system

Abstract

We provide the system. [Solution] A means of receiving mood information entered by the user, A means for selecting an appropriate location corresponding to received mood information using a generative model, A means of presenting the user with a list of selected locations, A means for calculating the optimal route from a given location to a location selected by the user, A system that includes means of proposing transportation to a selected location.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance as a 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] We will describe the 'problems to be solved' and'means for solving the problems'.

[0005] Conventional map applications focus on efficient movement and mainly provide functions such as offering the shortest and cheapest routes and searching for stores and facilities near the current location. Such applications are suitable for users' daily movement needs, but there is a problem that there are few opportunities for new discoveries and mood changes due to repeating the same routine.

[0006] In modern society, many users simply commute between work and home, limiting their opportunities for refreshment and new experiences. In this environment, there is a need for applications that allow users to discover new places and provide refreshment and new experiences according to their mood at the time. [Means for solving the problem]

[0007] This invention solves the above problem by providing a system that selects an appropriate location using a generative model based on mood information entered by the user and presents that location to the user.

[0008] This system includes the following means: First, means for receiving mood information entered by the user. Second, means for selecting an appropriate location corresponding to the received mood information using a generative model. And means for presenting a list of selected locations to the user. Furthermore, means for calculating the optimal route to the location selected by the user and for suggesting means of transportation.

[0009] In addition, this system can provide more suitable suggestions to users by including a means to narrow down potential locations by matching user profile information with past reviews. Furthermore, it can further improve user convenience by including a means to arrange transportation to the user's selected location in cooperation with transportation service providers.

[0010] Below are definitions of key terms included in the patent claims.

[0011] A "user" refers to an individual who uses an application to input mood information and selects a location suggested by the system.

[0012] "Mood information" refers to information that users input, indicating their current emotional state and desires.

[0013] A "generative model" refers to an algorithm or computational model used to select an appropriate location based on input mood information.

[0014] "The right place" refers to a location selected by the generative model based on the user's mood information, which corresponds to the user's current mood and needs.

[0015] "Selection method" refers to the function that uses a generative model to perform the process of identifying locations that fit the mood information.

[0016] A "list" refers to a presentation format that groups together several selected, appropriate locations.

[0017] "Means of presentation" refers to the ability to display a list of selected locations to the user visually or in other ways.

[0018] "Optimal route" refers to the most efficient path from the user's current location to the selected destination.

[0019] "Transportation" refers to the methods of travel available to the user along the optimal route (such as public transport or ride-hailing services).

[0020] "Profile information" refers to registered information about a user, including their personal preferences and past behavioral data.

[0021] A "review" refers to the ratings and comments that past users have provided about a place.

[0022] "Ride-hailing" refers to the process of booking a taxi or other vehicle as a means of transportation to a location specified by the user.

[0023] A "transportation service provider" refers to a company or organization that provides means of transportation, such as public transport or ride-hailing services. [Brief explanation of the drawing]

[0024] [Figure 1] It is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] It shows an emotion map to which 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

[0025] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0026] First, let's explain the terminology used in the following explanation.

[0027] 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).

[0028] 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.

[0029] 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.

[0030] 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).

[0031] 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."

[0032] [First Embodiment]

[0033] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0034] 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.

[0035] 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).

[0036] 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.

[0037] 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.

[0038] 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.

[0039] 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.

[0040] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0041] 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.

[0042] 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.

[0043] 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.

[0044] 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".

[0045] Below is a description of the "modes for carrying out the invention" as stated in the patent specification for a map app with emotions.

[0046] Modes for carrying out the invention

[0047] This invention relates to a map application system that suggests appropriate locations based on the user's mood information, enabling the user to make new discoveries and feel refreshed. This system involves the server, terminal, and user working together to process information.

[0048] Explanation of the program's processing

[0049] 1. User Login

[0050] The user launches the app and accesses the login screen. After entering their login information (username and password), the device sends this information to the server for authentication. The server verifies the received authentication information against its database and returns the authentication result to the device. If authentication is successful, the server retrieves the user's profile information and sends it to the device. After receiving the profile information, the device transitions to the main screen.

[0051] 2. Mood Input

[0052] The user enters their current mood (e.g., "I'm tired," "I want to eat something spicy") on the main screen. This mood information is sent from the device to the server.

[0053] 3. Emotional Analysis and Spot Selection

[0054] The server inputs the user's mood information into a generative model and initiates a process to select an appropriate location based on that mood. The generative model uses the user's profile information, past reviews, and rating data to search the database for locations that match the mood information and lists several candidate locations.

[0055] For example, if a user types "I'm tired," the server will search for places like relaxation spots or quiet cafes. If a user types "I want to eat something spicy," the server will suggest restaurants that specialize in spicy food.

[0056] 4. Suggestions for recommended places

[0057] The server sends a list of selected locations to the terminal. The terminal displays this list visually to the user. The user selects a location they want to go to from this list.

[0058] 5. Route guidance

[0059] When the user selects a destination, the device sends this selection information to the server. The server calculates the optimal route from the user's current location to the selected destination and also presents alternative modes of transportation (e.g., bus, train, ride-hailing service). The calculation results are sent to the device and displayed to the user. The user reviews the displayed route guidance and selects a ride-hailing service if necessary. The device then initiates the ride-hailing arrangement and coordinates with the ride-hailing service provider to arrange a taxi.

[0060] 6. Feedback after the visit

[0061] After visiting a location, the user enters feedback and reviews about the place within the app. The device sends this information to the server. The server stores the received feedback in a database and uses it as training data to improve the accuracy of future suggestions.

[0062] Specific usage examples

[0063] One day, a user exhausted from work launches the app and enters their mood as "tired." The app then suggests quiet cafes and relaxation salons. When the user selects a cafe, the app suggests the best route and transportation options. If the user chooses a ride-hailing service, the app arranges a taxi, and the user refreshes themselves at the cafe. After the visit, the user provides feedback on the cafe within the app, and this information is used to improve future suggestions.

[0064] The above describes embodiments of the present invention, which are designed to allow users to discover new places and refresh themselves according to their mood.

[0065] The following describes the processing flow.

[0066] The following explains the program's processing steps for the emotionally-driven map app "YALL".

[0067] Step 1:

[0068] The user launches the app and accesses the login screen. They enter their login information (username and password).

[0069] Step 2:

[0070] The terminal sends the login information entered by the user to the server. The server checks the received authentication information against its database and returns the authentication result to the terminal.

[0071] Step 3:

[0072] Upon successful authentication, the server retrieves the user's profile information and sends it to the device. After receiving the profile information, the device transitions to the main screen.

[0073] Step 4:

[0074] On the main screen, the user enters their current mood. For example, they might enter "I'm tired" or "I want to eat something spicy." This mood information is sent from the device to the server.

[0075] Step 5:

[0076] The server launches a generative model to analyze the received mood information. The generative model uses the user's profile information, past reviews, and rating data to search the database for locations that match the mood information.

[0077] Step 6:

[0078] The server lists several suitable locations based on the results of the generative model. The list includes places to relax, restaurants, and other establishments that correspond to the mood information.

[0079] Step 7:

[0080] The server sends the information of the listed locations to the terminal. The terminal then displays this information to the user.

[0081] Step 8:

[0082] The user selects a desired location from a list of presented locations. Once the selection is complete, the device sends the selection information to the server.

[0083] Step 9:

[0084] The server calculates the optimal route from the origin (the user's current location) to the destination (the selected location) based on the received selection information. The calculation also includes transportation options (e.g., bus, train, ride-hailing service, etc.).

[0085] Step 10:

[0086] The server sends the calculation results to the terminal. The terminal displays the optimal route and transportation options to the user.

[0087] Step 11:

[0088] The user checks the route guidance and selects a ride-hailing service if necessary. If a ride-hailing service is selected, the device connects with the transportation service provider (e.g., Uber) to arrange the ride.

[0089] Step 12:

[0090] After visiting a location, users enter feedback and reviews about the place they visited within the app. This feedback information is sent from the device to the server.

[0091] Step 13:

[0092] The server stores the received feedback in a database. This data is then used as training data to improve the accuracy of future suggestions.

[0093] The above outlines the specific processing steps of the "YALL" program.

[0094] (Example 1)

[0095] 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."

[0096] Traditional map applications have limited functionality in suggesting locations based on the user's mood, and therefore have the problem of not being able to adequately meet the needs of individual users. Furthermore, even if local information and transportation options are provided, this does not always match the user's mood or situation at the time, often leaving users seeking relaxation or new experiences dissatisfied.

[0097] 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.

[0098] In this invention, the server includes means for receiving mood information entered by the user, means for selecting an appropriate location corresponding to the received mood information using a generative model, means for presenting a list of selected locations to the user, means for calculating the optimal route from the presented locations to a location selected by the user, means for suggesting means of transportation to the selected location, means for generating a list of candidate locations by utilizing the received mood information and user profile information by providing prompts to the generative model, and means for suggesting relaxation spots, restaurants, etc., based on the mood information entered by the user. As a result, the user can receive suggestions for places that suit their mood and refresh themselves efficiently.

[0099] "Mood information" refers to data entered by users in the form of text or choices, indicating the emotions and moods they are currently experiencing.

[0100] A "generative model" refers to a machine learning algorithm or AI model that generates new information or suggestions based on received data.

[0101] "User profile information" refers to data that includes a user's personal information, past behavioral history, preferences, and so on.

[0102] A "list of candidate locations" is a list of several suitable locations proposed by the generative model.

[0103] The "optimal route" is the most efficient and time-saving path to reach the location selected by the user.

[0104] "Transportation" refers to means of getting around, such as buses, trains, taxis, and ride-hailing services.

[0105] A "prompt" is an instruction or command given to input specific information into a generative model.

[0106] A "relaxation spot" is a place where users can relax, and examples include cafes and spas.

[0107] A "restaurant" is a facility that provides food and beverage services to users.

[0108] Modes for carrying out the invention

[0109] This invention relates to a map application system that suggests appropriate locations based on the user's mood information, enabling the user to make new discoveries and feel refreshed. This system involves the server, terminal, and user working together to process information.

[0110] This system includes the following elements:

[0111] 1. Means for receiving mood information entered by the user:

[0112] The user launches the application on their device, enters their username and password, and logs in.

[0113] The device sends this information to the server, which then authenticates by matching it against its database. If authentication is successful, the server sends the user's profile information to the device.

[0114] 2. Means for selecting an appropriate location corresponding to received mood information using a generative model:

[0115] The user enters their current mood on the main screen (e.g., "I'm tired," "I want to eat something spicy," etc.). The device then sends this mood information to the server.

[0116] The server prompts the generating AI model and uses the received mood information and user profile information to generate a list of candidate locations.

[0117] 3. Means of presenting the user with a list of selected locations:

[0118] The server sends the generated list of candidate locations to the terminal, which then visually displays the list to the user.

[0119] 4. Means for calculating the optimal route from a given location to a location selected by the user:

[0120] When a user selects a desired location from a list, the device sends that information to the server.

[0121] The server calculates the optimal route from the user's current location to the selected destination and sends the result to the terminal.

[0122] 5. Means of proposing transportation to the selected location:

[0123] The server suggests multiple modes of transportation (e.g., bus, train, ride-hailing service, etc.) based on the calculated route and sends them to the terminal.

[0124] Users can view the displayed route guidance and select a ride-hailing service as needed. The terminal arranges the ride and coordinates with the ride-hailing service provider to dispatch a taxi.

[0125] 6. A means of generating a list of candidate locations by providing prompts to a generative model, utilizing the received mood information and user profile information:

[0126] Specifically, the server sends a prompt message to the mood-generating AI model (e.g., GPT-3) as follows: "Please list places you can suggest if the user is feeling relaxed. This user likes cafes and has past review data indicating a preference for quiet places."

[0127] A concrete example of how this system can be used is when a user, exhausted from work one day, launches the app and enters their mood as "tired." The app then suggests quiet cafes and relaxation salons. If the user selects a cafe, the app also suggests the best route and transportation options. If the user chooses a ride-hailing service, the app arranges a taxi, and the user refreshes themselves at the cafe. After the visit, the user enters feedback about the cafe within the app, and this information is used to improve future suggestions.

[0128] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0129] Step 1:

[0130] The user launches the app on their mobile device. They enter their username and password to log in.

[0131] Specific operation: The user taps the app icon to launch it and enters their username and password on the login screen.

[0132] Enter: Username, Password

[0133] Output: Sending a login request

[0134] Step 2:

[0135] The device sends the user's login information to the server. The server verifies the received authentication information against its database and sends the authentication result back to the device. If authentication is successful, it retrieves the profile information and sends it to the device.

[0136] Specific operation: The server uses SQL queries against the database to verify the authentication information.

[0137] Enter: Username, Password

[0138] Output: Authentication result, profile information

[0139] Step 3:

[0140] The device receives profile information and transitions to the main screen. The user enters their current mood.

[0141] Specific actions: The user taps the mood input button and enters their mood in the text box (for example, "tired"). They then tap the send button.

[0142] Input: Mood information (e.g., "tired")

[0143] Output: Mood information transmission

[0144] Step 4:

[0145] The terminal sends the entered mood information to the server. The server receives the mood information and provides prompts to the generating AI model. These prompts also include user profile information.

[0146] Specific operation: The server sends mood information and profile information as prompt messages to the AI ​​model that generates the data.

[0147] Input: Mood information, profile information

[0148] Output: Sending prompt messages to the generating AI model

[0149] Step 5:

[0150] The generative AI model lists appropriate locations based on mood information. The server receives responses from the generative AI model and builds a list of candidate locations.

[0151] Specific operation: The generative AI model uses an algorithm to generate a list of candidate locations based on the user's mood information and profile information.

[0152] Input: Prompt message

[0153] Output: Candidate location list

[0154] Step 6:

[0155] The server sends a list of potential locations to the terminal, which then displays it visually to the user. The user then selects the location they want to go to.

[0156] Specific operation: The server sends a list of candidate locations to the device in JSON format, and the device displays it in a UI component. The user taps a location from the list to select it.

[0157] Input: Candidate location list

[0158] Output: Information on the selected location

[0159] Step 7:

[0160] The terminal sends information about the selected location to the server. The server calculates the optimal route from the user's current location to the selected location and sends the result to the terminal.

[0161] Specific operation: The server uses a map API (e.g., Google Maps API) to calculate the optimal route.

[0162] Input: User's current location, information on the selected location

[0163] Output: Optimal route information

[0164] Step 8:

[0165] The server suggests multiple modes of transport based on the calculated optimal route and sends them to the terminal. The terminal displays this to the user. The user selects a ride-hailing service as needed.

[0166] Specific operation: The server sends a list of transportation options (bus, train, ride-hailing service, etc.) to the device in JSON format, and the device displays them in the UI. The user taps the "Use ride-hailing service" button.

[0167] Input: Optimal route information

[0168] Output: Options for transportation, information on selecting a ride-hailing service.

[0169] Step 9:

[0170] The terminal arranges for a ride and coordinates with the ride-hailing service provider to dispatch a taxi.

[0171] Specific operation: The device calls the ride-hailing service's API to arrange a taxi.

[0172] Input: Ride-hailing service selection information

[0173] Output: Vehicle dispatch confirmation information

[0174] Step 10:

[0175] Users enter feedback and reviews about places they visit within the app. The device sends this information to a server, which stores it in a database.

[0176] Specific operation: The user enters feedback into the evaluation form and taps the submit button. The device sends the feedback to the server via a POST request.

[0177] Input: Feedback Information

[0178] Output: Sending feedback, saving to database

[0179] (Application Example 1)

[0180] 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."

[0181] Traditional food delivery services have had the problem that it is difficult for users to choose the best meal according to their mood and preferences. Furthermore, the lack of a system that makes suggestions based on the user's mood has made improving the user experience a challenge.

[0182] 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.

[0183] In this invention, the server includes means for receiving mood information entered by the user, means for selecting appropriate delivery items corresponding to the received mood information using a generative AI model, means for presenting a list of selected delivery items to the user, and means for notifying the user of the delivery status of the ordered delivery items. This makes it possible to quickly suggest the optimal delivery items corresponding to the user's mood and improve the user experience.

[0184] 1. "User-inputted mood information" refers to information that users provide to the application through input methods such as text or buttons, indicating their current emotions or state (for example, "tired" or "needs energy").

[0185] 2. A "generative AI model" is an artificial intelligence algorithm used to analyze mood information entered by the user and select appropriate delivery items based on that information.

[0186] 3. "Appropriate delivery items" are food and beverage options selected based on the user's mood information, providing the refreshment and nutrition the user desires.

[0187] 4. "Means of presenting to the user" refers to methods and technologies for visually displaying a list of appropriate delivery items selected by the generative AI model on the user's device screen.

[0188] 5. "Ordered delivery items" refer to food and drinks that the user selects and decides to purchase from the presented list.

[0189] 6. "Means of notifying users of delivery status" refers to methods or systems that update and inform users in real time through an application about the process of how ordered delivery items reach the user.

[0190] 7. "User profile information" refers to data that includes basic information about the user (e.g., name, address, food preferences, etc.) and is used to select delivery items.

[0191] 8. "Past order history" refers to a record of the food and drinks a user has ordered in the past. This is used to understand the user's preferences and make suggestions more accurate.

[0192] 9. A "delivery service provider" is a business or company that is responsible for delivering items ordered by a user to a specified address.

[0193] 10. "Arranging delivery of delivery items" is the process of notifying the delivery service provider of the order after the user's order has been confirmed and preparing to deliver the food and drinks to the user.

[0194] This invention relates to a map application system that suggests appropriate delivery items based on the user's mood information. This system involves the server, terminal, and user working together to process the data.

[0195] Explanation of the program's processing

[0196] 1. Login and profile retrieval

[0197] The user launches the app and accesses the login screen. After entering their login information (username and password), the device sends this information to the server for authentication. The server verifies the received authentication information against its database and returns the authentication result to the device. If authentication is successful, the server retrieves the user's profile information and sends it to the device. After receiving the profile information, the device transitions to the main screen.

[0198] 2. Enter and submit your mood.

[0199] The user enters their current mood (e.g., "tired," "needs energy") on the main screen. This mood information is then sent from the device to the server.

[0200] 3. Analysis of mood information and item selection

[0201] The server inputs the user's mood information into a generative AI model, initiating a process to select appropriate delivery items based on that information. The generative AI model utilizes the user's profile information, past order history, and reviews to search the database for delivery items that match the mood information and lists several candidates. For example, if the user inputs "tired," the server will select items such as "energy-boosting rice bowl" or "relaxation drink."

[0202] 4. Item suggestions and ordering

[0203] The server sends a list of selected delivery items to the terminal. The terminal displays this visually to the user, who then selects the items they wish to order from this list. The selection information is sent from the terminal to the server, which notifies the delivery service provider of the order and arranges for delivery.

[0204] 5. Delivery status notification

[0205] Once the order has been processed, the server monitors the delivery status in real time and notifies the user. The terminal receives updates on the delivery status and displays them to the user as needed.

[0206] Hardware and software to use

[0207] Server: Cloud servers such as AWS

[0208] Device: Smartphone

[0209] Software: Flask (a Python micro web framework), generative AI model

[0210] As a specific example

[0211] When a user feels tired and enters "tired," the app suggests delivery items such as energizing rice bowls or relaxation drinks. Once the user selects a relaxation drink and places an order, the server notifies the delivery service provider of the arrangement, and the device receives real-time updates on the delivery status. In this way, users can quickly receive a meal tailored to their mood through a delivery service.

[0212] Example of a prompt

[0213] "Users input their mood, such as 'tired':

[0214] Question: What kinds of food and drinks are suitable?

[0215] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0216] Step 1: The user launches the app, accesses the login screen, and enters their username and password. The entered login information is sent from the device to the server. The server compares the received login information with its database and generates an authentication result. If successful, the server retrieves the user's profile information and sends it to the device. The device receives the profile information and transitions to the main screen.

[0217] Enter: Username, Password

[0218] Output: Authentication results, user profile information

[0219] Data processing and calculation: Database matching, profile information acquisition.

[0220] Step 2: The user enters their current mood on the main screen (for example, "tired" or "needs energy"), and the device sends this mood information to the server. The server inputs the received mood information into the generating AI model.

[0221] Input: Mood information

[0222] Output: Received mood information

[0223] Data processing and calculation: Inputting mood information into AI models.

[0224] Step 3: The server uses a generative AI model to analyze the user's mood information and select appropriate delivery items. The generative AI model utilizes user profile information, past order history, and reviews to search the database for and list delivery items that match the user's mood information.

[0225] Input: Mood information, user profile information, past order history

[0226] Output: List of delivery items

[0227] Data processing and computation: Database search, generation, and analysis using AI models.

[0228] Step 4: The server sends a list of selected delivery items to the terminal. The terminal displays this on its screen, and the user selects items from the suggested list. The selection information is then sent from the terminal to the server.

[0229] Input: List of delivery items

[0230] Output: User selection information

[0231] Data processing and calculation: Sending lists, receiving item selections.

[0232] Step 5: Based on the user's selection information, the server sends the order information to the delivery service provider and arranges delivery. The server notifies the delivery service provider of the arrangement.

[0233] Input: User's selection information

[0234] Output: Delivery arrangement information

[0235] Data processing and calculation: Sending order information, notifying delivery arrangements.

[0236] Step 6: After the delivery arrangements are complete, the server monitors the delivery status in real time and sends updates to the terminal. The terminal displays the delivery status to the user as needed, providing updates until delivery is complete.

[0237] Input: Delivery status information

[0238] Output: Delivery status update information

[0239] Data processing and calculation: Real-time monitoring, delivery status notification.

[0240] 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.

[0241] Below is a description of the "Modes for Carrying Out the Invention" from the patent specification concerning a map application system that incorporates an emotion engine.

[0242] Modes for carrying out the invention

[0243] This invention is a map application system that suggests appropriate locations based on the user's mood information, providing new discoveries and refreshment. This system is realized through the mutual cooperation of a server, terminal, user, and emotion engine.

[0244] Explanation of the program's processing

[0245] 1. User Login

[0246] The user launches the app and attempts to log in by entering their username and password on the login screen. The device sends this login information to the server, which verifies the authentication information against its database and sends the authentication result back to the device. If authentication is successful, the server retrieves profile information and sends it to the device, and the device transitions to the main screen.

[0247] 2. Mood input / emotion recognition

[0248] The user enters their current mood in text or voice on the main screen. The device sends this mood information to the server, while the emotion engine analyzes the user's input to identify their emotions. The emotion engine also refers to the user's past input history to predict emotions and sends this prediction to the server as well.

[0249] For example, if a user types "I feel kind of tired," the emotion engine recognizes this as "fatigue" and sends that information to the server. It also refers to past input history to make predictions such as "a refreshing spot would be preferred."

[0250] 3. Emotional Analysis and Spot Selection

[0251] The server activates a generative model based on the sentiment and prediction information obtained from the sentiment engine to select an appropriate location. The generative model uses user profile information, past reviews, and rating data to search the database for locations that match the sentiment information and lists multiple candidate locations.

[0252] For example, if the emotion engine recognizes the user's emotion as "fatigue," the server will search for relaxation spots and cafes, taking into account the user's historical data to select the most appropriate location.

[0253] 4. Suggestions for recommended places

[0254] The server sends information about the listed locations to the terminal, which then displays this information visually to the user. The user selects a desired location from the displayed list. For example, they might choose from a list of places like a "quiet cafe" or a "massage parlor."

[0255] 5. Route guidance

[0256] When a user selects a destination, the device sends this selection information to the server. The server calculates the optimal route from the user's current location to the selected destination and also presents transportation options (e.g., bus, train, ride-hailing service). The calculation results are sent to the device and displayed to the user.

[0257] 6. Vehicle dispatch arrangement

[0258] If a user selects a ride-hailing service, the terminal will work with the transportation service provider to arrange the ride. For example, if a user selects a taxi, the terminal will work with the ride-hailing service provider to arrange a taxi.

[0259] 7. Feedback after the visit

[0260] After visiting a location, users enter feedback and reviews about the place they visited within the app. The device sends this information to the server, which stores the received feedback in a database. The sentiment engine also uses this feedback as training data to improve the accuracy of future suggestions.

[0261] Specific usage examples

[0262] One day, a user tired from work launches the app and texts, "I feel kind of tired." The emotion engine recognizes the feeling of "fatigue" and, using past data, suggests relaxation spots. If the user selects "a quiet cafe," the app suggests the best route and mode of transportation (e.g., a 5-minute walk from the nearest station, or a 10-minute taxi ride). If the user selects a taxi, the app arranges one. After visiting, the user provides feedback on the cafe, and this information is used to improve future suggestions.

[0263] The above describes embodiments of the present invention, which aim to allow users to discover new places and refresh themselves according to their mood.

[0264] The following describes the processing flow.

[0265] The following explains the program processing for the MAP application system that incorporates an emotion engine, broken down into specific steps.

[0266] Step 1:

[0267] The user launches the app and accesses the login screen.

[0268] Step 2:

[0269] The user enters their login information (username and password), and the device sends this information to the server.

[0270] Step 3:

[0271] The server compares the received authentication information with the database and sends the authentication result to the terminal. If authentication is successful, the server retrieves the user's profile information and sends it to the terminal.

[0272] Step 4:

[0273] The device receives the profile information and transitions to the main screen. Here, the user's mood input interface is displayed.

[0274] Step 5:

[0275] Users input their current mood in text or voice. For example, they might input "I'm tired" or "I want to eat something spicy."

[0276] Step 6:

[0277] The device sends the mood information entered by the user to the server. Simultaneously, the emotion engine analyzes the mood information and identifies the emotions.

[0278] Step 7:

[0279] The emotion engine analyzes the user's input and past input history to identify the user's emotional state as "fatigue" or "stress," and sends it to the server.

[0280] Step 8:

[0281] The server receives emotion information from the emotion engine and activates the generation model. The generation model searches for appropriate locations from the database based on the emotion information.

[0282] Step 9:

[0283] Based on the results of the generation model, the server lists up multiple candidate locations and generates a list. For example, relaxation spots, restaurants, etc. are included in the list.

[0284] Step 10:

[0285] The server sends this list to the terminal, and the terminal displays the list to the user.

[0286] Step 11:

[0287] The user selects a place to go from the list. For example, select "a quiet café" or "a massage shop".

[0288] Step 12:

[0289] The terminal sends the selection information to the server, and the server calculates the optimal route from the user's current location to the destination.

[0290] Step 13:

[0291] The server calculates the optimal route and transportation options (e.g., bus, train, carpool service, etc.) and sends the results to the terminal.

[0292] Step 14: [[ID=۴۶]]

[0293] The terminal displays the optimal route and transportation options to the user, and the user selects a means of transportation.

[0294] Step 15: [[ID=5৪]]

[0295] When a user selects a ride-hailing service, the terminal works with the transportation service provider to arrange the ride.

[0296] Step 16:

[0297] After visiting a place, users can enter feedback and reviews about the place they visited within the app.

[0298] Step 17:

[0299] The device sends feedback information to the server, which stores it in a database. The emotion engine also uses this feedback as training data.

[0300] The above outlines the specific processing steps of the program for the MAP application system that incorporates an emotion engine.

[0301] (Example 2)

[0302] 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".

[0303] Traditional map applications lacked the ability to suggest appropriate locations based on the user's mood and feelings, resulting in an unsatisfactory user experience. Furthermore, users often spent a considerable amount of time searching for a suitable location, potentially increasing stress. Additionally, the lack of consistent suggestions for transportation and optimal routes reduced user convenience.

[0304] The specific processing by the specific processing unit 290 of the data processing apparatus 12 in Example 2 is realized by the following means. In this invention, the server includes: means for receiving the mood information input by the user; means for analyzing the received mood information using an emotion engine and identifying emotion information; means for selecting an appropriate location based on the identified emotion information using a generation model; means for presenting a list of the selected locations to the user; means for calculating an optimal route from the presented locations to the location selected by the user; and means for proposing a means of transportation to the selected location. Thereby, it becomes possible to propose an appropriate location according to the user's mood and emotion, improving the user's usage experience, shortening the search time, and providing a highly convenient service.

[0305] The "user's mood information" is text or voice data input by the user to express the current emotion or mood.

[0306] The "emotion engine" is a software component that analyzes the received mood information, identifies the user's emotion, and outputs the result.

[0307] The "generation model" is an algorithm or AI model for selecting an appropriate location based on the user's emotion information.

[0308] The "list of locations" is a list including a plurality of locations selected to match the user's emotion information.

[0309] The "optimal route" is the most efficient route from the user's current location to the destination selected by the user.

[0310] The "means of transportation" is a means of movement (e.g., walking, public transportation, carpooling service, etc.) that the user may use to reach the destination.

[0311] The "transportation service provider" is an operator that provides a carpooling service or public transportation used by the user for movement.

[0312] Modes for carrying out the invention

[0313] This invention is a map application system that suggests appropriate locations based on the user's mood information, providing new discoveries and refreshment. This system is realized through the mutual cooperation of a server, terminal, user, and emotion engine.

[0314] User Login

[0315] The user launches the app and attempts to log in by entering their username and password on the login screen. The device sends this login information to the server, which verifies the authentication information against a database (e.g., a MySQL database) and sends the authentication result back to the device. If authentication is successful, the server retrieves profile information and sends it to the device, and the device transitions to the main screen.

[0316] Mood input / emotion recognition

[0317] The user enters their current mood in text or voice on the main screen. The terminal sends this mood information to the server, while an emotion engine (e.g., IBM Watson) analyzes the user's input to identify the emotion. The emotion engine also refers to the user's past input history to predict the emotion and sends it to the server.

[0318] For example, if a user types "I feel kind of tired," the emotion engine recognizes it as "fatigue" and sends that information to the server. It also refers to past input history to make predictions such as "a refreshing spot would be preferred."

[0319] Emotional analysis and spot selection

[0320] The server launches a generative AI model (e.g., TensorFlow) based on sentiment and prediction information obtained from the sentiment engine to select an appropriate location. The generative model utilizes user profile information, past reviews, and rating data to search a database (e.g., MongoDB) for locations that match the sentiment information and lists multiple candidate locations.

[0321] For example, if the emotion engine recognizes the user's emotion as "fatigue," the server will search for relaxation spots and cafes, taking into account the user's historical data to select the most appropriate location.

[0322] Suggestions for recommended places

[0323] The server sends information about the listed locations to the terminal, which then displays this information visually to the user. The user selects a desired location from the displayed list. For example, they might choose from a list of places like a "quiet cafe" or a "massage parlor."

[0324] Route guidance

[0325] When a user selects a destination, the device sends this selection information to the server. The server calculates the optimal route from the user's current location to the selected destination (for example, using the Google Maps API) and also presents transportation options (e.g., bus, train, ride-hailing service, etc.). The calculation results are sent to the device and displayed to the user.

[0326] Vehicle dispatch arrangement

[0327] If the user selects a ride-hailing service, the device will work with the transportation service provider (for example, using the Uber API) to arrange the ride. For example, if the user selects a taxi, the device will work with the ride-hailing service provider to arrange a taxi.

[0328] Feedback after the visit

[0329] After a visit, users enter feedback and reviews about the visited location within the app. The device sends this to a server, which stores the received feedback in a database (e.g., Elasticsearch). The sentiment engine also uses this feedback as training data to improve the accuracy of future suggestions.

[0330] Specific usage examples

[0331] One day, a user tired from work launches the app and texts, "I feel kind of tired." The emotion engine recognizes the feeling of "fatigue" and, using past data, suggests relaxation spots. If the user selects "a quiet cafe," the app suggests the best route and mode of transportation (e.g., a 5-minute walk from the nearest station, or a 10-minute taxi ride). If the user selects a taxi, the app arranges one. After visiting, the user provides feedback on the cafe, and this information is used to improve future suggestions.

[0332] Example of a prompt:

[0333] Please enter your current mood. Example: "I feel kind of tired."

[0334] The purpose of this system is for users to discover new places and refresh themselves according to their mood.

[0335] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0336] Step 1:

[0337] User Login

[0338] Specific actions:

[0339] The user launches the app and enters the username "test_user" and password "password123" on the login screen.

[0340] Enter: Username and password

[0341] Data processing: The terminal converts user input information into an HTTPS request and sends it to the server.

[0342] Output: Request data (JSON including username and password)

[0343] The server compares the received JSON-formatted login information with the database (MySQL).

[0344] Input: Request data (JSON format)

[0345] Data calculation: The server matches the authentication information in the MySQL database.

[0346] Output: Authentication result (success or failure)

[0347] If authentication is successful, the server retrieves the profile information and sends it to the device in JSON format.

[0348] Input: Authentication result

[0349] Data processing: Acquisition of profile information

[0350] Output: JSON containing profile information

[0351] The device analyzes the received profile information and displays it on the main screen.

[0352] Input: Profile information (JSON format)

[0353] Data processing: JSON parsing

[0354] Output: Main screen display

[0355] Step 2:

[0356] Mood input / emotion recognition

[0357] Specific actions:

[0358] The user types the text "I feel kind of tired" on the main screen and presses the send button.

[0359] Input: Text about mood

[0360] Data processing: The device converts text data into an API request and sends it to the server.

[0361] Output: Request data (JSON including text)

[0362] The server receives the request data and sends it to the emotion engine (IBM Watson).

[0363] Input: Request data (JSON format)

[0364] Data processing: Sentiment analysis using IBM Watson

[0365] Output: Emotional information (e.g., "fatigue")

[0366] The emotion engine identifies emotional information and sends the results back to the server. The server then refers to the user's past input history and generates predictive information.

[0367] Input: Emotional information

[0368] Data calculation: Matching with past input history

[0369] Output: Sentiment information and prediction information (JSON format)

[0370] Step 3:

[0371] Emotional analysis and spot selection

[0372] Specific actions:

[0373] The server launches a generative AI model (TensorFlow) based on the emotional and predictive information obtained from the emotion engine.

[0374] Input: Sentiment information and prediction information (JSON format)

[0375] Data processing: Spot selection using generative AI models

[0376] Output: Candidate location list (JSON format)

[0377] The generative AI model uses user profile information, past reviews, and rating data to select the appropriate location.

[0378] Input: Profile information, past reviews and evaluation data

[0379] Data processing: Data integration and analysis

[0380] Output: List of multiple candidate locations (JSON format)

[0381] The server sends the list of selected candidate locations to the terminal.

[0382] Input: Candidate location list (JSON format)

[0383] Data transmission: Transmission via HTTPS request

[0384] Output: Received candidate location list

[0385] Step 4:

[0386] Suggestions for recommended places

[0387] Specific actions:

[0388] The device analyzes the received list of potential locations and displays it visually. The user selects the desired location from the list.

[0389] Input: Candidate location list

[0390] Data processing: List analysis and screen display

[0391] Output: Display of candidate locations and user selection

[0392] Step 5:

[0393] Route guidance

[0394] Specific actions:

[0395] The device sends information about the destination the user wants to go to to the server. The server obtains the user's current location and calculates the optimal route to the selected location.

[0396] Input: Selection information and current location information

[0397] Data calculation: Calculation of the optimal route (using Google Maps API)

[0398] Output: Route information and transportation options (JSON format)

[0399] The server sends route information and transportation details to the terminal. The terminal then displays this information to the user.

[0400] Input: Route information and mode of transport (JSON format)

[0401] Data processing: JSON parsing and screen display

[0402] Output: Route guidance and transportation options provided.

[0403] Step 6:

[0404] Vehicle dispatch arrangement

[0405] Specific actions:

[0406] If the user selects a ride-hailing service, the terminal will work with the transportation service provider to arrange the ride.

[0407] Input: Information on vehicle selection

[0408] Data transmission: Ride-hailing request (e.g., using the Uber API)

[0409] Output: Vehicle dispatch completion notification and detailed information

[0410] Step 7:

[0411] Feedback after the visit

[0412] Specific actions:

[0413] After a visit, the user enters feedback about the visited location within the app. The device then sends this feedback to the server.

[0414] Input: Feedback text

[0415] Data processing: Sending feedback data (JSON format)

[0416] Output: Sending feedback data to the server

[0417] The server stores the received feedback in a database (Elasticsearch), and the sentiment engine uses this feedback as training data.

[0418] Input: Feedback data

[0419] Data processing: Data storage and use as training data.

[0420] Output: Storage of feedback information and updating of the sentiment engine.

[0421] (Application Example 2)

[0422] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0423] Traditional map applications have made it difficult for users to discover new places based on their mood or feelings. Furthermore, they have not effectively facilitated user-selected locations or collected feedback after visits. Therefore, there is a need for a system that allows users to find places to refresh themselves according to their mood, facilitates easy travel, and improves the accuracy of future suggestions.

[0424] 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.

[0425] In this invention, the server includes means for receiving mood information entered by the user, means for selecting an appropriate location corresponding to the received mood information using a generative model, means for presenting a list of selected locations to the user, means for calculating the optimal route from the presented locations to a location selected by the user, means for suggesting means of transportation to the selected location, means for transmitting route information to an autonomous mobile vehicle and guiding the user, and means for receiving user feedback information and storing it as data to improve the accuracy of future suggestions. This makes it possible for the user to find an appropriate location according to their mood, travel easily, and improve the accuracy of future suggestions.

[0426] A "user" refers to a person who uses this system to input their mood information and receive guidance to an appropriate location.

[0427] "Mood information" refers to information that users input, indicating their current emotions and moods.

[0428] A "generative model" refers to an algorithm or program used to select an appropriate location based on mood information.

[0429] An "appropriate place" refers to a location selected based on the user's mood information, where the user can make new discoveries and feel refreshed.

[0430] A "list" refers to a list used to present users with a selection of suitable locations.

[0431] The "optimal route" refers to the path that allows the user to reach their chosen location most efficiently.

[0432] "Transportation" refers to the means of transport or methods available to the user to reach their chosen destination.

[0433] An "autonomous mobile vehicle" refers to a means of transportation that automatically guides the user to a selected location.

[0434] "Feedback information" refers to the impressions and evaluations that users provide after a visit, and this data is used to improve the accuracy of future suggestions.

[0435] Modes for carrying out the invention

[0436] This invention is a system that suggests appropriate locations based on the user's mood information, providing new discoveries and refreshment. This system is realized through the mutual cooperation of a server, terminal, user, and emotion engine.

[0437] Explanation of the program's processing

[0438] 1. User Login

[0439] The user launches the smartphone app and attempts to log in by entering their username and password on the login screen. The device sends this login information to the server, which verifies the authentication information against a database and sends the authentication result back to the device. If authentication is successful, the server retrieves profile information and sends it to the device, and the device transitions to the main screen.

[0440] 2. Mood input / emotion recognition

[0441] The user enters their current mood in text or voice on the main screen. The device sends this mood information to the server, while the emotion engine analyzes the user's input to identify their emotions. The emotion engine also refers to the user's past input history to predict emotions and sends this prediction to the server as well.

[0442] For example, if a user types "I feel kind of tired," the emotion engine recognizes this as "fatigue" and sends that information to the server. It also refers to past input history to make predictions such as "a refreshing spot would be preferred."

[0443] 3. Emotional Analysis and Spot Selection

[0444] The server activates a generative model based on the sentiment and prediction information obtained from the sentiment engine to select an appropriate location. The generative model uses user profile information, past reviews, and rating data to search the database for locations that match the sentiment information and lists multiple candidate locations.

[0445] For example, if the emotion engine recognizes the user's emotion as "fatigue," the server will search for relaxation spots and cafes, taking into account the user's historical data to select the most appropriate location.

[0446] 4. Suggestions for recommended places

[0447] The server sends information about the listed locations to the terminal, which then displays this information visually to the user. The user selects a desired location from the displayed list. For example, they might choose from a list of places like a "quiet cafe" or a "massage parlor."

[0448] 5. Route guidance

[0449] When a user selects a destination, the device sends this selection information to the server. The server calculates the optimal route from the user's current location to the selected destination and also presents transportation options (e.g., bus, train, autonomous vehicle, etc.). The calculation results are sent to the device and displayed to the user.

[0450] Hardware and software used

[0451] Smartphone: Used by users to run applications.

[0452] Emotion engine: Used to analyze user emotions. Specific examples include natural language processing APIs.

[0453] Server: Used for managing proposal information and calculating routes. A specific example is a cloud service platform.

[0454] Autonomous mobile vehicles: Used to guide users to a selected location. A specific example is an autonomous driving vehicle system.

[0455] Database: Used to manage user profile information and feedback. A specific example is a relational database.

[0456] Specific usage examples and prompt messages

[0457] One day, a user launches the "Emotion Navi" app and enters "I'm feeling a little stressed" on the input screen. The emotion engine recognizes this as "stress" and, based on past data, suggests places where the user can relax (for example, a park or an art museum). If the user selects "park," the optimal route is calculated, and an autonomous vehicle guides the user to that park.

[0458] Example of a prompt

[0459] User: "I'm feeling a little stressed."

[0460] Emotional engine: Recognizes "stress"

[0461] Server: "I suggest places where you can relax. Parks, art museums, cafes."

[0462] User: "Select a park"

[0463] Server: "Calculating the optimal route"

[0464] Autonomous mobile vehicle: "Starting route guidance"

[0465] As described above, this system allows users to easily find the optimal location according to their mood and travel comfortably using autonomous vehicles. Furthermore, feedback after visits will be used to improve the accuracy of future suggestions.

[0466] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0467] Step 1:

[0468] The user launches the smartphone app and attempts to log in by entering their username and password on the login screen. The device sends this login information to the server, which verifies the authentication information against a database and sends the authentication result back to the device. The input is the username and password, and the output is the authentication result. If authentication is successful, the server retrieves profile information and sends it to the device, and the device transitions to the main screen.

[0469] Step 2:

[0470] The user enters their current mood as text or voice on the main screen. The input is mood information in text or voice. The terminal sends this mood information to the server, where the emotion engine analyzes the user's input to identify the emotion. The emotion engine also refers to the user's past input history to predict the emotion and sends it to the server. The output is the analyzed emotion information.

[0471] Step 3:

[0472] The server activates a generative model based on sentiment and prediction information obtained from the sentiment engine to select an appropriate location. The input is sentiment and prediction information, and the output is a list of multiple candidate locations. The generative model uses user profile information, past reviews, and rating data to search the database for locations that match the sentiment information and lists multiple candidate locations.

[0473] Step 4:

[0474] The server sends information about the listed locations to the terminal, which then displays this information visually to the user. The input is a list of multiple possible locations, and the output is a list of locations displayed to the user. The user selects a desired location from the displayed list.

[0475] Step 5:

[0476] When the user selects a destination, the device sends this selection information to the server. The server calculates the optimal route from the user's current location to the selected destination and also presents transportation options (e.g., bus, train, autonomous vehicle, etc.). The inputs are the user's current location and the selected destination, and the output is the optimal route and transportation options. The calculation results are sent to the device and displayed to the user.

[0477] Step 6:

[0478] Route information is transmitted to the autonomous vehicle to guide the user. The server transmits optimal route information to the autonomous vehicle; the input is the optimal route information, and the output is the start of route guidance by the autonomous vehicle.

[0479] Step 7:

[0480] After a user visits a place, they input feedback and reviews on their device. The device sends this information to the server; the input is feedback data, and the output is saved to a database. The server stores the received feedback in the database, and the sentiment engine uses this feedback as training data to improve the accuracy of future suggestions.

[0481] Through these steps, users can easily find the perfect location based on their mood and travel comfortably using autonomous vehicles. Furthermore, feedback after visits will be used to improve the accuracy of future recommendations.

[0482] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating 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.

[0483] 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.

[0484] 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.

[0485] [Second Embodiment]

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

[0487] 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.

[0488] 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).

[0489] 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.

[0490] 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.

[0491] 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).

[0492] 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.

[0493] 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.

[0494] 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.

[0495] 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.

[0496] 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.

[0497] 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".

[0498] Below is a description of the "modes for carrying out the invention" as stated in the patent specification for a map app with emotions.

[0499] Modes for carrying out the invention

[0500] This invention relates to a map application system that suggests appropriate locations based on the user's mood information, enabling the user to make new discoveries and feel refreshed. This system involves the server, terminal, and user working together to process information.

[0501] Explanation of the program's processing

[0502] 1. User Login

[0503] The user launches the app and accesses the login screen. After entering their login information (username and password), the device sends this information to the server for authentication. The server verifies the received authentication information against its database and returns the authentication result to the device. If authentication is successful, the server retrieves the user's profile information and sends it to the device. After receiving the profile information, the device transitions to the main screen.

[0504] 2. Mood Input

[0505] The user enters their current mood (e.g., "I'm tired," "I want to eat something spicy") on the main screen. This mood information is sent from the device to the server.

[0506] 3. Emotional Analysis and Spot Selection

[0507] The server inputs the user's mood information into a generative model and initiates a process to select an appropriate location based on that mood. The generative model uses the user's profile information, past reviews, and rating data to search the database for locations that match the mood information and lists several candidate locations.

[0508] For example, if a user types "I'm tired," the server will search for places like relaxation spots or quiet cafes. If a user types "I want to eat something spicy," the server will suggest restaurants that specialize in spicy food.

[0509] 4. Suggestions for recommended places

[0510] The server sends a list of selected locations to the terminal. The terminal displays this list visually to the user. The user selects a location they want to go to from this list.

[0511] 5. Route guidance

[0512] When the user selects a destination, the device sends this selection information to the server. The server calculates the optimal route from the user's current location to the selected destination and also presents alternative modes of transportation (e.g., bus, train, ride-hailing service). The calculation results are sent to the device and displayed to the user. The user reviews the displayed route guidance and selects a ride-hailing service if necessary. The device then initiates the ride-hailing arrangement and coordinates with the ride-hailing service provider to arrange a taxi.

[0513] 6. Feedback after the visit

[0514] After visiting a location, the user enters feedback and reviews about the place within the app. The device sends this information to the server. The server stores the received feedback in a database and uses it as training data to improve the accuracy of future suggestions.

[0515] Specific usage examples

[0516] One day, a user exhausted from work launches the app and enters their mood as "tired." The app then suggests quiet cafes and relaxation salons. When the user selects a cafe, the app suggests the best route and transportation options. If the user chooses a ride-hailing service, the app arranges a taxi, and the user refreshes themselves at the cafe. After the visit, the user provides feedback on the cafe within the app, and this information is used to improve future suggestions.

[0517] The above describes embodiments of the present invention, which are designed to allow users to discover new places and refresh themselves according to their mood.

[0518] The following describes the processing flow.

[0519] The following explains the program's processing steps for the emotionally-driven map app "YALL".

[0520] Step 1:

[0521] The user launches the app and accesses the login screen. They enter their login information (username and password).

[0522] Step 2:

[0523] The terminal sends the login information entered by the user to the server. The server checks the received authentication information against its database and returns the authentication result to the terminal.

[0524] Step 3:

[0525] Upon successful authentication, the server retrieves the user's profile information and sends it to the device. After receiving the profile information, the device transitions to the main screen.

[0526] Step 4:

[0527] On the main screen, the user enters their current mood. For example, they might enter "I'm tired" or "I want to eat something spicy." This mood information is sent from the device to the server.

[0528] Step 5:

[0529] The server launches a generative model to analyze the received mood information. The generative model uses the user's profile information, past reviews, and rating data to search the database for locations that match the mood information.

[0530] Step 6:

[0531] The server lists several suitable locations based on the results of the generative model. The list includes places to relax, restaurants, and other establishments that correspond to the mood information.

[0532] Step 7:

[0533] The server sends the information of the listed locations to the terminal. The terminal then displays this information to the user.

[0534] Step 8:

[0535] The user selects a desired location from a list of presented locations. Once the selection is complete, the device sends the selection information to the server.

[0536] Step 9:

[0537] The server calculates the optimal route from the origin (the user's current location) to the destination (the selected location) based on the received selection information. The calculation also includes transportation options (e.g., bus, train, ride-hailing service, etc.).

[0538] Step 10:

[0539] The server sends the calculation results to the terminal. The terminal displays the optimal route and transportation options to the user.

[0540] Step 11:

[0541] The user checks the route guidance and selects a ride-hailing service if necessary. If a ride-hailing service is selected, the device connects with the transportation service provider (e.g., Uber) to arrange the ride.

[0542] Step 12:

[0543] After visiting a location, users enter feedback and reviews about the place they visited within the app. This feedback information is sent from the device to the server.

[0544] Step 13:

[0545] The server stores the received feedback in a database. This data is then used as training data to improve the accuracy of future suggestions.

[0546] The above outlines the specific processing steps of the "YALL" program.

[0547] (Example 1)

[0548] 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."

[0549] Traditional map applications have limited functionality in suggesting locations based on the user's mood, and therefore have the problem of not being able to adequately meet the needs of individual users. Furthermore, even if local information and transportation options are provided, this does not always match the user's mood or situation at the time, often leaving users seeking relaxation or new experiences dissatisfied.

[0550] 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.

[0551] In this invention, the server includes means for receiving mood information entered by the user, means for selecting an appropriate location corresponding to the received mood information using a generative model, means for presenting a list of selected locations to the user, means for calculating the optimal route from the presented locations to a location selected by the user, means for suggesting means of transportation to the selected location, means for generating a list of candidate locations by utilizing the received mood information and user profile information by providing prompts to the generative model, and means for suggesting relaxation spots, restaurants, etc., based on the mood information entered by the user. As a result, the user can receive suggestions for places that suit their mood and refresh themselves efficiently.

[0552] "Mood information" refers to data entered by users in the form of text or choices, indicating the emotions and moods they are currently experiencing.

[0553] A "generative model" refers to a machine learning algorithm or AI model that generates new information or suggestions based on received data.

[0554] "User profile information" refers to data that includes a user's personal information, past behavioral history, preferences, and so on.

[0555] A "list of candidate locations" is a list of several suitable locations proposed by the generative model.

[0556] The "optimal route" is the most efficient and time-saving path to reach the location selected by the user.

[0557] "Transportation" refers to means of getting around, such as buses, trains, taxis, and ride-hailing services.

[0558] A "prompt" is an instruction or command given to input specific information into a generative model.

[0559] A "relaxation spot" is a place where users can relax, and examples include cafes and spas.

[0560] A "restaurant" is a facility that provides food and beverage services to users.

[0561] Modes for carrying out the invention

[0562] This invention relates to a map application system that suggests appropriate locations based on the user's mood information, enabling the user to make new discoveries and feel refreshed. This system involves the server, terminal, and user working together to process information.

[0563] This system includes the following elements:

[0564] 1. Means for receiving mood information entered by the user:

[0565] The user launches the application on their device, enters their username and password, and logs in.

[0566] The device sends this information to the server, which then authenticates by matching it against its database. If authentication is successful, the server sends the user's profile information to the device.

[0567] 2. Means for selecting an appropriate location corresponding to received mood information using a generative model:

[0568] The user enters their current mood on the main screen (e.g., "I'm tired," "I want to eat something spicy," etc.). The device then sends this mood information to the server.

[0569] The server prompts the generating AI model and uses the received mood information and user profile information to generate a list of candidate locations.

[0570] 3. Means of presenting the user with a list of selected locations:

[0571] The server sends the generated list of candidate locations to the terminal, which then visually displays the list to the user.

[0572] 4. Means for calculating the optimal route from a given location to a location selected by the user:

[0573] When a user selects a desired location from a list, the device sends that information to the server.

[0574] The server calculates the optimal route from the user's current location to the selected destination and sends the result to the terminal.

[0575] 5. Means of proposing transportation to the selected location:

[0576] The server suggests multiple modes of transportation (e.g., bus, train, ride-hailing service, etc.) based on the calculated route and sends them to the terminal.

[0577] Users can view the displayed route guidance and select a ride-hailing service as needed. The terminal arranges the ride and coordinates with the ride-hailing service provider to dispatch a taxi.

[0578] 6. A means of generating a list of candidate locations by providing prompts to a generative model, utilizing the received mood information and user profile information:

[0579] Specifically, the server sends a prompt message to the mood-generating AI model (e.g., GPT-3) as follows: "Please list places you can suggest if the user is feeling relaxed. This user likes cafes and has past review data indicating a preference for quiet places."

[0580] A concrete example of how this system can be used is when a user, exhausted from work one day, launches the app and enters their mood as "tired." The app then suggests quiet cafes and relaxation salons. If the user selects a cafe, the app also suggests the best route and transportation options. If the user chooses a ride-hailing service, the app arranges a taxi, and the user refreshes themselves at the cafe. After the visit, the user enters feedback about the cafe within the app, and this information is used to improve future suggestions.

[0581] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0582] Step 1:

[0583] The user launches the app on their mobile device. They enter their username and password to log in.

[0584] Specific operation: The user taps the app icon to launch it and enters their username and password on the login screen.

[0585] Enter: Username, Password

[0586] Output: Sending a login request

[0587] Step 2:

[0588] The device sends the user's login information to the server. The server verifies the received authentication information against its database and sends the authentication result back to the device. If authentication is successful, it retrieves the profile information and sends it to the device.

[0589] Specific operation: The server uses SQL queries against the database to verify the authentication information.

[0590] Enter: Username, Password

[0591] Output: Authentication result, profile information

[0592] Step 3:

[0593] The device receives profile information and transitions to the main screen. The user enters their current mood.

[0594] Specific actions: The user taps the mood input button and enters their mood in the text box (for example, "tired"). They then tap the send button.

[0595] Input: Mood information (e.g., "tired")

[0596] Output: Mood information transmission

[0597] Step 4:

[0598] The terminal sends the entered mood information to the server. The server receives the mood information and provides prompts to the generating AI model. These prompts also include user profile information.

[0599] Specific operation: The server sends mood information and profile information as prompt messages to the AI ​​model that generates the data.

[0600] Input: Mood information, profile information

[0601] Output: Sending prompt messages to the generating AI model

[0602] Step 5:

[0603] The generative AI model lists appropriate locations based on mood information. The server receives responses from the generative AI model and builds a list of candidate locations.

[0604] Specific operation: The generative AI model uses an algorithm to generate a list of candidate locations based on the user's mood information and profile information.

[0605] Input: Prompt message

[0606] Output: Candidate location list

[0607] Step 6:

[0608] The server sends a list of potential locations to the terminal, which then displays it visually to the user. The user then selects the location they want to go to.

[0609] Specific operation: The server sends a list of candidate locations to the device in JSON format, and the device displays it in a UI component. The user taps a location from the list to select it.

[0610] Input: Candidate location list

[0611] Output: Information on the selected location

[0612] Step 7:

[0613] The terminal sends information about the selected location to the server. The server calculates the optimal route from the user's current location to the selected location and sends the result to the terminal.

[0614] Specific operation: The server uses a map API (e.g., Google Maps API) to calculate the optimal route.

[0615] Input: User's current location, information on the selected location

[0616] Output: Optimal route information

[0617] Step 8:

[0618] The server suggests multiple modes of transport based on the calculated optimal route and sends them to the terminal. The terminal displays this to the user. The user selects a ride-hailing service as needed.

[0619] Specific operation: The server sends a list of transportation options (bus, train, ride-hailing service, etc.) to the device in JSON format, and the device displays them in the UI. The user taps the "Use ride-hailing service" button.

[0620] Input: Optimal route information

[0621] Output: Options for transportation, information on selecting a ride-hailing service.

[0622] Step 9:

[0623] The terminal arranges for a ride and coordinates with the ride-hailing service provider to dispatch a taxi.

[0624] Specific operation: The device calls the ride-hailing service's API to arrange a taxi.

[0625] Input: Ride-hailing service selection information

[0626] Output: Vehicle dispatch confirmation information

[0627] Step 10:

[0628] Users enter feedback and reviews about places they visit within the app. The device sends this information to a server, which stores it in a database.

[0629] Specific operation: The user enters feedback into the evaluation form and taps the submit button. The device sends the feedback to the server via a POST request.

[0630] Input: Feedback Information

[0631] Output: Sending feedback, saving to database

[0632] (Application Example 1)

[0633] 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."

[0634] Traditional food delivery services have had the problem that it is difficult for users to choose the best meal according to their mood and preferences. Furthermore, the lack of a system that makes suggestions based on the user's mood has made improving the user experience a challenge.

[0635] 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.

[0636] In this invention, the server includes means for receiving mood information entered by the user, means for selecting appropriate delivery items corresponding to the received mood information using a generative AI model, means for presenting a list of selected delivery items to the user, and means for notifying the user of the delivery status of the ordered delivery items. This makes it possible to quickly suggest the optimal delivery items corresponding to the user's mood and improve the user experience.

[0637] 1. "User-inputted mood information" refers to information that users provide to the application through input methods such as text or buttons, indicating their current emotions or state (for example, "tired" or "needs energy").

[0638] 2. A "generative AI model" is an artificial intelligence algorithm used to analyze mood information entered by the user and select appropriate delivery items based on that information.

[0639] 3. "Appropriate delivery items" are food and beverage options selected based on the user's mood information, providing the refreshment and nutrition the user desires.

[0640] 4. "Means of presenting to the user" refers to methods and technologies for visually displaying a list of appropriate delivery items selected by the generative AI model on the user's device screen.

[0641] 5. "Ordered delivery items" refer to food and drinks that the user selects and decides to purchase from the presented list.

[0642] 6. "Means of notifying users of delivery status" refers to methods or systems that update and inform users in real time through an application about the process of how ordered delivery items reach the user.

[0643] 7. "User profile information" refers to data that includes basic information about the user (e.g., name, address, food preferences, etc.) and is used to select delivery items.

[0644] 8. "Past order history" refers to a record of the food and drinks a user has ordered in the past. This is used to understand the user's preferences and make suggestions more accurate.

[0645] 9. A "delivery service provider" is a business or company that is responsible for delivering items ordered by a user to a specified address.

[0646] 10. "Arranging delivery of delivery items" is the process of notifying the delivery service provider of the order after the user's order has been confirmed and preparing to deliver the food and drinks to the user.

[0647] This invention relates to a map application system that suggests appropriate delivery items based on the user's mood information. This system involves the server, terminal, and user working together to process the data.

[0648] Explanation of the program's processing

[0649] 1. Login and profile retrieval

[0650] The user launches the app and accesses the login screen. After entering their login information (username and password), the device sends this information to the server for authentication. The server verifies the received authentication information against its database and returns the authentication result to the device. If authentication is successful, the server retrieves the user's profile information and sends it to the device. After receiving the profile information, the device transitions to the main screen.

[0651] 2. Enter and submit your mood.

[0652] The user enters their current mood (e.g., "tired," "needs energy") on the main screen. This mood information is then sent from the device to the server.

[0653] 3. Analysis of mood information and item selection

[0654] The server inputs the user's mood information into a generative AI model, initiating a process to select appropriate delivery items based on that information. The generative AI model utilizes the user's profile information, past order history, and reviews to search the database for delivery items that match the mood information and lists several candidates. For example, if the user inputs "tired," the server will select items such as "energy-boosting rice bowl" or "relaxation drink."

[0655] 4. Item suggestions and ordering

[0656] The server sends a list of selected delivery items to the terminal. The terminal displays this visually to the user, who then selects the items they wish to order from this list. The selection information is sent from the terminal to the server, which notifies the delivery service provider of the order and arranges for delivery.

[0657] 5. Delivery status notification

[0658] Once the order has been processed, the server monitors the delivery status in real time and notifies the user. The terminal receives updates on the delivery status and displays them to the user as needed.

[0659] Hardware and software to use

[0660] Server: Cloud servers such as AWS

[0661] Device: Smartphone

[0662] Software: Flask (a Python micro web framework), generative AI model

[0663] As a specific example

[0664] When a user feels tired and enters "tired," the app suggests delivery items such as energizing rice bowls or relaxation drinks. Once the user selects a relaxation drink and places an order, the server notifies the delivery service provider of the arrangement, and the device receives real-time updates on the delivery status. In this way, users can quickly receive a meal tailored to their mood through a delivery service.

[0665] Example of a prompt

[0666] "Users input their mood, such as 'tired':

[0667] Question: What kinds of food and drinks are suitable?

[0668] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0669] Step 1: The user launches the app, accesses the login screen, and enters their username and password. The entered login information is sent from the device to the server. The server compares the received login information with its database and generates an authentication result. If successful, the server retrieves the user's profile information and sends it to the device. The device receives the profile information and transitions to the main screen.

[0670] Enter: Username, Password

[0671] Output: Authentication results, user profile information

[0672] Data processing and calculation: Database matching, profile information acquisition.

[0673] Step 2: The user enters their current mood on the main screen (for example, "tired" or "needs energy"), and the device sends this mood information to the server. The server inputs the received mood information into the generating AI model.

[0674] Input: Mood information

[0675] Output: Received mood information

[0676] Data processing and calculation: Inputting mood information into AI models.

[0677] Step 3: The server uses a generative AI model to analyze the user's mood information and select appropriate delivery items. The generative AI model utilizes user profile information, past order history, and reviews to search the database for and list delivery items that match the user's mood information.

[0678] Input: Mood information, user profile information, past order history

[0679] Output: List of delivery items

[0680] Data processing and computation: Database search, generation, and analysis using AI models.

[0681] Step 4: The server sends a list of selected delivery items to the terminal. The terminal displays this on its screen, and the user selects items from the suggested list. The selection information is then sent from the terminal to the server.

[0682] Input: List of delivery items

[0683] Output: User selection information

[0684] Data processing and calculation: Sending lists, receiving item selections.

[0685] Step 5: Based on the user's selection information, the server sends the order information to the delivery service provider and arranges delivery. The server notifies the delivery service provider of the arrangement.

[0686] Input: User's selection information

[0687] Output: Delivery arrangement information

[0688] Data processing and calculation: Sending order information, notifying delivery arrangements.

[0689] Step 6: After the delivery arrangements are complete, the server monitors the delivery status in real time and sends updates to the terminal. The terminal displays the delivery status to the user as needed, providing updates until delivery is complete.

[0690] Input: Delivery status information

[0691] Output: Delivery status update information

[0692] Data processing and calculation: Real-time monitoring, delivery status notification.

[0693] 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.

[0694] Below is a description of the "Modes for Carrying Out the Invention" from the patent specification concerning a map application system that incorporates an emotion engine.

[0695] Modes for carrying out the invention

[0696] This invention is a map application system that suggests appropriate locations based on the user's mood information, providing new discoveries and refreshment. This system is realized through the mutual cooperation of a server, terminal, user, and emotion engine.

[0697] Explanation of the program's processing

[0698] 1. User Login

[0699] The user launches the app and attempts to log in by entering their username and password on the login screen. The device sends this login information to the server, which verifies the authentication information against its database and sends the authentication result back to the device. If authentication is successful, the server retrieves profile information and sends it to the device, and the device transitions to the main screen.

[0700] 2. Mood input / emotion recognition

[0701] The user enters their current mood in text or voice on the main screen. The device sends this mood information to the server, while the emotion engine analyzes the user's input to identify their emotions. The emotion engine also refers to the user's past input history to predict emotions and sends this prediction to the server as well.

[0702] For example, if a user types "I feel kind of tired," the emotion engine recognizes this as "fatigue" and sends that information to the server. It also refers to past input history to make predictions such as "a refreshing spot would be preferred."

[0703] 3. Emotional Analysis and Spot Selection

[0704] The server activates a generative model based on the sentiment and prediction information obtained from the sentiment engine to select an appropriate location. The generative model uses user profile information, past reviews, and rating data to search the database for locations that match the sentiment information and lists multiple candidate locations.

[0705] For example, if the emotion engine recognizes the user's emotion as "fatigue," the server will search for relaxation spots and cafes, taking into account the user's historical data to select the most appropriate location.

[0706] 4. Suggestions for recommended places

[0707] The server sends information about the listed locations to the terminal, which then displays this information visually to the user. The user selects a desired location from the displayed list. For example, they might choose from a list of places like a "quiet cafe" or a "massage parlor."

[0708] 5. Route guidance

[0709] When a user selects a destination, the device sends this selection information to the server. The server calculates the optimal route from the user's current location to the selected destination and also presents transportation options (e.g., bus, train, ride-hailing service). The calculation results are sent to the device and displayed to the user.

[0710] 6. Vehicle dispatch arrangement

[0711] If a user selects a ride-hailing service, the terminal will work with the transportation service provider to arrange the ride. For example, if a user selects a taxi, the terminal will work with the ride-hailing service provider to arrange a taxi.

[0712] 7. Feedback after the visit

[0713] After visiting a location, users enter feedback and reviews about the place they visited within the app. The device sends this information to the server, which stores the received feedback in a database. The sentiment engine also uses this feedback as training data to improve the accuracy of future suggestions.

[0714] Specific usage examples

[0715] One day, a user tired from work launches the app and texts, "I feel kind of tired." The emotion engine recognizes the feeling of "fatigue" and, using past data, suggests relaxation spots. If the user selects "a quiet cafe," the app suggests the best route and mode of transportation (e.g., a 5-minute walk from the nearest station, or a 10-minute taxi ride). If the user selects a taxi, the app arranges one. After visiting, the user provides feedback on the cafe, and this information is used to improve future suggestions.

[0716] The above describes embodiments of the present invention, which aim to allow users to discover new places and refresh themselves according to their mood.

[0717] The following describes the processing flow.

[0718] The following explains the program processing for the MAP application system that incorporates an emotion engine, broken down into specific steps.

[0719] Step 1:

[0720] The user launches the app and accesses the login screen.

[0721] Step 2:

[0722] The user enters their login information (username and password), and the device sends this information to the server.

[0723] Step 3:

[0724] The server compares the received authentication information with the database and sends the authentication result to the terminal. If authentication is successful, the server retrieves the user's profile information and sends it to the terminal.

[0725] Step 4:

[0726] The device receives the profile information and transitions to the main screen. Here, the user's mood input interface is displayed.

[0727] Step 5:

[0728] Users input their current mood in text or voice. For example, they might input "I'm tired" or "I want to eat something spicy."

[0729] Step 6:

[0730] The device sends the mood information entered by the user to the server. Simultaneously, the emotion engine analyzes the mood information and identifies the emotions.

[0731] Step 7:

[0732] The emotion engine analyzes the user's input and past input history to identify the user's emotional state as "fatigue" or "stress," and sends it to the server.

[0733] Step 8:

[0734] The server receives sentiment information from the sentiment engine and invokes the generative model. The generative model searches the database for the appropriate location based on the sentiment information.

[0735] Step 9:

[0736] Based on the results of the generative model, the server lists multiple candidate locations and generates a list. For example, the list might include relaxation spots and restaurants.

[0737] Step 10:

[0738] The server sends this list to the terminal, and the terminal displays the list to the user.

[0739] Step 11:

[0740] The user selects a place they want to go from a list. For example, they might choose a "quiet cafe" or a "massage parlor."

[0741] Step 12:

[0742] The device sends selection information to the server, which calculates the optimal route from the user's current location to their destination.

[0743] Step 13:

[0744] The server calculates the optimal route and transportation options (e.g., bus, train, ride-hailing service, etc.) and sends the results to the terminal.

[0745] Step 14:

[0746] The device displays optimal route and transportation options to the user, who then selects their preferred mode of transport.

[0747] Step 15:

[0748] When a user selects a ride-hailing service, the terminal works with the transportation service provider to arrange the ride.

[0749] Step 16:

[0750] After visiting a place, users can enter feedback and reviews about the place they visited within the app.

[0751] Step 17:

[0752] The device sends feedback information to the server, which stores it in a database. The emotion engine also uses this feedback as training data.

[0753] The above outlines the specific processing steps of the program for the MAP application system that incorporates an emotion engine.

[0754] (Example 2)

[0755] 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".

[0756] Traditional map applications lacked the ability to suggest appropriate locations based on the user's mood and feelings, resulting in an unsatisfactory user experience. Furthermore, users often spent a considerable amount of time searching for a suitable location, potentially increasing stress. Additionally, the lack of consistent suggestions for transportation and optimal routes reduced user convenience.

[0757] The identification processing by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for receiving mood information entered by the user, means for analyzing the received mood information using an emotion engine and identifying the emotion information, means for selecting an appropriate location based on the identified emotion information using a generative model, means for presenting a list of selected locations to the user, means for calculating the optimal route from the presented locations to a location selected by the user, and means for suggesting means of transportation to the selected location. This makes it possible to suggest appropriate locations according to the user's mood and emotions, improving the user experience, shortening search time, and providing a highly convenient service.

[0758] "User mood information" refers to text or audio data that users input to express their current emotions or moods.

[0759] An "emotion engine" is a software component that analyzes received mood information to identify the user's emotions and outputs the result.

[0760] A "generative model" is an algorithm or AI model that selects appropriate locations based on user sentiment information.

[0761] A "list of places" is a list containing multiple locations selected to match the user's emotional information.

[0762] The "optimal route" is the most efficient path from the user's current location to the destination selected by the user.

[0763] "Transportation" refers to the means of travel that a user may use to reach their destination (e.g., walking, public transport, ride-hailing service, etc.).

[0764] A "transportation service provider" is a business that provides ride-hailing services or public transportation that users use for getting around.

[0765] Modes for carrying out the invention

[0766] This invention is a map application system that suggests appropriate locations based on the user's mood information, providing new discoveries and refreshment. This system is realized through the mutual cooperation of a server, terminal, user, and emotion engine.

[0767] User Login

[0768] The user launches the app and attempts to log in by entering their username and password on the login screen. The device sends this login information to the server, which verifies the authentication information against a database (e.g., a MySQL database) and sends the authentication result back to the device. If authentication is successful, the server retrieves profile information and sends it to the device, and the device transitions to the main screen.

[0769] Mood input / emotion recognition

[0770] The user enters their current mood in text or voice on the main screen. The terminal sends this mood information to the server, while an emotion engine (e.g., IBM Watson) analyzes the user's input to identify the emotion. The emotion engine also refers to the user's past input history to predict the emotion and sends it to the server.

[0771] For example, if a user types "I feel kind of tired," the emotion engine recognizes it as "fatigue" and sends that information to the server. It also refers to past input history to make predictions such as "a refreshing spot would be preferred."

[0772] Emotional analysis and spot selection

[0773] The server launches a generative AI model (e.g., TensorFlow) based on sentiment and prediction information obtained from the sentiment engine to select an appropriate location. The generative model utilizes user profile information, past reviews, and rating data to search a database (e.g., MongoDB) for locations that match the sentiment information and lists multiple candidate locations.

[0774] For example, if the emotion engine recognizes the user's emotion as "fatigue," the server will search for relaxation spots and cafes, taking into account the user's historical data to select the most appropriate location.

[0775] Suggestions for recommended places

[0776] The server sends information about the listed locations to the terminal, which then displays this information visually to the user. The user selects a desired location from the displayed list. For example, they might choose from a list of places like a "quiet cafe" or a "massage parlor."

[0777] Route guidance

[0778] When a user selects a destination, the device sends this selection information to the server. The server calculates the optimal route from the user's current location to the selected destination (for example, using the Google Maps API) and also presents transportation options (e.g., bus, train, ride-hailing service, etc.). The calculation results are sent to the device and displayed to the user.

[0779] Vehicle dispatch arrangement

[0780] If the user selects a ride-hailing service, the device will work with the transportation service provider (for example, using the Uber API) to arrange the ride. For example, if the user selects a taxi, the device will work with the ride-hailing service provider to arrange a taxi.

[0781] Feedback after the visit

[0782] After a visit, users enter feedback and reviews about the visited location within the app. The device sends this to a server, which stores the received feedback in a database (e.g., Elasticsearch). The sentiment engine also uses this feedback as training data to improve the accuracy of future suggestions.

[0783] Specific usage examples

[0784] One day, a user tired from work launches the app and texts, "I feel kind of tired." The emotion engine recognizes the feeling of "fatigue" and, using past data, suggests relaxation spots. If the user selects "a quiet cafe," the app suggests the best route and mode of transportation (e.g., a 5-minute walk from the nearest station, or a 10-minute taxi ride). If the user selects a taxi, the app arranges one. After visiting, the user provides feedback on the cafe, and this information is used to improve future suggestions.

[0785] Example of a prompt:

[0786] Please enter your current mood. Example: "I feel kind of tired."

[0787] The purpose of this system is for users to discover new places and refresh themselves according to their mood.

[0788] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0789] Step 1:

[0790] User Login

[0791] Specific actions:

[0792] The user launches the app and enters the username "test_user" and password "password123" on the login screen.

[0793] Enter: Username and password

[0794] Data processing: The terminal converts user input information into an HTTPS request and sends it to the server.

[0795] Output: Request data (JSON including username and password)

[0796] The server compares the received JSON-formatted login information with the database (MySQL).

[0797] Input: Request data (JSON format)

[0798] Data calculation: The server matches the authentication information in the MySQL database.

[0799] Output: Authentication result (success or failure)

[0800] If authentication is successful, the server retrieves the profile information and sends it to the device in JSON format.

[0801] Input: Authentication result

[0802] Data processing: Acquisition of profile information

[0803] Output: JSON containing profile information

[0804] The device analyzes the received profile information and displays it on the main screen.

[0805] Input: Profile information (JSON format)

[0806] Data processing: JSON parsing

[0807] Output: Main screen display

[0808] Step 2:

[0809] Mood input / emotion recognition

[0810] Specific actions:

[0811] The user types the text "I feel kind of tired" on the main screen and presses the send button.

[0812] Input: Text about mood

[0813] Data processing: The device converts text data into an API request and sends it to the server.

[0814] Output: Request data (JSON including text)

[0815] The server receives the request data and sends it to the emotion engine (IBM Watson).

[0816] Input: Request data (JSON format)

[0817] Data processing: Sentiment analysis using IBM Watson

[0818] Output: Emotional information (e.g., "fatigue")

[0819] The emotion engine identifies emotional information and sends the results back to the server. The server then refers to the user's past input history and generates predictive information.

[0820] Input: Emotional information

[0821] Data calculation: Matching with past input history

[0822] Output: Sentiment information and prediction information (JSON format)

[0823] Step 3:

[0824] Emotional analysis and spot selection

[0825] Specific actions:

[0826] The server launches a generative AI model (TensorFlow) based on the emotional and predictive information obtained from the emotion engine.

[0827] Input: Sentiment information and prediction information (JSON format)

[0828] Data processing: Spot selection using generative AI models

[0829] Output: Candidate location list (JSON format)

[0830] The generative AI model uses user profile information, past reviews, and rating data to select the appropriate location.

[0831] Input: Profile information, past reviews and evaluation data

[0832] Data processing: Data integration and analysis

[0833] Output: List of multiple candidate locations (JSON format)

[0834] The server sends the list of selected candidate locations to the terminal.

[0835] Input: Candidate location list (JSON format)

[0836] Data transmission: Transmission via HTTPS request

[0837] Output: Received candidate location list

[0838] Step 4:

[0839] Suggestions for recommended places

[0840] Specific actions:

[0841] The device analyzes the received list of potential locations and displays it visually. The user selects the desired location from the list.

[0842] Input: Candidate location list

[0843] Data processing: List analysis and screen display

[0844] Output: Display of candidate locations and user selection

[0845] Step 5:

[0846] Route guidance

[0847] Specific actions:

[0848] The device sends information about the destination the user wants to go to to the server. The server obtains the user's current location and calculates the optimal route to the selected location.

[0849] Input: Selection information and current location information

[0850] Data calculation: Calculation of the optimal route (using Google Maps API)

[0851] Output: Route information and transportation options (JSON format)

[0852] The server sends route information and transportation details to the terminal. The terminal then displays this information to the user.

[0853] Input: Route information and mode of transport (JSON format)

[0854] Data processing: JSON parsing and screen display

[0855] Output: Route guidance and transportation options provided.

[0856] Step 6:

[0857] Vehicle dispatch arrangement

[0858] Specific actions:

[0859] If the user selects a ride-hailing service, the terminal will work with the transportation service provider to arrange the ride.

[0860] Input: Information on vehicle selection

[0861] Data transmission: Ride-hailing request (e.g., using the Uber API)

[0862] Output: Vehicle dispatch completion notification and detailed information

[0863] Step 7:

[0864] Feedback after the visit

[0865] Specific actions:

[0866] After a visit, the user enters feedback about the visited location within the app. The device then sends this feedback to the server.

[0867] Input: Feedback text

[0868] Data processing: Sending feedback data (JSON format)

[0869] Output: Sending feedback data to the server

[0870] The server stores the received feedback in a database (Elasticsearch), and the sentiment engine uses this feedback as training data.

[0871] Input: Feedback data

[0872] Data processing: Data storage and use as training data.

[0873] Output: Storage of feedback information and updating of the sentiment engine.

[0874] (Application Example 2)

[0875] 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."

[0876] Traditional map applications have made it difficult for users to discover new places based on their mood or feelings. Furthermore, they have not effectively facilitated user-selected locations or collected feedback after visits. Therefore, there is a need for a system that allows users to find places to refresh themselves according to their mood, facilitates easy travel, and improves the accuracy of future suggestions.

[0877] 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.

[0878] In this invention, the server includes means for receiving mood information entered by the user, means for selecting an appropriate location corresponding to the received mood information using a generative model, means for presenting a list of selected locations to the user, means for calculating the optimal route from the presented locations to a location selected by the user, means for suggesting means of transportation to the selected location, means for transmitting route information to an autonomous mobile vehicle and guiding the user, and means for receiving user feedback information and storing it as data to improve the accuracy of future suggestions. This makes it possible for the user to find an appropriate location according to their mood, travel easily, and improve the accuracy of future suggestions.

[0879] A "user" refers to a person who uses this system to input their mood information and receive guidance to an appropriate location.

[0880] "Mood information" refers to information that users input, indicating their current emotions and moods.

[0881] A "generative model" refers to an algorithm or program used to select an appropriate location based on mood information.

[0882] An "appropriate place" refers to a location selected based on the user's mood information, where the user can make new discoveries and feel refreshed.

[0883] A "list" refers to a list used to present users with a selection of suitable locations.

[0884] The "optimal route" refers to the path that allows the user to reach their chosen location most efficiently.

[0885] "Transportation" refers to the means of transport or methods available to the user to reach their chosen destination.

[0886] An "autonomous mobile vehicle" refers to a means of transportation that automatically guides the user to a selected location.

[0887] "Feedback information" refers to the impressions and evaluations that users provide after a visit, and this data is used to improve the accuracy of future suggestions.

[0888] Modes for carrying out the invention

[0889] This invention is a system that suggests appropriate locations based on the user's mood information, providing new discoveries and refreshment. This system is realized through the mutual cooperation of a server, terminal, user, and emotion engine.

[0890] Explanation of the program's processing

[0891] 1. User Login

[0892] The user launches the smartphone app and attempts to log in by entering their username and password on the login screen. The device sends this login information to the server, which verifies the authentication information against a database and sends the authentication result back to the device. If authentication is successful, the server retrieves profile information and sends it to the device, and the device transitions to the main screen.

[0893] 2. Mood input / emotion recognition

[0894] The user enters their current mood in text or voice on the main screen. The device sends this mood information to the server, while the emotion engine analyzes the user's input to identify their emotions. The emotion engine also refers to the user's past input history to predict emotions and sends this prediction to the server as well.

[0895] For example, if a user types "I feel kind of tired," the emotion engine recognizes this as "fatigue" and sends that information to the server. It also refers to past input history to make predictions such as "a refreshing spot would be preferred."

[0896] 3. Emotional Analysis and Spot Selection

[0897] The server activates a generative model based on the sentiment and prediction information obtained from the sentiment engine to select an appropriate location. The generative model uses user profile information, past reviews, and rating data to search the database for locations that match the sentiment information and lists multiple candidate locations.

[0898] For example, if the emotion engine recognizes the user's emotion as "fatigue," the server will search for relaxation spots and cafes, taking into account the user's historical data to select the most appropriate location.

[0899] 4. Suggestions for recommended places

[0900] The server sends information about the listed locations to the terminal, which then displays this information visually to the user. The user selects a desired location from the displayed list. For example, they might choose from a list of places like a "quiet cafe" or a "massage parlor."

[0901] 5. Route guidance

[0902] When a user selects a destination, the device sends this selection information to the server. The server calculates the optimal route from the user's current location to the selected destination and also presents transportation options (e.g., bus, train, autonomous vehicle, etc.). The calculation results are sent to the device and displayed to the user.

[0903] Hardware and software used

[0904] Smartphone: Used by users to run applications.

[0905] Emotion engine: Used to analyze user emotions. Specific examples include natural language processing APIs.

[0906] Server: Used for managing proposal information and calculating routes. A specific example is a cloud service platform.

[0907] Autonomous mobile vehicles: Used to guide users to a selected location. A specific example is an autonomous driving vehicle system.

[0908] Database: Used to manage user profile information and feedback. A specific example is a relational database.

[0909] Specific usage examples and prompt messages

[0910] One day, a user launches the "Emotion Navi" app and enters "I'm feeling a little stressed" on the input screen. The emotion engine recognizes this as "stress" and, based on past data, suggests places where the user can relax (for example, a park or an art museum). If the user selects "park," the optimal route is calculated, and an autonomous vehicle guides the user to that park.

[0911] Example of a prompt

[0912] User: "I'm feeling a little stressed."

[0913] Emotional engine: Recognizes "stress"

[0914] Server: "I suggest places where you can relax. Parks, art museums, cafes."

[0915] User: "Select a park"

[0916] Server: "Calculating the optimal route"

[0917] Autonomous mobile vehicle: "Starting route guidance"

[0918] As described above, this system allows users to easily find the optimal location according to their mood and travel comfortably using autonomous vehicles. Furthermore, feedback after visits will be used to improve the accuracy of future suggestions.

[0919] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0920] Step 1:

[0921] The user launches the smartphone app and attempts to log in by entering their username and password on the login screen. The device sends this login information to the server, which verifies the authentication information against a database and sends the authentication result back to the device. The input is the username and password, and the output is the authentication result. If authentication is successful, the server retrieves profile information and sends it to the device, and the device transitions to the main screen.

[0922] Step 2:

[0923] The user enters their current mood as text or voice on the main screen. The input is mood information in text or voice. The terminal sends this mood information to the server, where the emotion engine analyzes the user's input to identify the emotion. The emotion engine also refers to the user's past input history to predict the emotion and sends it to the server. The output is the analyzed emotion information.

[0924] Step 3:

[0925] The server activates a generative model based on sentiment and prediction information obtained from the sentiment engine to select an appropriate location. The input is sentiment and prediction information, and the output is a list of multiple candidate locations. The generative model uses user profile information, past reviews, and rating data to search the database for locations that match the sentiment information and lists multiple candidate locations.

[0926] Step 4:

[0927] The server sends information about the listed locations to the terminal, which then displays this information visually to the user. The input is a list of multiple possible locations, and the output is a list of locations displayed to the user. The user selects a desired location from the displayed list.

[0928] Step 5:

[0929] When the user selects a destination, the device sends this selection information to the server. The server calculates the optimal route from the user's current location to the selected destination and also presents transportation options (e.g., bus, train, autonomous vehicle, etc.). The inputs are the user's current location and the selected destination, and the output is the optimal route and transportation options. The calculation results are sent to the device and displayed to the user.

[0930] Step 6:

[0931] Route information is transmitted to the autonomous vehicle to guide the user. The server transmits optimal route information to the autonomous vehicle; the input is the optimal route information, and the output is the start of route guidance by the autonomous vehicle.

[0932] Step 7:

[0933] After a user visits a place, they input feedback and reviews on their device. The device sends this information to the server; the input is feedback data, and the output is saved to a database. The server stores the received feedback in the database, and the sentiment engine uses this feedback as training data to improve the accuracy of future suggestions.

[0934] Through these steps, users can easily find the perfect location based on their mood and travel comfortably using autonomous vehicles. Furthermore, feedback after visits will be used to improve the accuracy of future recommendations.

[0935] 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.

[0936] 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.

[0937] 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.

[0938] [Third Embodiment]

[0939] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0940] 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.

[0941] 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).

[0942] 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.

[0943] 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.

[0944] 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).

[0945] 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.

[0946] 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.

[0947] 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.

[0948] 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.

[0949] 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.

[0950] 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".

[0951] Below is a description of the "modes for carrying out the invention" as stated in the patent specification for a map app with emotions.

[0952] Modes for carrying out the invention

[0953] This invention relates to a map application system that suggests appropriate locations based on the user's mood information, enabling the user to make new discoveries and feel refreshed. This system involves the server, terminal, and user working together to process information.

[0954] Explanation of the program's processing

[0955] 1. User Login

[0956] The user launches the app and accesses the login screen. After entering their login information (username and password), the device sends this information to the server for authentication. The server verifies the received authentication information against its database and returns the authentication result to the device. If authentication is successful, the server retrieves the user's profile information and sends it to the device. After receiving the profile information, the device transitions to the main screen.

[0957] 2. Mood Input

[0958] The user enters their current mood (e.g., "I'm tired," "I want to eat something spicy") on the main screen. This mood information is sent from the device to the server.

[0959] 3. Emotional Analysis and Spot Selection

[0960] The server inputs the user's mood information into a generative model and initiates a process to select an appropriate location based on that mood. The generative model uses the user's profile information, past reviews, and rating data to search the database for locations that match the mood information and lists several candidate locations.

[0961] For example, if a user types "I'm tired," the server will search for places like relaxation spots or quiet cafes. If a user types "I want to eat something spicy," the server will suggest restaurants that specialize in spicy food.

[0962] 4. Suggestions for recommended places

[0963] The server sends a list of selected locations to the terminal. The terminal displays this list visually to the user. The user selects a location they want to go to from this list.

[0964] 5. Route guidance

[0965] When the user selects a destination, the device sends this selection information to the server. The server calculates the optimal route from the user's current location to the selected destination and also presents alternative modes of transportation (e.g., bus, train, ride-hailing service). The calculation results are sent to the device and displayed to the user. The user reviews the displayed route guidance and selects a ride-hailing service if necessary. The device then initiates the ride-hailing arrangement and coordinates with the ride-hailing service provider to arrange a taxi.

[0966] 6. Feedback after the visit

[0967] After visiting a location, the user enters feedback and reviews about the place within the app. The device sends this information to the server. The server stores the received feedback in a database and uses it as training data to improve the accuracy of future suggestions.

[0968] Specific usage examples

[0969] One day, a user exhausted from work launches the app and enters their mood as "tired." The app then suggests quiet cafes and relaxation salons. When the user selects a cafe, the app suggests the best route and transportation options. If the user chooses a ride-hailing service, the app arranges a taxi, and the user refreshes themselves at the cafe. After the visit, the user provides feedback on the cafe within the app, and this information is used to improve future suggestions.

[0970] The above describes embodiments of the present invention, which are designed to allow users to discover new places and refresh themselves according to their mood.

[0971] The following describes the processing flow.

[0972] The following explains the program's processing steps for the emotionally-driven map app "YALL".

[0973] Step 1:

[0974] The user launches the app and accesses the login screen. They enter their login information (username and password).

[0975] Step 2:

[0976] The terminal sends the login information entered by the user to the server. The server checks the received authentication information against its database and returns the authentication result to the terminal.

[0977] Step 3:

[0978] Upon successful authentication, the server retrieves the user's profile information and sends it to the device. After receiving the profile information, the device transitions to the main screen.

[0979] Step 4:

[0980] On the main screen, the user enters their current mood. For example, they might enter "I'm tired" or "I want to eat something spicy." This mood information is sent from the device to the server.

[0981] Step 5:

[0982] The server launches a generative model to analyze the received mood information. The generative model uses the user's profile information, past reviews, and rating data to search the database for locations that match the mood information.

[0983] Step 6:

[0984] The server lists several suitable locations based on the results of the generative model. The list includes places to relax, restaurants, and other establishments that correspond to the mood information.

[0985] Step 7:

[0986] The server sends the information of the listed locations to the terminal. The terminal then displays this information to the user.

[0987] Step 8:

[0988] The user selects a desired location from a list of presented locations. Once the selection is complete, the device sends the selection information to the server.

[0989] Step 9:

[0990] The server calculates the optimal route from the origin (the user's current location) to the destination (the selected location) based on the received selection information. The calculation also includes transportation options (e.g., bus, train, ride-hailing service, etc.).

[0991] Step 10:

[0992] The server sends the calculation results to the terminal. The terminal displays the optimal route and transportation options to the user.

[0993] Step 11:

[0994] The user checks the route guidance and selects a ride-hailing service if necessary. If a ride-hailing service is selected, the device connects with the transportation service provider (e.g., Uber) to arrange the ride.

[0995] Step 12:

[0996] After visiting a location, users enter feedback and reviews about the place they visited within the app. This feedback information is sent from the device to the server.

[0997] Step 13:

[0998] The server stores the received feedback in a database. This data is then used as training data to improve the accuracy of future suggestions.

[0999] The above outlines the specific processing steps of the "YALL" program.

[1000] (Example 1)

[1001] 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."

[1002] Traditional map applications have limited functionality in suggesting locations based on the user's mood, and therefore have the problem of not being able to adequately meet the needs of individual users. Furthermore, even if local information and transportation options are provided, this does not always match the user's mood or situation at the time, often leaving users seeking relaxation or new experiences dissatisfied.

[1003] 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.

[1004] In this invention, the server includes means for receiving mood information entered by the user, means for selecting an appropriate location corresponding to the received mood information using a generative model, means for presenting a list of selected locations to the user, means for calculating the optimal route from the presented locations to a location selected by the user, means for suggesting means of transportation to the selected location, means for generating a list of candidate locations by utilizing the received mood information and user profile information by providing prompts to the generative model, and means for suggesting relaxation spots, restaurants, etc., based on the mood information entered by the user. As a result, the user can receive suggestions for places that suit their mood and refresh themselves efficiently.

[1005] "Mood information" refers to data entered by users in the form of text or choices, indicating the emotions and moods they are currently experiencing.

[1006] A "generative model" refers to a machine learning algorithm or AI model that generates new information or suggestions based on received data.

[1007] "User profile information" refers to data that includes a user's personal information, past behavioral history, preferences, and so on.

[1008] A "list of candidate locations" is a list of several suitable locations proposed by the generative model.

[1009] The "optimal route" is the most efficient and time-saving path to reach the location selected by the user.

[1010] "Transportation" refers to means of getting around, such as buses, trains, taxis, and ride-hailing services.

[1011] A "prompt" is an instruction or command given to input specific information into a generative model.

[1012] A "relaxation spot" is a place where users can relax, and examples include cafes and spas.

[1013] A "restaurant" is a facility that provides food and beverage services to users.

[1014] Modes for carrying out the invention

[1015] This invention relates to a map application system that suggests appropriate locations based on the user's mood information, enabling the user to make new discoveries and feel refreshed. This system involves the server, terminal, and user working together to process information.

[1016] This system includes the following elements:

[1017] 1. Means for receiving mood information entered by the user:

[1018] The user launches the application on their device, enters their username and password, and logs in.

[1019] The device sends this information to the server, which then authenticates by matching it against its database. If authentication is successful, the server sends the user's profile information to the device.

[1020] 2. Means for selecting an appropriate location corresponding to received mood information using a generative model:

[1021] The user enters their current mood on the main screen (e.g., "I'm tired," "I want to eat something spicy," etc.). The device then sends this mood information to the server.

[1022] The server prompts the generating AI model and uses the received mood information and user profile information to generate a list of candidate locations.

[1023] 3. Means of presenting the user with a list of selected locations:

[1024] The server sends the generated list of candidate locations to the terminal, which then visually displays the list to the user.

[1025] 4. Means for calculating the optimal route from a given location to a location selected by the user:

[1026] When a user selects a desired location from a list, the device sends that information to the server.

[1027] The server calculates the optimal route from the user's current location to the selected destination and sends the result to the terminal.

[1028] 5. Means of proposing transportation to the selected location:

[1029] The server suggests multiple modes of transportation (e.g., bus, train, ride-hailing service, etc.) based on the calculated route and sends them to the terminal.

[1030] Users can view the displayed route guidance and select a ride-hailing service as needed. The terminal arranges the ride and coordinates with the ride-hailing service provider to dispatch a taxi.

[1031] 6. A means of generating a list of candidate locations by providing prompts to a generative model, utilizing the received mood information and user profile information:

[1032] Specifically, the server sends a prompt message to the mood-generating AI model (e.g., GPT-3) as follows: "Please list places you can suggest if the user is feeling relaxed. This user likes cafes and has past review data indicating a preference for quiet places."

[1033] A concrete example of how this system can be used is when a user, exhausted from work one day, launches the app and enters their mood as "tired." The app then suggests quiet cafes and relaxation salons. If the user selects a cafe, the app also suggests the best route and transportation options. If the user chooses a ride-hailing service, the app arranges a taxi, and the user refreshes themselves at the cafe. After the visit, the user enters feedback about the cafe within the app, and this information is used to improve future suggestions.

[1034] The flow of the specific processing in Example 1 will be explained using Figure 11.

[1035] Step 1:

[1036] The user launches the app on their mobile device. They enter their username and password to log in.

[1037] Specific operation: The user taps the app icon to launch it and enters their username and password on the login screen.

[1038] Enter: Username, Password

[1039] Output: Sending a login request

[1040] Step 2:

[1041] The device sends the user's login information to the server. The server verifies the received authentication information against its database and sends the authentication result back to the device. If authentication is successful, it retrieves the profile information and sends it to the device.

[1042] Specific operation: The server uses SQL queries against the database to verify the authentication information.

[1043] Enter: Username, Password

[1044] Output: Authentication result, profile information

[1045] Step 3:

[1046] The device receives profile information and transitions to the main screen. The user enters their current mood.

[1047] Specific actions: The user taps the mood input button and enters their mood in the text box (for example, "tired"). They then tap the send button.

[1048] Input: Mood information (e.g., "tired")

[1049] Output: Mood information transmission

[1050] Step 4:

[1051] The terminal sends the entered mood information to the server. The server receives the mood information and provides prompts to the generating AI model. These prompts also include user profile information.

[1052] Specific operation: The server sends mood information and profile information as prompt messages to the AI ​​model that generates the data.

[1053] Input: Mood information, profile information

[1054] Output: Sending prompt messages to the generating AI model

[1055] Step 5:

[1056] The generative AI model lists appropriate locations based on mood information. The server receives responses from the generative AI model and builds a list of candidate locations.

[1057] Specific operation: The generative AI model uses an algorithm to generate a list of candidate locations based on the user's mood information and profile information.

[1058] Input: Prompt message

[1059] Output: Candidate location list

[1060] Step 6:

[1061] The server sends a list of potential locations to the terminal, which then displays it visually to the user. The user then selects the location they want to go to.

[1062] Specific operation: The server sends a list of candidate locations to the device in JSON format, and the device displays it in a UI component. The user taps a location from the list to select it.

[1063] Input: Candidate location list

[1064] Output: Information on the selected location

[1065] Step 7:

[1066] The terminal sends information about the selected location to the server. The server calculates the optimal route from the user's current location to the selected location and sends the result to the terminal.

[1067] Specific operation: The server uses a map API (e.g., Google Maps API) to calculate the optimal route.

[1068] Input: User's current location, information on the selected location

[1069] Output: Optimal route information

[1070] Step 8:

[1071] The server suggests multiple modes of transport based on the calculated optimal route and sends them to the terminal. The terminal displays this to the user. The user selects a ride-hailing service as needed.

[1072] Specific operation: The server sends a list of transportation options (bus, train, ride-hailing service, etc.) to the device in JSON format, and the device displays them in the UI. The user taps the "Use ride-hailing service" button.

[1073] Input: Optimal route information

[1074] Output: Options for transportation, information on selecting a ride-hailing service.

[1075] Step 9:

[1076] The terminal arranges for a ride and coordinates with the ride-hailing service provider to dispatch a taxi.

[1077] Specific operation: The device calls the ride-hailing service's API to arrange a taxi.

[1078] Input: Ride-hailing service selection information

[1079] Output: Vehicle dispatch confirmation information

[1080] Step 10:

[1081] Users enter feedback and reviews about places they visit within the app. The device sends this information to a server, which stores it in a database.

[1082] Specific operation: The user enters feedback into the evaluation form and taps the submit button. The device sends the feedback to the server via a POST request.

[1083] Input: Feedback Information

[1084] Output: Sending feedback, saving to database

[1085] (Application Example 1)

[1086] 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."

[1087] Traditional food delivery services have had the problem that it is difficult for users to choose the best meal according to their mood and preferences. Furthermore, the lack of a system that makes suggestions based on the user's mood has made improving the user experience a challenge.

[1088] 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.

[1089] In this invention, the server includes means for receiving mood information entered by the user, means for selecting appropriate delivery items corresponding to the received mood information using a generative AI model, means for presenting a list of selected delivery items to the user, and means for notifying the user of the delivery status of the ordered delivery items. This makes it possible to quickly suggest the optimal delivery items corresponding to the user's mood and improve the user experience.

[1090] 1. "User-inputted mood information" refers to information that users provide to the application through input methods such as text or buttons, indicating their current emotions or state (for example, "tired" or "needs energy").

[1091] 2. A "generative AI model" is an artificial intelligence algorithm used to analyze mood information entered by the user and select appropriate delivery items based on that information.

[1092] 3. "Appropriate delivery items" are food and beverage options selected based on the user's mood information, providing the refreshment and nutrition the user desires.

[1093] 4. "Means of presenting to the user" refers to methods and technologies for visually displaying a list of appropriate delivery items selected by the generative AI model on the user's device screen.

[1094] 5. "Ordered delivery items" refer to food and drinks that the user selects and decides to purchase from the presented list.

[1095] 6. "Means of notifying users of delivery status" refers to methods or systems that update and inform users in real time through an application about the process of how ordered delivery items reach the user.

[1096] 7. "User profile information" refers to data that includes basic information about the user (e.g., name, address, food preferences, etc.) and is used to select delivery items.

[1097] 8. "Past order history" refers to a record of the food and drinks a user has ordered in the past. This is used to understand the user's preferences and make suggestions more accurate.

[1098] 9. A "delivery service provider" is a business or company that is responsible for delivering items ordered by a user to a specified address.

[1099] 10. "Arranging delivery of delivery items" is the process of notifying the delivery service provider of the order after the user's order has been confirmed and preparing to deliver the food and drinks to the user.

[1100] This invention relates to a map application system that suggests appropriate delivery items based on the user's mood information. This system involves the server, terminal, and user working together to process the data.

[1101] Explanation of the program's processing

[1102] 1. Login and profile retrieval

[1103] The user launches the app and accesses the login screen. After entering their login information (username and password), the device sends this information to the server for authentication. The server verifies the received authentication information against its database and returns the authentication result to the device. If authentication is successful, the server retrieves the user's profile information and sends it to the device. After receiving the profile information, the device transitions to the main screen.

[1104] 2. Enter and submit your mood.

[1105] The user enters their current mood (e.g., "tired," "needs energy") on the main screen. This mood information is then sent from the device to the server.

[1106] 3. Analysis of mood information and item selection

[1107] The server inputs the user's mood information into a generative AI model, initiating a process to select appropriate delivery items based on that information. The generative AI model utilizes the user's profile information, past order history, and reviews to search the database for delivery items that match the mood information and lists several candidates. For example, if the user inputs "tired," the server will select items such as "energy-boosting rice bowl" or "relaxation drink."

[1108] 4. Item suggestions and ordering

[1109] The server sends a list of selected delivery items to the terminal. The terminal displays this visually to the user, who then selects the items they wish to order from this list. The selection information is sent from the terminal to the server, which notifies the delivery service provider of the order and arranges for delivery.

[1110] 5. Delivery status notification

[1111] Once the order has been processed, the server monitors the delivery status in real time and notifies the user. The terminal receives updates on the delivery status and displays them to the user as needed.

[1112] Hardware and software to use

[1113] Server: Cloud servers such as AWS

[1114] Device: Smartphone

[1115] Software: Flask (a Python micro web framework), generative AI model

[1116] As a specific example

[1117] When a user feels tired and enters "tired," the app suggests delivery items such as energizing rice bowls or relaxation drinks. Once the user selects a relaxation drink and places an order, the server notifies the delivery service provider of the arrangement, and the device receives real-time updates on the delivery status. In this way, users can quickly receive a meal tailored to their mood through a delivery service.

[1118] Example of a prompt

[1119] "Users input their mood, such as 'tired':

[1120] Question: What kinds of food and drinks are suitable?

[1121] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[1122] Step 1: The user launches the app, accesses the login screen, and enters their username and password. The entered login information is sent from the device to the server. The server compares the received login information with its database and generates an authentication result. If successful, the server retrieves the user's profile information and sends it to the device. The device receives the profile information and transitions to the main screen.

[1123] Enter: Username, Password

[1124] Output: Authentication results, user profile information

[1125] Data processing and calculation: Database matching, profile information acquisition.

[1126] Step 2: The user enters their current mood on the main screen (for example, "tired" or "needs energy"), and the device sends this mood information to the server. The server inputs the received mood information into the generating AI model.

[1127] Input: Mood information

[1128] Output: Received mood information

[1129] Data processing and calculation: Inputting mood information into AI models.

[1130] Step 3: The server uses a generative AI model to analyze the user's mood information and select appropriate delivery items. The generative AI model utilizes user profile information, past order history, and reviews to search the database for and list delivery items that match the user's mood information.

[1131] Input: Mood information, user profile information, past order history

[1132] Output: List of delivery items

[1133] Data processing and computation: Database search, generation, and analysis using AI models.

[1134] Step 4: The server sends a list of selected delivery items to the terminal. The terminal displays this on its screen, and the user selects items from the suggested list. The selection information is then sent from the terminal to the server.

[1135] Input: List of delivery items

[1136] Output: User selection information

[1137] Data processing and calculation: Sending lists, receiving item selections.

[1138] Step 5: Based on the user's selection information, the server sends the order information to the delivery service provider and arranges delivery. The server notifies the delivery service provider of the arrangement.

[1139] Input: User's selection information

[1140] Output: Delivery arrangement information

[1141] Data processing and calculation: Sending order information, notifying delivery arrangements.

[1142] Step 6: After the delivery arrangements are complete, the server monitors the delivery status in real time and sends updates to the terminal. The terminal displays the delivery status to the user as needed, providing updates until delivery is complete.

[1143] Input: Delivery status information

[1144] Output: Delivery status update information

[1145] Data processing and calculation: Real-time monitoring, delivery status notification.

[1146] 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.

[1147] Below is a description of the "Modes for Carrying Out the Invention" from the patent specification concerning a map application system that incorporates an emotion engine.

[1148] Modes for carrying out the invention

[1149] This invention is a map application system that suggests appropriate locations based on the user's mood information, providing new discoveries and refreshment. This system is realized through the mutual cooperation of a server, terminal, user, and emotion engine.

[1150] Explanation of the program's processing

[1151] 1. User Login

[1152] The user launches the app and attempts to log in by entering their username and password on the login screen. The device sends this login information to the server, which verifies the authentication information against its database and sends the authentication result back to the device. If authentication is successful, the server retrieves profile information and sends it to the device, and the device transitions to the main screen.

[1153] 2. Mood input / emotion recognition

[1154] The user enters their current mood in text or voice on the main screen. The device sends this mood information to the server, while the emotion engine analyzes the user's input to identify their emotions. The emotion engine also refers to the user's past input history to predict emotions and sends this prediction to the server as well.

[1155] For example, if a user types "I feel kind of tired," the emotion engine recognizes this as "fatigue" and sends that information to the server. It also refers to past input history to make predictions such as "a refreshing spot would be preferred."

[1156] 3. Emotional Analysis and Spot Selection

[1157] The server activates a generative model based on the sentiment and prediction information obtained from the sentiment engine to select an appropriate location. The generative model uses user profile information, past reviews, and rating data to search the database for locations that match the sentiment information and lists multiple candidate locations.

[1158] For example, if the emotion engine recognizes the user's emotion as "fatigue," the server will search for relaxation spots and cafes, taking into account the user's historical data to select the most appropriate location.

[1159] 4. Suggestions for recommended places

[1160] The server sends information about the listed locations to the terminal, which then displays this information visually to the user. The user selects a desired location from the displayed list. For example, they might choose from a list of places like a "quiet cafe" or a "massage parlor."

[1161] 5. Route guidance

[1162] When a user selects a destination, the device sends this selection information to the server. The server calculates the optimal route from the user's current location to the selected destination and also presents transportation options (e.g., bus, train, ride-hailing service). The calculation results are sent to the device and displayed to the user.

[1163] 6. Vehicle dispatch arrangement

[1164] If a user selects a ride-hailing service, the terminal will work with the transportation service provider to arrange the ride. For example, if a user selects a taxi, the terminal will work with the ride-hailing service provider to arrange a taxi.

[1165] 7. Feedback after the visit

[1166] After visiting a location, users enter feedback and reviews about the place they visited within the app. The device sends this information to the server, which stores the received feedback in a database. The sentiment engine also uses this feedback as training data to improve the accuracy of future suggestions.

[1167] Specific usage examples

[1168] One day, a user tired from work launches the app and texts, "I feel kind of tired." The emotion engine recognizes the feeling of "fatigue" and, using past data, suggests relaxation spots. If the user selects "a quiet cafe," the app suggests the best route and mode of transportation (e.g., a 5-minute walk from the nearest station, or a 10-minute taxi ride). If the user selects a taxi, the app arranges one. After visiting, the user provides feedback on the cafe, and this information is used to improve future suggestions.

[1169] The above describes embodiments of the present invention, which aim to allow users to discover new places and refresh themselves according to their mood.

[1170] The following describes the processing flow.

[1171] The following explains the program processing for the MAP application system that incorporates an emotion engine, broken down into specific steps.

[1172] Step 1:

[1173] The user launches the app and accesses the login screen.

[1174] Step 2:

[1175] The user enters their login information (username and password), and the device sends this information to the server.

[1176] Step 3:

[1177] The server compares the received authentication information with the database and sends the authentication result to the terminal. If authentication is successful, the server retrieves the user's profile information and sends it to the terminal.

[1178] Step 4:

[1179] The device receives the profile information and transitions to the main screen. Here, the user's mood input interface is displayed.

[1180] Step 5:

[1181] Users input their current mood in text or voice. For example, they might input "I'm tired" or "I want to eat something spicy."

[1182] Step 6:

[1183] The device sends the mood information entered by the user to the server. Simultaneously, the emotion engine analyzes the mood information and identifies the emotions.

[1184] Step 7:

[1185] The emotion engine analyzes the user's input and past input history to identify the user's emotional state as "fatigue" or "stress," and sends it to the server.

[1186] Step 8:

[1187] The server receives sentiment information from the sentiment engine and invokes the generative model. The generative model searches the database for the appropriate location based on the sentiment information.

[1188] Step 9:

[1189] Based on the results of the generative model, the server lists multiple candidate locations and generates a list. For example, the list might include relaxation spots and restaurants.

[1190] Step 10:

[1191] The server sends this list to the terminal, and the terminal displays the list to the user.

[1192] Step 11:

[1193] The user selects a place they want to go from a list. For example, they might choose a "quiet cafe" or a "massage parlor."

[1194] Step 12:

[1195] The device sends selection information to the server, which calculates the optimal route from the user's current location to their destination.

[1196] Step 13:

[1197] The server calculates the optimal route and transportation options (e.g., bus, train, ride-hailing service, etc.) and sends the results to the terminal.

[1198] Step 14:

[1199] The device displays optimal route and transportation options to the user, who then selects their preferred mode of transport.

[1200] Step 15:

[1201] When a user selects a ride-hailing service, the terminal works with the transportation service provider to arrange the ride.

[1202] Step 16:

[1203] After visiting a place, users can enter feedback and reviews about the place they visited within the app.

[1204] Step 17:

[1205] The device sends feedback information to the server, which stores it in a database. The emotion engine also uses this feedback as training data.

[1206] The above outlines the specific processing steps of the program for the MAP application system that incorporates an emotion engine.

[1207] (Example 2)

[1208] 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."

[1209] Traditional map applications lacked the ability to suggest appropriate locations based on the user's mood and feelings, resulting in an unsatisfactory user experience. Furthermore, users often spent a considerable amount of time searching for a suitable location, potentially increasing stress. Additionally, the lack of consistent suggestions for transportation and optimal routes reduced user convenience.

[1210] The identification processing by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for receiving mood information entered by the user, means for analyzing the received mood information using an emotion engine and identifying the emotion information, means for selecting an appropriate location based on the identified emotion information using a generative model, means for presenting a list of selected locations to the user, means for calculating the optimal route from the presented locations to a location selected by the user, and means for suggesting means of transportation to the selected location. This makes it possible to suggest appropriate locations according to the user's mood and emotions, improving the user experience, shortening search time, and providing a highly convenient service.

[1211] "User mood information" refers to text or audio data that users input to express their current emotions or moods.

[1212] An "emotion engine" is a software component that analyzes received mood information to identify the user's emotions and outputs the result.

[1213] A "generative model" is an algorithm or AI model that selects appropriate locations based on user sentiment information.

[1214] A "list of places" is a list containing multiple locations selected to match the user's emotional information.

[1215] The "optimal route" is the most efficient path from the user's current location to the destination selected by the user.

[1216] "Transportation" refers to the means of travel that a user may use to reach their destination (e.g., walking, public transport, ride-hailing service, etc.).

[1217] A "transportation service provider" is a business that provides ride-hailing services or public transportation that users use for getting around.

[1218] Modes for carrying out the invention

[1219] This invention is a map application system that suggests appropriate locations based on the user's mood information, providing new discoveries and refreshment. This system is realized through the mutual cooperation of a server, terminal, user, and emotion engine.

[1220] User Login

[1221] The user launches the app and attempts to log in by entering their username and password on the login screen. The device sends this login information to the server, which verifies the authentication information against a database (e.g., a MySQL database) and sends the authentication result back to the device. If authentication is successful, the server retrieves profile information and sends it to the device, and the device transitions to the main screen.

[1222] Mood input / emotion recognition

[1223] The user enters their current mood in text or voice on the main screen. The terminal sends this mood information to the server, while an emotion engine (e.g., IBM Watson) analyzes the user's input to identify the emotion. The emotion engine also refers to the user's past input history to predict the emotion and sends it to the server.

[1224] For example, if a user types "I feel kind of tired," the emotion engine recognizes it as "fatigue" and sends that information to the server. It also refers to past input history to make predictions such as "a refreshing spot would be preferred."

[1225] Emotional analysis and spot selection

[1226] The server launches a generative AI model (e.g., TensorFlow) based on sentiment and prediction information obtained from the sentiment engine to select an appropriate location. The generative model utilizes user profile information, past reviews, and rating data to search a database (e.g., MongoDB) for locations that match the sentiment information and lists multiple candidate locations.

[1227] For example, if the emotion engine recognizes the user's emotion as "fatigue," the server will search for relaxation spots and cafes, taking into account the user's historical data to select the most appropriate location.

[1228] Suggestions for recommended places

[1229] The server sends information about the listed locations to the terminal, which then displays this information visually to the user. The user selects a desired location from the displayed list. For example, they might choose from a list of places like a "quiet cafe" or a "massage parlor."

[1230] Route guidance

[1231] When a user selects a destination, the device sends this selection information to the server. The server calculates the optimal route from the user's current location to the selected destination (for example, using the Google Maps API) and also presents transportation options (e.g., bus, train, ride-hailing service, etc.). The calculation results are sent to the device and displayed to the user.

[1232] Vehicle dispatch arrangement

[1233] If the user selects a ride-hailing service, the device will work with the transportation service provider (for example, using the Uber API) to arrange the ride. For example, if the user selects a taxi, the device will work with the ride-hailing service provider to arrange a taxi.

[1234] Feedback after the visit

[1235] After a visit, users enter feedback and reviews about the visited location within the app. The device sends this to a server, which stores the received feedback in a database (e.g., Elasticsearch). The sentiment engine also uses this feedback as training data to improve the accuracy of future suggestions.

[1236] Specific usage examples

[1237] One day, a user tired from work launches the app and texts, "I feel kind of tired." The emotion engine recognizes the feeling of "fatigue" and, using past data, suggests relaxation spots. If the user selects "a quiet cafe," the app suggests the best route and mode of transportation (e.g., a 5-minute walk from the nearest station, or a 10-minute taxi ride). If the user selects a taxi, the app arranges one. After visiting, the user provides feedback on the cafe, and this information is used to improve future suggestions.

[1238] Example of a prompt:

[1239] Please enter your current mood. Example: "I feel kind of tired."

[1240] The purpose of this system is for users to discover new places and refresh themselves according to their mood.

[1241] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1242] Step 1:

[1243] User Login

[1244] Specific actions:

[1245] The user launches the app and enters the username "test_user" and password "password123" on the login screen.

[1246] Enter: Username and password

[1247] Data processing: The terminal converts user input information into an HTTPS request and sends it to the server.

[1248] Output: Request data (JSON including username and password)

[1249] The server compares the received JSON-formatted login information with the database (MySQL).

[1250] Input: Request data (JSON format)

[1251] Data calculation: The server matches the authentication information in the MySQL database.

[1252] Output: Authentication result (success or failure)

[1253] If authentication is successful, the server retrieves the profile information and sends it to the device in JSON format.

[1254] Input: Authentication result

[1255] Data processing: Acquisition of profile information

[1256] Output: JSON containing profile information

[1257] The device analyzes the received profile information and displays it on the main screen.

[1258] Input: Profile information (JSON format)

[1259] Data processing: JSON parsing

[1260] Output: Main screen display

[1261] Step 2:

[1262] Mood input / emotion recognition

[1263] Specific actions:

[1264] The user types the text "I feel kind of tired" on the main screen and presses the send button.

[1265] Input: Text about mood

[1266] Data processing: The device converts text data into an API request and sends it to the server.

[1267] Output: Request data (JSON including text)

[1268] The server receives the request data and sends it to the emotion engine (IBM Watson).

[1269] Input: Request data (JSON format)

[1270] Data processing: Sentiment analysis using IBM Watson

[1271] Output: Emotional information (e.g., "fatigue")

[1272] The emotion engine identifies emotional information and sends the results back to the server. The server then refers to the user's past input history and generates predictive information.

[1273] Input: Emotional information

[1274] Data calculation: Matching with past input history

[1275] Output: Sentiment information and prediction information (JSON format)

[1276] Step 3:

[1277] Emotional analysis and spot selection

[1278] Specific actions:

[1279] The server launches a generative AI model (TensorFlow) based on the emotional and predictive information obtained from the emotion engine.

[1280] Input: Sentiment information and prediction information (JSON format)

[1281] Data processing: Spot selection using generative AI models

[1282] Output: Candidate location list (JSON format)

[1283] The generative AI model uses user profile information, past reviews, and rating data to select the appropriate location.

[1284] Input: Profile information, past reviews and evaluation data

[1285] Data processing: Data integration and analysis

[1286] Output: List of multiple candidate locations (JSON format)

[1287] The server sends the list of selected candidate locations to the terminal.

[1288] Input: Candidate location list (JSON format)

[1289] Data transmission: Transmission via HTTPS request

[1290] Output: Received candidate location list

[1291] Step 4:

[1292] Suggestions for recommended places

[1293] Specific actions:

[1294] The device analyzes the received list of potential locations and displays it visually. The user selects the desired location from the list.

[1295] Input: Candidate location list

[1296] Data processing: List analysis and screen display

[1297] Output: Display of candidate locations and user selection

[1298] Step 5:

[1299] Route guidance

[1300] Specific actions:

[1301] The device sends information about the destination the user wants to go to to the server. The server obtains the user's current location and calculates the optimal route to the selected location.

[1302] Input: Selection information and current location information

[1303] Data calculation: Calculation of the optimal route (using Google Maps API)

[1304] Output: Route information and transportation options (JSON format)

[1305] The server sends route information and transportation details to the terminal. The terminal then displays this information to the user.

[1306] Input: Route information and mode of transport (JSON format)

[1307] Data processing: JSON parsing and screen display

[1308] Output: Route guidance and transportation options provided.

[1309] Step 6:

[1310] Vehicle dispatch arrangement

[1311] Specific actions:

[1312] If the user selects a ride-hailing service, the terminal will work with the transportation service provider to arrange the ride.

[1313] Input: Information on vehicle selection

[1314] Data transmission: Ride-hailing request (e.g., using the Uber API)

[1315] Output: Vehicle dispatch completion notification and detailed information

[1316] Step 7:

[1317] Feedback after the visit

[1318] Specific actions:

[1319] After a visit, the user enters feedback about the visited location within the app. The device then sends this feedback to the server.

[1320] Input: Feedback text

[1321] Data processing: Sending feedback data (JSON format)

[1322] Output: Sending feedback data to the server

[1323] The server stores the received feedback in a database (Elasticsearch), and the sentiment engine uses this feedback as training data.

[1324] Input: Feedback data

[1325] Data processing: Data storage and use as training data.

[1326] Output: Storage of feedback information and updating of the sentiment engine.

[1327] (Application Example 2)

[1328] 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."

[1329] Traditional map applications have made it difficult for users to discover new places based on their mood or feelings. Furthermore, they have not effectively facilitated user-selected locations or collected feedback after visits. Therefore, there is a need for a system that allows users to find places to refresh themselves according to their mood, facilitates easy travel, and improves the accuracy of future suggestions.

[1330] 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.

[1331] In this invention, the server includes means for receiving mood information entered by the user, means for selecting an appropriate location corresponding to the received mood information using a generative model, means for presenting a list of selected locations to the user, means for calculating the optimal route from the presented locations to a location selected by the user, means for suggesting means of transportation to the selected location, means for transmitting route information to an autonomous mobile vehicle and guiding the user, and means for receiving user feedback information and storing it as data to improve the accuracy of future suggestions. This makes it possible for the user to find an appropriate location according to their mood, travel easily, and improve the accuracy of future suggestions.

[1332] A "user" refers to a person who uses this system to input their mood information and receive guidance to an appropriate location.

[1333] "Mood information" refers to information that users input, indicating their current emotions and moods.

[1334] A "generative model" refers to an algorithm or program used to select an appropriate location based on mood information.

[1335] An "appropriate place" refers to a location selected based on the user's mood information, where the user can make new discoveries and feel refreshed.

[1336] A "list" refers to a list used to present users with a selection of suitable locations.

[1337] The "optimal route" refers to the path that allows the user to reach their chosen location most efficiently.

[1338] "Transportation" refers to the means of transport or methods available to the user to reach their chosen destination.

[1339] An "autonomous mobile vehicle" refers to a means of transportation that automatically guides the user to a selected location.

[1340] "Feedback information" refers to the impressions and evaluations that users provide after a visit, and this data is used to improve the accuracy of future suggestions.

[1341] Modes for carrying out the invention

[1342] This invention is a system that suggests appropriate locations based on the user's mood information, providing new discoveries and refreshment. This system is realized through the mutual cooperation of a server, terminal, user, and emotion engine.

[1343] Explanation of the program's processing

[1344] 1. User Login

[1345] The user launches the smartphone app and attempts to log in by entering their username and password on the login screen. The device sends this login information to the server, which verifies the authentication information against a database and sends the authentication result back to the device. If authentication is successful, the server retrieves profile information and sends it to the device, and the device transitions to the main screen.

[1346] 2. Mood input / emotion recognition

[1347] The user enters their current mood in text or voice on the main screen. The device sends this mood information to the server, while the emotion engine analyzes the user's input to identify their emotions. The emotion engine also refers to the user's past input history to predict emotions and sends this prediction to the server as well.

[1348] For example, if a user types "I feel kind of tired," the emotion engine recognizes this as "fatigue" and sends that information to the server. It also refers to past input history to make predictions such as "a refreshing spot would be preferred."

[1349] 3. Emotional Analysis and Spot Selection

[1350] The server activates a generative model based on the sentiment and prediction information obtained from the sentiment engine to select an appropriate location. The generative model uses user profile information, past reviews, and rating data to search the database for locations that match the sentiment information and lists multiple candidate locations.

[1351] For example, if the emotion engine recognizes the user's emotion as "fatigue," the server will search for relaxation spots and cafes, taking into account the user's historical data to select the most appropriate location.

[1352] 4. Suggestions for recommended places

[1353] The server sends information about the listed locations to the terminal, which then displays this information visually to the user. The user selects a desired location from the displayed list. For example, they might choose from a list of places like a "quiet cafe" or a "massage parlor."

[1354] 5. Route guidance

[1355] When a user selects a destination, the device sends this selection information to the server. The server calculates the optimal route from the user's current location to the selected destination and also presents transportation options (e.g., bus, train, autonomous vehicle, etc.). The calculation results are sent to the device and displayed to the user.

[1356] Hardware and software used

[1357] Smartphone: Used by users to run applications.

[1358] Emotion engine: Used to analyze user emotions. Specific examples include natural language processing APIs.

[1359] Server: Used for managing proposal information and calculating routes. A specific example is a cloud service platform.

[1360] Autonomous mobile vehicles: Used to guide users to a selected location. A specific example is an autonomous driving vehicle system.

[1361] Database: Used to manage user profile information and feedback. A specific example is a relational database.

[1362] Specific usage examples and prompt messages

[1363] One day, a user launches the "Emotion Navi" app and enters "I'm feeling a little stressed" on the input screen. The emotion engine recognizes this as "stress" and, based on past data, suggests places where the user can relax (for example, a park or an art museum). If the user selects "park," the optimal route is calculated, and an autonomous vehicle guides the user to that park.

[1364] Example of a prompt

[1365] User: "I'm feeling a little stressed."

[1366] Emotional engine: Recognizes "stress"

[1367] Server: "I suggest places where you can relax. Parks, art museums, cafes."

[1368] User: "Select a park"

[1369] Server: "Calculating the optimal route"

[1370] Autonomous mobile vehicle: "Starting route guidance"

[1371] As described above, this system allows users to easily find the optimal location according to their mood and travel comfortably using autonomous vehicles. Furthermore, feedback after visits will be used to improve the accuracy of future suggestions.

[1372] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1373] Step 1:

[1374] The user launches the smartphone app and attempts to log in by entering their username and password on the login screen. The device sends this login information to the server, which verifies the authentication information against a database and sends the authentication result back to the device. The input is the username and password, and the output is the authentication result. If authentication is successful, the server retrieves profile information and sends it to the device, and the device transitions to the main screen.

[1375] Step 2:

[1376] The user enters their current mood as text or voice on the main screen. The input is mood information in text or voice. The terminal sends this mood information to the server, where the emotion engine analyzes the user's input to identify the emotion. The emotion engine also refers to the user's past input history to predict the emotion and sends it to the server. The output is the analyzed emotion information.

[1377] Step 3:

[1378] The server activates a generative model based on sentiment and prediction information obtained from the sentiment engine to select an appropriate location. The input is sentiment and prediction information, and the output is a list of multiple candidate locations. The generative model uses user profile information, past reviews, and rating data to search the database for locations that match the sentiment information and lists multiple candidate locations.

[1379] Step 4:

[1380] The server sends information about the listed locations to the terminal, which then displays this information visually to the user. The input is a list of multiple possible locations, and the output is a list of locations displayed to the user. The user selects a desired location from the displayed list.

[1381] Step 5:

[1382] When the user selects a destination, the device sends this selection information to the server. The server calculates the optimal route from the user's current location to the selected destination and also presents transportation options (e.g., bus, train, autonomous vehicle, etc.). The inputs are the user's current location and the selected destination, and the output is the optimal route and transportation options. The calculation results are sent to the device and displayed to the user.

[1383] Step 6:

[1384] Route information is transmitted to the autonomous vehicle to guide the user. The server transmits optimal route information to the autonomous vehicle; the input is the optimal route information, and the output is the start of route guidance by the autonomous vehicle.

[1385] Step 7:

[1386] After a user visits a place, they input feedback and reviews on their device. The device sends this information to the server; the input is feedback data, and the output is saved to a database. The server stores the received feedback in the database, and the sentiment engine uses this feedback as training data to improve the accuracy of future suggestions.

[1387] Through these steps, users can easily find the perfect location based on their mood and travel comfortably using autonomous vehicles. Furthermore, feedback after visits will be used to improve the accuracy of future recommendations.

[1388] 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.

[1389] 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.

[1390] 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.

[1391] [Fourth Embodiment]

[1392] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[1393] 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.

[1394] 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).

[1395] 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.

[1396] 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.

[1397] 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).

[1398] 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.

[1399] 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.

[1400] 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.

[1401] 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.

[1402] 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.

[1403] 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.

[1404] 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".

[1405] Below is a description of the "modes for carrying out the invention" as stated in the patent specification for a map app with emotions.

[1406] Modes for carrying out the invention

[1407] This invention relates to a map application system that suggests appropriate locations based on the user's mood information, enabling the user to make new discoveries and feel refreshed. This system involves the server, terminal, and user working together to process information.

[1408] Explanation of the program's processing

[1409] 1. User Login

[1410] The user launches the app and accesses the login screen. After entering their login information (username and password), the device sends this information to the server for authentication. The server verifies the received authentication information against its database and returns the authentication result to the device. If authentication is successful, the server retrieves the user's profile information and sends it to the device. After receiving the profile information, the device transitions to the main screen.

[1411] 2. Mood Input

[1412] The user enters their current mood (e.g., "I'm tired," "I want to eat something spicy") on the main screen. This mood information is sent from the device to the server.

[1413] 3. Emotional Analysis and Spot Selection

[1414] The server inputs the user's mood information into a generative model and initiates a process to select an appropriate location based on that mood. The generative model uses the user's profile information, past reviews, and rating data to search the database for locations that match the mood information and lists several candidate locations.

[1415] For example, if a user types "I'm tired," the server will search for places like relaxation spots or quiet cafes. If a user types "I want to eat something spicy," the server will suggest restaurants that specialize in spicy food.

[1416] 4. Suggestions for recommended places

[1417] The server sends a list of selected locations to the terminal. The terminal displays this list visually to the user. The user selects a location they want to go to from this list.

[1418] 5. Route guidance

[1419] When the user selects a destination, the device sends this selection information to the server. The server calculates the optimal route from the user's current location to the selected destination and also presents alternative modes of transportation (e.g., bus, train, ride-hailing service). The calculation results are sent to the device and displayed to the user. The user reviews the displayed route guidance and selects a ride-hailing service if necessary. The device then initiates the ride-hailing arrangement and coordinates with the ride-hailing service provider to arrange a taxi.

[1420] 6. Feedback after the visit

[1421] After visiting a location, the user enters feedback and reviews about the place within the app. The device sends this information to the server. The server stores the received feedback in a database and uses it as training data to improve the accuracy of future suggestions.

[1422] Specific usage examples

[1423] One day, a user exhausted from work launches the app and enters their mood as "tired." The app then suggests quiet cafes and relaxation salons. When the user selects a cafe, the app suggests the best route and transportation options. If the user chooses a ride-hailing service, the app arranges a taxi, and the user refreshes themselves at the cafe. After the visit, the user provides feedback on the cafe within the app, and this information is used to improve future suggestions.

[1424] The above describes embodiments of the present invention, which are designed to allow users to discover new places and refresh themselves according to their mood.

[1425] The following describes the processing flow.

[1426] The following explains the program's processing steps for the emotionally-driven map app "YALL".

[1427] Step 1:

[1428] The user launches the app and accesses the login screen. They enter their login information (username and password).

[1429] Step 2:

[1430] The terminal sends the login information entered by the user to the server. The server checks the received authentication information against its database and returns the authentication result to the terminal.

[1431] Step 3:

[1432] Upon successful authentication, the server retrieves the user's profile information and sends it to the device. After receiving the profile information, the device transitions to the main screen.

[1433] Step 4:

[1434] On the main screen, the user enters their current mood. For example, they might enter "I'm tired" or "I want to eat something spicy." This mood information is sent from the device to the server.

[1435] Step 5:

[1436] The server launches a generative model to analyze the received mood information. The generative model uses the user's profile information, past reviews, and rating data to search the database for locations that match the mood information.

[1437] Step 6:

[1438] The server lists several suitable locations based on the results of the generative model. The list includes places to relax, restaurants, and other establishments that correspond to the mood information.

[1439] Step 7:

[1440] The server sends the information of the listed locations to the terminal. The terminal then displays this information to the user.

[1441] Step 8:

[1442] The user selects a desired location from a list of presented locations. Once the selection is complete, the device sends the selection information to the server.

[1443] Step 9:

[1444] The server calculates the optimal route from the origin (the user's current location) to the destination (the selected location) based on the received selection information. The calculation also includes transportation options (e.g., bus, train, ride-hailing service, etc.).

[1445] Step 10:

[1446] The server sends the calculation results to the terminal. The terminal displays the optimal route and transportation options to the user.

[1447] Step 11:

[1448] The user checks the route guidance and selects a ride-hailing service if necessary. If a ride-hailing service is selected, the device connects with the transportation service provider (e.g., Uber) to arrange the ride.

[1449] Step 12:

[1450] After visiting a location, users enter feedback and reviews about the place they visited within the app. This feedback information is sent from the device to the server.

[1451] Step 13:

[1452] The server stores the received feedback in a database. This data is then used as training data to improve the accuracy of future suggestions.

[1453] The above outlines the specific processing steps of the "YALL" program.

[1454] (Example 1)

[1455] 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".

[1456] Traditional map applications have limited functionality in suggesting locations based on the user's mood, and therefore have the problem of not being able to adequately meet the needs of individual users. Furthermore, even if local information and transportation options are provided, this does not always match the user's mood or situation at the time, often leaving users seeking relaxation or new experiences dissatisfied.

[1457] 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.

[1458] In this invention, the server includes means for receiving mood information entered by the user, means for selecting an appropriate location corresponding to the received mood information using a generative model, means for presenting a list of selected locations to the user, means for calculating the optimal route from the presented locations to a location selected by the user, means for suggesting means of transportation to the selected location, means for generating a list of candidate locations by utilizing the received mood information and user profile information by providing prompts to the generative model, and means for suggesting relaxation spots, restaurants, etc., based on the mood information entered by the user. As a result, the user can receive suggestions for places that suit their mood and refresh themselves efficiently.

[1459] "Mood information" refers to data entered by users in the form of text or choices, indicating the emotions and moods they are currently experiencing.

[1460] A "generative model" refers to a machine learning algorithm or AI model that generates new information or suggestions based on received data.

[1461] "User profile information" refers to data that includes a user's personal information, past behavioral history, preferences, and so on.

[1462] A "list of candidate locations" is a list of several suitable locations proposed by the generative model.

[1463] The "optimal route" is the most efficient and time-saving path to reach the location selected by the user.

[1464] "Transportation" refers to means of getting around, such as buses, trains, taxis, and ride-hailing services.

[1465] A "prompt" is an instruction or command given to input specific information into a generative model.

[1466] A "relaxation spot" is a place where users can relax, and examples include cafes and spas.

[1467] A "restaurant" is a facility that provides food and beverage services to users.

[1468] Modes for carrying out the invention

[1469] This invention relates to a map application system that suggests appropriate locations based on the user's mood information, enabling the user to make new discoveries and feel refreshed. This system involves the server, terminal, and user working together to process information.

[1470] This system includes the following elements:

[1471] 1. Means for receiving mood information entered by the user:

[1472] The user launches the application on their device, enters their username and password, and logs in.

[1473] The device sends this information to the server, which then authenticates by matching it against its database. If authentication is successful, the server sends the user's profile information to the device.

[1474] 2. Means for selecting an appropriate location corresponding to received mood information using a generative model:

[1475] The user enters their current mood on the main screen (e.g., "I'm tired," "I want to eat something spicy," etc.). The device then sends this mood information to the server.

[1476] The server prompts the generating AI model and uses the received mood information and user profile information to generate a list of candidate locations.

[1477] 3. Means of presenting the user with a list of selected locations:

[1478] The server sends the generated list of candidate locations to the terminal, which then visually displays the list to the user.

[1479] 4. Means for calculating the optimal route from a given location to a location selected by the user:

[1480] When a user selects a desired location from a list, the device sends that information to the server.

[1481] The server calculates the optimal route from the user's current location to the selected destination and sends the result to the terminal.

[1482] 5. Means of proposing transportation to the selected location:

[1483] The server suggests multiple modes of transportation (e.g., bus, train, ride-hailing service, etc.) based on the calculated route and sends them to the terminal.

[1484] Users can view the displayed route guidance and select a ride-hailing service as needed. The terminal arranges the ride and coordinates with the ride-hailing service provider to dispatch a taxi.

[1485] 6. A means of generating a list of candidate locations by providing prompts to a generative model, utilizing the received mood information and user profile information:

[1486] Specifically, the server sends a prompt message to the mood-generating AI model (e.g., GPT-3) as follows: "Please list places you can suggest if the user is feeling relaxed. This user likes cafes and has past review data indicating a preference for quiet places."

[1487] A concrete example of how this system can be used is when a user, exhausted from work one day, launches the app and enters their mood as "tired." The app then suggests quiet cafes and relaxation salons. If the user selects a cafe, the app also suggests the best route and transportation options. If the user chooses a ride-hailing service, the app arranges a taxi, and the user refreshes themselves at the cafe. After the visit, the user enters feedback about the cafe within the app, and this information is used to improve future suggestions.

[1488] The flow of the specific processing in Example 1 will be explained using Figure 11.

[1489] Step 1:

[1490] The user launches the app on their mobile device. They enter their username and password to log in.

[1491] Specific operation: The user taps the app icon to launch it and enters their username and password on the login screen.

[1492] Enter: Username, Password

[1493] Output: Sending a login request

[1494] Step 2:

[1495] The device sends the user's login information to the server. The server verifies the received authentication information against its database and sends the authentication result back to the device. If authentication is successful, it retrieves the profile information and sends it to the device.

[1496] Specific operation: The server uses SQL queries against the database to verify the authentication information.

[1497] Enter: Username, Password

[1498] Output: Authentication result, profile information

[1499] Step 3:

[1500] The device receives profile information and transitions to the main screen. The user enters their current mood.

[1501] Specific actions: The user taps the mood input button and enters their mood in the text box (for example, "tired"). They then tap the send button.

[1502] Input: Mood information (e.g., "tired")

[1503] Output: Mood information transmission

[1504] Step 4:

[1505] The terminal sends the entered mood information to the server. The server receives the mood information and provides prompts to the generating AI model. These prompts also include user profile information.

[1506] Specific operation: The server sends mood information and profile information as prompt messages to the AI ​​model that generates the data.

[1507] Input: Mood information, profile information

[1508] Output: Sending prompt messages to the generating AI model

[1509] Step 5:

[1510] The generative AI model lists appropriate locations based on mood information. The server receives responses from the generative AI model and builds a list of candidate locations.

[1511] Specific operation: The generative AI model uses an algorithm to generate a list of candidate locations based on the user's mood information and profile information.

[1512] Input: Prompt message

[1513] Output: Candidate location list

[1514] Step 6:

[1515] The server sends a list of potential locations to the terminal, which then displays it visually to the user. The user then selects the location they want to go to.

[1516] Specific operation: The server sends a list of candidate locations to the device in JSON format, and the device displays it in a UI component. The user taps a location from the list to select it.

[1517] Input: Candidate location list

[1518] Output: Information on the selected location

[1519] Step 7:

[1520] The terminal sends information about the selected location to the server. The server calculates the optimal route from the user's current location to the selected location and sends the result to the terminal.

[1521] Specific operation: The server uses a map API (e.g., Google Maps API) to calculate the optimal route.

[1522] Input: User's current location, information on the selected location

[1523] Output: Optimal route information

[1524] Step 8:

[1525] The server suggests multiple modes of transport based on the calculated optimal route and sends them to the terminal. The terminal displays this to the user. The user selects a ride-hailing service as needed.

[1526] Specific operation: The server sends a list of transportation options (bus, train, ride-hailing service, etc.) to the device in JSON format, and the device displays them in the UI. The user taps the "Use ride-hailing service" button.

[1527] Input: Optimal route information

[1528] Output: Options for transportation, information on selecting a ride-hailing service.

[1529] Step 9:

[1530] The terminal arranges for a ride and coordinates with the ride-hailing service provider to dispatch a taxi.

[1531] Specific operation: The device calls the ride-hailing service's API to arrange a taxi.

[1532] Input: Ride-hailing service selection information

[1533] Output: Vehicle dispatch confirmation information

[1534] Step 10:

[1535] Users enter feedback and reviews about places they visit within the app. The device sends this information to a server, which stores it in a database.

[1536] Specific operation: The user enters feedback into the evaluation form and taps the submit button. The device sends the feedback to the server via a POST request.

[1537] Input: Feedback Information

[1538] Output: Sending feedback, saving to database

[1539] (Application Example 1)

[1540] 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".

[1541] Traditional food delivery services have had the problem that it is difficult for users to choose the best meal according to their mood and preferences. Furthermore, the lack of a system that makes suggestions based on the user's mood has made improving the user experience a challenge.

[1542] 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.

[1543] In this invention, the server includes means for receiving mood information entered by the user, means for selecting appropriate delivery items corresponding to the received mood information using a generative AI model, means for presenting a list of selected delivery items to the user, and means for notifying the user of the delivery status of the ordered delivery items. This makes it possible to quickly suggest the optimal delivery items corresponding to the user's mood and improve the user experience.

[1544] 1. "User-inputted mood information" refers to information that users provide to the application through input methods such as text or buttons, indicating their current emotions or state (for example, "tired" or "needs energy").

[1545] 2. A "generative AI model" is an artificial intelligence algorithm used to analyze mood information entered by the user and select appropriate delivery items based on that information.

[1546] 3. "Appropriate delivery items" are food and beverage options selected based on the user's mood information, providing the refreshment and nutrition the user desires.

[1547] 4. "Means of presenting to the user" refers to methods and technologies for visually displaying a list of appropriate delivery items selected by the generative AI model on the user's device screen.

[1548] 5. "Ordered delivery items" refer to food and drinks that the user selects and decides to purchase from the presented list.

[1549] 6. "Means of notifying users of delivery status" refers to methods or systems that update and inform users in real time through an application about the process of how ordered delivery items reach the user.

[1550] 7. "User profile information" refers to data that includes basic information about the user (e.g., name, address, food preferences, etc.) and is used to select delivery items.

[1551] 8. "Past order history" refers to a record of the food and drinks a user has ordered in the past. This is used to understand the user's preferences and make suggestions more accurate.

[1552] 9. A "delivery service provider" is a business or company that is responsible for delivering items ordered by a user to a specified address.

[1553] 10. "Arranging delivery of delivery items" is the process of notifying the delivery service provider of the order after the user's order has been confirmed and preparing to deliver the food and drinks to the user.

[1554] This invention relates to a map application system that suggests appropriate delivery items based on the user's mood information. This system involves the server, terminal, and user working together to process the data.

[1555] Explanation of the program's processing

[1556] 1. Login and profile retrieval

[1557] The user launches the app and accesses the login screen. After entering their login information (username and password), the device sends this information to the server for authentication. The server verifies the received authentication information against its database and returns the authentication result to the device. If authentication is successful, the server retrieves the user's profile information and sends it to the device. After receiving the profile information, the device transitions to the main screen.

[1558] 2. Enter and submit your mood.

[1559] The user enters their current mood (e.g., "tired," "needs energy") on the main screen. This mood information is then sent from the device to the server.

[1560] 3. Analysis of mood information and item selection

[1561] The server inputs the user's mood information into a generative AI model, initiating a process to select appropriate delivery items based on that information. The generative AI model utilizes the user's profile information, past order history, and reviews to search the database for delivery items that match the mood information and lists several candidates. For example, if the user inputs "tired," the server will select items such as "energy-boosting rice bowl" or "relaxation drink."

[1562] 4. Item suggestions and ordering

[1563] The server sends a list of selected delivery items to the terminal. The terminal displays this visually to the user, who then selects the items they wish to order from this list. The selection information is sent from the terminal to the server, which notifies the delivery service provider of the order and arranges for delivery.

[1564] 5. Delivery status notification

[1565] Once the order has been processed, the server monitors the delivery status in real time and notifies the user. The terminal receives updates on the delivery status and displays them to the user as needed.

[1566] Hardware and software to use

[1567] Server: Cloud servers such as AWS

[1568] Device: Smartphone

[1569] Software: Flask (a Python micro web framework), generative AI model

[1570] As a specific example

[1571] When a user feels tired and enters "tired," the app suggests delivery items such as energizing rice bowls or relaxation drinks. Once the user selects a relaxation drink and places an order, the server notifies the delivery service provider of the arrangement, and the device receives real-time updates on the delivery status. In this way, users can quickly receive a meal tailored to their mood through a delivery service.

[1572] Example of a prompt

[1573] "Users input their mood, such as 'tired':

[1574] Question: What kinds of food and drinks are suitable?

[1575] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[1576] Step 1: The user launches the app, accesses the login screen, and enters their username and password. The entered login information is sent from the device to the server. The server compares the received login information with its database and generates an authentication result. If successful, the server retrieves the user's profile information and sends it to the device. The device receives the profile information and transitions to the main screen.

[1577] Enter: Username, Password

[1578] Output: Authentication results, user profile information

[1579] Data processing and calculation: Database matching, profile information acquisition.

[1580] Step 2: The user enters their current mood on the main screen (for example, "tired" or "needs energy"), and the device sends this mood information to the server. The server inputs the received mood information into the generating AI model.

[1581] Input: Mood information

[1582] Output: Received mood information

[1583] Data processing and calculation: Inputting mood information into AI models.

[1584] Step 3: The server uses a generative AI model to analyze the user's mood information and select appropriate delivery items. The generative AI model utilizes user profile information, past order history, and reviews to search the database for and list delivery items that match the user's mood information.

[1585] Input: Mood information, user profile information, past order history

[1586] Output: List of delivery items

[1587] Data processing and computation: Database search, generation, and analysis using AI models.

[1588] Step 4: The server sends a list of selected delivery items to the terminal. The terminal displays this on its screen, and the user selects items from the suggested list. The selection information is then sent from the terminal to the server.

[1589] Input: List of delivery items

[1590] Output: User selection information

[1591] Data processing and calculation: Sending lists, receiving item selections.

[1592] Step 5: Based on the user's selection information, the server sends the order information to the delivery service provider and arranges delivery. The server notifies the delivery service provider of the arrangement.

[1593] Input: User's selection information

[1594] Output: Delivery arrangement information

[1595] Data processing and calculation: Sending order information, notifying delivery arrangements.

[1596] Step 6: After the delivery arrangements are complete, the server monitors the delivery status in real time and sends updates to the terminal. The terminal displays the delivery status to the user as needed, providing updates until delivery is complete.

[1597] Input: Delivery status information

[1598] Output: Delivery status update information

[1599] Data processing and calculation: Real-time monitoring, delivery status notification.

[1600] 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.

[1601] Below is a description of the "Modes for Carrying Out the Invention" from the patent specification concerning a map application system that incorporates an emotion engine.

[1602] Modes for carrying out the invention

[1603] This invention is a map application system that suggests appropriate locations based on the user's mood information, providing new discoveries and refreshment. This system is realized through the mutual cooperation of a server, terminal, user, and emotion engine.

[1604] Explanation of the program's processing

[1605] 1. User Login

[1606] The user launches the app and attempts to log in by entering their username and password on the login screen. The device sends this login information to the server, which verifies the authentication information against its database and sends the authentication result back to the device. If authentication is successful, the server retrieves profile information and sends it to the device, and the device transitions to the main screen.

[1607] 2. Mood input / emotion recognition

[1608] The user enters their current mood in text or voice on the main screen. The device sends this mood information to the server, while the emotion engine analyzes the user's input to identify their emotions. The emotion engine also refers to the user's past input history to predict emotions and sends this prediction to the server as well.

[1609] For example, if a user types "I feel kind of tired," the emotion engine recognizes this as "fatigue" and sends that information to the server. It also refers to past input history to make predictions such as "a refreshing spot would be preferred."

[1610] 3. Emotional Analysis and Spot Selection

[1611] The server activates a generative model based on the sentiment and prediction information obtained from the sentiment engine to select an appropriate location. The generative model uses user profile information, past reviews, and rating data to search the database for locations that match the sentiment information and lists multiple candidate locations.

[1612] For example, if the emotion engine recognizes the user's emotion as "fatigue," the server will search for relaxation spots and cafes, taking into account the user's historical data to select the most appropriate location.

[1613] 4. Suggestions for recommended places

[1614] The server sends information about the listed locations to the terminal, which then displays this information visually to the user. The user selects a desired location from the displayed list. For example, they might choose from a list of places like a "quiet cafe" or a "massage parlor."

[1615] 5. Route guidance

[1616] When a user selects a destination, the device sends this selection information to the server. The server calculates the optimal route from the user's current location to the selected destination and also presents transportation options (e.g., bus, train, ride-hailing service). The calculation results are sent to the device and displayed to the user.

[1617] 6. Vehicle dispatch arrangement

[1618] If a user selects a ride-hailing service, the terminal will work with the transportation service provider to arrange the ride. For example, if a user selects a taxi, the terminal will work with the ride-hailing service provider to arrange a taxi.

[1619] 7. Feedback after the visit

[1620] After visiting a location, users enter feedback and reviews about the place they visited within the app. The device sends this information to the server, which stores the received feedback in a database. The sentiment engine also uses this feedback as training data to improve the accuracy of future suggestions.

[1621] Specific usage examples

[1622] One day, a user tired from work launches the app and texts, "I feel kind of tired." The emotion engine recognizes the feeling of "fatigue" and, using past data, suggests relaxation spots. If the user selects "a quiet cafe," the app suggests the best route and mode of transportation (e.g., a 5-minute walk from the nearest station, or a 10-minute taxi ride). If the user selects a taxi, the app arranges one. After visiting, the user provides feedback on the cafe, and this information is used to improve future suggestions.

[1623] The above describes embodiments of the present invention, which aim to allow users to discover new places and refresh themselves according to their mood.

[1624] The following describes the processing flow.

[1625] The following explains the program processing for the MAP application system that incorporates an emotion engine, broken down into specific steps.

[1626] Step 1:

[1627] The user launches the app and accesses the login screen.

[1628] Step 2:

[1629] The user enters their login information (username and password), and the device sends this information to the server.

[1630] Step 3:

[1631] The server compares the received authentication information with the database and sends the authentication result to the terminal. If authentication is successful, the server retrieves the user's profile information and sends it to the terminal.

[1632] Step 4:

[1633] The device receives the profile information and transitions to the main screen. Here, the user's mood input interface is displayed.

[1634] Step 5:

[1635] Users input their current mood in text or voice. For example, they might input "I'm tired" or "I want to eat something spicy."

[1636] Step 6:

[1637] The device sends the mood information entered by the user to the server. Simultaneously, the emotion engine analyzes the mood information and identifies the emotions.

[1638] Step 7:

[1639] The emotion engine analyzes the user's input and past input history to identify the user's emotional state as "fatigue" or "stress," and sends it to the server.

[1640] Step 8:

[1641] The server receives sentiment information from the sentiment engine and invokes the generative model. The generative model searches the database for the appropriate location based on the sentiment information.

[1642] Step 9:

[1643] Based on the results of the generative model, the server lists multiple candidate locations and generates a list. For example, the list might include relaxation spots and restaurants.

[1644] Step 10:

[1645] The server sends this list to the terminal, and the terminal displays the list to the user.

[1646] Step 11:

[1647] The user selects a place they want to go from a list. For example, they might choose a "quiet cafe" or a "massage parlor."

[1648] Step 12:

[1649] The device sends selection information to the server, which calculates the optimal route from the user's current location to their destination.

[1650] Step 13:

[1651] The server calculates the optimal route and transportation options (e.g., bus, train, ride-hailing service, etc.) and sends the results to the terminal.

[1652] Step 14:

[1653] The device displays optimal route and transportation options to the user, who then selects their preferred mode of transport.

[1654] Step 15:

[1655] When a user selects a ride-hailing service, the terminal works with the transportation service provider to arrange the ride.

[1656] Step 16:

[1657] After visiting a place, users can enter feedback and reviews about the place they visited within the app.

[1658] Step 17:

[1659] The device sends feedback information to the server, which stores it in a database. The emotion engine also uses this feedback as training data.

[1660] The above outlines the specific processing steps of the program for the MAP application system that incorporates an emotion engine.

[1661] (Example 2)

[1662] 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".

[1663] Traditional map applications lacked the ability to suggest appropriate locations based on the user's mood and feelings, resulting in an unsatisfactory user experience. Furthermore, users often spent a considerable amount of time searching for a suitable location, potentially increasing stress. Additionally, the lack of consistent suggestions for transportation and optimal routes reduced user convenience.

[1664] The identification processing by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for receiving mood information entered by the user, means for analyzing the received mood information using an emotion engine and identifying the emotion information, means for selecting an appropriate location based on the identified emotion information using a generative model, means for presenting a list of selected locations to the user, means for calculating the optimal route from the presented locations to a location selected by the user, and means for suggesting means of transportation to the selected location. This makes it possible to suggest appropriate locations according to the user's mood and emotions, improving the user experience, shortening search time, and providing a highly convenient service.

[1665] "User mood information" refers to text or audio data that users input to express their current emotions or moods.

[1666] An "emotion engine" is a software component that analyzes received mood information to identify the user's emotions and outputs the result.

[1667] A "generative model" is an algorithm or AI model that selects appropriate locations based on user sentiment information.

[1668] A "list of places" is a list containing multiple locations selected to match the user's emotional information.

[1669] The "optimal route" is the most efficient path from the user's current location to the destination selected by the user.

[1670] "Transportation" refers to the means of travel that a user may use to reach their destination (e.g., walking, public transport, ride-hailing service, etc.).

[1671] A "transportation service provider" is a business that provides ride-hailing services or public transportation that users use for getting around.

[1672] Modes for carrying out the invention

[1673] This invention is a map application system that suggests appropriate locations based on the user's mood information, providing new discoveries and refreshment. This system is realized through the mutual cooperation of a server, terminal, user, and emotion engine.

[1674] User Login

[1675] The user launches the app and attempts to log in by entering their username and password on the login screen. The device sends this login information to the server, which verifies the authentication information against a database (e.g., a MySQL database) and sends the authentication result back to the device. If authentication is successful, the server retrieves profile information and sends it to the device, and the device transitions to the main screen.

[1676] Mood input / emotion recognition

[1677] The user enters their current mood in text or voice on the main screen. The terminal sends this mood information to the server, while an emotion engine (e.g., IBM Watson) analyzes the user's input to identify the emotion. The emotion engine also refers to the user's past input history to predict the emotion and sends it to the server.

[1678] For example, if a user types "I feel kind of tired," the emotion engine recognizes it as "fatigue" and sends that information to the server. It also refers to past input history to make predictions such as "a refreshing spot would be preferred."

[1679] Emotional analysis and spot selection

[1680] The server launches a generative AI model (e.g., TensorFlow) based on sentiment and prediction information obtained from the sentiment engine to select an appropriate location. The generative model utilizes user profile information, past reviews, and rating data to search a database (e.g., MongoDB) for locations that match the sentiment information and lists multiple candidate locations.

[1681] For example, if the emotion engine recognizes the user's emotion as "fatigue," the server will search for relaxation spots and cafes, taking into account the user's historical data to select the most appropriate location.

[1682] Suggestions for recommended places

[1683] The server sends information about the listed locations to the terminal, which then displays this information visually to the user. The user selects a desired location from the displayed list. For example, they might choose from a list of places like a "quiet cafe" or a "massage parlor."

[1684] Route guidance

[1685] When a user selects a destination, the device sends this selection information to the server. The server calculates the optimal route from the user's current location to the selected destination (for example, using the Google Maps API) and also presents transportation options (e.g., bus, train, ride-hailing service, etc.). The calculation results are sent to the device and displayed to the user.

[1686] Vehicle dispatch arrangement

[1687] If the user selects a ride-hailing service, the device will work with the transportation service provider (for example, using the Uber API) to arrange the ride. For example, if the user selects a taxi, the device will work with the ride-hailing service provider to arrange a taxi.

[1688] Feedback after the visit

[1689] After a visit, users enter feedback and reviews about the visited location within the app. The device sends this to a server, which stores the received feedback in a database (e.g., Elasticsearch). The sentiment engine also uses this feedback as training data to improve the accuracy of future suggestions.

[1690] Specific usage examples

[1691] One day, a user tired from work launches the app and texts, "I feel kind of tired." The emotion engine recognizes the feeling of "fatigue" and, using past data, suggests relaxation spots. If the user selects "a quiet cafe," the app suggests the best route and mode of transportation (e.g., a 5-minute walk from the nearest station, or a 10-minute taxi ride). If the user selects a taxi, the app arranges one. After visiting, the user provides feedback on the cafe, and this information is used to improve future suggestions.

[1692] Example of a prompt:

[1693] Please enter your current mood. Example: "I feel kind of tired."

[1694] The purpose of this system is for users to discover new places and refresh themselves according to their mood.

[1695] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1696] Step 1:

[1697] User Login

[1698] Specific actions:

[1699] The user launches the app and enters the username "test_user" and password "password123" on the login screen.

[1700] Enter: Username and password

[1701] Data processing: The terminal converts user input information into an HTTPS request and sends it to the server.

[1702] Output: Request data (JSON including username and password)

[1703] The server compares the received JSON-formatted login information with the database (MySQL).

[1704] Input: Request data (JSON format)

[1705] Data calculation: The server matches the authentication information in the MySQL database.

[1706] Output: Authentication result (success or failure)

[1707] If authentication is successful, the server retrieves the profile information and sends it to the device in JSON format.

[1708] Input: Authentication result

[1709] Data processing: Acquisition of profile information

[1710] Output: JSON containing profile information

[1711] The device analyzes the received profile information and displays it on the main screen.

[1712] Input: Profile information (JSON format)

[1713] Data processing: JSON parsing

[1714] Output: Main screen display

[1715] Step 2:

[1716] Mood input / emotion recognition

[1717] Specific actions:

[1718] The user types the text "I feel kind of tired" on the main screen and presses the send button.

[1719] Input: Text about mood

[1720] Data processing: The device converts text data into an API request and sends it to the server.

[1721] Output: Request data (JSON including text)

[1722] The server receives the request data and sends it to the emotion engine (IBM Watson).

[1723] Input: Request data (JSON format)

[1724] Data processing: Sentiment analysis using IBM Watson

[1725] Output: Emotional information (e.g., "fatigue")

[1726] The emotion engine identifies emotional information and sends the results back to the server. The server then refers to the user's past input history and generates predictive information.

[1727] Input: Emotional information

[1728] Data calculation: Matching with past input history

[1729] Output: Sentiment information and prediction information (JSON format)

[1730] Step 3:

[1731] Emotional analysis and spot selection

[1732] Specific actions:

[1733] The server launches a generative AI model (TensorFlow) based on the emotional and predictive information obtained from the emotion engine.

[1734] Input: Sentiment information and prediction information (JSON format)

[1735] Data processing: Spot selection using generative AI models

[1736] Output: Candidate location list (JSON format)

[1737] The generative AI model uses user profile information, past reviews, and rating data to select the appropriate location.

[1738] Input: Profile information, past reviews and evaluation data

[1739] Data processing: Data integration and analysis

[1740] Output: List of multiple candidate locations (JSON format)

[1741] The server sends the list of selected candidate locations to the terminal.

[1742] Input: Candidate location list (JSON format)

[1743] Data transmission: Transmission via HTTPS request

[1744] Output: Received candidate location list

[1745] Step 4:

[1746] Suggestions for recommended places

[1747] Specific actions:

[1748] The device analyzes the received list of potential locations and displays it visually. The user selects the desired location from the list.

[1749] Input: Candidate location list

[1750] Data processing: List analysis and screen display

[1751] Output: Display of candidate locations and user selection

[1752] Step 5:

[1753] Route guidance

[1754] Specific actions:

[1755] The device sends information about the destination the user wants to go to to the server. The server obtains the user's current location and calculates the optimal route to the selected location.

[1756] Input: Selection information and current location information

[1757] Data calculation: Calculation of the optimal route (using Google Maps API)

[1758] Output: Route information and transportation options (JSON format)

[1759] The server sends route information and transportation details to the terminal. The terminal then displays this information to the user.

[1760] Input: Route information and mode of transport (JSON format)

[1761] Data processing: JSON parsing and screen display

[1762] Output: Route guidance and transportation options provided.

[1763] Step 6:

[1764] Vehicle dispatch arrangement

[1765] Specific actions:

[1766] If the user selects a ride-hailing service, the terminal will work with the transportation service provider to arrange the ride.

[1767] Input: Information on vehicle selection

[1768] Data transmission: Ride-hailing request (e.g., using the Uber API)

[1769] Output: Vehicle dispatch completion notification and detailed information

[1770] Step 7:

[1771] Feedback after the visit

[1772] Specific actions:

[1773] After a visit, the user enters feedback about the visited location within the app. The device then sends this feedback to the server.

[1774] Input: Feedback text

[1775] Data processing: Sending feedback data (JSON format)

[1776] Output: Sending feedback data to the server

[1777] The server stores the received feedback in a database (Elasticsearch), and the sentiment engine uses this feedback as training data.

[1778] Input: Feedback data

[1779] Data processing: Data storage and use as training data.

[1780] Output: Storage of feedback information and updating of the sentiment engine.

[1781] (Application Example 2)

[1782] 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".

[1783] Traditional map applications have made it difficult for users to discover new places based on their mood or feelings. Furthermore, they have not effectively facilitated user-selected locations or collected feedback after visits. Therefore, there is a need for a system that allows users to find places to refresh themselves according to their mood, facilitates easy travel, and improves the accuracy of future suggestions.

[1784] 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.

[1785] In this invention, the server includes means for receiving mood information entered by the user, means for selecting an appropriate location corresponding to the received mood information using a generative model, means for presenting a list of selected locations to the user, means for calculating the optimal route from the presented locations to a location selected by the user, means for suggesting means of transportation to the selected location, means for transmitting route information to an autonomous mobile vehicle and guiding the user, and means for receiving user feedback information and storing it as data to improve the accuracy of future suggestions. This makes it possible for the user to find an appropriate location according to their mood, travel easily, and improve the accuracy of future suggestions.

[1786] A "user" refers to a person who uses this system to input their mood information and receive guidance to an appropriate location.

[1787] "Mood information" refers to information that users input, indicating their current emotions and moods.

[1788] A "generative model" refers to an algorithm or program used to select an appropriate location based on mood information.

[1789] An "appropriate place" refers to a location selected based on the user's mood information, where the user can make new discoveries and feel refreshed.

[1790] A "list" refers to a list used to present users with a selection of suitable locations.

[1791] The "optimal route" refers to the path that allows the user to reach their chosen location most efficiently.

[1792] "Transportation" refers to the means of transport or methods available to the user to reach their chosen destination.

[1793] An "autonomous mobile vehicle" refers to a means of transportation that automatically guides the user to a selected location.

[1794] "Feedback information" refers to the impressions and evaluations that users provide after a visit, and this data is used to improve the accuracy of future suggestions.

[1795] Modes for carrying out the invention

[1796] This invention is a system that suggests appropriate locations based on the user's mood information, providing new discoveries and refreshment. This system is realized through the mutual cooperation of a server, terminal, user, and emotion engine.

[1797] Explanation of the program's processing

[1798] 1. User Login

[1799] The user launches the smartphone app and attempts to log in by entering their username and password on the login screen. The device sends this login information to the server, which verifies the authentication information against a database and sends the authentication result back to the device. If authentication is successful, the server retrieves profile information and sends it to the device, and the device transitions to the main screen.

[1800] 2. Mood input / emotion recognition

[1801] The user enters their current mood in text or voice on the main screen. The device sends this mood information to the server, while the emotion engine analyzes the user's input to identify their emotions. The emotion engine also refers to the user's past input history to predict emotions and sends this prediction to the server as well.

[1802] For example, if a user types "I feel kind of tired," the emotion engine recognizes this as "fatigue" and sends that information to the server. It also refers to past input history to make predictions such as "a refreshing spot would be preferred."

[1803] 3. Emotional Analysis and Spot Selection

[1804] The server activates a generative model based on the sentiment and prediction information obtained from the sentiment engine to select an appropriate location. The generative model uses user profile information, past reviews, and rating data to search the database for locations that match the sentiment information and lists multiple candidate locations.

[1805] For example, if the emotion engine recognizes the user's emotion as "fatigue," the server will search for relaxation spots and cafes, taking into account the user's historical data to select the most appropriate location.

[1806] 4. Suggestions for recommended places

[1807] The server sends information about the listed locations to the terminal, which then displays this information visually to the user. The user selects a desired location from the displayed list. For example, they might choose from a list of places like a "quiet cafe" or a "massage parlor."

[1808] 5. Route guidance

[1809] When a user selects a destination, the device sends this selection information to the server. The server calculates the optimal route from the user's current location to the selected destination and also presents transportation options (e.g., bus, train, autonomous vehicle, etc.). The calculation results are sent to the device and displayed to the user.

[1810] Hardware and software used

[1811] Smartphone: Used by users to run applications.

[1812] Emotion engine: Used to analyze user emotions. Specific examples include natural language processing APIs.

[1813] Server: Used for managing proposal information and calculating routes. A specific example is a cloud service platform.

[1814] Autonomous mobile vehicles: Used to guide users to a selected location. A specific example is an autonomous driving vehicle system.

[1815] Database: Used to manage user profile information and feedback. A specific example is a relational database.

[1816] Specific usage examples and prompt messages

[1817] One day, a user launches the "Emotion Navi" app and enters "I'm feeling a little stressed" on the input screen. The emotion engine recognizes this as "stress" and, based on past data, suggests places where the user can relax (for example, a park or an art museum). If the user selects "park," the optimal route is calculated, and an autonomous vehicle guides the user to that park.

[1818] Example of a prompt

[1819] User: "I'm feeling a little stressed."

[1820] Emotional engine: Recognizes "stress"

[1821] Server: "I suggest places where you can relax. Parks, art museums, cafes."

[1822] User: "Select a park"

[1823] Server: "Calculating the optimal route"

[1824] Autonomous mobile vehicle: "Starting route guidance"

[1825] As described above, this system allows users to easily find the optimal location according to their mood and travel comfortably using autonomous vehicles. Furthermore, feedback after visits will be used to improve the accuracy of future suggestions.

[1826] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1827] Step 1:

[1828] The user launches the smartphone app and attempts to log in by entering their username and password on the login screen. The device sends this login information to the server, which verifies the authentication information against a database and sends the authentication result back to the device. The input is the username and password, and the output is the authentication result. If authentication is successful, the server retrieves profile information and sends it to the device, and the device transitions to the main screen.

[1829] Step 2:

[1830] The user enters their current mood as text or voice on the main screen. The input is mood information in text or voice. The terminal sends this mood information to the server, where the emotion engine analyzes the user's input to identify the emotion. The emotion engine also refers to the user's past input history to predict the emotion and sends it to the server. The output is the analyzed emotion information.

[1831] Step 3:

[1832] The server activates a generative model based on sentiment and prediction information obtained from the sentiment engine to select an appropriate location. The input is sentiment and prediction information, and the output is a list of multiple candidate locations. The generative model uses user profile information, past reviews, and rating data to search the database for locations that match the sentiment information and lists multiple candidate locations.

[1833] Step 4:

[1834] The server sends information about the listed locations to the terminal, which then displays this information visually to the user. The input is a list of multiple possible locations, and the output is a list of locations displayed to the user. The user selects a desired location from the displayed list.

[1835] Step 5:

[1836] When the user selects a destination, the device sends this selection information to the server. The server calculates the optimal route from the user's current location to the selected destination and also presents transportation options (e.g., bus, train, autonomous vehicle, etc.). The inputs are the user's current location and the selected destination, and the output is the optimal route and transportation options. The calculation results are sent to the device and displayed to the user.

[1837] Step 6:

[1838] Route information is transmitted to the autonomous vehicle to guide the user. The server transmits optimal route information to the autonomous vehicle; the input is the optimal route information, and the output is the start of route guidance by the autonomous vehicle.

[1839] Step 7:

[1840] After a user visits a place, they input feedback and reviews on their device. The device sends this information to the server; the input is feedback data, and the output is saved to a database. The server stores the received feedback in the database, and the sentiment engine uses this feedback as training data to improve the accuracy of future suggestions.

[1841] Through these steps, users can easily find the perfect location based on their mood and travel comfortably using autonomous vehicles. Furthermore, feedback after visits will be used to improve the accuracy of future recommendations.

[1842] 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.

[1843] 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.

[1844] 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.

[1845] 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.

[1846] 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.

[1847] 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.

[1848] 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.

[1849] 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.

[1850] 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."

[1851] 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.

[1852] 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.

[1853] 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.

[1854] 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.

[1855] 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.

[1856] 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.

[1857] 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.

[1858] 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.

[1859] 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.

[1860] 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.

[1861] 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.

[1862] 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.

[1863] The following is further disclosed regarding the embodiments described above.

[1864] Understood. Please draft the claims for a system of emotionally charged map applications following the format below.

[1865] (Claim 1)

[1866] A means of receiving mood information entered by the user,

[1867] A means for selecting an appropriate location corresponding to received mood information using a generative model,

[1868] A means of presenting the user with a list of selected locations,

[1869] A means for calculating the optimal route from a given location to a location selected by the user,

[1870] A system that includes means of proposing transportation to a selected location.

[1871] (Claim 2)

[1872] The system according to claim 1, comprising means for narrowing down the list of potential locations to be selected by matching the user's profile information with past reviews.

[1873] (Claim 3)

[1874] The system according to claim 1, comprising means for coordinating with a transportation service provider to arrange for a vehicle to be dispatched to a location selected by the user.

[1875] "Example 1"

[1876] (Claim 1)

[1877] A means of receiving mood information entered by the user,

[1878] A means for selecting an appropriate location corresponding to received mood information using a generative model,

[1879] A means of presenting the user with a list of selected locations,

[1880] A means for calculating the optimal route from a given location to a location selected by the user,

[1881] A means of proposing transportation to the selected location,

[1882] A means of generating a list of candidate locations by providing prompts to a generative model, utilizing received mood information and user profile information,

[1883] A system that includes a means of suggesting relaxation spots, restaurants, and other places based on the mood information entered by the user.

[1884] (Claim 2)

[1885] The system according to claim 1, comprising means for narrowing down the list of potential locations to be selected by matching the user's profile information with past reviews.

[1886] (Claim 3)

[1887] The system according to claim 1, comprising means for coordinating with a transportation service provider to arrange for a vehicle to be dispatched to a location selected by the user.

[1888] "Application Example 1"

[1889] (Claim 1)

[1890] A means of receiving mood information entered by the user,

[1891] A means for selecting appropriate delivery items corresponding to received mood information using a generative AI model,

[1892] A means of presenting the user with a list of selected delivery items,

[1893] A means for the user to order delivery items selected from the items presented,

[1894] A system that includes means for notifying users of the delivery status of ordered delivery items.

[1895] (Claim 2)

[1896] The system according to claim 1, comprising means for narrowing down candidates for delivery items by matching the user's profile information with past order history.

[1897] (Claim 3)

[1898] The system according to claim 1, comprising means for coordinating with a delivery service provider to arrange for the delivery of a delivery item selected by the user.

[1899] "Example 2 of combining an emotion engine"

[1900] (Claim 1)

[1901] A means of receiving mood information entered by the user,

[1902] A means for analyzing received mood information using an emotion engine and identifying emotional information,

[1903] A means for selecting an appropriate location based on identified sentiment information using a generative model,

[1904] A means of presenting the user with a list of selected locations,

[1905] A means for calculating the optimal route from a given location to a location selected by the user,

[1906] A system that includes means of proposing transportation to a selected location.

[1907] (Claim 2)

[1908] The system according to claim 1, comprising means for narrowing down the list of potential locations to be selected by matching the user's profile information with past reviews.

[1909] (Claim 3)

[1910] The system according to claim 1, comprising means for coordinating with a transportation service provider to arrange for a vehicle to be dispatched to a location selected by the user.

[1911] "Application example 2 when combining with an emotional engine"

[1912] (Claim 1)

[1913] A means of receiving mood information entered by the user,

[1914] A means for selecting an appropriate location corresponding to received mood information using a generative model,

[1915] A means of presenting the user with a list of selected locations,

[1916] A means for calculating the optimal route from a given location to a location selected by the user,

[1917] A means of proposing transportation to the selected location,

[1918] A means of transmitting route information to an autonomous mobile vehicle and guiding the user,

[1919] A system that includes means for receiving user feedback information and storing it as data to improve the accuracy of future suggestions.

[1920] (Claim 2)

[1921] The system according to claim 1, comprising means for narrowing down the list of potential locations to be selected by matching the user's profile information with past reviews.

[1922] (Claim 3)

[1923] The system according to claim 1, comprising means for coordinating with a transportation service provider to arrange for a vehicle to be dispatched to a location selected by the user. [Explanation of Symbols]

[1924] 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 mood information entered by the user, A means for selecting an appropriate location corresponding to received mood information using a generative model, A means of presenting the user with a list of selected locations, A means for calculating the optimal route from a given location to a location selected by the user, A system that includes means of proposing transportation to a selected location.

2. The system according to claim 1, comprising means for narrowing down the list of potential locations to be selected by comparing the user's profile information with past reviews.

3. The system according to claim 1, comprising means for coordinating with a transportation service provider to arrange for a vehicle to be dispatched to a location selected by the user.

Images