.

[0702] Finally, the response generation function sends the acquired restaurant information back to the user in text format. The specific response message would look something like this: "Looks like you're hungry! Here's a recommendation for the nearest restaurant, Restaurant A. Make a reservation here: https: / / example.com / reserve / a".

[0703] System implementation example

[0704] The hardware used to implement this system includes a standard server, and the software includes Python, Flask, and the Hugging Face transformers library, while the database uses SQL, etc. This enables a series of processes that receive user input, perform sentiment analysis, and provide optimal information in text format.

[0705] Example of a prompt

[0706] "I'm really hungry, so please tell me about a restaurant we can go to right away."

[0707] When this prompt message is sent to the system, the emotion engine interprets it as "very hungry," and the user is then provided with restaurant information that can be used quickly.

[0708] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0709] Step 1:

[0710] The user enters an inquiry message and current location information on their mobile device.

[0711] Input: User's message "I'm really hungry, please tell me a restaurant I can go to right away," and current location information (e.g., latitude and longitude).

[0712] Processing: The mobile device retrieves the message and location information and sends it to the server.

[0713] Output: The message and location information are sent to the server.

[0714] Step 2:

[0715] The server receives the inquiry message and location information.

[0716] Input: Inquiry message and location information submitted by the user

[0717] Processing: The server receives these as HTTP requests.

[0718] Output: Received messages and location information

[0719] Step 3:

[0720] The server's emotion engine analyzes the received messages and recognizes the user's emotions.

[0721] Input: Received inquiry message

[0722] Processing: Use the "transformers" library from Hugging Face to analyze the sentiment of the message.

[0723] Output: User sentiment data (e.g., "Very hungry")

[0724] Step 4:

[0725] The server analyzes messages based on emotion to identify the user's specific requests.

[0726] Input: Message and sentiment data

[0727] Processing: Use message analysis to identify that the user is looking for "restaurants they can go to right away."

[0728] Output: Request from the identified user

[0729] Step 5:

[0730] The server accesses the database and retrieves the nearest destination information based on the user's current location.

[0731] Input: User's current location and specified request

[0732] Processing: Use SQL queries or similar methods to search and retrieve information about the nearest destination from the database.

[0733] Output: Information on the nearest restaurants

[0734] Step 6:

[0735] The server formats the destination information it has obtained into text format and returns it to the user.

[0736] Input: Nearest restaurant information

[0737] Process: Generate a message in text format that reads, "Looks like you're hungry! Here's a recommendation for the nearest restaurant, Restaurant A. Make a reservation here: https: / / example.com / reserve / a".

[0738] Output: Generated response message

[0739] Step 7:

[0740] The server generates a message and sends it to the user's mobile device.

[0741] Input: Generated response message

[0742] Processing: Send the response message as an HTTP response.

[0743] Output: Message sent to the user's mobile device

[0744] Step 8:

[0745] The system displays messages received by the user's mobile device in the user interface.

[0746] Input: Received message

[0747] Processing: Display the message as text in the user interface.

[0748] Output: The user confirms the information displayed on their mobile device screen.

[0749] 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.

[0750] 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.

[0751] 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.

[0752] [Third Embodiment]

[0753] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0754] 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.

[0755] 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).

[0756] 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.

[0757] 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.

[0758] 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).

[0759] 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.

[0760] 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.

[0761] 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.

[0762] 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.

[0763] 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.

[0764] 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".

[0765] This invention provides a system that allows users to easily obtain information about their nearest destination using a mobile device, and to access detailed information and even make reservations. The system receives inquiry messages from users, analyzes them, and provides appropriate information. In particular, it provides high convenience to users by generating responses that include detailed information about places such as restaurants and reservation links.

[0766] Description of the system's programs and processes

[0767] Main components of the program

[0768] The system includes the following main features:

[0769] 1. Message receiving function

[0770] The server receives inquiry messages and current location information from the user.

[0771] 2. Message analysis function

[0772] The server analyzes the received message and identifies the user's request from its content.

[0773] 3. Database search function

[0774] The server accesses the database and retrieves the nearest destination information based on the user's current location.

[0775] 4. Response generation function

[0776] The server formats the acquired destination information into text format and returns it to the user.

[0777] Processing flow

[0778] The following is a specific example of the system's processing.

[0779] 1. User requests are submitted.

[0780] The user types "Please tell me the nearest restaurant" on their mobile device.

[0781] The device obtains the user's current location information (latitude and longitude) and sends it to the server.

[0782] 2. Receiving and parsing server requests

[0783] The server receives requests from users.

[0784] Identify the user's request (in this case, "nearest restaurant") from the received message.

[0785] 3. Retrieving information from the database

[0786] The server retrieves restaurant information from a dummy database.

[0787] The system will select the nearest restaurant based on your current location.

[0788] 4. Generating responses to users

[0789] The server formats the information of the selected restaurants into text format.

[0790] As a concrete example, it generates a response message such as, "The nearest restaurant is Restaurant A. Address: Example Town 1-1. Make a reservation here: https: / / example.com / reserve / a".

[0791] 5. Sending responses to users

[0792] The server sends the generated message to the terminal, and the message is displayed on the terminal.

[0793] Users can view this information on their devices and make reservations as needed.

[0794] This system configuration allows users to obtain necessary information in real time through an interactive format and even make reservations on the spot. This process provides a far more natural and flexible user experience compared to maps or static information.

[0795] The following describes the processing flow.

[0796] Step 1:

[0797] The user types "Please tell me the nearest restaurant" on their mobile device. The device retrieves the user's current location information (e.g., latitude and longitude).

[0798] Step 2:

[0799] The device sends the user's inquiry message and current location information to the server. Specifically, it sends a data packet containing the inquiry message and location information as an HTTP request.

[0800] Step 3:

[0801] The server parses the received request data. The query message and location information are separated into data structures to prepare for the next processing step.

[0802] Step 4:

[0803] The server performs natural language analysis on the query message to identify the user's request. In this example, it extracts the request "Please tell me the nearest restaurant" from the query message.

[0804] Step 5:

[0805] The server accesses the database and searches for the nearest restaurant based on location information. Using database queries, it calculates the distance from the current location and identifies the closest restaurant.

[0806] Step 6:

[0807] The system formats the restaurant information (name, address, reservation URL, etc.) obtained by the server into text format. For example, it generates a response message such as, "The nearest restaurant is Restaurant A. Address: Example Town 1-1. Make a reservation here: https: / / example.com / reserve / a".

[0808] Step 7:

[0809] The server sends the generated response message back to the user's device. It is sent as an HTTP response.

[0810] Step 8:

[0811] The terminal displays received messages on the user interface. Users can review the information displayed on the screen and complete their online reservation by clicking the reservation link as needed.

[0812] (Example 1)

[0813] 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."

[0814] Traditional location-based information systems have suffered from a poor user experience due to the cumbersome process required to obtain detailed information about the user's current location. Furthermore, the need for users to utilize multiple platforms when making reservations at restaurants and other establishments, resulting in a lack of convenience, is also a challenge.

[0815] 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.

[0816] In this invention, the server includes means for receiving inquiry messages and location information from a user, means for analyzing the received inquiry messages and identifying the user's request, means for obtaining the nearest destination information from a database based on the location information, means for obtaining the user's current location using GPS functionality, and means for generating the obtained destination information in text format and responding to the user. This allows the user to easily obtain information on the nearest restaurant and proceed directly to making a reservation.

[0817] An "inquiry message" is a message, such as text, that a user enters and sends to a server in order to obtain information.

[0818] "Location information" refers to data indicating the user's current latitude and longitude, obtained using methods such as GPS.

[0819] The "GPS function" is a function that uses a global positioning system to obtain geographical location information.

[0820] "Means of receiving" refers to hardware or software mechanisms that can receive data transmitted by a user.

[0821] "Means of analysis" refers to a mechanism that includes algorithms and programs for analyzing received data.

[0822] "Means for identifying user requirements" refer to processes and mechanisms for clarifying the information and actions that users desire based on analyzed data.

[0823] A "database" is a management system that systematically stores information and allows it to be searched and retrieved as needed.

[0824] "Nearest destination information" refers to data that indicates the location closest to the user's current location.

[0825] "Means of generating in text format" refers to a mechanism for formatting acquired information into text that is easy for the user to understand.

[0826] "Means of responding" refers to the process or device that sends the generated text message to the user.

[0827] "Restaurant information" refers to data such as the name, address, contact information, and business hours of restaurants and cafes.

[0828] A "reservation link" is a URL that allows direct access to the reservation page of a specific restaurant.

[0829] This invention provides a system that allows users to easily obtain information on nearby restaurants using their mobile devices, and to access detailed information and even make reservations. The system receives inquiry messages from users, analyzes them, and provides appropriate information. In particular, it provides high convenience to users by generating responses that include detailed information about restaurants and other locations, as well as reservation links.

[0830] This system primarily includes the following key features:

[0831] 1. Message receiving function

[0832] The server receives inquiry messages and current location information from the user. The message entered by the user on their mobile device and the current latitude and longitude information obtained using the device's GPS function are sent to the server.

[0833] 2. Message analysis function

[0834] The server analyzes the received message using a natural language processing engine (e.g., NLTK or spaCy) and identifies the user's request from its content.

[0835] 3. Database search function

[0836] The server accesses a database (e.g., MySQL) to retrieve information about the nearest restaurant based on the user's current location. The database stores information such as the restaurant's name, address, contact information, and business hours.

[0837] 4. Response generation function

[0838] The server formats the retrieved restaurant information into text format. For example, it generates a response message such as, "The nearest restaurant is Restaurant A. Address: Example Town 1-1. Make a reservation here: https: / / example.com / reserve / a".

[0839] 5. Sending responses to users

[0840] The server sends the generated message to the terminal, and the message is displayed on the terminal. The user checks this information on the terminal and makes a reservation if necessary.

[0841] The following is an example of the system's specific operation:

[0842] When a user types "Tell me the nearest restaurant" using their mobile device, the device uses its GPS function to obtain its current location and sends it to the server along with the inquiry message. The server parses the received message to understand that the user is looking for a restaurant and retrieves information about the nearest restaurant from its database. The retrieved information is formatted into a text message and sent to the user's device as a reply. Upon receiving this message on their device, the user can review the displayed restaurant information and click the link to proceed with the reservation process.

[0843] The following is an example of a specific prompt statement:

[0844] Prompt message:

[0845] This document describes the processing of a location-based restaurant search system using mobile devices. It details how the process works, what data is used, and includes user-side operating procedures.

[0846] This provides detailed clues to gain a concrete understanding of the system's operation and processing flow.

[0847] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0848] Step 1: User submits request

[0849] Input: The user launches the application on their mobile device and enters "Please tell me the nearest restaurant" in the text input field.

[0850] Specific operation: The user opens the app on their smartphone, types "What are some restaurants near me?" into the search bar, and taps the send button. The device uses its GPS function to obtain the user's current location (latitude and longitude) and sends it to the server along with the text input.

[0851] Output: The user's current location information and inquiry message are sent to the server.

[0852] Step 2: Server receives request

[0853] Input: Inquiry message and current location information sent from the user's mobile device.

[0854] Specific operation: The server-side API endpoint receives the HTTP request and extracts the message content and location information.

[0855] Output: The received message and location information are passed to the analysis module.

[0856] Step 3: Message Analysis

[0857] Input: Received inquiry message.

[0858] Specific operation: The server uses a natural language processing engine (e.g., NLTK or spaCy) to analyze the user's input text, "What are some nearby restaurants?", and extracts the keywords "restaurants" and the information "nearest". This identifies the user's request.

[0859] Output: The information the user is looking for (nearest restaurants) is identified.

[0860] Step 4: Database Search

[0861] Input: User's current location and specified request (nearest restaurant).

[0862] Specific operation: The server executes a query like "SELECT FROM restaurants WHERE ST_Distance(location, ST_Point(user's latitude, user's longitude)) ORDER BY distance LIMIT 1" on the database (e.g., MySQL). This retrieves the record of the nearest restaurant.

[0863] Output: Information about the nearest restaurant (name, address, reservation link, etc.) is retrieved.

[0864] Step 5: Generate response

[0865] Input: Information about the nearest restaurant obtained.

[0866] Specific operation: The server uses a template engine (e.g., Jinja) to format the retrieved restaurant information into text format and generate a text message with the following content: "The nearest restaurant is Restaurant A. Address: Example Town 1-1. Make a reservation here: https: / / example.com / reserve / a"

[0867] Output: The generated text message is prepared as a reply message to the user.

[0868] Step 6: Sending responses to users

[0869] Input: The generated text message.

[0870] Specific operation: The server converts the generated text message into JSON format and sends it to the user's terminal as an HTTP response. The terminal displays the received message in its user interface.

[0871] Output: The response message displayed on the user's device. The user reviews the displayed information, and if necessary, taps the displayed reservation link to open a browser and proceed with the reservation process.

[0872] (Application Example 1)

[0873] 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."

[0874] Currently, when users search for restaurant information, view details, make reservations, and place orders, they have to go through multiple applications and websites. This process is time-consuming and degrades the user experience. In particular, when using food delivery services, a smooth information retrieval and ordering process is required, but the current system does not adequately meet this need.

[0875] 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.

[0876] In this invention, the server includes means for receiving inquiry messages and location information from a user, means for analyzing the received inquiry messages and identifying the user's request, means for obtaining the nearest destination information from a database based on the location information, means for generating the obtained destination information in text format and responding to the user, and means for generating a link that allows the user to complete an order based on the obtained information. This enables the user to smoothly complete everything from searching for restaurant information to making reservations and placing orders within a single application.

[0877] "User inquiry messages" refer to questions or requests about locations or services sent by users of the system.

[0878] "Location information" refers to data on the user's current location, identified by latitude and longitude.

[0879] "Means of receiving" refers to functions and devices for acquiring messages and location information from users.

[0880] "Means of analysis and identification of user requests" refers to functions or software that analyze the content of received messages and identify the information or services that the user is seeking.

[0881] "Means of obtaining the nearest destination information from a database based on location information" refers to functions or systems that retrieve data on the nearest destination (e.g., a restaurant) from a database based on the user's location information.

[0882] "Means of generating and responding in text format" refers to functions or software that convert acquired destination information into a text message and send it to the user.

[0883] "Means of generating a link that allows an order to be completed based on acquired information" refers to a function or system that creates a web link to complete an order based on information specified by the user and provides that link to the user.

[0884] This section describes the system for realizing this invention. The overall system configuration and processing details are described in detail below.

[0885] In this invention, the server includes the following means:

[0886] 1. Means for receiving user inquiry messages and location information

[0887] Users use a mobile device, such as a smartphone, to input and send inquiry messages about restaurants. During this process, the mobile device uses its GPS function to obtain its current location information (latitude and longitude) and sends it to the server along with the message.

[0888] 2. Means for analyzing received inquiry messages and identifying user requests.

[0889] The server analyzes the received message. This analysis uses natural language processing to identify the user's specific request from the message content. For example, if the message "Tell me the nearest restaurant that can deliver quickly" is entered, the server will identify "restaurants that can deliver quickly."

[0890] 3. Means for obtaining the nearest destination information from a database based on location information.

[0891] The server accesses its database and searches for information on the nearest restaurants based on the user's current location. This database includes restaurant locations, names, addresses, delivery times, and menu links.

[0892] 4. A means of generating acquired destination information in text format and returning it to the user.

[0893] The server generates a text message to respond to the user based on the acquired restaurant information. For example, it might be in the format of: "The nearest restaurant is Restaurant A. Address: Example Town 1-1. Delivery time: 30 minutes. Menu available here: https: / / example.com / menu / a".

[0894] 5. Means for generating a link that allows the customer to complete an order based on the information obtained.

[0895] The server generates a web link to complete the order based on the restaurant information specified by the user and provides it to the user. This link allows the user to complete the order with a single tap from within the application.

[0896] Hardware and software to be used

[0897] Hardware: Smartphone (iOS / Android)

[0898] Software: Python (programming language), Geopy (Python location acquisition library), requests (HTTP request library)

[0899] Specific example

[0900] Let's say a user opens a food delivery app and enters a request saying, "Tell me the nearest restaurant in Shinjuku Ward that can deliver quickly." The app obtains the user's current location information and sends it to the server. The server analyzes the received message and identifies the user's request. Next, it searches its database for information on the nearest restaurants that can deliver quickly and generates the details of the relevant restaurants in text format. This is sent back to the user, who can complete the order by tapping the link.

[0901] Example of a prompt

[0902] If a user enters "Tell me the nearest restaurant in Shinjuku Ward that offers fast delivery," the AI ​​model will input the following prompt:

[0903] Design a Python program that, when a user enters "Tell me the nearest restaurant in Shinjuku Ward that offers fast delivery," retrieves the user's location, searches for nearby delivery restaurants, and provides detailed information (restaurant name, address, delivery time, menu link, etc.).

[0904] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0905] Step 1:

[0906] A user types "Tell me the nearest restaurant in Shinjuku Ward that offers fast delivery" on their mobile device. The device retrieves this inquiry message and location information (latitude and longitude) and sends it to the server. The input is the user's request and current location, and the output is the data transmission to the server.

[0907] Step 2:

[0908] The server receives an inquiry message sent by the user. The input data consists of the user's inquiry message and location information. The server analyzes this data to identify the information the user is looking for (in this case, the nearest restaurant with fast delivery). Natural language processing is used for the analysis, and the output is the identification of the necessary information.

[0909] Step 3:

[0910] The server queries the database based on the analysis results. The database stores information such as the location, name, address, delivery time, and menu links of restaurants. The input is the user's request and location information, and the output retrieved from the database is information about the most suitable restaurant. The server retrieves the restaurant information closest to the user's current location.

[0911] Step 4:

[0912] The server formats the retrieved restaurant information into text format. Specifically, it generates a reply message containing information such as the restaurant name, address, delivery time, and menu links. The input is restaurant information retrieved from the database, and the output is a text message to be sent to the user. The server prepares this for replying to the user's mobile device.

[0913] Step 5:

[0914] The server sends a final response message to the user's mobile device. The user's device receives this message and displays its contents. The input is the text message sent from the server, and the output is the display on the user's device screen. This allows the user to check information about nearby restaurants and complete their order by clicking on menu links.

[0915] Step 6:

[0916] The server then generates a link that allows the user to complete their order based on the restaurant information they specify. The input is the restaurant details, and the output is a link to the order page. The server also provides this link to the user. For example, there is a specific example: "The nearest restaurant is Restaurant A. Address: Example Town 1-1. Delivery time: 30 minutes. Menu available here: https: / / example.com / menu / a".

[0917] 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.

[0918] This invention provides a system that allows users to interactively obtain information about their nearest destination using a mobile device and also make reservations for that location. This system differs from conventional systems in that it analyzes user inquiry messages using an emotion engine and provides appropriate information. In particular, it generates responses including detailed information about places such as restaurants and reservation links, while simultaneously adjusting the way information is provided based on the user's emotions, thereby providing a better user experience.

[0919] Description of the system's programs and processes

[0920] Main components of the program

[0921] The system includes the following main features:

[0922] 1. Message receiving function

[0923] The server receives inquiry messages and current location information from the user.

[0924] 2. Emotional Engine

[0925] The server analyzes the received inquiry message and recognizes the user's emotions.

[0926] 3. Message analysis function

[0927] The server further analyzes the message, which includes emotional data added by the emotion engine, to identify the user's request.

[0928] 4. Database search function

[0929] The server accesses the database and retrieves the nearest destination information based on the user's current location. It also uses an emotion engine to tailor the suggested information.

[0930] 5. Response generation function

[0931] The server formats the acquired destination information into text format and sends it back to the user. It also adjusts the content and tone of the response message according to the user's mood.

[0932] Processing flow

[0933] The following is a specific example of the system's processing.

[0934] 1. User requests are submitted.

[0935] The user types on their mobile device, "I'm really hungry, please tell me a restaurant I can go to right away."

[0936] The device obtains the user's current location information (e.g., latitude and longitude) and sends it to the server.

[0937] 2. Receiving and parsing server requests

[0938] The server receives requests from users.

[0939] The emotion engine analyzes the user's emotions (in this example, "very hungry") from the received message.

[0940] 3. Message analysis and request identification

[0941] The server analyzes the message based on the results of the emotion engine and identifies the user's specific request (in this case, "a restaurant I can go to right away").

[0942] 4. Retrieving information from the database

[0943] The server retrieves restaurant information from the database. Based on the current location, it selects the nearest restaurant.

[0944] The restaurant information provided will be adjusted based on the user's preferences. For example, restaurants that are easily accessible will be prioritized.

[0945] 5. Generating responses to users

[0946] The server formats the information of the selected restaurant into text format. For example, it generates a response message such as, "The nearest restaurant is Restaurant A. Address: Example Town 1-1. Make a reservation here: https: / / example.com / reserve / a".

[0947] It also generates responses in a tone that matches the user's mood, such as, "You seem hungry! I'll recommend restaurant A, which we can go to right away."

[0948] 6. Sending responses to users

[0949] The server sends the generated message back to the user's terminal. It sends it as an HTTP response.

[0950] 7. Display by device

[0951] The terminal displays received messages on the user interface. Users can review the information displayed on the screen and complete their online reservation by clicking the reservation link as needed.

[0952] Thus, the system of the present invention receives user inquiry messages and location information, recognizes and analyzes the user's emotions using an emotion engine, and provides optimal information. As a result, users can obtain the necessary information in a dialogue format and receive detailed responses tailored to their emotions.

[0953] The following describes the processing flow.

[0954] Step 1:

[0955] The user types "I'm really hungry, please tell me a restaurant I can go to right away" on their mobile device. The device then retrieves the user's current location information (e.g., latitude and longitude).

[0956] Step 2:

[0957] The device sends the user's inquiry message and current location information to the server. Specifically, it sends a data packet containing the inquiry message and location information to the server as an HTTP request.

[0958] Step 3:

[0959] The server parses the received request. First, the inquiry message and location information are separated into data structures, and then the message is passed to the sentiment engine to analyze the user's sentiment.

[0960] Step 4:

[0961] The emotion engine recognizes emotions from the received query message. For example, it identifies the emotion "I'm hungry" as "very hungry" and returns that emotion data to the server.

[0962] Step 5:

[0963] The server identifies the user's request based on the sentiment data returned from the sentiment engine. In this case, it determines that the user is looking for a "restaurant they can go to right away."

[0964] Step 6:

[0965] The server accesses the database and searches for the nearest restaurant based on the user's current location. At the same time, it filters the results based on the user's sentiment data, prioritizing restaurants suitable for those in a hurry.

[0966] Step 7:

[0967] The server formats the acquired restaurant information into text format. For example, it generates a response message such as, "Looks like you're hungry! The nearest restaurant is Restaurant A. Address: Example Town 1-1. Make a reservation here: https: / / example.com / reserve / a".

[0968] Step 8:

[0969] The server sends the generated response message back to the user's terminal. It is sent as an HTTP response, preparing the terminal to display the received data.

[0970] Step 9:

[0971] The terminal displays received messages on the user interface. Users can review the information displayed on the screen and complete their online reservation by clicking the reservation link as needed.

[0972] In this way, the system improves the user experience and enables flexible, real-time information delivery by providing optimal information while taking user emotions into consideration.

[0973] (Example 2)

[0974] 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."

[0975] Traditional information systems simply provided information in response to user inquiries, lacking the flexibility to adapt to users' emotions and circumstances. As a result, users often felt that the information they received didn't match their feelings or urgency, leading to decreased satisfaction. For example, even if someone was extremely hungry and urgently needed a meal, they might only be provided with information about nearby restaurants, which was insufficient in situations requiring immediate assistance.

[0976] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0977] In this invention, the server includes means for receiving inquiry messages and location information from a user; means for analyzing the sentiment of the received inquiry message using an emotion engine; means for analyzing the message based on the analyzed sentiment data and identifying the user's request; means for obtaining the nearest destination information from a database based on location information and adjusting the information based on sentiment data; means for generating the obtained destination information in text format and adjusting the content of the response message according to the user's sentiment; means for sending the generated response message to the user; and means for displaying the received response message on a user interface and making it operable. This enables the provision of optimal information according to the user's sentiment and urgency, thereby improving the user experience.

[0978] An "inquiry message" is text or audio data that a user enters when asking a question or requesting information from the system.

[0979] "Location information" refers to latitude and longitude data that indicates the user's current location.

[0980] An "emotion engine" is an algorithm or software that analyzes the content of inquiry messages received from users and identifies and classifies the user's emotional state.

[0981] "Target destination information" refers to information about a specific destination that the user is looking for, and includes data such as detailed information about restaurants, shops, and facilities.

[0982] "Text format" refers to a format in which information is represented as a string of characters, providing data in a way that is easy for users to read.

[0983] A "response message" is text data containing answers or suggestions that the system generates and provides in response to a user's inquiry.

[0984] A "user interface" refers to the screens and control elements that allow a user to interact with a system, and are the elements that display information to the user and accept input.

[0985] "Dining establishment information" refers to information containing detailed data about restaurants, cafes, and other food establishments, including location, business hours, and menus.

[0986] A "reservation link" is a URL or web address that allows a user to make a reservation for a specific service or facility.

[0987] This invention provides a system that allows users to interactively obtain information about their nearest destination using a mobile device and also make reservations for that location. This system differs from conventional systems in that it analyzes user inquiry messages using an emotion engine and provides appropriate information. In particular, it generates responses including detailed information about places such as restaurants and reservation links, while simultaneously adjusting the way information is provided based on the user's emotions, thereby providing a better user experience.

[0988] The system configuration includes the following main functions:

[0989] 1. Message receiving function

[0990] The server receives inquiry messages and current location information from the user. To implement this functionality, an HTTP server is used to receive data. Specifically, the API can be built using programming languages ​​such as Python or Node.js.

[0991] 2. Emotional Engine

[0992] The server analyzes the received inquiry message to recognize the user's emotions. The emotion engine uses natural language processing libraries such as the Google NLP API and IBM Watson. Based on this analysis, the server can determine the user's emotions, such as "very hungry."

[0993] 3. Message analysis function

[0994] The server further analyzes the message, which includes sentiment data added by the sentiment engine, to identify the user's request. In this step, the server clarifies the user's specific request by referring to the sentiment data.

[0995] 4. Database search function

[0996] The server accesses the database and retrieves the nearest destination information based on the user's current location. It also uses an emotion engine to refine the suggested information. MySQL and MongoDB are suitable databases for this purpose. Specifically, SQL queries are used to retrieve the necessary information.

[0997] 5. Response generation function

[0998] The server formats the acquired destination information into text format and sends it back to the user. Here, the content and tone of the response message are adjusted according to the user's mood. For example, it might generate a response with a tone that matches the user's mood, such as, "You seem hungry! I'll recommend restaurant A, which we can go to right away."

[0999] 6. Function to send responses to users

[1000] The server sends the generated response message back to the user's terminal. Specifically, it sends it as an HTTP response.

[1001] 7. Display function on the device

[1002] The terminal displays received messages on the user interface. Users can review the information displayed on the screen and complete their online reservation by clicking the reservation link as needed.

[1003] Specific example

[1004] The following is a concrete example of how the system processes:

[1005] The user types on their mobile device, "I'm really hungry, please tell me a restaurant I can go to right away."

[1006] The device obtains the user's current location information (e.g., latitude and longitude) and sends it to the server.

[1007] The server uses an emotion engine to analyze the user's emotions (in this example, "very hungry") from the received request message.

[1008] The server analyzes the message based on the results from the emotion engine and identifies the user's specific request (in this case, "a restaurant I can go to right away").

[1009] The server retrieves restaurant information from the database. Based on the current location, it selects the nearest restaurant.

[1010] The server formats the restaurant information into text format and generates a response that matches the user's mood, such as, "Looks like you're hungry! Here's a restaurant A that's right there. Address: Example-cho 1-1. Make a reservation here: https: / / example.com / reserve / a".

[1011] The server sends the generated message back to the user's terminal.

[1012] The terminal displays received messages on the user interface, and the user can review the displayed information. If necessary, they can click the reservation link to complete the online reservation.

[1013] Example of a prompt:

[1014] "I'm really hungry, so please tell me a restaurant we can go to right away."

[1015] "Can you recommend a cafe I can get to quickly?"

[1016] Thus, the system of the present invention receives user inquiry messages and location information, recognizes and analyzes the user's emotions using an emotion engine, and provides optimal information. As a result, users can obtain the necessary information in a dialogue format and receive detailed responses tailored to their emotions.

[1017] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1018] Step 1: User submits request

[1019] The user opens their mobile device and launches a messaging app. The user types, "I'm really hungry, please tell me a restaurant I can get to right away." The device uses its built-in GPS to obtain the current latitude (35.6895) and longitude (139.6917). This input data (user's text and location information) is sent to the server as an HTTP POST request.

[1020] Step 2: Server receives and parses requests

[1021] The server receives an HTTP POST request at the API endpoint. The server extracts the inquiry content and location information from the received message. This data is passed to the sentiment engine for analysis. The sentiment engine identifies the emotion as "very hungry." The input data consists of the user's inquiry message and location information, and the output is the sentiment data as a result of the analysis.

[1022] Step 3: Analyze the message and identify the request.

[1023] The server identifies the user's request specifically based on the analysis results of the emotion engine. It analyzes the received message and emotion data to identify a "restaurant you can go to right away." The input data is emotion data and the user's message, and the output is the identified request.

[1024] Step 4: Retrieve information from the database

[1025] The server accesses the database and retrieves information on the nearest restaurant based on the user's current location. Based on sentiment data, it prioritizes selecting restaurants that are easily accessible. Specifically, it executes an SQL query to search for restaurants within a 500m radius of the user's current location (35.6895,139.6917). The input data consists of location information and sentiment data, and the output is information on the selected restaurants.

[1026] Step 5: Generate responses for the user

[1027] The server generates a response message in text format based on the acquired restaurant information. It references sentiment data and makes adjustments according to the sentiment. For example, it might generate a message like, "Looks like you're hungry! Here's a restaurant A that's right away. Address: Example Town 1-1. Make a reservation here: https: / / example.com / reserve / a". The input data is restaurant information and sentiment data, and the output is the response message.

[1028] Step 6: Sending responses to users

[1029] The server sends the generated response message to the user's terminal as an HTTP response. Specifically, it returns a response with HTTP status code 200. The input data is the response message, and the output is the HTTP response.

[1030] Step 7: Display on the device

[1031] The terminal displays the received response message on the screen. The user can see the message, "Looks like you're hungry! Here's a restaurant A that's close by. Address: Example-cho 1-1. Make a reservation here: https: / / example.com / reserve / a", and click the reservation link to complete the online reservation. The input data is the received response, and the output is the displayed content.

[1032] (Application Example 2)

[1033] 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."

[1034] Traditional user interfaces suffered from a poor user experience because they mechanically provided information without considering the user's emotions. Furthermore, they lacked mechanisms to quickly and appropriately provide information on nearby destinations such as restaurants, which hindered user satisfaction.

[1035] 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.

[1036] In this invention, the server includes means for receiving inquiry messages and location information from a user, means for analyzing the received inquiry messages and recognizing the user's emotions, means for identifying the user's requests based on the analyzed emotions, means for obtaining the nearest destination information from a database based on the current location information, and means for generating the obtained destination information in text format and responding to the user. This makes it possible to provide timely and appropriate information based on the user's emotions and improve the user experience.

[1037] A "user inquiry message" refers to an information request that a user sends to the system via a mobile device or other device.

[1038] "Location information" refers to data that indicates the user's current location, and is usually expressed as latitude and longitude.

[1039] "Means of receiving" refers to the mechanism by which the server obtains inquiry messages and location information sent by the user.

[1040] "Means of analysis and recognition of user emotions" refer to algorithms and models that process received inquiry messages and identify user emotions.

[1041] "Methods for identifying user requests based on analyzed emotions" refers to a process for identifying the information and services that users truly want, while taking their emotions into consideration.

[1042] A "database" is a repository of information that stores destination information and other data to be provided to users.

[1043] "Means for obtaining the nearest destination information based on current location information" refers to a function that searches for and obtains information about the nearest destination based on the user's current location.

[1044] "Destination information" refers to detailed information such as the name, address, and contact information of restaurants and other establishments.

[1045] "A means of generating and responding to the user in text format" refers to a mechanism for formatting destination information as a string and providing it to the user.

[1046] "Restaurant information" refers to information such as the location, menu, and business hours of restaurants and cafes.

[1047] A "reservation link" is a URL or web address that allows users to make reservations directly via the internet.

[1048] This invention provides a system that allows users to interactively obtain information about their nearest destination using a mobile device and also make reservations for that location. This system is characterized by its ability to analyze the user's emotions and provide optimal information.

[1049] Main components of the program

[1050] The system includes the following main features:

[1051] 1. Message receiving function

[1052] The server receives inquiry messages and current location information from the user. This allows the server to understand what the user is requesting.

[1053] 2. Emotional Engine

[1054] The server analyzes the received inquiry message and recognizes the user's emotions. This emotion analysis uses the "transformers" library from Hugging Face.

[1055] 3. Message analysis function

[1056] The server further analyzes the message, which includes emotional data added by the emotion engine, to identify the user's specific request.

[1057] 4. Database search function

[1058] The server accesses the database and retrieves the nearest destination information based on the user's current location. The information retrieved includes restaurant information.

[1059] 5. Response generation function

[1060] The server formats the retrieved destination information into text format and sends it back to the user. It also adjusts the content and tone of the response message according to the user's mood. The response also includes a booking link.

[1061] System operation

[1062] The system starts working when a user sends a specific request. For example, a user might type, "I'm really hungry, please tell me a restaurant I can go to right away." Along with this input message, the user's current location is sent to the server.

[1063] The server analyzes the received request using an emotion engine to identify the user's emotion (in this case, "very hungry"). Next, the message analysis function extracts the user's specific request ("a restaurant I can go to right now").

[1064] Next, the database search function searches for the nearest restaurant based on the user's current location and retrieves that information. At this stage, the results of the sentiment engine are also considered to select the most appropriate restaurant.

[1065] Finally, the response generation function sends the acquired restaurant information back to the user in text format. The specific response message would look something like this: "Looks like you're hungry! Here's a recommendation for the nearest restaurant, Restaurant A. Make a reservation here: https: / / example.com / reserve / a".

[1066] System implementation example

[1067] The hardware used to implement this system includes a standard server, and the software includes Python, Flask, and the Hugging Face transformers library, while the database uses SQL, etc. This enables a series of processes that receive user input, perform sentiment analysis, and provide optimal information in text format.

[1068] Example of a prompt

[1069] "I'm really hungry, so please tell me about a restaurant we can go to right away."

[1070] When this prompt message is sent to the system, the emotion engine interprets it as "very hungry," and the user is then provided with restaurant information that can be used quickly.

[1071] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1072] Step 1:

[1073] The user enters an inquiry message and current location information on their mobile device.

[1074] Input: User's message "I'm really hungry, please tell me a restaurant I can go to right away," and current location information (e.g., latitude and longitude).

[1075] Processing: The mobile device retrieves the message and location information and sends it to the server.

[1076] Output: The message and location information are sent to the server.

[1077] Step 2:

[1078] The server receives the inquiry message and location information.

[1079] Input: Inquiry message and location information submitted by the user

[1080] Processing: The server receives these as HTTP requests.

[1081] Output: Received messages and location information

[1082] Step 3:

[1083] The server's emotion engine analyzes the received messages and recognizes the user's emotions.

[1084] Input: Received inquiry message

[1085] Processing: Use the "transformers" library from Hugging Face to analyze the sentiment of the message.

[1086] Output: User sentiment data (e.g., "Very hungry")

[1087] Step 4:

[1088] The server analyzes messages based on emotion to identify the user's specific requests.

[1089] Input: Message and sentiment data

[1090] Processing: Use message analysis to identify that the user is looking for "restaurants they can go to right away."

[1091] Output: Request from the identified user

[1092] Step 5:

[1093] The server accesses the database and retrieves the nearest destination information based on the user's current location.

[1094] Input: User's current location and specified request

[1095] Processing: Use SQL queries or similar methods to search and retrieve information about the nearest destination from the database.

[1096] Output: Information on the nearest restaurants

[1097] Step 6:

[1098] The server formats the destination information it has obtained into text format and returns it to the user.

[1099] Input: Nearest restaurant information

[1100] Process: Generate a message in text format that reads, "Looks like you're hungry! Here's a recommendation for the nearest restaurant, Restaurant A. Make a reservation here: https: / / example.com / reserve / a".

[1101] Output: Generated response message

[1102] Step 7:

[1103] The server generates a message and sends it to the user's mobile device.

[1104] Input: Generated response message

[1105] Processing: Send the response message as an HTTP response.

[1106] Output: Message sent to the user's mobile device

[1107] Step 8:

[1108] The system displays messages received by the user's mobile device in the user interface.

[1109] Input: Received message

[1110] Processing: Display the message as text in the user interface.

[1111] Output: The user confirms the information displayed on their mobile device screen.

[1112] 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.

[1113] 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.

[1114] 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.

[1115] [Fourth Embodiment]

[1116] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[1117] 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.

[1118] 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).

[1119] 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.

[1120] 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.

[1121] 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).

[1122] 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.

[1123] 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.

[1124] 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.

[1125] 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.

[1126] 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.

[1127] 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.

[1128] 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".

[1129] This invention provides a system that allows users to easily obtain information about their nearest destination using a mobile device, and to access detailed information and even make reservations. The system receives inquiry messages from users, analyzes them, and provides appropriate information. In particular, it provides high convenience to users by generating responses that include detailed information about places such as restaurants and reservation links.

[1130] Description of the system's programs and processes

[1131] Main components of the program

[1132] The system includes the following main features:

[1133] 1. Message receiving function

[1134] The server receives inquiry messages and current location information from the user.

[1135] 2. Message analysis function

[1136] The server analyzes the received message and identifies the user's request from its content.

[1137] 3. Database search function

[1138] The server accesses the database and retrieves the nearest destination information based on the user's current location.

[1139] 4. Response generation function

[1140] The server formats the acquired destination information into text format and returns it to the user.

[1141] Processing flow

[1142] The following is a specific example of the system's processing.

[1143] 1. User requests are submitted.

[1144] The user types "Please tell me the nearest restaurant" on their mobile device.

[1145] The device obtains the user's current location information (latitude and longitude) and sends it to the server.

[1146] 2. Receiving and parsing server requests

[1147] The server receives requests from users.

[1148] Identify the user's request (in this case, "nearest restaurant") from the received message.

[1149] 3. Retrieving information from the database

[1150] The server retrieves restaurant information from a dummy database.

[1151] The system will select the nearest restaurant based on your current location.

[1152] 4. Generating responses to users

[1153] The server formats the information of the selected restaurants into text format.

[1154] As a concrete example, it generates a response message such as, "The nearest restaurant is Restaurant A. Address: Example Town 1-1. Make a reservation here: https: / / example.com / reserve / a".

[1155] 5. Sending responses to users

[1156] The server sends the generated message to the terminal, and the message is displayed on the terminal.

[1157] Users can view this information on their devices and make reservations as needed.

[1158] This system configuration allows users to obtain necessary information in real time through an interactive format and even make reservations on the spot. This process provides a far more natural and flexible user experience compared to maps or static information.

[1159] The following describes the processing flow.

[1160] Step 1:

[1161] The user types "Please tell me the nearest restaurant" on their mobile device. The device retrieves the user's current location information (e.g., latitude and longitude).

[1162] Step 2:

[1163] The device sends the user's inquiry message and current location information to the server. Specifically, it sends a data packet containing the inquiry message and location information as an HTTP request.

[1164] Step 3:

[1165] The server parses the received request data. The query message and location information are separated into data structures to prepare for the next processing step.

[1166] Step 4:

[1167] The server performs natural language analysis on the query message to identify the user's request. In this example, it extracts the request "Please tell me the nearest restaurant" from the query message.

[1168] Step 5:

[1169] The server accesses the database and searches for the nearest restaurant based on location information. Using database queries, it calculates the distance from the current location and identifies the closest restaurant.

[1170] Step 6:

[1171] The system formats the restaurant information (name, address, reservation URL, etc.) obtained by the server into text format. For example, it generates a response message such as, "The nearest restaurant is Restaurant A. Address: Example Town 1-1. Make a reservation here: https: / / example.com / reserve / a".

[1172] Step 7:

[1173] The server sends the generated response message back to the user's device. It is sent as an HTTP response.

[1174] Step 8:

[1175] The terminal displays received messages on the user interface. Users can review the information displayed on the screen and complete their online reservation by clicking the reservation link as needed.

[1176] (Example 1)

[1177] 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".

[1178] Traditional location-based information systems have suffered from a poor user experience due to the cumbersome process required to obtain detailed information about the user's current location. Furthermore, the need for users to utilize multiple platforms when making reservations at restaurants and other establishments, resulting in a lack of convenience, is also a challenge.

[1179] 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.

[1180] In this invention, the server includes means for receiving inquiry messages and location information from a user, means for analyzing the received inquiry messages and identifying the user's request, means for obtaining the nearest destination information from a database based on the location information, means for obtaining the user's current location using GPS functionality, and means for generating the obtained destination information in text format and responding to the user. This allows the user to easily obtain information on the nearest restaurant and proceed directly to making a reservation.

[1181] An "inquiry message" is a message, such as text, that a user enters and sends to a server in order to obtain information.

[1182] "Location information" refers to data indicating the user's current latitude and longitude, obtained using methods such as GPS.

[1183] The "GPS function" is a function that uses a global positioning system to obtain geographical location information.

[1184] "Means of receiving" refers to hardware or software mechanisms that can receive data transmitted by a user.

[1185] "Means of analysis" refers to a mechanism that includes algorithms and programs for analyzing received data.

[1186] "Means for identifying user requirements" refer to processes and mechanisms for clarifying the information and actions that users desire based on analyzed data.

[1187] A "database" is a management system that systematically stores information and allows it to be searched and retrieved as needed.

[1188] "Nearest destination information" refers to data that indicates the location closest to the user's current location.

[1189] "Means of generating in text format" refers to a mechanism for formatting acquired information into text that is easy for the user to understand.

[1190] "Means of responding" refers to the process or device that sends the generated text message to the user.

[1191] "Restaurant information" refers to data such as the name, address, contact information, and business hours of restaurants and cafes.

[1192] A "reservation link" is a URL that allows direct access to the reservation page of a specific restaurant.

[1193] This invention provides a system that allows users to easily obtain information on nearby restaurants using their mobile devices, and to access detailed information and even make reservations. The system receives inquiry messages from users, analyzes them, and provides appropriate information. In particular, it provides high convenience to users by generating responses that include detailed information about restaurants and other locations, as well as reservation links.

[1194] This system primarily includes the following key features:

[1195] 1. Message receiving function

[1196] The server receives inquiry messages and current location information from the user. The message entered by the user on their mobile device and the current latitude and longitude information obtained using the device's GPS function are sent to the server.

[1197] 2. Message analysis function

[1198] The server analyzes the received message using a natural language processing engine (e.g., NLTK or spaCy) and identifies the user's request from its content.

[1199] 3. Database search function

[1200] The server accesses a database (e.g., MySQL) to retrieve information about the nearest restaurant based on the user's current location. The database stores information such as the restaurant's name, address, contact information, and business hours.

[1201] 4. Response generation function

[1202] The server formats the retrieved restaurant information into text format. For example, it generates a response message such as, "The nearest restaurant is Restaurant A. Address: Example Town 1-1. Make a reservation here: https: / / example.com / reserve / a".

[1203] 5. Sending responses to users

[1204] The server sends the generated message to the terminal, and the message is displayed on the terminal. The user checks this information on the terminal and makes a reservation if necessary.

[1205] The following is an example of the system's specific operation:

[1206] When a user types "Tell me the nearest restaurant" using their mobile device, the device uses its GPS function to obtain its current location and sends it to the server along with the inquiry message. The server parses the received message to understand that the user is looking for a restaurant and retrieves information about the nearest restaurant from its database. The retrieved information is formatted into a text message and sent to the user's device as a reply. Upon receiving this message on their device, the user can review the displayed restaurant information and click the link to proceed with the reservation process.

[1207] The following is an example of a specific prompt statement:

[1208] Prompt message:

[1209] This document describes the processing of a location-based restaurant search system using mobile devices. It details how the process works, what data is used, and includes user-side operating procedures.

[1210] This provides detailed clues to gain a concrete understanding of the system's operation and processing flow.

[1211] The flow of the specific processing in Example 1 will be explained using Figure 11.

[1212] Step 1: User submits request

[1213] Input: The user launches the application on their mobile device and enters "Please tell me the nearest restaurant" in the text input field.

[1214] Specific operation: The user opens the app on their smartphone, types "What are some restaurants near me?" into the search bar, and taps the send button. The device uses its GPS function to obtain the user's current location (latitude and longitude) and sends it to the server along with the text input.

[1215] Output: The user's current location information and inquiry message are sent to the server.

[1216] Step 2: Server receives request

[1217] Input: Inquiry message and current location information sent from the user's mobile device.

[1218] Specific operation: The server-side API endpoint receives the HTTP request and extracts the message content and location information.

[1219] Output: The received message and location information are passed to the analysis module.

[1220] Step 3: Message Analysis

[1221] Input: Received inquiry message.

[1222] Specific operation: The server uses a natural language processing engine (e.g., NLTK or spaCy) to analyze the user's input text, "What are some nearby restaurants?", and extracts the keywords "restaurants" and the information "nearest". This identifies the user's request.

[1223] Output: The information the user is looking for (nearest restaurants) is identified.

[1224] Step 4: Database Search

[1225] Input: User's current location and specified request (nearest restaurant).

[1226] Specific operation: The server executes a query like "SELECT FROM restaurants WHERE ST_Distance(location, ST_Point(user's latitude, user's longitude)) ORDER BY distance LIMIT 1" on the database (e.g., MySQL). This retrieves the record of the nearest restaurant.

[1227] Output: Information about the nearest restaurant (name, address, reservation link, etc.) is retrieved.

[1228] Step 5: Generate response

[1229] Input: Information about the nearest restaurant obtained.

[1230] Specific operation: The server uses a template engine (e.g., Jinja) to format the retrieved restaurant information into text format and generate a text message with the following content: "The nearest restaurant is Restaurant A. Address: Example Town 1-1. Make a reservation here: https: / / example.com / reserve / a"

[1231] Output: The generated text message is prepared as a reply message to the user.

[1232] Step 6: Sending responses to users

[1233] Input: The generated text message.

[1234] Specific operation: The server converts the generated text message into JSON format and sends it to the user's terminal as an HTTP response. The terminal displays the received message in its user interface.

[1235] Output: The response message displayed on the user's device. The user reviews the displayed information, and if necessary, taps the displayed reservation link to open a browser and proceed with the reservation process.

[1236] (Application Example 1)

[1237] 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".

[1238] Currently, when users search for restaurant information, view details, make reservations, and place orders, they have to go through multiple applications and websites. This process is time-consuming and degrades the user experience. In particular, when using food delivery services, a smooth information retrieval and ordering process is required, but the current system does not adequately meet this need.

[1239] 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.

[1240] In this invention, the server includes means for receiving inquiry messages and location information from a user, means for analyzing the received inquiry messages and identifying the user's request, means for obtaining the nearest destination information from a database based on the location information, means for generating the obtained destination information in text format and responding to the user, and means for generating a link that allows the user to complete an order based on the obtained information. This enables the user to smoothly complete everything from searching for restaurant information to making reservations and placing orders within a single application.

[1241] "User inquiry messages" refer to questions or requests about locations or services sent by users of the system.

[1242] "Location information" refers to data on the user's current location, identified by latitude and longitude.

[1243] "Means of receiving" refers to functions and devices for acquiring messages and location information from users.

[1244] "Means of analysis and identification of user requests" refers to functions or software that analyze the content of received messages and identify the information or services that the user is seeking.

[1245] "Means of obtaining the nearest destination information from a database based on location information" refers to functions or systems that retrieve data on the nearest destination (e.g., a restaurant) from a database based on the user's location information.

[1246] "Means of generating and responding in text format" refers to functions or software that convert acquired destination information into a text message and send it to the user.

[1247] "Means of generating a link that allows an order to be completed based on acquired information" refers to a function or system that creates a web link to complete an order based on information specified by the user and provides that link to the user.

[1248] This section describes the system for realizing this invention. The overall system configuration and processing details are described in detail below.

[1249] In this invention, the server includes the following means:

[1250] 1. Means for receiving user inquiry messages and location information

[1251] Users use a mobile device, such as a smartphone, to input and send inquiry messages about restaurants. During this process, the mobile device uses its GPS function to obtain its current location information (latitude and longitude) and sends it to the server along with the message.

[1252] 2. Means for analyzing received inquiry messages and identifying user requests.

[1253] The server analyzes the received message. This analysis uses natural language processing to identify the user's specific request from the message content. For example, if the message "Tell me the nearest restaurant that can deliver quickly" is entered, the server will identify "restaurants that can deliver quickly."

[1254] 3. Means for obtaining the nearest destination information from a database based on location information.

[1255] The server accesses its database and searches for information on the nearest restaurants based on the user's current location. This database includes restaurant locations, names, addresses, delivery times, and menu links.

[1256] 4. A means of generating acquired destination information in text format and returning it to the user.

[1257] The server generates a text message to respond to the user based on the acquired restaurant information. For example, it might be in the format of: "The nearest restaurant is Restaurant A. Address: Example Town 1-1. Delivery time: 30 minutes. Menu available here: https: / / example.com / menu / a".

[1258] 5. Means for generating a link that allows the customer to complete an order based on the information obtained.

[1259] The server generates a web link to complete the order based on the restaurant information specified by the user and provides it to the user. This link allows the user to complete the order with a single tap from within the application.

[1260] Hardware and software to be used

[1261] Hardware: Smartphone (iOS / Android)

[1262] Software: Python (programming language), Geopy (Python location acquisition library), requests (HTTP request library)

[1263] Specific example

[1264] Let's say a user opens a food delivery app and enters a request saying, "Tell me the nearest restaurant in Shinjuku Ward that can deliver quickly." The app obtains the user's current location information and sends it to the server. The server analyzes the received message and identifies the user's request. Next, it searches its database for information on the nearest restaurants that can deliver quickly and generates the details of the relevant restaurants in text format. This is sent back to the user, who can complete the order by tapping the link.

[1265] Example of a prompt

[1266] If a user enters "Tell me the nearest restaurant in Shinjuku Ward that offers fast delivery," the AI ​​model will input the following prompt:

[1267] Design a Python program that, when a user enters "Tell me the nearest restaurant in Shinjuku Ward that offers fast delivery," retrieves the user's location, searches for nearby delivery restaurants, and provides detailed information (restaurant name, address, delivery time, menu link, etc.).

[1268] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[1269] Step 1:

[1270] A user types "Tell me the nearest restaurant in Shinjuku Ward that offers fast delivery" on their mobile device. The device retrieves this inquiry message and location information (latitude and longitude) and sends it to the server. The input is the user's request and current location, and the output is the data transmission to the server.

[1271] Step 2:

[1272] The server receives an inquiry message sent by the user. The input data consists of the user's inquiry message and location information. The server analyzes this data to identify the information the user is looking for (in this case, the nearest restaurant with fast delivery). Natural language processing is used for the analysis, and the output is the identification of the necessary information.

[1273] Step 3:

[1274] The server queries the database based on the analysis results. The database stores information such as the location, name, address, delivery time, and menu links of restaurants. The input is the user's request and location information, and the output retrieved from the database is information about the most suitable restaurant. The server retrieves the restaurant information closest to the user's current location.

[1275] Step 4:

[1276] The server formats the retrieved restaurant information into text format. Specifically, it generates a reply message containing information such as the restaurant name, address, delivery time, and menu links. The input is restaurant information retrieved from the database, and the output is a text message to be sent to the user. The server prepares this for replying to the user's mobile device.

[1277] Step 5:

[1278] The server sends a final response message to the user's mobile device. The user's device receives this message and displays its contents. The input is the text message sent from the server, and the output is the display on the user's device screen. This allows the user to check information about nearby restaurants and complete their order by clicking on menu links.

[1279] Step 6:

[1280] The server then generates a link that allows the user to complete their order based on the restaurant information they specify. The input is the restaurant details, and the output is a link to the order page. The server also provides this link to the user. For example, there is a specific example: "The nearest restaurant is Restaurant A. Address: Example Town 1-1. Delivery time: 30 minutes. Menu available here: https: / / example.com / menu / a".

[1281] 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.

[1282] This invention provides a system that allows users to interactively obtain information about their nearest destination using a mobile device and also make reservations for that location. This system differs from conventional systems in that it analyzes user inquiry messages using an emotion engine and provides appropriate information. In particular, it generates responses including detailed information about places such as restaurants and reservation links, while simultaneously adjusting the way information is provided based on the user's emotions, thereby providing a better user experience.

[1283] Description of the system's programs and processes

[1284] Main components of the program

[1285] The system includes the following main features:

[1286] 1. Message receiving function

[1287] The server receives inquiry messages and current location information from the user.

[1288] 2. Emotional Engine

[1289] The server analyzes the received inquiry message and recognizes the user's emotions.

[1290] 3. Message analysis function

[1291] The server further analyzes the message, which includes emotional data added by the emotion engine, to identify the user's request.

[1292] 4. Database search function

[1293] The server accesses the database and retrieves the nearest destination information based on the user's current location. It also uses an emotion engine to tailor the suggested information.

[1294] 5. Response generation function

[1295] The server formats the acquired destination information into text format and sends it back to the user. It also adjusts the content and tone of the response message according to the user's mood.

[1296] Processing flow

[1297] The following is a specific example of the system's processing.

[1298] 1. User requests are submitted.

[1299] The user types on their mobile device, "I'm really hungry, please tell me a restaurant I can go to right away."

[1300] The device obtains the user's current location information (e.g., latitude and longitude) and sends it to the server.

[1301] 2. Receiving and parsing server requests

[1302] The server receives requests from users.

[1303] The emotion engine analyzes the user's emotions (in this example, "very hungry") from the received message.

[1304] 3. Message analysis and request identification

[1305] The server analyzes the message based on the results of the emotion engine and identifies the user's specific request (in this case, "a restaurant I can go to right away").

[1306] 4. Retrieving information from the database

[1307] The server retrieves restaurant information from the database. Based on the current location, it selects the nearest restaurant.

[1308] The restaurant information provided will be adjusted based on the user's preferences. For example, restaurants that are easily accessible will be prioritized.

[1309] 5. Generating responses to users

[1310] The server formats the information of the selected restaurant into text format. For example, it generates a response message such as, "The nearest restaurant is Restaurant A. Address: Example Town 1-1. Make a reservation here: https: / / example.com / reserve / a".

[1311] It also generates responses in a tone that matches the user's mood, such as, "You seem hungry! I'll recommend restaurant A, which we can go to right away."

[1312] 6. Sending responses to users

[1313] The server sends the generated message back to the user's terminal. It sends it as an HTTP response.

[1314] 7. Display by device

[1315] The terminal displays received messages on the user interface. Users can review the information displayed on the screen and complete their online reservation by clicking the reservation link as needed.

[1316] Thus, the system of the present invention receives user inquiry messages and location information, recognizes and analyzes the user's emotions using an emotion engine, and provides optimal information. As a result, users can obtain the necessary information in a dialogue format and receive detailed responses tailored to their emotions.

[1317] The following describes the processing flow.

[1318] Step 1:

[1319] The user types "I'm really hungry, please tell me a restaurant I can go to right away" on their mobile device. The device then retrieves the user's current location information (e.g., latitude and longitude).

[1320] Step 2:

[1321] The device sends the user's inquiry message and current location information to the server. Specifically, it sends a data packet containing the inquiry message and location information to the server as an HTTP request.

[1322] Step 3:

[1323] The server parses the received request. First, the inquiry message and location information are separated into data structures, and then the message is passed to the sentiment engine to analyze the user's sentiment.

[1324] Step 4:

[1325] The emotion engine recognizes emotions from the received query message. For example, it identifies the emotion "I'm hungry" as "very hungry" and returns that emotion data to the server.

[1326] Step 5:

[1327] The server identifies the user's request based on the sentiment data returned from the sentiment engine. In this case, it determines that the user is looking for a "restaurant they can go to right away."

[1328] Step 6:

[1329] The server accesses the database and searches for the nearest restaurant based on the user's current location. At the same time, it filters the results based on the user's sentiment data, prioritizing restaurants suitable for those in a hurry.

[1330] Step 7:

[1331] The server formats the acquired restaurant information into text format. For example, it generates a response message such as, "Looks like you're hungry! The nearest restaurant is Restaurant A. Address: Example Town 1-1. Make a reservation here: https: / / example.com / reserve / a".

[1332] Step 8:

[1333] The server sends the generated response message back to the user's terminal. It is sent as an HTTP response, preparing the terminal to display the received data.

[1334] Step 9:

[1335] The terminal displays received messages on the user interface. Users can review the information displayed on the screen and complete their online reservation by clicking the reservation link as needed.

[1336] In this way, the system improves the user experience and enables flexible, real-time information delivery by providing optimal information while taking user emotions into consideration.

[1337] (Example 2)

[1338] 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".

[1339] Traditional information systems simply provided information in response to user inquiries, lacking the flexibility to adapt to users' emotions and circumstances. As a result, users often felt that the information they received didn't match their feelings or urgency, leading to decreased satisfaction. For example, even if someone was extremely hungry and urgently needed a meal, they might only be provided with information about nearby restaurants, which was insufficient in situations requiring immediate assistance.

[1340] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[1341] In this invention, the server includes means for receiving inquiry messages and location information from a user; means for analyzing the sentiment of the received inquiry message using an emotion engine; means for analyzing the message based on the analyzed sentiment data and identifying the user's request; means for obtaining the nearest destination information from a database based on location information and adjusting the information based on sentiment data; means for generating the obtained destination information in text format and adjusting the content of the response message according to the user's sentiment; means for sending the generated response message to the user; and means for displaying the received response message on a user interface and making it operable. This enables the provision of optimal information according to the user's sentiment and urgency, thereby improving the user experience.

[1342] An "inquiry message" is text or audio data that a user enters when asking a question or requesting information from the system.

[1343] "Location information" refers to latitude and longitude data that indicates the user's current location.

[1344] An "emotion engine" is an algorithm or software that analyzes the content of inquiry messages received from users and identifies and classifies the user's emotional state.

[1345] "Target destination information" refers to information about a specific destination that the user is looking for, and includes data such as detailed information about restaurants, shops, and facilities.

[1346] "Text format" refers to a format in which information is represented as a string of characters, providing data in a way that is easy for users to read.

[1347] A "response message" is text data containing answers or suggestions that the system generates and provides in response to a user's inquiry.

[1348] A "user interface" refers to the screens and control elements that allow a user to interact with a system, and are the elements that display information to the user and accept input.

[1349] "Dining establishment information" refers to information containing detailed data about restaurants, cafes, and other food establishments, including location, business hours, and menus.

[1350] A "reservation link" is a URL or web address that allows a user to make a reservation for a specific service or facility.

[1351] This invention provides a system that allows users to interactively obtain information about their nearest destination using a mobile device and also make reservations for that location. This system differs from conventional systems in that it analyzes user inquiry messages using an emotion engine and provides appropriate information. In particular, it generates responses including detailed information about places such as restaurants and reservation links, while simultaneously adjusting the way information is provided based on the user's emotions, thereby providing a better user experience.

[1352] The system configuration includes the following main functions:

[1353] 1. Message receiving function

[1354] The server receives inquiry messages and current location information from the user. To implement this functionality, an HTTP server is used to receive data. Specifically, the API can be built using programming languages ​​such as Python or Node.js.

[1355] 2. Emotional Engine

[1356] The server analyzes the received inquiry message to recognize the user's emotions. The emotion engine uses natural language processing libraries such as the Google NLP API and IBM Watson. Based on this analysis, the server can determine the user's emotions, such as "very hungry."

[1357] 3. Message analysis function

[1358] The server further analyzes the message, which includes sentiment data added by the sentiment engine, to identify the user's request. In this step, the server clarifies the user's specific request by referring to the sentiment data.

[1359] 4. Database search function

[1360] The server accesses the database and retrieves the nearest destination information based on the user's current location. It also uses an emotion engine to refine the suggested information. MySQL and MongoDB are suitable databases for this purpose. Specifically, SQL queries are used to retrieve the necessary information.

[1361] 5. Response generation function

[1362] The server formats the acquired destination information into text format and sends it back to the user. Here, the content and tone of the response message are adjusted according to the user's mood. For example, it might generate a response with a tone that matches the user's mood, such as, "You seem hungry! I'll recommend restaurant A, which we can go to right away."

[1363] 6. Function to send responses to users

[1364] The server sends the generated response message back to the user's terminal. Specifically, it sends it as an HTTP response.

[1365] 7. Display function on the device

[1366] The terminal displays received messages on the user interface. Users can review the information displayed on the screen and complete their online reservation by clicking the reservation link as needed.

[1367] Specific example

[1368] The following is a concrete example of how the system processes:

[1369] The user types on their mobile device, "I'm really hungry, please tell me a restaurant I can go to right away."

[1370] The device obtains the user's current location information (e.g., latitude and longitude) and sends it to the server.

[1371] The server uses an emotion engine to analyze the user's emotions (in this example, "very hungry") from the received request message.

[1372] The server analyzes the message based on the results from the emotion engine and identifies the user's specific request (in this case, "a restaurant I can go to right away").

[1373] The server retrieves restaurant information from the database. Based on the current location, it selects the nearest restaurant.

[1374] The server formats the restaurant information into text format and generates a response that matches the user's mood, such as, "Looks like you're hungry! Here's a restaurant A that's right there. Address: Example-cho 1-1. Make a reservation here: https: / / example.com / reserve / a".

[1375] The server sends the generated message back to the user's terminal.

[1376] The terminal displays received messages on the user interface, and the user can review the displayed information. If necessary, they can click the reservation link to complete the online reservation.

[1377] Example of a prompt:

[1378] "I'm really hungry, so please tell me a restaurant we can go to right away."

[1379] "Can you recommend a cafe I can get to quickly?"

[1380] Thus, the system of the present invention receives user inquiry messages and location information, recognizes and analyzes the user's emotions using an emotion engine, and provides optimal information. As a result, users can obtain the necessary information in a dialogue format and receive detailed responses tailored to their emotions.

[1381] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1382] Step 1: User submits request

[1383] The user opens their mobile device and launches a messaging app. The user types, "I'm really hungry, please tell me a restaurant I can get to right away." The device uses its built-in GPS to obtain the current latitude (35.6895) and longitude (139.6917). This input data (user's text and location information) is sent to the server as an HTTP POST request.

[1384] Step 2: Server receives and parses requests

[1385] The server receives an HTTP POST request at the API endpoint. The server extracts the inquiry content and location information from the received message. This data is passed to the sentiment engine for analysis. The sentiment engine identifies the emotion as "very hungry." The input data consists of the user's inquiry message and location information, and the output is the sentiment data as a result of the analysis.

[1386] Step 3: Analyze the message and identify the request.

[1387] The server identifies the user's request specifically based on the analysis results of the emotion engine. It analyzes the received message and emotion data to identify a "restaurant you can go to right away." The input data is emotion data and the user's message, and the output is the identified request.

[1388] Step 4: Retrieve information from the database

[1389] The server accesses the database and retrieves information on the nearest restaurant based on the user's current location. Based on sentiment data, it prioritizes selecting restaurants that are easily accessible. Specifically, it executes an SQL query to search for restaurants within a 500m radius of the user's current location (35.6895,139.6917). The input data consists of location information and sentiment data, and the output is information on the selected restaurants.

[1390] Step 5: Generate responses for the user

[1391] The server generates a response message in text format based on the acquired restaurant information. It references sentiment data and makes adjustments according to the sentiment. For example, it might generate a message like, "Looks like you're hungry! Here's a restaurant A that's right away. Address: Example Town 1-1. Make a reservation here: https: / / example.com / reserve / a". The input data is restaurant information and sentiment data, and the output is the response message.

[1392] Step 6: Sending responses to users

[1393] The server sends the generated response message to the user's terminal as an HTTP response. Specifically, it returns a response with HTTP status code 200. The input data is the response message, and the output is the HTTP response.

[1394] Step 7: Display on the device

[1395] The terminal displays the received response message on the screen. The user can see the message, "Looks like you're hungry! Here's a restaurant A that's close by. Address: Example-cho 1-1. Make a reservation here: https: / / example.com / reserve / a", and click the reservation link to complete the online reservation. The input data is the received response, and the output is the displayed content.

[1396] (Application Example 2)

[1397] 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".

[1398] Traditional user interfaces suffered from a poor user experience because they mechanically provided information without considering the user's emotions. Furthermore, they lacked mechanisms to quickly and appropriately provide information on nearby destinations such as restaurants, which hindered user satisfaction.

[1399] 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.

[1400] In this invention, the server includes means for receiving inquiry messages and location information from a user, means for analyzing the received inquiry messages and recognizing the user's emotions, means for identifying the user's requests based on the analyzed emotions, means for obtaining the nearest destination information from a database based on the current location information, and means for generating the obtained destination information in text format and responding to the user. This makes it possible to provide timely and appropriate information based on the user's emotions and improve the user experience.

[1401] A "user inquiry message" refers to an information request that a user sends to the system via a mobile device or other device.

[1402] "Location information" refers to data that indicates the user's current location, and is usually expressed as latitude and longitude.

[1403] "Means of receiving" refers to the mechanism by which the server obtains inquiry messages and location information sent by the user.

[1404] "Means of analysis and recognition of user emotions" refer to algorithms and models that process received inquiry messages and identify user emotions.

[1405] "Methods for identifying user requests based on analyzed emotions" refers to a process for identifying the information and services that users truly want, while taking their emotions into consideration.

[1406] A "database" is a repository of information that stores destination information and other data to be provided to users.

[1407] "Means for obtaining the nearest destination information based on current location information" refers to a function that searches for and obtains information about the nearest destination based on the user's current location.

[1408] "Destination information" refers to detailed information such as the name, address, and contact information of restaurants and other establishments.

[1409] "A means of generating and responding to the user in text format" refers to a mechanism for formatting destination information as a string and providing it to the user.

[1410] "Restaurant information" refers to information such as the location, menu, and business hours of restaurants and cafes.

[1411] A "reservation link" is a URL or web address that allows users to make reservations directly via the internet.

[1412] This invention provides a system that allows users to interactively obtain information about their nearest destination using a mobile device and also make reservations for that location. This system is characterized by its ability to analyze the user's emotions and provide optimal information.

[1413] Main components of the program

[1414] The system includes the following main features:

[1415] 1. Message receiving function

[1416] The server receives inquiry messages and current location information from the user. This allows the server to understand what the user is requesting.

[1417] 2. Emotional Engine

[1418] The server analyzes the received inquiry message and recognizes the user's emotions. This emotion analysis uses the "transformers" library from Hugging Face.

[1419] 3. Message analysis function

[1420] The server further analyzes the message, which includes emotional data added by the emotion engine, to identify the user's specific request.

[1421] 4. Database search function

[1422] The server accesses the database and retrieves the nearest destination information based on the user's current location. The information retrieved includes restaurant information.

[1423] 5. Response generation function

[1424] The server formats the retrieved destination information into text format and sends it back to the user. It also adjusts the content and tone of the response message according to the user's mood. The response also includes a booking link.

[1425] System operation

[1426] The system starts working when a user sends a specific request. For example, a user might type, "I'm really hungry, please tell me a restaurant I can go to right away." Along with this input message, the user's current location is sent to the server.

[1427] The server analyzes the received request using an emotion engine to identify the user's emotion (in this case, "very hungry"). Next, the message analysis function extracts the user's specific request ("a restaurant I can go to right now").

[1428] Next, the database search function searches for the nearest restaurant based on the user's current location and retrieves that information. At this stage, the results of the sentiment engine are also considered to select the most appropriate restaurant.

[1429] Finally, the response generation function sends the acquired restaurant information back to the user in text format. The specific response message would look something like this: "Looks like you're hungry! Here's a recommendation for the nearest restaurant, Restaurant A. Make a reservation here: https: / / example.com / reserve / a".

[1430] System implementation example

[1431] The hardware used to implement this system includes a standard server, and the software includes Python, Flask, and the Hugging Face transformers library, while the database uses SQL, etc. This enables a series of processes that receive user input, perform sentiment analysis, and provide optimal information in text format.

[1432] Example of a prompt

[1433] "I'm really hungry, so please tell me about a restaurant we can go to right away."

[1434] When this prompt message is sent to the system, the emotion engine interprets it as "very hungry," and the user is then provided with restaurant information that can be used quickly.

[1435] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1436] Step 1:

[1437] The user enters an inquiry message and current location information on their mobile device.

[1438] Input: User's message "I'm really hungry, please tell me a restaurant I can go to right away," and current location information (e.g., latitude and longitude).

[1439] Processing: The mobile device retrieves the message and location information and sends it to the server.

[1440] Output: The message and location information are sent to the server.

[1441] Step 2:

[1442] The server receives the inquiry message and location information.

[1443] Input: Inquiry message and location information submitted by the user

[1444] Processing: The server receives these as HTTP requests.

[1445] Output: Received messages and location information

[1446] Step 3:

[1447] The server's emotion engine analyzes the received messages and recognizes the user's emotions.

[1448] Input: Received inquiry message

[1449] Processing: Use the "transformers" library from Hugging Face to analyze the sentiment of the message.

[1450] Output: User sentiment data (e.g., "Very hungry")

[1451] Step 4:

[1452] The server analyzes messages based on emotion to identify the user's specific requests.

[1453] Input: Message and sentiment data

[1454] Processing: Use message analysis to identify that the user is looking for "restaurants they can go to right away."

[1455] Output: Request from the identified user

[1456] Step 5:

[1457] The server accesses the database and retrieves the nearest destination information based on the user's current location.

[1458] Input: User's current location and specified request

[1459] Processing: Use SQL queries or similar methods to search and retrieve information about the nearest destination from the database.

[1460] Output: Information on the nearest restaurants

[1461] Step 6:

[1462] The server formats the destination information it has obtained into text format and returns it to the user.

[1463] Input: Nearest restaurant information

[1464] Process: Generate a message in text format that reads, "Looks like you're hungry! Here's a recommendation for the nearest restaurant, Restaurant A. Make a reservation here: https: / / example.com / reserve / a".

[1465] Output: Generated response message

[1466] Step 7:

[1467] The server generates a message and sends it to the user's mobile device.

[1468] Input: Generated response message

[1469] Processing: Send the response message as an HTTP response.

[1470] Output: Message sent to the user's mobile device

[1471] Step 8:

[1472] The system displays messages received by the user's mobile device in the user interface.

[1473] Input: Received message

[1474] Processing: Display the message as text in the user interface.

[1475] Output: The user confirms the information displayed on their mobile device screen.

[1476] 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.

[1477] 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.

[1478] 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.

[1479] 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.

[1480] 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.

[1481] 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.

[1482] 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.

[1483] 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.

[1484] 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."

[1485] 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.

[1486] 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.

[1487] 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.

[1488] 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.

[1489] 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.

[1490] 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.

[1491] 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.

[1492] 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.

[1493] 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.

[1494] 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.

[1495] 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.

[1496] 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.

[1497] The following is further disclosed regarding the embodiments described above.

[1498] (Claim 1)

[1499] A means of receiving inquiry messages and location information from users,

[1500] A means for analyzing the received inquiry message and identifying the user's request,

[1501] A means of obtaining the nearest destination information from a database based on location information,

[1502] A system including means for generating the acquired destination information in text format and returning it to the user.

[1503] (Claim 2)

[1504] The system according to claim 1, in which the acquired destination information includes restaurant information.

[1505] (Claim 3)

[1506] The system according to claim 1, which includes a reservation link in the acquired restaurant information.

[1507] "Example 1"

[1508] (Claim 1)

[1509] A means of receiving inquiry messages and location information from users,

[1510] A means for analyzing the received inquiry message and identifying the user's request,

[1511] A means of obtaining the nearest destination information from a database based on location information,

[1512] A means for generating the acquired destination information in text format and returning it to the user,

[1513] A system that includes a means of obtaining the user's current location using GPS functionality.

[1514] (Claim 2)

[1515] The system according to claim 1, in which the acquired destination information includes restaurant information.

[1516] (Claim 3)

[1517] The system according to claim 1, which includes a reservation link in the acquired restaurant information.

[1518] "Application Example 1"

[1519] (Claim 1)

[1520] A means of receiving inquiry messages and location information from users,

[1521] A means for analyzing the received inquiry message and identifying the user's request,

[1522] A means of obtaining the nearest destination information from a database based on location information,

[1523] A means for generating the acquired destination information in text format and returning it to the user,

[1524] A system including means for generating a link that allows an order to be completed based on the information obtained.

[1525] (Claim 2)

[1526] The system according to claim 1, in which the acquired destination information includes restaurant information.

[1527] (Claim 3)

[1528] The system according to claim 1, which includes a reservation link in the acquired restaurant information.

[1529] "Example 2 of combining an emotion engine"

[1530] (Claim 1)

[1531] A means of receiving inquiry messages and location information from users,

[1532] A means for analyzing the sentiment of the received inquiry message using an emotion engine,

[1533] A means for analyzing the message based on the analyzed sentiment data and identifying the user's request,

[1534] A means of obtaining the nearest target location information from a database based on location data and adjusting the information based on sentiment data,

[1535] A means for generating the acquired target location information in text format and adjusting the content of the response message according to the user's emotions,

[1536] A system including means for sending the generated response message to the user.

[1537] (Claim 2)

[1538] The system according to claim 1, in which the acquired target location information includes restaurant information.

[1539] (Claim 3)

[1540] The system according to claim 1, which includes a reservation link in the acquired restaurant information.

[1541] "Application example 2 of combining emotional engines"

[1542] (Claim 1)

[1543] A means of receiving inquiry messages and location information from users,

[1544] A means for analyzing the received inquiry message and recognizing the user's emotions,

[1545] A means for identifying user requests based on the analyzed emotions,

[1546] A means of obtaining the nearest destination information from a database based on current location information,

[1547] A system including means for generating the acquired destination information in text format and returning it to the user.

[1548] (Claim 2)

[1549] The system according to claim 1, in which the acquired destination information includes restaurant information.

[1550] (Claim 3)

[1551] The system according to claim 1, which includes a reservation link in the acquired restaurant information. [Explanation of Symbols]

[1552] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of receiving inquiry messages and location information from users, A means for analyzing the received inquiry message and identifying the user's request, A means of obtaining the nearest destination information from a database based on location information, A system including means for generating the acquired destination information in text format and returning it to the user.

2. The system according to claim 1, in which the acquired destination information includes restaurant information.

3. The system according to claim 1, which includes a reservation link in the acquired restaurant information.

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