System

Abstract

A system is provided.SOLUTION: This system is provided with a user information input means for allowing a user to input basic information and desired conditions, a data receiving means for receiving information from the user information input means, an analyzing means for analyzing the received user information, and for extracting the desired conditions of the user, and a database for storing a plurality of vehicle and driver information. The system includes a matching means for selecting an optimum vehicle and driver for the user, a vehicle allocation information transmission means for transmitting information on the selected vehicle and driver to the user, a route management means for tracking position information on the selected vehicle and driver and providing an optimum route, and an evaluation data collection means for receiving an evaluation from the user and collecting and learning the evaluation data.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] While the usage rate of taxi dispatch apps has increased in recent years, they are still unable to fully address users' specific needs. In particular, it is difficult to provide vehicles and drivers that meet users' desired travel conditions and individual needs. Furthermore, users are increasingly demanding comfort while driving, and flexible dispatch systems that can meet this demand are needed. Furthermore, taxi companies are expected to efficiently manage their operations by understanding the routes and driving quality of their drivers. The present invention aims to solve these problems, increase user satisfaction, and improve the operational efficiency of taxi companies. [Means for solving the problem]

[0005] The present invention is a system comprising: a user information input means for a user to input basic information and desired conditions; a data receiving means for receiving information from the user information input means; an analysis means for analyzing the received user information and extracting the user's desired conditions; a database storing information on multiple vehicles and drivers; a matching means for selecting the most suitable vehicle and driver for the user based on the user's desired conditions extracted by the analysis means; a dispatch information sending means for sending information on the selected vehicle and driver to the user; a route management means for tracking the location information of the selected vehicle and driver and providing the optimal route; and an evaluation data collection means for receiving evaluations from users and collecting and learning from the evaluation data.

[0006] This system allows users to receive the optimal vehicle and driver match based on their specific needs, improving the comfort of their trip. Furthermore, it allows taxi companies to efficiently manage driver and vehicle information, improving operational efficiency and service quality.

[0007] "User information input means" refers to an interface that allows a user to input their own basic information and desired conditions for travel.

[0008] "Data receiving means" refers to a device or system that receives information sent from the user information input means and sends it to the next step for analysis.

[0009] "Analysis means" refers to a device or algorithm for analyzing received user information and extracting the user's desired conditions.

[0010] "Database" refers to a storage system that stores multiple vehicle and driver information items and allows them to be searched and analyzed as needed.

[0011] "Matching means" refers to a device or algorithm that selects the optimal vehicle and driver based on the user's desired conditions extracted by the analysis means.

[0012] "Vehicle dispatch information transmission means" refers to a device or system for transmitting information about the vehicle and driver selected by the matching means to the user.

[0013] "Route management means" refers to a device or system that tracks the location information of selected vehicles and drivers and provides optimal routes taking into account traffic and accident information.

[0014] "Evaluation data collection means" refers to a device or system that receives evaluations from users, collects and learns from the data, and uses it to improve the system. [Brief explanation of the drawings]

[0015] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9]1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0016] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0017] First, the terms used in the following description will be explained.

[0018] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0019] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0020] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0021] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0022] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0023] [First embodiment]

[0024] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0025] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0026] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0027] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0028] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0029] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0030] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0031] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0032] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0033] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0034] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0035] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0036] The present invention is a system that matches users with the most suitable taxi vehicle and driver based on their basic information and desired conditions. The program processing of this system is explained below in natural language.

[0037] Entering and collecting user data

[0038] User

[0039] Users open the smartphone application and enter basic information such as their name, age, gender, and address.

[0040] The app allows users to input their specific travel needs, such as "I want to arrive quickly," "No conversation required," "Multilingual support," "I want to share a ride," "Car sickness prevention," and "Wheelchair and stroller access."

[0041] Data submission and initial analysis

[0042] Terminal

[0043] The information entered by the user is sent to a server via the Internet.

[0044] server

[0045] The server passes the received user information and requests to an analysis module.

[0046] The analytical module analyzes the user's specific needs and lifestyle and extracts relevant parameters.

[0047] Selection of the best vehicle and driver

[0048] server

[0049] The server checks the database of available taxi vehicles and drivers in real time.

[0050] The system uses data such as each driver's preferred routes, past ratings, and driving quality to select the most suitable driver and vehicle.

[0051] A matching algorithm is used to select the vehicle and driver that best suits the user's requirements.

[0052] Vehicle dispatch proposal

[0053] server

[0054] Information about the matched driver and vehicle is sent to the user's device.

[0055] Terminal

[0056] The user terminal displays driver and vehicle information (e.g., driver's name, vehicle number, estimated arrival time).

[0057] The user reviews and confirms the proposed vehicle and driver.

[0058] Movement initiation and path tracking

[0059] server

[0060] As the driver heads to the user's pickup location, the server tracks the vehicle's location in real time.

[0061] The server calculates the optimal route based on traffic and congestion information and provides it to the driver and user.

[0062] User

[0063] Users can check the driver's arrival on the app and then get in the car.

[0064] While traveling, users can check their route and estimated arrival time through the app.

[0065] Post-move evaluation and feedback

[0066] User

[0067] After completing a trip, users can rate the driver and vehicle through the app, including by leaving a star rating and comments.

[0068] server

[0069] The server passes the collected evaluation data to an analysis module, which then learns to improve matching accuracy for the next time.

[0070] This will improve future matching accuracy and increase user satisfaction.

[0071] Specific examples

[0072] 1. Users

[0073] Tanaka wants to leave for work at 9 a.m. He enters the conditions "I want to travel quickly" and "No conversation" into the app.

[0074] 2. Terminal

[0075] Tanaka's input data is sent to the server.

[0076] 3. Server

[0077] The AI ​​module analyzes Tanaka's request and selects a driver, Yamada, who can provide a quick and quiet trip.

[0078] 4. Server

[0079] Yamada's vehicle information is sent to Tanaka's device.

[0080] 5. Terminal

[0081] Tanaka checks the proposed content and approves it.

[0082] 6. Server

[0083] Gives pickup instructions to Yamada and manages the route.

[0084] 7. Users

[0085] After the trip is completed, Tanaka praises Yamada's driving and enters feedback.

[0086] 8. Server

[0087] The collected evaluation data will be analyzed by AI and used for the next match.

[0088] In this way, AI-powered dispatch systems will provide users with an optimal travel experience tailored to their individual needs, while also helping taxi companies improve their operational efficiency.

[0089] The processing flow will be explained below.

[0090] Step 1: Enter your user information and preferences

[0091] User

[0092] Users open the smartphone application and enter their basic information (e.g., name, age, gender, address).

[0093] Users input specific travel requests (e.g., early arrival, no conversation, multilingual support, ride-sharing, car sickness prevention, wheelchair and stroller accessibility).

[0094] Step 2: Send data

[0095] Terminal

[0096] The terminal sends the basic information and desired conditions entered by the user to the server.

[0097] Step 3: Receiving user information

[0098] server

[0099] The server receives the user information and desired conditions sent from the terminal.

[0100] Step 4: Analyze user information

[0101] server

[0102] The server transfers the received user information to the AI ​​analysis module.

[0103] The AI ​​analysis module extracts the user's desired conditions and generates analysis results.

[0104] Step 5: Vehicle and driver database matching

[0105] server

[0106] The server searches a database of available vehicles and drivers in real time.

[0107] The database stores information such as the driver's strengths and weaknesses in routes, past evaluations, and driving quality.

[0108] Step 6: Generate the best match

[0109] server

[0110] Based on the output of the AI ​​analysis module, the server selects the vehicle and driver that best meets the user's desired conditions.

[0111] A matching algorithm is used to create a final list of selected drivers and vehicles.

[0112] Step 7: Submit your trip

[0113] server

[0114] The server sends information about the selected vehicle and driver, as well as the estimated arrival time, to the user's terminal.

[0115] Step 8: Review the trip offer

[0116] Terminal

[0117] The user's device will display details of the ride offer (e.g., driver's name, vehicle number, estimated arrival time).

[0118] The user checks the proposed vehicle and driver information and presses the confirm button.

[0119] Step 9: Notification of arrival at pickup point

[0120] Terminal

[0121] The user's device is notified that the driver has arrived at the pickup location.

[0122] Users can check the driver's arrival via the app.

[0123] Step 10: Route Management and Tracking

[0124] server

[0125] The server tracks the vehicle's location in real time and calculates the optimal route based on traffic and congestion information.

[0126] The calculated optimal route is notified to the driver and user.

[0127] Step 11: Notification of completion of move

[0128] server

[0129] When the trip is complete, the server receives a notification from the driver that the trip is complete and notifies the user.

[0130] Step 12: Enter your rating

[0131] User

[0132] After completing the trip, users can rate the driver and vehicle through the app (e.g., star rating, comments).

[0133] Step 13: Collect evaluation data

[0134] server

[0135] The server transfers user evaluation data to an analysis module, which learns from it to help improve the matching process next time.

[0136] Example 1

[0137] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0138] In modern society, it is difficult to quickly and accurately match the optimal means of transportation and operators that meet the diverse needs of users, and there is a lack of mechanisms to respond to immediate and personalized requests. Furthermore, conventional systems have difficulty effectively utilizing collected evaluation data to improve the accuracy of the next match, which limits the ability to continuously improve the quality of the user experience.

[0139] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0140] In this invention, the server includes an information input means for a user to input basic information and desired conditions, a data receiving means for receiving information from the information input means, and an analysis means for analyzing the received user information and extracting the user's desired conditions. This enables quick and accurate matching of optimal transportation means and operators according to the user's diverse needs. Furthermore, by using a generative AI model to collect and learn evaluation data, the accuracy of the next match can be improved, and the quality of the user experience can be continuously improved.

[0141] The "information input means" is a means for the user to input basic information and desired conditions.

[0142] The "data receiving means" is a means for receiving information from the information input means.

[0143] The "analysis means" is a means for analyzing the received user information and extracting the user's desired conditions.

[0144] "Database" means data storage for storing multiple modes of transportation and operator information.

[0145] The "matching means" is a means for selecting the most suitable means of transportation and operator for the user based on the user's desired conditions extracted by the analysis means.

[0146] The "vehicle dispatch information transmission means" is a means for transmitting information about the selected transportation means and the driver to the user.

[0147] The "route management means" is a means for tracking the location information of the selected means of transportation and the operator, and providing the optimal route.

[0148] The "rating data collection means" is a means for receiving ratings from users and collecting and learning rating data.

[0149] A "generative AI model" is an artificial intelligence technology that analyzes and learns from evaluation data to improve matching accuracy the next time.

[0150] This invention is a system that matches the optimal means of transportation and operator based on the user's input of basic information and desired conditions. This system is composed of components of a server, a terminal, and a user, and is specifically implemented as follows.

[0151] A user opens a smartphone application and enters basic information such as name, age, gender, and address. For example, the user enters "Ichiro Tanaka" as the user name, "30 years old," "male," and "Shinjuku-ku, Tokyo" as the address. The user also enters specific travel requests. For example, from options such as "Arrive quickly," "No conversation," "Multilingual support," "Prefer carpooling," "Car sickness prevention," and "Wheelchair and stroller access," the user selects "Arrive quickly" and "No conversation."

[0152] The terminal automatically transmits the information entered by the user to the server via the Internet, at which point the terminal waits for a response to confirm that the data transmission was successful.

[0153] The server passes the user information and desired conditions received from the device to a dedicated analysis module. The analysis module analyzes the user's desired conditions and lifestyle and extracts relevant parameters. For example, if the user requests a "quick arrival," the server will issue instructions to prioritize the selection of drivers who can provide a fast route.

[0154] The server compares a database of available transportation methods and drivers in real time. The database includes each driver's location information, preferred routes, past evaluations, etc. The server narrows down the candidates based on each driver's preferred routes, past evaluations, and driving quality. For example, driver Taro Yamada is good at speedy driving, which meets the condition of "I want to arrive quickly." A matching algorithm is used to select the driver and transportation method that best suits the user's requirements. In this process, a generative AI model is used to analyze and learn from the evaluation data.

[0155] The server sends information about the selected driver and transportation means to the user's terminal. The information sent includes the driver's name, transportation means number, and estimated arrival time. The driver and transportation means information is displayed on the user's terminal. For example, it may show "Driver's name: Yamada Taro, Transportation means number: AB-1234, Estimated arrival time: 9:00." The user confirms the information presented and presses the "Confirm" button to indicate their consent.

[0156] As the driver heads to the user's pickup point, the server tracks the vehicle's location in real time. The driver's current location is monitored using GPS data. The server calculates the optimal route based on traffic and congestion information. This information is provided to the driver and user in real time. The user checks the driver's arrival in the app and waits at the specified pickup point. A notification is sent when the driver approaches the arrival point. The user gets into the driver's car and begins traveling. During the trip, the user can check the route and estimated arrival time through the app.

[0157] After completing a trip, the user can rate the driver and the means of transportation through the app. For example, they could rate the driver 5 stars and comment that the ride was very quick and quiet. The server then passes the collected rating data to an analysis module, which uses the data to learn how to improve the accuracy of the next match. The rating data is then analyzed by a generative AI model, which updates the driver's rating score. This improves future matching accuracy and continuously improves the quality of the user experience.

[0158] This system allows users to enjoy an optimal travel experience tailored to their individual needs, and also enables taxi companies and drivers to improve the quality of their services. Examples of prompt sentences include "Mr. Tanaka wishes to travel quickly," and "Mr. Yamada is good at driving quickly."

[0159] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0160] Step 1: Entering User Data

[0161] The user opens the smartphone application and enters basic information such as name, age, gender, and address, as well as desired conditions for travel. The specific data entered includes the user name "Tanaka Ichiro," age "30," gender "male," address "Shinjuku Ward, Tokyo," and desired conditions "arrive quickly" and "no conversation." This information is stored on the device as input data (basic information and desired conditions).

[0162] Step 2: Send data

[0163] The terminal sends the information entered by the user to the server via the Internet. The input is the user data stored in the previous step, and the output is sent to the server. At this time, the terminal waits for a response to confirm the successful data transmission.

[0164] Step 3: Initial analysis

[0165] The server passes the user data received from the device to the analysis module. The received data consists of the user's basic information and desired conditions. The analysis module analyzes the user's desired conditions and lifestyle and extracts related parameters. For example, based on the desired condition of "I want to arrive quickly," the module prepares to find a driver who can provide a fast route. The output is the extracted analysis results (parameters related to the user's desired conditions).

[0166] Step 4: Select the best transportation and driver

[0167] The server compares the analysis results with a database of available means of transportation and drivers in real time. The input data are the analysis results and database information. The server narrows down the most suitable candidates based on data such as each driver's location information, preferred routes, and past evaluations. For example, it confirms that driver "Yamada Taro" is good at speedy driving, which meets the condition of "wanting to arrive quickly." The output is the selection result of the most suitable means of transportation and driver.

[0168] Step 5: Ride proposal

[0169] The server sends the information of the selected driver and transportation means to the user's terminal. The input is the information of the selected driver and transportation means, and the output is the dispatch information (driver's name, transportation means number, estimated arrival time) sent to the user's terminal.

[0170] Step 6: View and confirm your trip information

[0171] The terminal receives the dispatch information from the server and displays it to the user. The displayed information is "Driver's name: Yamada Taro, Transportation number: AB-1234, Estimated arrival time: 9:00." The user confirms the information presented and presses the "Confirm" button to accept it. The input is the dispatch information from the server, and the output is the user's confirmation and confirmation action.

[0172] Step 7: Path tracing

[0173] The server tracks the vehicle's location in real time as the driver heads to the user's pickup point. The input is GPS data and traffic information, and the output is the calculation of the optimal route. The server calculates the optimal route based on traffic and congestion information and provides this information to the driver and user.

[0174] Step 8: Driver arrives and begins travel

[0175] The user checks the driver's arrival through the app. A notification is sent when the user approaches the destination. The user waits at the designated pickup point, gets into the driver's car, and begins their journey. During the journey, the user can check the route and estimated arrival time through the app. The input is the arrival notification and route information from the server, and the output is the user's journey start and route confirmation.

[0176] Step 9: Post-move evaluation and feedback

[0177] After completing the trip, the user evaluates the driver and the means of transportation through the app. For example, the user might enter "star rating: 5, comment: The drive was very quick and quiet." The input is the user's evaluation data, and the output is the transmission of the evaluation data to the server.

[0178] Step 10: Collect evaluation data and learn

[0179] The server passes the collected evaluation data to an analysis module, which then performs learning to improve the accuracy of the next match. The input is the user's evaluation data, and the output is the pilot's evaluation score analyzed and updated by the generative AI model. This improves future matching accuracy and continuously improves the quality of the user experience.

[0180] (Application example 1)

[0181] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0182] Conventional taxi dispatch systems have difficulty reflecting detailed user preferences and providing services that meet some customization requests. In particular, they lack accuracy in providing specific travel requests and optimal route guidance, leaving users wanting more. Similar challenges exist in the food delivery field, where it is difficult to efficiently select the optimal delivery person and route.

[0183] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0184] In this invention, the server includes: a user information input means for a user to input basic information and desired conditions; a data receiving means for receiving information from the user information input means; an analysis means for analyzing the received user information and extracting the user's desired conditions; a database storing information on multiple vehicles and drivers; a matching means for selecting the most suitable vehicle and driver for the user based on the user's desired conditions extracted by the analysis means; a dispatch information sending means for sending information on the selected vehicle and driver to the user; a route management means for tracking the location information of the selected vehicle and driver and providing the optimal route; an evaluation data collection means for receiving evaluations from users and collecting and learning the evaluation data; and a means for selecting the most suitable delivery person and route based on the basic information and desired conditions input by the user. This makes it possible to provide an optimal service that meets the user's detailed desired conditions and specific requests.

[0185] "User information input means" refers to a device or function that allows a user to input basic information and desired conditions.

[0186] The "data receiving means" is a device or function that receives information from the user information input means.

[0187] The "analysis means" is a device or function that analyzes the received user information and extracts the user's desired conditions.

[0188] A "database" is a storage device for storing information on a plurality of vehicles and drivers.

[0189] The "matching means" is a device or function that selects the most suitable vehicle and driver based on the user's desired conditions extracted by the analysis means.

[0190] The "vehicle dispatch information transmission means" is a device or function that transmits information about the selected vehicle and driver to the user.

[0191] The "route management means" is a device or function that tracks the location information of the selected vehicle and driver and provides the optimal route.

[0192] The "evaluation data collection means" is a device or function that receives evaluations from users and collects and learns evaluation data.

[0193] A "delivery staff member" is a person who delivers items along the optimal route based on the basic information and desired conditions entered by the user.

[0194] An "optimal route" is the best travel route calculated taking into account traffic conditions and the user's desired conditions.

[0195]

[0196] The system for implementing this invention includes a user information input means through which the user inputs basic information and desired conditions. This information is input via a mobile device such as a smartphone. Specifically, the user inputs their name, age, address, and desired conditions for travel (e.g., conditions such as "I want to arrive quickly," "No conversation," and "I want contactless delivery").

[0197] The entered data is sent to a server via the Internet. The server has a data receiving means for receiving this data. The received user information is analyzed using an analysis means, and the user's desired conditions are extracted. This analysis means includes an AI module using Python, which efficiently analyzes user information.

[0198] The analyzed data is compared with a database that stores information on multiple vehicles and drivers. The server's database contains detailed information on each driver, such as their preferred routes, past ratings, and driving quality. The server also has a matching means for selecting the optimal vehicle and driver. This matching means selects the vehicle and driver that best meets the user's desired conditions.

[0199] The selected vehicle and driver information will be sent to the user's terminal via the vehicle dispatch information sending means. The user will then confirm and confirm the proposed vehicle and driver information (e.g., driver's name, vehicle license plate number, estimated arrival time).

[0200] The server then tracks the location information of the vehicle and the driver using a route management means, which calculates the optimal route taking into account traffic and congestion information.

[0201] After completing a delivery, the user can rate the driver and vehicle via their device. This rating is in the form of a star rating or a comment, and is sent to the server via a data collection tool. The server then uses AI to analyze the collected rating data and use it to improve the accuracy of the next match.

[0202] For example, the user inputs requests such as "I want to arrive quickly" or "I want contactless delivery." This information is sent to the server, and the AI ​​module analyzes it, selecting the most suitable driver to deliver quickly and contactlessly. This driver's information is sent to the user's device, and the user confirms it, and the delivery begins. Below is an example of a prompt:

[0203] User: Name: Taro Tanaka, Age: 30, Address: Shibuya-ku, Tokyo

[0204] Desired conditions: Delivery by 12:00, no-contact delivery, early arrival

[0205] This will enable us to provide optimal services that meet the user's detailed requirements and specific requests.

[0206] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0207] Step 1:

[0208] The user starts the smartphone application and inputs their name, age, address, and desired conditions. Specific desired conditions include "quick arrival" and "contactless delivery." This input information is sent to the server by the data receiving means.

[0209] Input: User's basic information and desired conditions

[0210] Output: Data sent to the data receiver

[0211] Step 2:

[0212] The server's data receiving means acquires the received user information and passes it to the analyzing means, which extracts the user's desired conditions from the received data.

[0213] Input: Data sent to the data receiving means

[0214] Output: Extracted user preferences

[0215] Step 3:

[0216] The server's analytical means uses a generative AI model using Python to analyze the user's input data, and this data analysis extracts the user's desired conditions.

[0217] Input: Received user information

[0218] Output: Parsed desired conditions

[0219] Step 4:

[0220] The server's matching means selects the optimal delivery person and route from a database based on the desired conditions obtained from the analysis means. This database stores information such as each driver's preferred routes, past evaluations, and driving quality.

[0221] Input: Parsed desired conditions

[0222] Output: Selection of optimal delivery personnel and route

[0223] Step 5:

[0224] The server's dispatch information transmission means transmits the selected delivery person and route information to the user terminal, and the user confirms and approves the received information.

[0225] Input: Selection of optimal delivery personnel and route

[0226] Output: Information sent to the user's terminal

[0227] Step 6:

[0228] The server's route management means tracks the location information of selected drivers and delivery routes in real time and provides the optimal route, using an algorithm that takes into account traffic and accident information to optimize the route.

[0229] Input: Selected driver and delivery route information

[0230] Output: Optimal route information

[0231] Step 7:

[0232] After the delivery is completed, the user can rate the driver and the vehicle through the smartphone application, which will then be sent to the server's rating data collection means.

[0233] Input: User rating data

[0234] Output: Rating data sent to the server

[0235] Step 8:

[0236] The server's evaluation data collection means analyzes the collected evaluation data and trains the generative AI model to improve matching accuracy next time. This continuous learning process improves the quality of service and increases user satisfaction.

[0237] Input: User-submitted rating data

[0238] Output: The learning results of the generative AI model

[0239] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0240] The present invention is a system that combines a system that matches the optimal vehicle and driver by inputting the user's basic information and desired conditions with an emotion engine that recognizes and processes the user's emotions. Below, the program processing of this system is specifically explained in natural language.

[0241] Entering and collecting user data

[0242] User

[0243] Users open the smartphone application and enter their basic information (e.g., name, age, gender, address).

[0244] The app allows users to input their specific travel needs, such as "I want to arrive quickly," "No conversation required," "Multilingual support," "I want to share a ride," "Car sickness prevention," and "Wheelchair and stroller access."

[0245] Data submission and initial analysis

[0246] Terminal

[0247] The information entered by the user is sent to a server via the Internet.

[0248] server

[0249] The server passes the received user information and requests to the analysis module and emotion engine.

[0250] The analytical module analyzes the user's specific needs and lifestyle and extracts relevant parameters.

[0251] At the same time, the emotion engine recognizes the user's emotional state based on user input data and real-time interactions (e.g., voice, facial expressions, text).

[0252] Selection of the best vehicle and driver

[0253] server

[0254] The server checks the database of available taxi vehicles and drivers in real time.

[0255] The system uses data such as each driver's preferred routes, past ratings, and driving quality to select the most suitable driver and vehicle.

[0256] It also incorporates the output of the emotion engine to select the vehicle and driver that best suits the user's current emotional state.

[0257] Vehicle dispatch proposal

[0258] server

[0259] Information about the matched driver and vehicle is sent to the user's device.

[0260] Terminal

[0261] The user terminal displays driver and vehicle information (e.g., driver's name, vehicle number, estimated arrival time).

[0262] The user checks the proposed vehicle and driver information and presses the confirm button.

[0263] Movement initiation and path tracking

[0264] server

[0265] As the driver heads to the user's pickup location, the server tracks the vehicle's location in real time.

[0266] The server calculates the optimal route based on traffic and congestion information and provides it to the driver and user.

[0267] User

[0268] Users can check the driver's arrival on the app and then get in the car.

[0269] While traveling, users can check their route and estimated arrival time through the app.

[0270] Emotion Monitoring and Alerts

[0271] server

[0272] The emotion engine monitors the user's emotional state in real time while on the move.

[0273] If the server detects any emotion indicative of anxiety or discomfort from the user, it will provide warnings and guidance to the driver.

[0274] Post-move evaluation and feedback

[0275] User

[0276] After completing a trip, users can rate the driver and vehicle through the app, including by leaving a star rating and comments.

[0277] server

[0278] The server passes the collected evaluation data and emotion data to an analysis module, which then performs learning to improve matching accuracy for the next time.

[0279] This will improve future matching accuracy and increase user satisfaction.

[0280] Specific examples

[0281] 1. Users

[0282] Tanaka wants to leave for work at 9 a.m. He enters the conditions "I want to travel quickly" and "No conversation" into the app.

[0283] 2. Terminal

[0284] Tanaka's input data is sent to the server.

[0285] 3. Server

[0286] The analysis module analyzes Tanaka's desired conditions, and the emotion engine evaluates Tanaka's current emotional state. A driver, Yamada, who can provide a fast, quiet, and reassuring ride is selected.

[0287] 4. Server

[0288] Yamada's vehicle information is sent to Tanaka's device.

[0289] 5. Terminal

[0290] Tanaka checks the proposed content and approves it.

[0291] 6. Server

[0292] It issues pickup instructions to Yamada and manages the route. The emotion engine monitors Tanaka's emotional state and sends a warning to Yamada if anxiety is detected.

[0293] 7. Users

[0294] After the trip is completed, Tanaka praises Yamada's driving and enters feedback.

[0295] 8. Server

[0296] The collected evaluation and emotional data is analyzed by AI to help improve the accuracy of the next match.

[0297] In this way, a dispatch system that combines an emotion engine can provide an optimal travel experience tailored to the individual needs and emotional state of the user, while also helping to improve the operational efficiency of taxi companies.

[0298] The processing flow will be explained below.

[0299] Step 1: Enter your user information and preferences

[0300] User

[0301] Users open the smartphone application and enter their basic information (e.g., name, age, gender, address).

[0302] Users input specific travel requests (e.g., early arrival, no conversation, multilingual support, ride-sharing, car sickness prevention, wheelchair and stroller accessibility).

[0303] Step 2: Submit your user information and preferences

[0304] Terminal

[0305] The terminal sends the basic information and desired conditions entered by the user to the server.

[0306] Step 3: Receiving and analyzing user information

[0307] server

[0308] The server receives the user information and desired conditions sent from the terminal.

[0309] The server uses an AI analysis module to analyze the user's desired conditions and extract relevant parameters.

[0310] Step 4: Emotional state analysis by the emotion engine

[0311] server

[0312] The emotion engine in the server analyzes the user's input data and real-time interactions (e.g., voice, facial expressions, text) to assess the user's emotional state.

[0313] Step 5: Vehicle and driver database matching

[0314] server

[0315] The server searches a database of available vehicles and drivers in real time.

[0316] The database contains information such as the driver's preferred routes, past ratings, and driving quality.

[0317] Step 6: Selecting the best vehicle and driver

[0318] server

[0319] Based on the output of the AI ​​analysis module and the evaluation of the emotion engine, the server selects the vehicle and driver that best suits the user's desired conditions and emotional state.

[0320] Step 7: Submit your trip

[0321] server

[0322] The server sends information about the selected vehicle and driver, as well as the estimated arrival time, to the user's terminal.

[0323] Step 8: Review the trip offer

[0324] Terminal

[0325] The user's device will display details of the ride offer (e.g., driver's name, vehicle number, estimated arrival time).

[0326] The user checks the proposed vehicle and driver information and presses the confirm button.

[0327] Step 9: Notification of arrival at pickup point

[0328] Terminal

[0329] The user's device is notified that the driver has arrived at the pickup location.

[0330] Users can check the driver's arrival via the app.

[0331] Step 10: Route Management and Tracking

[0332] server

[0333] The server tracks the vehicle's location in real time and calculates the optimal route based on traffic and congestion information.

[0334] The calculated optimal route is notified to the driver and user.

[0335] Step 11: Emotion Monitoring and Alerts

[0336] server

[0337] The emotion engine monitors the user's emotional state in real time while on the move.

[0338] If the server detects any emotion indicative of anxiety or discomfort from the user, it will provide warnings and guidance to the driver.

[0339] Step 12: Notification of completion of move

[0340] server

[0341] When the trip is complete, the server receives a notification from the driver that the trip is complete and notifies the user.

[0342] Step 13: Enter your rating

[0343] User

[0344] After completing the trip, users can rate the driver and vehicle through the app (e.g., star rating, comments).

[0345] Step 14: Collect and analyze assessment data

[0346] server

[0347] The server transfers user evaluation and sentiment data to an analysis module, which learns from the data to help improve the matching process next time.

[0348] Example 2

[0349] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0350] In modern mobility services, it is important to select a vehicle and driver that takes into account the user's basic information and desired conditions. Additionally, there is a need for a system that can recognize the user's emotional state in real time and respond appropriately. However, conventional systems have difficulty matching users while taking their emotions into account, limiting their ability to improve satisfaction. Furthermore, they are insufficient in utilizing real-time emotion monitoring and evaluation data. To address these issues, the present invention aims to provide a system that selects the optimal vehicle and driver, taking into account the user's emotional state.

[0351] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0352] In this invention, the server includes: a user information input means for a user to input basic information and desired conditions; a data receiving means for receiving information from the user information input means; an analysis means for analyzing the received user information and extracting the user's desired conditions; an emotion recognition means for recognizing and processing the user's emotional state; a database storing multiple vehicle and driver information; a matching means for selecting the optimal vehicle and driver for the user based on the user's desired conditions and emotional state extracted by the analysis means and the emotion recognition means; a vehicle dispatch information sending means for sending information on the selected vehicle and driver to the user; a route management means for tracking the location information of the selected vehicle and driver and providing the optimal route; and an evaluation data collection means for receiving evaluations from users and collecting and learning the evaluation data. This enables the optimal vehicle and driver to be matched taking the user's emotional state into consideration, thereby improving user satisfaction.

[0353] "User information input means" refers to the device or software functions that allow users to input their own basic information and desired conditions.

[0354] "Data receiving means" refers to a function for receiving information sent from the user information input means.

[0355] "Analysis means" refers to a function for analyzing received user information and extracting the user's desired conditions.

[0356] "Emotion recognizer" refers to technology or software that recognizes and processes a user's emotional state.

[0357] "Database" refers to a data storage system that stores and retrieves information about multiple vehicles and drivers.

[0358] The "matching means" refers to a function that selects the most suitable vehicle and driver based on the user's desired conditions and emotional state extracted by the analysis means and emotion recognition means.

[0359] "Vehicle dispatch information transmission means" refers to a function for transmitting information about the selected vehicle and driver to the user.

[0360] "Route management means" refers to a function for tracking the location information of selected vehicles and drivers, and providing the optimal route taking into account traffic and obstacle information.

[0361] "Evaluation data collection means" refers to a function for receiving, collecting, and learning from evaluations from users.

[0362] "Vehicle" refers to a means of transportation for the purpose of moving a user, and examples include taxis and buses.

[0363] "Driver" means a person who drives a vehicle for the purpose of transporting users.

[0364] This invention is a system that matches the optimal vehicle and driver by inputting a user's basic information and desired conditions, and also includes an emotion engine that recognizes and processes the user's emotions in real time. Specific embodiments of this system are described below.

[0365] System configuration

[0366] The system includes the following components:

[0367] User information input method

[0368] Data Receiving Method

[0369] Analysis means

[0370] emotion recognition means

[0371] A database that stores vehicle and driver information

[0372] Matching Method

[0373] Vehicle dispatch information transmission means

[0374] Route Management Method

[0375] Evaluation data collection method

[0376] Hardware and Software Configuration

[0377] User

[0378] Using a smartphone application, users enter basic information and desired conditions, such as name, age, gender, and address, as well as detailed desired conditions such as "quick arrival," "no conversation required," and "multilingual support."

[0379] Terminal

[0380] It receives the information entered by the user and sends it to a server over the Internet, where the data is typically transmitted securely using the HTTPS protocol.

[0381] server

[0382] It works with a database and processes the received data using analytical and emotion recognition techniques, using machine learning and image processing libraries such as Python, TensorFlow, and OpenCV as specific software technologies.

[0383] The analysis means analyzes the data input by the user and extracts specific parameters. For example, if the user inputs a desire to arrive early, parameters based on this desire are extracted.

[0384] The emotion recognition means analyzes real-time data such as voice and facial expressions to evaluate the user's emotional state. For example, if the user has an anxious expression, the emotional state is recognized.

[0385] The matching means selects the most suitable vehicle and driver based on data from the analysis means and emotion recognition means, using an algorithm that references the driver's preferred routes and past evaluation data available in real time.

[0386] The vehicle dispatch information transmitting means transmits information about the most suitable vehicle and driver to the user's terminal, which displays information such as the driver's name, vehicle number, and estimated arrival time.

[0387] The route management tool tracks the location of selected vehicles in real time using GPS data and calculates the optimal route, taking into account traffic and obstacle information.

[0388] The evaluation data collection means collects evaluation data from users after the movement, and the collected data is used to improve the accuracy of the next matching.

[0389] Specific examples

[0390] 1. Users

[0391] To leave for work at 9 a.m., the user enters the conditions "I want to arrive early" and "No conversation" into the smartphone application.

[0392] 2. Terminal

[0393] Sends user input data to the server.

[0394] 3. Server

[0395] The analysis means analyzes the user's desired conditions, and the emotion recognition means evaluates the user's emotional state (e.g., no anxiety). Based on the analysis results and the emotional state, a driver who can provide a sense of security and speed is selected.

[0396] 4. Server

[0397] The driver's vehicle information is sent to the user's device.

[0398] 5. Users

[0399] The user reviews and accepts the proposed content.

[0400] 6. Server

[0401] The system issues dispatch instructions to the driver and manages routes. The emotion recognition system monitors the user's emotional state while traveling and sends warnings to the driver as necessary.

[0402] Prompt Sentence Examples

[0403] "I want to arrive early at 9 a.m. I'm looking for a driver who can comfortably travel without conversation."

[0404] "Designing a system to select the optimal driver and vehicle based on the user's emotional state"

[0405] As described above, the system starts with the user's basic information and desired conditions, recognizes their emotional state, selects the most suitable vehicle and driver, and performs real-time tracking and route management to provide a comfortable and safe travel experience.

[0406] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0407] System program processing flow

[0408] Step 1:

[0409] User

[0410] Users open the smartphone application and enter basic information (name, age, gender, address), followed by their desired travel conditions (e.g., "I want to arrive quickly," "No conversation required," "Multilingual support," "I want to share a ride," etc.).

[0411] Input: Basic information and desired conditions

[0412] Output: Basic information and desired conditions entered

[0413] Step 2:

[0414] Terminal

[0415] The device receives the basic information and desired conditions entered by the user and sends it to a server via the Internet, using the HTTPS protocol to ensure data security.

[0416] Input: Basic information and desired conditions

[0417] Output: User's basic information and preferences sent to the server

[0418] Step 3:

[0419] server

[0420] The server passes the received user information to an analysis module, which extracts parameters related to the user's specific preferences and lifestyle, using natural language processing (NLP) technology.

[0421] Input: Received basic information and desired conditions

[0422] Output: Extracted parameters

[0423] Step 4:

[0424] server

[0425] At the same time, the server uses emotion recognition means to analyze the user's emotional state, using technologies such as voice analysis and facial expression analysis, and determines the user's current emotional state based on real-time data.

[0426] Input: Real-time user voice and facial expression data

[0427] Output: Perceived emotional state

[0428] Step 5:

[0429] server

[0430] The server accesses the database based on data from the analysis and emotion recognition methods to select the most suitable vehicle and driver, using a machine learning algorithm to take into account the driver's past evaluations, preferred routes, driving quality, and other factors.

[0431] Input: extracted parameters and recognized emotional state

[0432] Output: Optimal vehicle and driver selection results

[0433] Step 6:

[0434] server

[0435] The server sends the selected vehicle and driver information to the user's terminal, including the driver's name, vehicle license plate number, estimated arrival time, etc.

[0436] Input: Optimal vehicle and driver selection results

[0437] Output: Vehicle and driver information sent to the user device

[0438] Step 7:

[0439] User

[0440] The user checks the vehicle and driver information displayed on the terminal and presses the OK button, which confirms the vehicle dispatch.

[0441] Input: Submitted vehicle and driver information

[0442] Output: Confirmation of dispatch

[0443] Step 8:

[0444] server

[0445] The server issues pickup instructions to the driver, tracks the vehicle's location in real time, calculates the optimal route taking into account traffic and obstacle information, and provides it to the driver and user.

[0446] Input: Trip confirmation and real-time GPS data

[0447] Output: Optimal route and location information

[0448] Step 9:

[0449] User

[0450] Users can check the driver's arrival time and get in the vehicle through the app. During the journey, users can check the route and estimated arrival time through the app.

[0451] Input: Real-time location

[0452] Output: Arrival confirmation and route information

[0453] Step 10:

[0454] server

[0455] The emotion recognition means monitors the user's emotional state while driving, and if the user shows signs of anxiety or discomfort, the server will provide warnings and guidance to the driver.

[0456] Input: Real-time user sentiment data

[0457] Output: Warnings and guidance to the driver

[0458] Step 11:

[0459] User

[0460] After completing a trip, users can rate the driver and vehicle through the app, in the form of a star rating and comments.

[0461] Input: Evaluation data after movement completion

[0462] Output: Collected evaluation data

[0463] Step 12:

[0464] server

[0465] The server analyzes the collected evaluation and emotion data and learns to improve matching accuracy for the next time, thereby strengthening the machine learning algorithm and increasing user satisfaction.

[0466] Input: Rating and sentiment data

[0467] Output: Improved matching algorithm

[0468] Through the above processing steps, the system achieves optimal vehicle and driver matching, taking into account the user's basic information, desired conditions, and even emotional state.

[0469] (Application example 2)

[0470] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0471] While conventional ride-hailing systems allow users to input their basic information and desired conditions, they do not take into account the user's emotional state when matching rides or monitor their emotions in real time while driving, limiting the improvement of the user experience. Furthermore, when using autonomous vehicles, the driving mode cannot be adjusted to match the user's emotional state while traveling, leaving issues in terms of safety and comfort.

[0472] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0473] In this invention, the server includes information input means for a user to input basic information and desired conditions, data receiving means for receiving information from the information input means, analysis means for analyzing the received user information and extracting the user's desired conditions, a database storing multiple vehicle and driver information, matching means for selecting the vehicle and driver most suitable for the user based on the user's desired conditions extracted by the analysis means, vehicle allocation information sending means for sending information on the selected vehicle and driver to the user, route management means for tracking the location information of the selected vehicle and driver and providing the optimal route, evaluation data collection means for receiving evaluations from users and collecting and learning the evaluation data, emotion recognition means for recognizing user emotion information in real time, and driving mode adjustment means for adjusting the driving mode during travel based on the emotion information obtained by the emotion recognition means. This enables optimal vehicle allocation matching according to the user's emotional state, thereby providing a safe and comfortable travel experience.

[0474] The "user information input means" is a means for a user to input basic information and desired conditions.

[0475] The "data receiving means" is a means for receiving information from the user information input means.

[0476] The "analysis means" is a means for analyzing the received user information and extracting the desired conditions of the user.

[0477] A "database" is a storage means for storing information on a plurality of vehicles and drivers.

[0478] The "matching means" is a means for selecting the vehicle and driver that are most suitable for the user based on the desired conditions of the user extracted by the analysis means.

[0479] The "vehicle allocation information transmission means" is a means for transmitting information about the selected vehicle and driver to the user.

[0480] The "route management means" is a means for tracking the location information of the selected vehicle and driver and providing the optimal route.

[0481] The "evaluation data collection means" is a means for receiving evaluations from users and collecting and learning the evaluation data.

[0482] The "emotion recognition means" is a means for recognizing the user's emotional information in real time.

[0483] The "driving mode adjustment means" is a means for adjusting the driving mode during movement based on the emotion information obtained by the emotion recognition means.

[0484] The present invention provides a system that matches the optimal vehicle and driver by inputting a user's basic information and desired conditions, as well as technology that recognizes the user's emotions in real time and adjusts the driving mode based on them.

[0485] Hardware and Software

[0486] Hardware:

[0487] Smartphone or tablet: A device where users can enter basic information and preferences. Emotional information is collected using audio and video input.

[0488] Server: Hardware for performing the following steps: data reception, analysis, matching, route management, evaluation data collection and learning, emotion recognition, and driving mode adjustment.

[0489] software:

[0490] User information input application: An application installed on a smartphone or tablet that allows users to enter basic information and desired conditions.

[0491] EmotionEngine: A software module used as an emotion recognition tool. It analyzes audio and video data to recognize the user's emotions.

[0492] Route Optimization Module: A software module used as a route management tool that calculates the optimal route based on traffic and accident information.

[0493] Processing flow

[0494] User:

[0495] Users open the smartphone application and enter their basic information (e.g., name, age, gender, address), as well as specific travel requests (e.g., "I want to arrive quickly," "No conversation required," "Multilingual support," etc.) into the application.

[0496] The user's audio and video data is also collected through the application and sent to the Emotion Engine.

[0497] server:

[0498] The server receives and analyzes the user's basic information and desired conditions, and extracts the user's desired conditions based on that data.

[0499] The analysis module selects the most suitable vehicle and driver from a database based on the analyzed information.

[0500] The emotion recognition means (Emotion Engine) analyzes the user's emotional information in real time and recognizes the corresponding emotional state.

[0501] The server sends information about the selected vehicle and driver to the user's terminal.

[0502] The route management module takes into account current traffic and accident information, calculates the optimal route and provides it to the vehicle and driver.

[0503] The emotion information is sent to the driving mode adjustment means to adjust the driving mode (e.g., speed, route selection, etc.) during travel.

[0504] After the move is complete, evaluation data from the user is received and used to improve the accuracy of the next match.

[0505] Specific examples

[0506] A specific example will be used to explain this.

[0507] A user inputs conditions such as "I want to arrive quickly" and "No conversation" and sends emotional information (e.g., audio and video data) through the application. The server analyzes this information and selects the self-driving vehicle and driver that best meets the desired conditions. The Emotion Engine recognizes the user's emotional state as "calm" and sets the appropriate driving mode. The route management module calculates the optimal route based on traffic information, providing safe and fast travel.

[0508] In this way, the present invention can provide an optimal travel experience according to the user's individual needs and emotional state.

[0509] Example prompt sentence:

[0510] Username: Yamada

[0511] Age: 40

[0512] Gender: Male

[0513] Address: Osaka Prefecture

[0514] Desired conditions: Arrive early, no conversation

[0515] Audio file: audio_sample.wav

[0516] Video file: video_sample.mp4

[0517] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0518] Step 1:

[0519] Entering user information

[0520] Input: The user launches the smartphone app and enters basic information (name, age, gender, address) and desired conditions (e.g., "I want to arrive quickly," "No conversation required," "Multilingual support," etc.).

[0521] What it does: As the user enters information, the application collects and formats the data for each field in the form.

[0522] Output: User's basic information and preferences are compiled in JSON format.

[0523] Step 2:

[0524] Sending data

[0525] Input: User information and desired conditions collected in the previous step.

[0526] Specific operation: The smartphone application sends the entered information to the server.

[0527] Output: The server receives the user's basic information and preferences.

[0528] Step 3:

[0529] Analysis of user information

[0530] Input: User's basic information and preferences received by the server.

[0531] Specific operation: The analysis module analyzes the received information and extracts the user's desired conditions.

[0532] Output: Extracted desired conditions.

[0533] Step 4:

[0534] Collecting emotional information

[0535] Input: Audio and video data from the user (e.g., "audio_sample.wav" and "video_sample.mp4").

[0536] Specific operation: The smartphone application collects audio and video data and sends it to the emotion recognition module (EmotionEngine).

[0537] Output: Audio and video data forwarded to the emotion recognition module.

[0538] Step 5:

[0539] Emotional information analysis

[0540] Input: Audio and video data collected in the previous step.

[0541] How it works: EmotionEngine analyzes audio and video data to recognize the user's emotional state in real time.

[0542] Output: Perceived emotional state of the user.

[0543] Step 6:

[0544] Vehicle and driver selection

[0545] Input: Extracted desired conditions and perceived emotional states.

[0546] Specific operation: The server refers to a database containing information on multiple vehicles and drivers, and selects the optimal vehicle and driver based on the desired conditions and emotional state.

[0547] Output: Information on the selected vehicle and driver.

[0548] Step 7:

[0549] Sending dispatch information

[0550] Input: Selected vehicle and driver information.

[0551] Specific operation: The server sends the selection information to the user's smartphone application.

[0552] Output: Vehicle and driver information displayed on the user's smartphone.

[0553] Step 8:

[0554] Route Optimization

[0555] Input: Vehicle and driver location and traffic information.

[0556] Specific operation: The server's route management module calculates the optimal route based on the received information.

[0557] Output: Optimized route information.

[0558] Step 9:

[0559] Adjusting the driving mode

[0560] Input: Perceived user emotional state and optimized route information.

[0561] Specific operation: The driving mode adjusting means adjusts the driving mode based on the emotional state to provide a safe and comfortable driving experience.

[0562] Output: Coordinated driving mode.

[0563] Step 10:

[0564] Collecting user ratings

[0565] Input: User feedback after the move is complete (e.g., star rating and comments).

[0566] Specific operation: The smartphone application collects ratings from users and sends them to the server.

[0567] Output: Collected evaluation data on the server.

[0568] Step 11:

[0569] Learning evaluation data

[0570] Input: Collected assessment data.

[0571] Specific operation: The server analyzes the evaluation data and learns to improve matching accuracy next time.

[0572] Output: Improved matching algorithm.

[0573] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0574] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0575] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0576] [Second embodiment]

[0577] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0578] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0579] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0580] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0581] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0582] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0583] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0584] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0585] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0586] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0587] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0588] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0589] The present invention is a system that matches users with the most suitable taxi vehicle and driver based on their basic information and desired conditions. The program processing of this system is explained below in natural language.

[0590] Entering and collecting user data

[0591] User

[0592] Users open the smartphone application and enter basic information such as their name, age, gender, and address.

[0593] The app allows users to input their specific travel needs, such as "I want to arrive quickly," "No conversation required," "Multilingual support," "I want to share a ride," "Car sickness prevention," and "Wheelchair and stroller access."

[0594] Data submission and initial analysis

[0595] Terminal

[0596] The information entered by the user is sent to a server via the Internet.

[0597] server

[0598] The server passes the received user information and requests to an analysis module.

[0599] The analytical module analyzes the user's specific needs and lifestyle and extracts relevant parameters.

[0600] Selection of the best vehicle and driver

[0601] server

[0602] The server checks the database of available taxi vehicles and drivers in real time.

[0603] The system uses data such as each driver's preferred routes, past ratings, and driving quality to select the most suitable driver and vehicle.

[0604] A matching algorithm is used to select the vehicle and driver that best suits the user's requirements.

[0605] Vehicle dispatch proposal

[0606] server

[0607] Information about the matched driver and vehicle is sent to the user's device.

[0608] Terminal

[0609] The user terminal displays driver and vehicle information (e.g., driver's name, vehicle number, estimated arrival time).

[0610] The user reviews and confirms the proposed vehicle and driver.

[0611] Movement initiation and path tracking

[0612] server

[0613] As the driver heads to the user's pickup location, the server tracks the vehicle's location in real time.

[0614] The server calculates the optimal route based on traffic and congestion information and provides it to the driver and user.

[0615] User

[0616] Users can check the driver's arrival on the app and then get in the car.

[0617] While traveling, users can check their route and estimated arrival time through the app.

[0618] Post-move evaluation and feedback

[0619] User

[0620] After completing a trip, users can rate the driver and vehicle through the app, including by leaving a star rating and comments.

[0621] server

[0622] The server passes the collected evaluation data to an analysis module, which then learns to improve matching accuracy for the next time.

[0623] This will improve future matching accuracy and increase user satisfaction.

[0624] Specific examples

[0625] 1. Users

[0626] Tanaka wants to leave for work at 9 a.m. He enters the conditions "I want to travel quickly" and "No conversation" into the app.

[0627] 2. Terminal

[0628] Tanaka's input data is sent to the server.

[0629] 3. Server

[0630] The AI ​​module analyzes Tanaka's request and selects a driver, Yamada, who can provide a quick and quiet trip.

[0631] 4. Server

[0632] Yamada's vehicle information is sent to Tanaka's device.

[0633] 5. Terminal

[0634] Tanaka checks the proposed content and approves it.

[0635] 6. Server

[0636] Gives pickup instructions to Yamada and manages the route.

[0637] 7. Users

[0638] After the trip is completed, Tanaka praises Yamada's driving and enters feedback.

[0639] 8. Server

[0640] The collected evaluation data will be analyzed by AI and used for the next match.

[0641] In this way, AI-powered dispatch systems will provide users with an optimal travel experience tailored to their individual needs, while also helping taxi companies improve their operational efficiency.

[0642] The processing flow will be explained below.

[0643] Step 1: Enter your user information and preferences

[0644] User

[0645] Users open the smartphone application and enter their basic information (e.g., name, age, gender, address).

[0646] Users input specific travel requests (e.g., early arrival, no conversation, multilingual support, ride-sharing, car sickness prevention, wheelchair and stroller accessibility).

[0647] Step 2: Send data

[0648] Terminal

[0649] The terminal sends the basic information and desired conditions entered by the user to the server.

[0650] Step 3: Receiving user information

[0651] server

[0652] The server receives the user information and desired conditions sent from the terminal.

[0653] Step 4: Analyze user information

[0654] server

[0655] The server transfers the received user information to the AI ​​analysis module.

[0656] The AI ​​analysis module extracts the user's desired conditions and generates analysis results.

[0657] Step 5: Vehicle and driver database matching

[0658] server

[0659] The server searches a database of available vehicles and drivers in real time.

[0660] The database stores information such as the driver's strengths and weaknesses in routes, past evaluations, and driving quality.

[0661] Step 6: Generate the best match

[0662] server

[0663] Based on the output of the AI ​​analysis module, the server selects the vehicle and driver that best meets the user's desired conditions.

[0664] A matching algorithm is used to create a final list of selected drivers and vehicles.

[0665] Step 7: Submit your trip

[0666] server

[0667] The server sends information about the selected vehicle and driver, as well as the estimated arrival time, to the user's terminal.

[0668] Step 8: Review the trip offer

[0669] Terminal

[0670] The user's device will display details of the ride offer (e.g., driver's name, vehicle number, estimated arrival time).

[0671] The user checks the proposed vehicle and driver information and presses the confirm button.

[0672] Step 9: Notification of arrival at pickup point

[0673] Terminal

[0674] The user's device is notified that the driver has arrived at the pickup location.

[0675] Users can check the driver's arrival via the app.

[0676] Step 10: Route Management and Tracking

[0677] server

[0678] The server tracks the vehicle's location in real time and calculates the optimal route based on traffic and congestion information.

[0679] The calculated optimal route is notified to the driver and user.

[0680] Step 11: Notification of completion of move

[0681] server

[0682] When the trip is complete, the server receives a notification from the driver that the trip is complete and notifies the user.

[0683] Step 12: Enter your rating

[0684] User

[0685] After completing the trip, users can rate the driver and vehicle through the app (e.g., star rating, comments).

[0686] Step 13: Collect evaluation data

[0687] server

[0688] The server transfers user evaluation data to an analysis module, which learns from it to help improve the matching process next time.

[0689] Example 1

[0690] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0691] In modern society, it is difficult to quickly and accurately match the optimal means of transportation and operators that meet the diverse needs of users, and there is a lack of mechanisms to respond to immediate and personalized requests. Furthermore, conventional systems have difficulty effectively utilizing collected evaluation data to improve the accuracy of the next match, which limits the ability to continuously improve the quality of the user experience.

[0692] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0693] In this invention, the server includes an information input means for a user to input basic information and desired conditions, a data receiving means for receiving information from the information input means, and an analysis means for analyzing the received user information and extracting the user's desired conditions. This enables quick and accurate matching of optimal transportation means and operators according to the user's diverse needs. Furthermore, by using a generative AI model to collect and learn evaluation data, the accuracy of the next match can be improved, and the quality of the user experience can be continuously improved.

[0694] The "information input means" is a means for the user to input basic information and desired conditions.

[0695] The "data receiving means" is a means for receiving information from the information input means.

[0696] The "analysis means" is a means for analyzing the received user information and extracting the user's desired conditions.

[0697] "Database" means data storage for storing multiple modes of transportation and operator information.

[0698] The "matching means" is a means for selecting the most suitable means of transportation and operator for the user based on the user's desired conditions extracted by the analysis means.

[0699] The "vehicle dispatch information transmission means" is a means for transmitting information about the selected transportation means and the driver to the user.

[0700] The "route management means" is a means for tracking the location information of the selected means of transportation and the operator, and providing the optimal route.

[0701] The "rating data collection means" is a means for receiving ratings from users and collecting and learning rating data.

[0702] A "generative AI model" is an artificial intelligence technology that analyzes and learns from evaluation data to improve matching accuracy the next time.

[0703] This invention is a system that matches the optimal means of transportation and operator based on the user's input of basic information and desired conditions. This system is composed of components of a server, a terminal, and a user, and is specifically implemented as follows.

[0704] A user opens a smartphone application and enters basic information such as name, age, gender, and address. For example, the user enters "Ichiro Tanaka" as the user name, "30 years old," "male," and "Shinjuku-ku, Tokyo" as the address. The user also enters specific travel requests. For example, from options such as "Arrive quickly," "No conversation," "Multilingual support," "Prefer carpooling," "Car sickness prevention," and "Wheelchair and stroller access," the user selects "Arrive quickly" and "No conversation."

[0705] The terminal automatically transmits the information entered by the user to the server via the Internet, at which point the terminal waits for a response to confirm that the data transmission was successful.

[0706] The server passes the user information and desired conditions received from the device to a dedicated analysis module. The analysis module analyzes the user's desired conditions and lifestyle and extracts relevant parameters. For example, if the user requests a "quick arrival," the server will issue instructions to prioritize the selection of drivers who can provide a fast route.

[0707] The server compares a database of available transportation methods and drivers in real time. The database includes each driver's location information, preferred routes, past evaluations, etc. The server narrows down the candidates based on each driver's preferred routes, past evaluations, and driving quality. For example, driver Taro Yamada is good at speedy driving, which meets the condition of "I want to arrive quickly." A matching algorithm is used to select the driver and transportation method that best suits the user's requirements. In this process, a generative AI model is used to analyze and learn from the evaluation data.

[0708] The server sends information about the selected driver and transportation means to the user's terminal. The information sent includes the driver's name, transportation means number, and estimated arrival time. The driver and transportation means information is displayed on the user's terminal. For example, it may show "Driver's name: Yamada Taro, Transportation means number: AB-1234, Estimated arrival time: 9:00." The user confirms the information presented and presses the "Confirm" button to indicate their consent.

[0709] As the driver heads to the user's pickup point, the server tracks the vehicle's location in real time. The driver's current location is monitored using GPS data. The server calculates the optimal route based on traffic and congestion information. This information is provided to the driver and user in real time. The user checks the driver's arrival in the app and waits at the specified pickup point. A notification is sent when the driver approaches the arrival point. The user gets into the driver's car and begins traveling. During the trip, the user can check the route and estimated arrival time through the app.

[0710] After completing a trip, the user can rate the driver and the means of transportation through the app. For example, they could rate the driver 5 stars and comment that the ride was very quick and quiet. The server then passes the collected rating data to an analysis module, which uses the data to learn how to improve the accuracy of the next match. The rating data is then analyzed by a generative AI model, which updates the driver's rating score. This improves future matching accuracy and continuously improves the quality of the user experience.

[0711] This system allows users to enjoy an optimal travel experience tailored to their individual needs, and also enables taxi companies and drivers to improve the quality of their services. Examples of prompt sentences include "Mr. Tanaka wishes to travel quickly," and "Mr. Yamada is good at driving quickly."

[0712] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0713] Step 1: Entering User Data

[0714] The user opens the smartphone application and enters basic information such as name, age, gender, and address, as well as desired conditions for travel. The specific data entered includes the user name "Tanaka Ichiro," age "30," gender "male," address "Shinjuku Ward, Tokyo," and desired conditions "arrive quickly" and "no conversation." This information is stored on the device as input data (basic information and desired conditions).

[0715] Step 2: Send data

[0716] The terminal sends the information entered by the user to the server via the Internet. The input is the user data stored in the previous step, and the output is sent to the server. At this time, the terminal waits for a response to confirm the successful data transmission.

[0717] Step 3: Initial analysis

[0718] The server passes the user data received from the device to the analysis module. The received data consists of the user's basic information and desired conditions. The analysis module analyzes the user's desired conditions and lifestyle and extracts related parameters. For example, based on the desired condition of "I want to arrive quickly," the module prepares to find a driver who can provide a fast route. The output is the extracted analysis results (parameters related to the user's desired conditions).

[0719] Step 4: Select the best transportation and driver

[0720] The server compares the analysis results with a database of available means of transportation and drivers in real time. The input data are the analysis results and database information. The server narrows down the most suitable candidates based on data such as each driver's location information, preferred routes, and past evaluations. For example, it confirms that driver "Yamada Taro" is good at speedy driving, which meets the condition of "wanting to arrive quickly." The output is the selection result of the most suitable means of transportation and driver.

[0721] Step 5: Ride proposal

[0722] The server sends the information of the selected driver and transportation means to the user's terminal. The input is the information of the selected driver and transportation means, and the output is the dispatch information (driver's name, transportation means number, estimated arrival time) sent to the user's terminal.

[0723] Step 6: View and confirm your trip information

[0724] The terminal receives the dispatch information from the server and displays it to the user. The displayed information is "Driver's name: Yamada Taro, Transportation number: AB-1234, Estimated arrival time: 9:00." The user confirms the information presented and presses the "Confirm" button to accept it. The input is the dispatch information from the server, and the output is the user's confirmation and confirmation action.

[0725] Step 7: Path tracing

[0726] The server tracks the vehicle's location in real time as the driver heads to the user's pickup point. The input is GPS data and traffic information, and the output is the calculation of the optimal route. The server calculates the optimal route based on traffic and congestion information and provides this information to the driver and user.

[0727] Step 8: Driver arrives and begins travel

[0728] The user checks the driver's arrival through the app. A notification is sent when the user approaches the destination. The user waits at the designated pickup point, gets into the driver's car, and begins their journey. During the journey, the user can check the route and estimated arrival time through the app. The input is the arrival notification and route information from the server, and the output is the user's journey start and route confirmation.

[0729] Step 9: Post-move evaluation and feedback

[0730] After completing the trip, the user evaluates the driver and the means of transportation through the app. For example, the user might enter "star rating: 5, comment: The drive was very quick and quiet." The input is the user's evaluation data, and the output is the transmission of the evaluation data to the server.

[0731] Step 10: Collect evaluation data and learn

[0732] The server passes the collected evaluation data to an analysis module, which then performs learning to improve the accuracy of the next match. The input is the user's evaluation data, and the output is the pilot's evaluation score analyzed and updated by the generative AI model. This improves future matching accuracy and continuously improves the quality of the user experience.

[0733] (Application example 1)

[0734] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0735] Conventional taxi dispatch systems have difficulty reflecting detailed user preferences and providing services that meet some customization requests. In particular, they lack accuracy in providing specific travel requests and optimal route guidance, leaving users wanting more. Similar challenges exist in the food delivery field, where it is difficult to efficiently select the optimal delivery person and route.

[0736] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0737] In this invention, the server includes: a user information input means for a user to input basic information and desired conditions; a data receiving means for receiving information from the user information input means; an analysis means for analyzing the received user information and extracting the user's desired conditions; a database storing information on multiple vehicles and drivers; a matching means for selecting the most suitable vehicle and driver for the user based on the user's desired conditions extracted by the analysis means; a dispatch information sending means for sending information on the selected vehicle and driver to the user; a route management means for tracking the location information of the selected vehicle and driver and providing the optimal route; an evaluation data collection means for receiving evaluations from users and collecting and learning the evaluation data; and a means for selecting the most suitable delivery person and route based on the basic information and desired conditions input by the user. This makes it possible to provide an optimal service that meets the user's detailed desired conditions and specific requests.

[0738] "User information input means" refers to a device or function that allows a user to input basic information and desired conditions.

[0739] The "data receiving means" is a device or function that receives information from the user information input means.

[0740] The "analysis means" is a device or function that analyzes the received user information and extracts the user's desired conditions.

[0741] A "database" is a storage device for storing information on a plurality of vehicles and drivers.

[0742] The "matching means" is a device or function that selects the most suitable vehicle and driver based on the user's desired conditions extracted by the analysis means.

[0743] The "vehicle dispatch information transmission means" is a device or function that transmits information about the selected vehicle and driver to the user.

[0744] The "route management means" is a device or function that tracks the location information of the selected vehicle and driver and provides the optimal route.

[0745] The "evaluation data collection means" is a device or function that receives evaluations from users and collects and learns evaluation data.

[0746] A "delivery staff member" is a person who delivers items along the optimal route based on the basic information and desired conditions entered by the user.

[0747] An "optimal route" is the best travel route calculated taking into account traffic conditions and the user's desired conditions.

[0748]

[0749] The system for implementing this invention includes a user information input means through which the user inputs basic information and desired conditions. This information is input via a mobile device such as a smartphone. Specifically, the user inputs their name, age, address, and desired conditions for travel (e.g., conditions such as "I want to arrive quickly," "No conversation," and "I want contactless delivery").

[0750] The entered data is sent to a server via the Internet. The server has a data receiving means for receiving this data. The received user information is analyzed using an analysis means, and the user's desired conditions are extracted. This analysis means includes an AI module using Python, which efficiently analyzes user information.

[0751] The analyzed data is compared with a database that stores information on multiple vehicles and drivers. The server's database contains detailed information on each driver, such as their preferred routes, past ratings, and driving quality. The server also has a matching means for selecting the optimal vehicle and driver. This matching means selects the vehicle and driver that best meets the user's desired conditions.

[0752] The selected vehicle and driver information will be sent to the user's terminal via the vehicle dispatch information sending means. The user will then confirm and confirm the proposed vehicle and driver information (e.g., driver's name, vehicle license plate number, estimated arrival time).

[0753] The server then tracks the location information of the vehicle and the driver using a route management means, which calculates the optimal route taking into account traffic and congestion information.

[0754] After completing a delivery, the user can rate the driver and vehicle via their device. This rating is in the form of a star rating or a comment, and is sent to the server via a data collection tool. The server then uses AI to analyze the collected rating data and use it to improve the accuracy of the next match.

[0755] For example, the user inputs requests such as "I want to arrive quickly" or "I want contactless delivery." This information is sent to the server, and the AI ​​module analyzes it, selecting the most suitable driver to deliver quickly and contactlessly. This driver's information is sent to the user's device, and the user confirms it, and the delivery begins. Below is an example of a prompt:

[0756] User: Name: Taro Tanaka, Age: 30, Address: Shibuya-ku, Tokyo

[0757] Desired conditions: Delivery by 12:00, no-contact delivery, early arrival

[0758] This will enable us to provide optimal services that meet the user's detailed requirements and specific requests.

[0759] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0760] Step 1:

[0761] The user starts the smartphone application and inputs their name, age, address, and desired conditions. Specific desired conditions include "quick arrival" and "contactless delivery." This input information is sent to the server by the data receiving means.

[0762] Input: User's basic information and desired conditions

[0763] Output: Data sent to the data receiver

[0764] Step 2:

[0765] The server's data receiving means acquires the received user information and passes it to the analyzing means, which extracts the user's desired conditions from the received data.

[0766] Input: Data sent to the data receiving means

[0767] Output: Extracted user preferences

[0768] Step 3:

[0769] The server's analytical means uses a generative AI model using Python to analyze the user's input data, and this data analysis extracts the user's desired conditions.

[0770] Input: Received user information

[0771] Output: Parsed desired conditions

[0772] Step 4:

[0773] The server's matching means selects the optimal delivery person and route from a database based on the desired conditions obtained from the analysis means. This database stores information such as each driver's preferred routes, past evaluations, and driving quality.

[0774] Input: Parsed desired conditions

[0775] Output: Selection of optimal delivery personnel and route

[0776] Step 5:

[0777] The server's dispatch information transmission means transmits the selected delivery person and route information to the user terminal, and the user confirms and approves the received information.

[0778] Input: Selection of optimal delivery personnel and route

[0779] Output: Information sent to the user's terminal

[0780] Step 6:

[0781] The server's route management means tracks the location information of selected drivers and delivery routes in real time and provides the optimal route, using an algorithm that takes into account traffic and accident information to optimize the route.

[0782] Input: Selected driver and delivery route information

[0783] Output: Optimal route information

[0784] Step 7:

[0785] After the delivery is completed, the user can rate the driver and the vehicle through the smartphone application, which will then be sent to the server's rating data collection means.

[0786] Input: User rating data

[0787] Output: Rating data sent to the server

[0788] Step 8:

[0789] The server's evaluation data collection means analyzes the collected evaluation data and trains the generative AI model to improve matching accuracy next time. This continuous learning process improves the quality of service and increases user satisfaction.

[0790] Input: User-submitted rating data

[0791] Output: The learning results of the generative AI model

[0792] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0793] The present invention is a system that combines a system that matches the optimal vehicle and driver by inputting the user's basic information and desired conditions with an emotion engine that recognizes and processes the user's emotions. Below, the program processing of this system is specifically explained in natural language.

[0794] Entering and collecting user data

[0795] User

[0796] Users open the smartphone application and enter their basic information (e.g., name, age, gender, address).

[0797] The app allows users to input their specific travel needs, such as "I want to arrive quickly," "No conversation required," "Multilingual support," "I want to share a ride," "Car sickness prevention," and "Wheelchair and stroller access."

[0798] Data submission and initial analysis

[0799] Terminal

[0800] The information entered by the user is sent to a server via the Internet.

[0801] server

[0802] The server passes the received user information and requests to the analysis module and emotion engine.

[0803] The analytical module analyzes the user's specific needs and lifestyle and extracts relevant parameters.

[0804] At the same time, the emotion engine recognizes the user's emotional state based on user input data and real-time interactions (e.g., voice, facial expressions, text).

[0805] Selection of the best vehicle and driver

[0806] server

[0807] The server checks the database of available taxi vehicles and drivers in real time.

[0808] The system uses data such as each driver's preferred routes, past ratings, and driving quality to select the most suitable driver and vehicle.

[0809] It also incorporates the output of the emotion engine to select the vehicle and driver that best suits the user's current emotional state.

[0810] Vehicle dispatch proposal

[0811] server

[0812] Information about the matched driver and vehicle is sent to the user's device.

[0813] Terminal

[0814] The user terminal displays driver and vehicle information (e.g., driver's name, vehicle number, estimated arrival time).

[0815] The user checks the proposed vehicle and driver information and presses the confirm button.

[0816] Movement initiation and path tracking

[0817] server

[0818] As the driver heads to the user's pickup location, the server tracks the vehicle's location in real time.

[0819] The server calculates the optimal route based on traffic and congestion information and provides it to the driver and user.

[0820] User

[0821] Users can check the driver's arrival on the app and then get in the car.

[0822] While traveling, users can check their route and estimated arrival time through the app.

[0823] Emotion Monitoring and Alerts

[0824] server

[0825] The emotion engine monitors the user's emotional state in real time while on the move.

[0826] If the server detects any emotion indicative of anxiety or discomfort from the user, it will provide warnings and guidance to the driver.

[0827] Post-move evaluation and feedback

[0828] User

[0829] After completing a trip, users can rate the driver and vehicle through the app, including by leaving a star rating and comments.

[0830] server

[0831] The server passes the collected evaluation data and emotion data to an analysis module, which then performs learning to improve matching accuracy for the next time.

[0832] This will improve future matching accuracy and increase user satisfaction.

[0833] Specific examples

[0834] 1. Users

[0835] Tanaka wants to leave for work at 9 a.m. He enters the conditions "I want to travel quickly" and "No conversation" into the app.

[0836] 2. Terminal

[0837] Tanaka's input data is sent to the server.

[0838] 3. Server

[0839] The analysis module analyzes Tanaka's desired conditions, and the emotion engine evaluates Tanaka's current emotional state. A driver, Yamada, who can provide a fast, quiet, and reassuring ride is selected.

[0840] 4. Server

[0841] Yamada's vehicle information is sent to Tanaka's device.

[0842] 5. Terminal

[0843] Tanaka checks the proposed content and approves it.

[0844] 6. Server

[0845] It issues pickup instructions to Yamada and manages the route. The emotion engine monitors Tanaka's emotional state and sends a warning to Yamada if anxiety is detected.

[0846] 7. Users

[0847] After the trip is completed, Tanaka praises Yamada's driving and enters feedback.

[0848] 8. Server

[0849] The collected evaluation and emotional data is analyzed by AI to help improve the accuracy of the next match.

[0850] In this way, a dispatch system that combines an emotion engine can provide an optimal travel experience tailored to the individual needs and emotional state of the user, while also helping to improve the operational efficiency of taxi companies.

[0851] The processing flow will be explained below.

[0852] Step 1: Enter your user information and preferences

[0853] User

[0854] Users open the smartphone application and enter their basic information (e.g., name, age, gender, address).

[0855] Users input specific travel requests (e.g., early arrival, no conversation, multilingual support, ride-sharing, car sickness prevention, wheelchair and stroller accessibility).

[0856] Step 2: Submit your user information and preferences

[0857] Terminal

[0858] The terminal sends the basic information and desired conditions entered by the user to the server.

[0859] Step 3: Receiving and analyzing user information

[0860] server

[0861] The server receives the user information and desired conditions sent from the terminal.

[0862] The server uses an AI analysis module to analyze the user's desired conditions and extract relevant parameters.

[0863] Step 4: Emotional state analysis by the emotion engine

[0864] server

[0865] The emotion engine in the server analyzes the user's input data and real-time interactions (e.g., voice, facial expressions, text) to assess the user's emotional state.

[0866] Step 5: Vehicle and driver database matching

[0867] server

[0868] The server searches a database of available vehicles and drivers in real time.

[0869] The database contains information such as the driver's preferred routes, past ratings, and driving quality.

[0870] Step 6: Selecting the best vehicle and driver

[0871] server

[0872] Based on the output of the AI ​​analysis module and the evaluation of the emotion engine, the server selects the vehicle and driver that best suits the user's desired conditions and emotional state.

[0873] Step 7: Submit your trip

[0874] server

[0875] The server sends information about the selected vehicle and driver, as well as the estimated arrival time, to the user's terminal.

[0876] Step 8: Review the trip offer

[0877] Terminal

[0878] The user's device will display details of the ride offer (e.g., driver's name, vehicle number, estimated arrival time).

[0879] The user checks the proposed vehicle and driver information and presses the confirm button.

[0880] Step 9: Notification of arrival at pickup point

[0881] Terminal

[0882] The user's device is notified that the driver has arrived at the pickup location.

[0883] Users can check the driver's arrival via the app.

[0884] Step 10: Route Management and Tracking

[0885] server

[0886] The server tracks the vehicle's location in real time and calculates the optimal route based on traffic and congestion information.

[0887] The calculated optimal route is notified to the driver and user.

[0888] Step 11: Emotion Monitoring and Alerts

[0889] server

[0890] The emotion engine monitors the user's emotional state in real time while on the move.

[0891] If the server detects any emotion indicative of anxiety or discomfort from the user, it will provide warnings and guidance to the driver.

[0892] Step 12: Notification of completion of move

[0893] server

[0894] When the trip is complete, the server receives a notification from the driver that the trip is complete and notifies the user.

[0895] Step 13: Enter your rating

[0896] User

[0897] After completing the trip, users can rate the driver and vehicle through the app (e.g., star rating, comments).

[0898] Step 14: Collect and analyze assessment data

[0899] server

[0900] The server transfers user evaluation and sentiment data to an analysis module, which learns from the data to help improve the matching process next time.

[0901] Example 2

[0902] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0903] In modern mobility services, it is important to select a vehicle and driver that takes into account the user's basic information and desired conditions. Additionally, there is a need for a system that can recognize the user's emotional state in real time and respond appropriately. However, conventional systems have difficulty matching users while taking their emotions into account, limiting their ability to improve satisfaction. Furthermore, they are insufficient in utilizing real-time emotion monitoring and evaluation data. To address these issues, the present invention aims to provide a system that selects the optimal vehicle and driver, taking into account the user's emotional state.

[0904] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0905] In this invention, the server includes: a user information input means for a user to input basic information and desired conditions; a data receiving means for receiving information from the user information input means; an analysis means for analyzing the received user information and extracting the user's desired conditions; an emotion recognition means for recognizing and processing the user's emotional state; a database storing multiple vehicle and driver information; a matching means for selecting the optimal vehicle and driver for the user based on the user's desired conditions and emotional state extracted by the analysis means and the emotion recognition means; a vehicle dispatch information sending means for sending information on the selected vehicle and driver to the user; a route management means for tracking the location information of the selected vehicle and driver and providing the optimal route; and an evaluation data collection means for receiving evaluations from users and collecting and learning the evaluation data. This enables the optimal vehicle and driver to be matched taking the user's emotional state into consideration, thereby improving user satisfaction.

[0906] "User information input means" refers to the device or software functions that allow users to input their own basic information and desired conditions.

[0907] "Data receiving means" refers to a function for receiving information sent from the user information input means.

[0908] "Analysis means" refers to a function for analyzing received user information and extracting the user's desired conditions.

[0909] "Emotion recognizer" refers to technology or software that recognizes and processes a user's emotional state.

[0910] "Database" refers to a data storage system that stores and retrieves information about multiple vehicles and drivers.

[0911] The "matching means" refers to a function that selects the most suitable vehicle and driver based on the user's desired conditions and emotional state extracted by the analysis means and emotion recognition means.

[0912] "Vehicle dispatch information transmission means" refers to a function for transmitting information about the selected vehicle and driver to the user.

[0913] "Route management means" refers to a function for tracking the location information of selected vehicles and drivers, and providing the optimal route taking into account traffic and obstacle information.

[0914] "Evaluation data collection means" refers to a function for receiving, collecting, and learning from evaluations from users.

[0915] "Vehicle" refers to a means of transportation for the purpose of moving a user, and examples include taxis and buses.

[0916] "Driver" means a person who drives a vehicle for the purpose of transporting users.

[0917] This invention is a system that matches the optimal vehicle and driver by inputting a user's basic information and desired conditions, and also includes an emotion engine that recognizes and processes the user's emotions in real time. Specific embodiments of this system are described below.

[0918] System configuration

[0919] The system includes the following components:

[0920] User information input method

[0921] Data Receiving Method

[0922] Analysis means

[0923] emotion recognition means

[0924] A database that stores vehicle and driver information

[0925] Matching Method

[0926] Vehicle dispatch information transmission means

[0927] Route Management Method

[0928] Evaluation data collection method

[0929] Hardware and Software Configuration

[0930] User

[0931] Using a smartphone application, users enter basic information and desired conditions, such as name, age, gender, and address, as well as detailed desired conditions such as "quick arrival," "no conversation required," and "multilingual support."

[0932] Terminal

[0933] It receives the information entered by the user and sends it to a server over the Internet, where the data is typically transmitted securely using the HTTPS protocol.

[0934] server

[0935] It works with a database and processes the received data using analytical and emotion recognition techniques, using machine learning and image processing libraries such as Python, TensorFlow, and OpenCV as specific software technologies.

[0936] The analysis means analyzes the data input by the user and extracts specific parameters. For example, if the user inputs a desire to arrive early, parameters based on this desire are extracted.

[0937] The emotion recognition means analyzes real-time data such as voice and facial expressions to evaluate the user's emotional state. For example, if the user has an anxious expression, the emotional state is recognized.

[0938] The matching means selects the most suitable vehicle and driver based on data from the analysis means and emotion recognition means, using an algorithm that references the driver's preferred routes and past evaluation data available in real time.

[0939] The vehicle dispatch information transmitting means transmits information about the most suitable vehicle and driver to the user's terminal, which displays information such as the driver's name, vehicle number, and estimated arrival time.

[0940] The route management tool tracks the location of selected vehicles in real time using GPS data and calculates the optimal route, taking into account traffic and obstacle information.

[0941] The evaluation data collection means collects evaluation data from users after the movement, and the collected data is used to improve the accuracy of the next matching.

[0942] Specific examples

[0943] 1. Users

[0944] To leave for work at 9 a.m., the user enters the conditions "I want to arrive early" and "No conversation" into the smartphone application.

[0945] 2. Terminal

[0946] Sends user input data to the server.

[0947] 3. Server

[0948] The analysis means analyzes the user's desired conditions, and the emotion recognition means evaluates the user's emotional state (e.g., no anxiety). Based on the analysis results and the emotional state, a driver who can provide a sense of security and speed is selected.

[0949] 4. Server

[0950] The driver's vehicle information is sent to the user's device.

[0951] 5. Users

[0952] The user reviews and accepts the proposed content.

[0953] 6. Server

[0954] The system issues dispatch instructions to the driver and manages routes. The emotion recognition system monitors the user's emotional state while traveling and sends warnings to the driver as necessary.

[0955] Prompt Sentence Examples

[0956] "I want to arrive early at 9 a.m. I'm looking for a driver who can comfortably travel without conversation."

[0957] "Designing a system to select the optimal driver and vehicle based on the user's emotional state"

[0958] As described above, the system starts with the user's basic information and desired conditions, recognizes their emotional state, selects the most suitable vehicle and driver, and performs real-time tracking and route management to provide a comfortable and safe travel experience.

[0959] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0960] System program processing flow

[0961] Step 1:

[0962] User

[0963] Users open the smartphone application and enter basic information (name, age, gender, address), followed by their desired travel conditions (e.g., "I want to arrive quickly," "No conversation required," "Multilingual support," "I want to share a ride," etc.).

[0964] Input: Basic information and desired conditions

[0965] Output: Basic information and desired conditions entered

[0966] Step 2:

[0967] Terminal

[0968] The device receives the basic information and desired conditions entered by the user and sends it to a server via the Internet, using the HTTPS protocol to ensure data security.

[0969] Input: Basic information and desired conditions

[0970] Output: User's basic information and preferences sent to the server

[0971] Step 3:

[0972] server

[0973] The server passes the received user information to an analysis module, which extracts parameters related to the user's specific preferences and lifestyle, using natural language processing (NLP) technology.

[0974] Input: Received basic information and desired conditions

[0975] Output: Extracted parameters

[0976] Step 4:

[0977] server

[0978] At the same time, the server uses emotion recognition means to analyze the user's emotional state, using technologies such as voice analysis and facial expression analysis, and determines the user's current emotional state based on real-time data.

[0979] Input: Real-time user voice and facial expression data

[0980] Output: Perceived emotional state

[0981] Step 5:

[0982] server

[0983] The server accesses the database based on data from the analysis and emotion recognition methods to select the most suitable vehicle and driver, using a machine learning algorithm to take into account the driver's past evaluations, preferred routes, driving quality, and other factors.

[0984] Input: extracted parameters and recognized emotional state

[0985] Output: Optimal vehicle and driver selection results

[0986] Step 6:

[0987] server

[0988] The server sends the selected vehicle and driver information to the user's terminal, including the driver's name, vehicle license plate number, estimated arrival time, etc.

[0989] Input: Optimal vehicle and driver selection results

[0990] Output: Vehicle and driver information sent to the user device

[0991] Step 7:

[0992] User

[0993] The user checks the vehicle and driver information displayed on the terminal and presses the OK button, which confirms the vehicle dispatch.

[0994] Input: Submitted vehicle and driver information

[0995] Output: Confirmation of dispatch

[0996] Step 8:

[0997] server

[0998] The server issues pickup instructions to the driver, tracks the vehicle's location in real time, calculates the optimal route taking into account traffic and obstacle information, and provides it to the driver and user.

[0999] Input: Trip confirmation and real-time GPS data

[1000] Output: Optimal route and location information

[1001] Step 9:

[1002] User

[1003] Users can check the driver's arrival time and get in the vehicle through the app. During the journey, users can check the route and estimated arrival time through the app.

[1004] Input: Real-time location

[1005] Output: Arrival confirmation and route information

[1006] Step 10:

[1007] server

[1008] The emotion recognition means monitors the user's emotional state while driving, and if the user shows signs of anxiety or discomfort, the server will provide warnings and guidance to the driver.

[1009] Input: Real-time user sentiment data

[1010] Output: Warnings and guidance to the driver

[1011] Step 11:

[1012] User

[1013] After completing a trip, users can rate the driver and vehicle through the app, in the form of a star rating and comments.

[1014] Input: Evaluation data after movement completion

[1015] Output: Collected evaluation data

[1016] Step 12:

[1017] server

[1018] The server analyzes the collected evaluation and emotion data and learns to improve matching accuracy for the next time, thereby strengthening the machine learning algorithm and increasing user satisfaction.

[1019] Input: Rating and sentiment data

[1020] Output: Improved matching algorithm

[1021] Through the above processing steps, the system achieves optimal vehicle and driver matching, taking into account the user's basic information, desired conditions, and even emotional state.

[1022] (Application example 2)

[1023] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1024] While conventional ride-hailing systems allow users to input their basic information and desired conditions, they do not take into account the user's emotional state when matching rides or monitor their emotions in real time while driving, limiting the improvement of the user experience. Furthermore, when using autonomous vehicles, the driving mode cannot be adjusted to match the user's emotional state while traveling, leaving issues in terms of safety and comfort.

[1025] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1026] In this invention, the server includes information input means for a user to input basic information and desired conditions, data receiving means for receiving information from the information input means, analysis means for analyzing the received user information and extracting the user's desired conditions, a database storing multiple vehicle and driver information, matching means for selecting the vehicle and driver most suitable for the user based on the user's desired conditions extracted by the analysis means, vehicle allocation information sending means for sending information on the selected vehicle and driver to the user, route management means for tracking the location information of the selected vehicle and driver and providing the optimal route, evaluation data collection means for receiving evaluations from users and collecting and learning the evaluation data, emotion recognition means for recognizing user emotion information in real time, and driving mode adjustment means for adjusting the driving mode during travel based on the emotion information obtained by the emotion recognition means. This enables optimal vehicle allocation matching according to the user's emotional state, thereby providing a safe and comfortable travel experience.

[1027] The "user information input means" is a means for a user to input basic information and desired conditions.

[1028] The "data receiving means" is a means for receiving information from the user information input means.

[1029] The "analysis means" is a means for analyzing the received user information and extracting the desired conditions of the user.

[1030] A "database" is a storage means for storing information on a plurality of vehicles and drivers.

[1031] The "matching means" is a means for selecting the vehicle and driver that are most suitable for the user based on the desired conditions of the user extracted by the analysis means.

[1032] The "vehicle allocation information transmission means" is a means for transmitting information about the selected vehicle and driver to the user.

[1033] The "route management means" is a means for tracking the location information of the selected vehicle and driver and providing the optimal route.

[1034] The "evaluation data collection means" is a means for receiving evaluations from users and collecting and learning the evaluation data.

[1035] The "emotion recognition means" is a means for recognizing the user's emotional information in real time.

[1036] The "driving mode adjustment means" is a means for adjusting the driving mode during movement based on the emotion information obtained by the emotion recognition means.

[1037] The present invention provides a system that matches the optimal vehicle and driver by inputting a user's basic information and desired conditions, as well as technology that recognizes the user's emotions in real time and adjusts the driving mode based on them.

[1038] Hardware and Software

[1039] Hardware:

[1040] Smartphone or tablet: A device where users can enter basic information and preferences. Emotional information is collected using audio and video input.

[1041] Server: Hardware for performing the following steps: data reception, analysis, matching, route management, evaluation data collection and learning, emotion recognition, and driving mode adjustment.

[1042] software:

[1043] User information input application: An application installed on a smartphone or tablet that allows users to enter basic information and desired conditions.

[1044] EmotionEngine: A software module used as an emotion recognition tool. It analyzes audio and video data to recognize the user's emotions.

[1045] Route Optimization Module: A software module used as a route management tool that calculates the optimal route based on traffic and accident information.

[1046] Processing flow

[1047] User:

[1048] Users open the smartphone application and enter their basic information (e.g., name, age, gender, address), as well as specific travel requests (e.g., "I want to arrive quickly," "No conversation required," "Multilingual support," etc.) into the application.

[1049] The user's audio and video data is also collected through the application and sent to the Emotion Engine.

[1050] server:

[1051] The server receives and analyzes the user's basic information and desired conditions, and extracts the user's desired conditions based on that data.

[1052] The analysis module selects the most suitable vehicle and driver from a database based on the analyzed information.

[1053] The emotion recognition means (Emotion Engine) analyzes the user's emotional information in real time and recognizes the corresponding emotional state.

[1054] The server sends information about the selected vehicle and driver to the user's terminal.

[1055] The route management module takes into account current traffic and accident information, calculates the optimal route and provides it to the vehicle and driver.

[1056] The emotion information is sent to the driving mode adjustment means to adjust the driving mode (e.g., speed, route selection, etc.) during travel.

[1057] After the move is complete, evaluation data from the user is received and used to improve the accuracy of the next match.

[1058] Specific examples

[1059] A specific example will be used to explain this.

[1060] A user inputs conditions such as "I want to arrive quickly" and "No conversation" and sends emotional information (e.g., audio and video data) through the application. The server analyzes this information and selects the self-driving vehicle and driver that best meets the desired conditions. The Emotion Engine recognizes the user's emotional state as "calm" and sets the appropriate driving mode. The route management module calculates the optimal route based on traffic information, providing safe and fast travel.

[1061] In this way, the present invention can provide an optimal travel experience according to the user's individual needs and emotional state.

[1062] Example prompt sentence:

[1063] Username: Yamada

[1064] Age: 40

[1065] Gender: Male

[1066] Address: Osaka Prefecture

[1067] Desired conditions: Arrive early, no conversation

[1068] Audio file: audio_sample.wav

[1069] Video file: video_sample.mp4

[1070] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1071] Step 1:

[1072] Entering user information

[1073] Input: The user launches the smartphone app and enters basic information (name, age, gender, address) and desired conditions (e.g., "I want to arrive quickly," "No conversation required," "Multilingual support," etc.).

[1074] What it does: As the user enters information, the application collects and formats the data for each field in the form.

[1075] Output: User's basic information and preferences are compiled in JSON format.

[1076] Step 2:

[1077] Sending data

[1078] Input: User information and desired conditions collected in the previous step.

[1079] Specific operation: The smartphone application sends the entered information to the server.

[1080] Output: The server receives the user's basic information and preferences.

[1081] Step 3:

[1082] Analysis of user information

[1083] Input: User's basic information and preferences received by the server.

[1084] Specific operation: The analysis module analyzes the received information and extracts the user's desired conditions.

[1085] Output: Extracted desired conditions.

[1086] Step 4:

[1087] Collecting emotional information

[1088] Input: Audio and video data from the user (e.g., "audio_sample.wav" and "video_sample.mp4").

[1089] Specific operation: The smartphone application collects audio and video data and sends it to the emotion recognition module (EmotionEngine).

[1090] Output: Audio and video data forwarded to the emotion recognition module.

[1091] Step 5:

[1092] Emotional information analysis

[1093] Input: Audio and video data collected in the previous step.

[1094] How it works: EmotionEngine analyzes audio and video data to recognize the user's emotional state in real time.

[1095] Output: Perceived emotional state of the user.

[1096] Step 6:

[1097] Vehicle and driver selection

[1098] Input: Extracted desired conditions and perceived emotional states.

[1099] Specific operation: The server refers to a database containing information on multiple vehicles and drivers, and selects the optimal vehicle and driver based on the desired conditions and emotional state.

[1100] Output: Information on the selected vehicle and driver.

[1101] Step 7:

[1102] Sending dispatch information

[1103] Input: Selected vehicle and driver information.

[1104] Specific operation: The server sends the selection information to the user's smartphone application.

[1105] Output: Vehicle and driver information displayed on the user's smartphone.

[1106] Step 8:

[1107] Route Optimization

[1108] Input: Vehicle and driver location and traffic information.

[1109] Specific operation: The server's route management module calculates the optimal route based on the received information.

[1110] Output: Optimized route information.

[1111] Step 9:

[1112] Adjusting the driving mode

[1113] Input: Perceived user emotional state and optimized route information.

[1114] Specific operation: The driving mode adjusting means adjusts the driving mode based on the emotional state to provide a safe and comfortable driving experience.

[1115] Output: Coordinated driving mode.

[1116] Step 10:

[1117] Collecting user ratings

[1118] Input: User feedback after the move is complete (e.g., star rating and comments).

[1119] Specific operation: The smartphone application collects ratings from users and sends them to the server.

[1120] Output: Collected evaluation data on the server.

[1121] Step 11:

[1122] Learning evaluation data

[1123] Input: Collected assessment data.

[1124] Specific operation: The server analyzes the evaluation data and learns to improve matching accuracy next time.

[1125] Output: Improved matching algorithm.

[1126] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1127] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1128] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[1129] [Third embodiment]

[1130] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1131] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[1132] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1133] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[1134] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1135] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1136] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1137] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1138] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1139] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1140] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1141] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[1142] The present invention is a system that matches users with the most suitable taxi vehicle and driver based on their basic information and desired conditions. The program processing of this system is explained below in natural language.

[1143] Entering and collecting user data

[1144] User

[1145] Users open the smartphone application and enter basic information such as their name, age, gender, and address.

[1146] The app allows users to input their specific travel needs, such as "I want to arrive quickly," "No conversation required," "Multilingual support," "I want to share a ride," "Car sickness prevention," and "Wheelchair and stroller access."

[1147] Data submission and initial analysis

[1148] Terminal

[1149] The information entered by the user is sent to a server via the Internet.

[1150] server

[1151] The server passes the received user information and requests to an analysis module.

[1152] The analytical module analyzes the user's specific needs and lifestyle and extracts relevant parameters.

[1153] Selection of the best vehicle and driver

[1154] server

[1155] The server checks the database of available taxi vehicles and drivers in real time.

[1156] The system uses data such as each driver's preferred routes, past ratings, and driving quality to select the most suitable driver and vehicle.

[1157] A matching algorithm is used to select the vehicle and driver that best suits the user's requirements.

[1158] Vehicle dispatch proposal

[1159] server

[1160] Information about the matched driver and vehicle is sent to the user's device.

[1161] Terminal

[1162] The user terminal displays driver and vehicle information (e.g., driver's name, vehicle number, estimated arrival time).

[1163] The user reviews and confirms the proposed vehicle and driver.

[1164] Movement initiation and path tracking

[1165] server

[1166] As the driver heads to the user's pickup location, the server tracks the vehicle's location in real time.

[1167] The server calculates the optimal route based on traffic and congestion information and provides it to the driver and user.

[1168] User

[1169] Users can check the driver's arrival on the app and then get in the car.

[1170] While traveling, users can check their route and estimated arrival time through the app.

[1171] Post-move evaluation and feedback

[1172] User

[1173] After completing a trip, users can rate the driver and vehicle through the app, including by leaving a star rating and comments.

[1174] server

[1175] The server passes the collected evaluation data to an analysis module, which then learns to improve matching accuracy for the next time.

[1176] This will improve future matching accuracy and increase user satisfaction.

[1177] Specific examples

[1178] 1. Users

[1179] Tanaka wants to leave for work at 9 a.m. He enters the conditions "I want to travel quickly" and "No conversation" into the app.

[1180] 2. Terminal

[1181] Tanaka's input data is sent to the server.

[1182] 3. Server

[1183] The AI ​​module analyzes Tanaka's request and selects a driver, Yamada, who can provide a quick and quiet trip.

[1184] 4. Server

[1185] Yamada's vehicle information is sent to Tanaka's device.

[1186] 5. Terminal

[1187] Tanaka checks the proposed content and approves it.

[1188] 6. Server

[1189] Gives pickup instructions to Yamada and manages the route.

[1190] 7. Users

[1191] After the trip is completed, Tanaka praises Yamada's driving and enters feedback.

[1192] 8. Server

[1193] The collected evaluation data will be analyzed by AI and used for the next match.

[1194] In this way, AI-powered dispatch systems will provide users with an optimal travel experience tailored to their individual needs, while also helping taxi companies improve their operational efficiency.

[1195] The processing flow will be explained below.

[1196] Step 1: Enter your user information and preferences

[1197] User

[1198] Users open the smartphone application and enter their basic information (e.g., name, age, gender, address).

[1199] Users input specific travel requests (e.g., early arrival, no conversation, multilingual support, ride-sharing, car sickness prevention, wheelchair and stroller accessibility).

[1200] Step 2: Send data

[1201] Terminal

[1202] The terminal sends the basic information and desired conditions entered by the user to the server.

[1203] Step 3: Receiving user information

[1204] server

[1205] The server receives the user information and desired conditions sent from the terminal.

[1206] Step 4: Analyze user information

[1207] server

[1208] The server transfers the received user information to the AI ​​analysis module.

[1209] The AI ​​analysis module extracts the user's desired conditions and generates analysis results.

[1210] Step 5: Vehicle and driver database matching

[1211] server

[1212] The server searches a database of available vehicles and drivers in real time.

[1213] The database stores information such as the driver's strengths and weaknesses in routes, past evaluations, and driving quality.

[1214] Step 6: Generate the best match

[1215] server

[1216] Based on the output of the AI ​​analysis module, the server selects the vehicle and driver that best meets the user's desired conditions.

[1217] A matching algorithm is used to create a final list of selected drivers and vehicles.

[1218] Step 7: Submit your trip

[1219] server

[1220] The server sends information about the selected vehicle and driver, as well as the estimated arrival time, to the user's terminal.

[1221] Step 8: Review the trip offer

[1222] Terminal

[1223] The user's device will display details of the ride offer (e.g., driver's name, vehicle number, estimated arrival time).

[1224] The user checks the proposed vehicle and driver information and presses the confirm button.

[1225] Step 9: Notification of arrival at pickup point

[1226] Terminal

[1227] The user's device is notified that the driver has arrived at the pickup location.

[1228] Users can check the driver's arrival via the app.

[1229] Step 10: Route Management and Tracking

[1230] server

[1231] The server tracks the vehicle's location in real time and calculates the optimal route based on traffic and congestion information.

[1232] The calculated optimal route is notified to the driver and user.

[1233] Step 11: Notification of completion of move

[1234] server

[1235] When the trip is complete, the server receives a notification from the driver that the trip is complete and notifies the user.

[1236] Step 12: Enter your rating

[1237] User

[1238] After completing the trip, users can rate the driver and vehicle through the app (e.g., star rating, comments).

[1239] Step 13: Collect evaluation data

[1240] server

[1241] The server transfers user evaluation data to an analysis module, which learns from it to help improve the matching process next time.

[1242] Example 1

[1243] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1244] In modern society, it is difficult to quickly and accurately match the optimal means of transportation and operators that meet the diverse needs of users, and there is a lack of mechanisms to respond to immediate and personalized requests. Furthermore, conventional systems have difficulty effectively utilizing collected evaluation data to improve the accuracy of the next match, which limits the ability to continuously improve the quality of the user experience.

[1245] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1246] In this invention, the server includes an information input means for a user to input basic information and desired conditions, a data receiving means for receiving information from the information input means, and an analysis means for analyzing the received user information and extracting the user's desired conditions. This enables quick and accurate matching of optimal transportation means and operators according to the user's diverse needs. Furthermore, by using a generative AI model to collect and learn evaluation data, the accuracy of the next match can be improved, and the quality of the user experience can be continuously improved.

[1247] The "information input means" is a means for the user to input basic information and desired conditions.

[1248] The "data receiving means" is a means for receiving information from the information input means.

[1249] The "analysis means" is a means for analyzing the received user information and extracting the user's desired conditions.

[1250] "Database" means data storage for storing multiple modes of transportation and operator information.

[1251] The "matching means" is a means for selecting the most suitable means of transportation and operator for the user based on the user's desired conditions extracted by the analysis means.

[1252] The "vehicle dispatch information transmission means" is a means for transmitting information about the selected transportation means and the driver to the user.

[1253] The "route management means" is a means for tracking the location information of the selected means of transportation and the operator, and providing the optimal route.

[1254] The "rating data collection means" is a means for receiving ratings from users and collecting and learning rating data.

[1255] A "generative AI model" is an artificial intelligence technology that analyzes and learns from evaluation data to improve matching accuracy the next time.

[1256] This invention is a system that matches the optimal means of transportation and operator based on the user's input of basic information and desired conditions. This system is composed of components of a server, a terminal, and a user, and is specifically implemented as follows.

[1257] A user opens a smartphone application and enters basic information such as name, age, gender, and address. For example, the user enters "Ichiro Tanaka" as the user name, "30 years old," "male," and "Shinjuku-ku, Tokyo" as the address. The user also enters specific travel requests. For example, from options such as "Arrive quickly," "No conversation," "Multilingual support," "Prefer carpooling," "Car sickness prevention," and "Wheelchair and stroller access," the user selects "Arrive quickly" and "No conversation."

[1258] The terminal automatically transmits the information entered by the user to the server via the Internet, at which point the terminal waits for a response to confirm that the data transmission was successful.

[1259] The server passes the user information and desired conditions received from the device to a dedicated analysis module. The analysis module analyzes the user's desired conditions and lifestyle and extracts relevant parameters. For example, if the user requests a "quick arrival," the server will issue instructions to prioritize the selection of drivers who can provide a fast route.

[1260] The server compares a database of available transportation methods and drivers in real time. The database includes each driver's location information, preferred routes, past evaluations, etc. The server narrows down the candidates based on each driver's preferred routes, past evaluations, and driving quality. For example, driver Taro Yamada is good at speedy driving, which meets the condition of "I want to arrive quickly." A matching algorithm is used to select the driver and transportation method that best suits the user's requirements. In this process, a generative AI model is used to analyze and learn from the evaluation data.

[1261] The server sends information about the selected driver and transportation means to the user's terminal. The information sent includes the driver's name, transportation means number, and estimated arrival time. The driver and transportation means information is displayed on the user's terminal. For example, it may show "Driver's name: Yamada Taro, Transportation means number: AB-1234, Estimated arrival time: 9:00." The user confirms the information presented and presses the "Confirm" button to indicate their consent.

[1262] As the driver heads to the user's pickup point, the server tracks the vehicle's location in real time. The driver's current location is monitored using GPS data. The server calculates the optimal route based on traffic and congestion information. This information is provided to the driver and user in real time. The user checks the driver's arrival in the app and waits at the specified pickup point. A notification is sent when the driver approaches the arrival point. The user gets into the driver's car and begins traveling. During the trip, the user can check the route and estimated arrival time through the app.

[1263] After completing a trip, the user can rate the driver and the means of transportation through the app. For example, they could rate the driver 5 stars and comment that the ride was very quick and quiet. The server then passes the collected rating data to an analysis module, which uses the data to learn how to improve the accuracy of the next match. The rating data is then analyzed by a generative AI model, which updates the driver's rating score. This improves future matching accuracy and continuously improves the quality of the user experience.

[1264] This system allows users to enjoy an optimal travel experience tailored to their individual needs, and also enables taxi companies and drivers to improve the quality of their services. Examples of prompt sentences include "Mr. Tanaka wishes to travel quickly," and "Mr. Yamada is good at driving quickly."

[1265] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1266] Step 1: Entering User Data

[1267] The user opens the smartphone application and enters basic information such as name, age, gender, and address, as well as desired conditions for travel. The specific data entered includes the user name "Tanaka Ichiro," age "30," gender "male," address "Shinjuku Ward, Tokyo," and desired conditions "arrive quickly" and "no conversation." This information is stored on the device as input data (basic information and desired conditions).

[1268] Step 2: Send data

[1269] The terminal sends the information entered by the user to the server via the Internet. The input is the user data stored in the previous step, and the output is sent to the server. At this time, the terminal waits for a response to confirm the successful data transmission.

[1270] Step 3: Initial analysis

[1271] The server passes the user data received from the device to the analysis module. The received data consists of the user's basic information and desired conditions. The analysis module analyzes the user's desired conditions and lifestyle and extracts related parameters. For example, based on the desired condition of "I want to arrive quickly," the module prepares to find a driver who can provide a fast route. The output is the extracted analysis results (parameters related to the user's desired conditions).

[1272] Step 4: Select the best transportation and driver

[1273] The server compares the analysis results with a database of available means of transportation and drivers in real time. The input data are the analysis results and database information. The server narrows down the most suitable candidates based on data such as each driver's location information, preferred routes, and past evaluations. For example, it confirms that driver "Yamada Taro" is good at speedy driving, which meets the condition of "wanting to arrive quickly." The output is the selection result of the most suitable means of transportation and driver.

[1274] Step 5: Ride proposal

[1275] The server sends the information of the selected driver and transportation means to the user's terminal. The input is the information of the selected driver and transportation means, and the output is the dispatch information (driver's name, transportation means number, estimated arrival time) sent to the user's terminal.

[1276] Step 6: View and confirm your trip information

[1277] The terminal receives the dispatch information from the server and displays it to the user. The displayed information is "Driver's name: Yamada Taro, Transportation number: AB-1234, Estimated arrival time: 9:00." The user confirms the information presented and presses the "Confirm" button to accept it. The input is the dispatch information from the server, and the output is the user's confirmation and confirmation action.

[1278] Step 7: Path tracing

[1279] The server tracks the vehicle's location in real time as the driver heads to the user's pickup point. The input is GPS data and traffic information, and the output is the calculation of the optimal route. The server calculates the optimal route based on traffic and congestion information and provides this information to the driver and user.

[1280] Step 8: Driver arrives and begins travel

[1281] The user checks the driver's arrival through the app. A notification is sent when the user approaches the destination. The user waits at the designated pickup point, gets into the driver's car, and begins their journey. During the journey, the user can check the route and estimated arrival time through the app. The input is the arrival notification and route information from the server, and the output is the user's journey start and route confirmation.

[1282] Step 9: Post-move evaluation and feedback

[1283] After completing the trip, the user evaluates the driver and the means of transportation through the app. For example, the user might enter "star rating: 5, comment: The drive was very quick and quiet." The input is the user's evaluation data, and the output is the transmission of the evaluation data to the server.

[1284] Step 10: Collect evaluation data and learn

[1285] The server passes the collected evaluation data to an analysis module, which then performs learning to improve the accuracy of the next match. The input is the user's evaluation data, and the output is the pilot's evaluation score analyzed and updated by the generative AI model. This improves future matching accuracy and continuously improves the quality of the user experience.

[1286] (Application example 1)

[1287] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1288] Conventional taxi dispatch systems have difficulty reflecting detailed user preferences and providing services that meet some customization requests. In particular, they lack accuracy in providing specific travel requests and optimal route guidance, leaving users wanting more. Similar challenges exist in the food delivery field, where it is difficult to efficiently select the optimal delivery person and route.

[1289] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1290] In this invention, the server includes: a user information input means for a user to input basic information and desired conditions; a data receiving means for receiving information from the user information input means; an analysis means for analyzing the received user information and extracting the user's desired conditions; a database storing information on multiple vehicles and drivers; a matching means for selecting the most suitable vehicle and driver for the user based on the user's desired conditions extracted by the analysis means; a dispatch information sending means for sending information on the selected vehicle and driver to the user; a route management means for tracking the location information of the selected vehicle and driver and providing the optimal route; an evaluation data collection means for receiving evaluations from users and collecting and learning the evaluation data; and a means for selecting the most suitable delivery person and route based on the basic information and desired conditions input by the user. This makes it possible to provide an optimal service that meets the user's detailed desired conditions and specific requests.

[1291] "User information input means" refers to a device or function that allows a user to input basic information and desired conditions.

[1292] The "data receiving means" is a device or function that receives information from the user information input means.

[1293] The "analysis means" is a device or function that analyzes the received user information and extracts the user's desired conditions.

[1294] A "database" is a storage device for storing information on a plurality of vehicles and drivers.

[1295] The "matching means" is a device or function that selects the most suitable vehicle and driver based on the user's desired conditions extracted by the analysis means.

[1296] The "vehicle dispatch information transmission means" is a device or function that transmits information about the selected vehicle and driver to the user.

[1297] The "route management means" is a device or function that tracks the location information of the selected vehicle and driver and provides the optimal route.

[1298] The "evaluation data collection means" is a device or function that receives evaluations from users and collects and learns evaluation data.

[1299] A "delivery staff member" is a person who delivers items along the optimal route based on the basic information and desired conditions entered by the user.

[1300] An "optimal route" is the best travel route calculated taking into account traffic conditions and the user's desired conditions.

[1301]

[1302] The system for implementing this invention includes a user information input means through which the user inputs basic information and desired conditions. This information is input via a mobile device such as a smartphone. Specifically, the user inputs their name, age, address, and desired conditions for travel (e.g., conditions such as "I want to arrive quickly," "No conversation," and "I want contactless delivery").

[1303] The entered data is sent to a server via the Internet. The server has a data receiving means for receiving this data. The received user information is analyzed using an analysis means, and the user's desired conditions are extracted. This analysis means includes an AI module using Python, which efficiently analyzes user information.

[1304] The analyzed data is compared with a database that stores information on multiple vehicles and drivers. The server's database contains detailed information on each driver, such as their preferred routes, past ratings, and driving quality. The server also has a matching means for selecting the optimal vehicle and driver. This matching means selects the vehicle and driver that best meets the user's desired conditions.

[1305] The selected vehicle and driver information will be sent to the user's terminal via the vehicle dispatch information sending means. The user will then confirm and confirm the proposed vehicle and driver information (e.g., driver's name, vehicle license plate number, estimated arrival time).

[1306] The server then tracks the location information of the vehicle and the driver using a route management means, which calculates the optimal route taking into account traffic and congestion information.

[1307] After completing a delivery, the user can rate the driver and vehicle via their device. This rating is in the form of a star rating or a comment, and is sent to the server via a data collection tool. The server then uses AI to analyze the collected rating data and use it to improve the accuracy of the next match.

[1308] For example, the user inputs requests such as "I want to arrive quickly" or "I want contactless delivery." This information is sent to the server, and the AI ​​module analyzes it, selecting the most suitable driver to deliver quickly and contactlessly. This driver's information is sent to the user's device, and the user confirms it, and the delivery begins. Below is an example of a prompt:

[1309] User: Name: Taro Tanaka, Age: 30, Address: Shibuya-ku, Tokyo

[1310] Desired conditions: Delivery by 12:00, no-contact delivery, early arrival

[1311] This will enable us to provide optimal services that meet the user's detailed requirements and specific requests.

[1312] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1313] Step 1:

[1314] The user starts the smartphone application and inputs their name, age, address, and desired conditions. Specific desired conditions include "quick arrival" and "contactless delivery." This input information is sent to the server by the data receiving means.

[1315] Input: User's basic information and desired conditions

[1316] Output: Data sent to the data receiver

[1317] Step 2:

[1318] The server's data receiving means acquires the received user information and passes it to the analyzing means, which extracts the user's desired conditions from the received data.

[1319] Input: Data sent to the data receiving means

[1320] Output: Extracted user preferences

[1321] Step 3:

[1322] The server's analytical means uses a generative AI model using Python to analyze the user's input data, and this data analysis extracts the user's desired conditions.

[1323] Input: Received user information

[1324] Output: Parsed desired conditions

[1325] Step 4:

[1326] The server's matching means selects the optimal delivery person and route from a database based on the desired conditions obtained from the analysis means. This database stores information such as each driver's preferred routes, past evaluations, and driving quality.

[1327] Input: Parsed desired conditions

[1328] Output: Selection of optimal delivery personnel and route

[1329] Step 5:

[1330] The server's dispatch information transmission means transmits the selected delivery person and route information to the user terminal, and the user confirms and approves the received information.

[1331] Input: Selection of optimal delivery personnel and route

[1332] Output: Information sent to the user's terminal

[1333] Step 6:

[1334] The server's route management means tracks the location information of selected drivers and delivery routes in real time and provides the optimal route, using an algorithm that takes into account traffic and accident information to optimize the route.

[1335] Input: Selected driver and delivery route information

[1336] Output: Optimal route information

[1337] Step 7:

[1338] After the delivery is completed, the user can rate the driver and the vehicle through the smartphone application, which will then be sent to the server's rating data collection means.

[1339] Input: User rating data

[1340] Output: Rating data sent to the server

[1341] Step 8:

[1342] The server's evaluation data collection means analyzes the collected evaluation data and trains the generative AI model to improve matching accuracy next time. This continuous learning process improves the quality of service and increases user satisfaction.

[1343] Input: User-submitted rating data

[1344] Output: The learning results of the generative AI model

[1345] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1346] The present invention is a system that combines a system that matches the optimal vehicle and driver by inputting the user's basic information and desired conditions with an emotion engine that recognizes and processes the user's emotions. Below, the program processing of this system is specifically explained in natural language.

[1347] Entering and collecting user data

[1348] User

[1349] Users open the smartphone application and enter their basic information (e.g., name, age, gender, address).

[1350] The app allows users to input their specific travel needs, such as "I want to arrive quickly," "No conversation required," "Multilingual support," "I want to share a ride," "Car sickness prevention," and "Wheelchair and stroller access."

[1351] Data submission and initial analysis

[1352] Terminal

[1353] The information entered by the user is sent to a server via the Internet.

[1354] server

[1355] The server passes the received user information and requests to the analysis module and emotion engine.

[1356] The analytical module analyzes the user's specific needs and lifestyle and extracts relevant parameters.

[1357] At the same time, the emotion engine recognizes the user's emotional state based on user input data and real-time interactions (e.g., voice, facial expressions, text).

[1358] Selection of the best vehicle and driver

[1359] server

[1360] The server checks the database of available taxi vehicles and drivers in real time.

[1361] The system uses data such as each driver's preferred routes, past ratings, and driving quality to select the most suitable driver and vehicle.

[1362] It also incorporates the output of the emotion engine to select the vehicle and driver that best suits the user's current emotional state.

[1363] Vehicle dispatch proposal

[1364] server

[1365] Information about the matched driver and vehicle is sent to the user's device.

[1366] Terminal

[1367] The user terminal displays driver and vehicle information (e.g., driver's name, vehicle number, estimated arrival time).

[1368] The user checks the proposed vehicle and driver information and presses the confirm button.

[1369] Movement initiation and path tracking

[1370] server

[1371] As the driver heads to the user's pickup location, the server tracks the vehicle's location in real time.

[1372] The server calculates the optimal route based on traffic and congestion information and provides it to the driver and user.

[1373] User

[1374] Users can check the driver's arrival on the app and then get in the car.

[1375] While traveling, users can check their route and estimated arrival time through the app.

[1376] Emotion Monitoring and Alerts

[1377] server

[1378] The emotion engine monitors the user's emotional state in real time while on the move.

[1379] If the server detects any emotion indicative of anxiety or discomfort from the user, it will provide warnings and guidance to the driver.

[1380] Post-move evaluation and feedback

[1381] User

[1382] After completing a trip, users can rate the driver and vehicle through the app, including by leaving a star rating and comments.

[1383] server

[1384] The server passes the collected evaluation data and emotion data to an analysis module, which then performs learning to improve matching accuracy for the next time.

[1385] This will improve future matching accuracy and increase user satisfaction.

[1386] Specific examples

[1387] 1. Users

[1388] Tanaka wants to leave for work at 9 a.m. He enters the conditions "I want to travel quickly" and "No conversation" into the app.

[1389] 2. Terminal

[1390] Tanaka's input data is sent to the server.

[1391] 3. Server

[1392] The analysis module analyzes Tanaka's desired conditions, and the emotion engine evaluates Tanaka's current emotional state. A driver, Yamada, who can provide a fast, quiet, and reassuring ride is selected.

[1393] 4. Server

[1394] Yamada's vehicle information is sent to Tanaka's device.

[1395] 5. Terminal

[1396] Tanaka checks the proposed content and approves it.

[1397] 6. Server

[1398] It issues pickup instructions to Yamada and manages the route. The emotion engine monitors Tanaka's emotional state and sends a warning to Yamada if anxiety is detected.

[1399] 7. Users

[1400] After the trip is completed, Tanaka praises Yamada's driving and enters feedback.

[1401] 8. Server

[1402] The collected evaluation and emotional data is analyzed by AI to help improve the accuracy of the next match.

[1403] In this way, a dispatch system that combines an emotion engine can provide an optimal travel experience tailored to the individual needs and emotional state of the user, while also helping to improve the operational efficiency of taxi companies.

[1404] The processing flow will be explained below.

[1405] Step 1: Enter your user information and preferences

[1406] User

[1407] Users open the smartphone application and enter their basic information (e.g., name, age, gender, address).

[1408] Users input specific travel requests (e.g., early arrival, no conversation, multilingual support, ride-sharing, car sickness prevention, wheelchair and stroller accessibility).

[1409] Step 2: Submit your user information and preferences

[1410] Terminal

[1411] The terminal sends the basic information and desired conditions entered by the user to the server.

[1412] Step 3: Receiving and analyzing user information

[1413] server

[1414] The server receives the user information and desired conditions sent from the terminal.

[1415] The server uses an AI analysis module to analyze the user's desired conditions and extract relevant parameters.

[1416] Step 4: Emotional state analysis by the emotion engine

[1417] server

[1418] The emotion engine in the server analyzes the user's input data and real-time interactions (e.g., voice, facial expressions, text) to assess the user's emotional state.

[1419] Step 5: Vehicle and driver database matching

[1420] server

[1421] The server searches a database of available vehicles and drivers in real time.

[1422] The database contains information such as the driver's preferred routes, past ratings, and driving quality.

[1423] Step 6: Selecting the best vehicle and driver

[1424] server

[1425] Based on the output of the AI ​​analysis module and the evaluation of the emotion engine, the server selects the vehicle and driver that best suits the user's desired conditions and emotional state.

[1426] Step 7: Submit your trip

[1427] server

[1428] The server sends information about the selected vehicle and driver, as well as the estimated arrival time, to the user's terminal.

[1429] Step 8: Review the trip offer

[1430] Terminal

[1431] The user's device will display details of the ride offer (e.g., driver's name, vehicle number, estimated arrival time).

[1432] The user checks the proposed vehicle and driver information and presses the confirm button.

[1433] Step 9: Notification of arrival at pickup point

[1434] Terminal

[1435] The user's device is notified that the driver has arrived at the pickup location.

[1436] Users can check the driver's arrival via the app.

[1437] Step 10: Route Management and Tracking

[1438] server

[1439] The server tracks the vehicle's location in real time and calculates the optimal route based on traffic and congestion information.

[1440] The calculated optimal route is notified to the driver and user.

[1441] Step 11: Emotion Monitoring and Alerts

[1442] server

[1443] The emotion engine monitors the user's emotional state in real time while on the move.

[1444] If the server detects any emotion indicative of anxiety or discomfort from the user, it will provide warnings and guidance to the driver.

[1445] Step 12: Notification of completion of move

[1446] server

[1447] When the trip is complete, the server receives a notification from the driver that the trip is complete and notifies the user.

[1448] Step 13: Enter your rating

[1449] User

[1450] After completing the trip, users can rate the driver and vehicle through the app (e.g., star rating, comments).

[1451] Step 14: Collect and analyze assessment data

[1452] server

[1453] The server transfers user evaluation and sentiment data to an analysis module, which learns from the data to help improve the matching process next time.

[1454] Example 2

[1455] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1456] In modern mobility services, it is important to select a vehicle and driver that takes into account the user's basic information and desired conditions. Additionally, there is a need for a system that can recognize the user's emotional state in real time and respond appropriately. However, conventional systems have difficulty matching users while taking their emotions into account, limiting their ability to improve satisfaction. Furthermore, they are insufficient in utilizing real-time emotion monitoring and evaluation data. To address these issues, the present invention aims to provide a system that selects the optimal vehicle and driver, taking into account the user's emotional state.

[1457] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1458] In this invention, the server includes: a user information input means for a user to input basic information and desired conditions; a data receiving means for receiving information from the user information input means; an analysis means for analyzing the received user information and extracting the user's desired conditions; an emotion recognition means for recognizing and processing the user's emotional state; a database storing multiple vehicle and driver information; a matching means for selecting the optimal vehicle and driver for the user based on the user's desired conditions and emotional state extracted by the analysis means and the emotion recognition means; a vehicle dispatch information sending means for sending information on the selected vehicle and driver to the user; a route management means for tracking the location information of the selected vehicle and driver and providing the optimal route; and an evaluation data collection means for receiving evaluations from users and collecting and learning the evaluation data. This enables the optimal vehicle and driver to be matched taking the user's emotional state into consideration, thereby improving user satisfaction.

[1459] "User information input means" refers to the device or software functions that allow users to input their own basic information and desired conditions.

[1460] "Data receiving means" refers to a function for receiving information sent from the user information input means.

[1461] "Analysis means" refers to a function for analyzing received user information and extracting the user's desired conditions.

[1462] "Emotion recognizer" refers to technology or software that recognizes and processes a user's emotional state.

[1463] "Database" refers to a data storage system that stores and retrieves information about multiple vehicles and drivers.

[1464] The "matching means" refers to a function that selects the most suitable vehicle and driver based on the user's desired conditions and emotional state extracted by the analysis means and emotion recognition means.

[1465] "Vehicle dispatch information transmission means" refers to a function for transmitting information about the selected vehicle and driver to the user.

[1466] "Route management means" refers to a function for tracking the location information of selected vehicles and drivers, and providing the optimal route taking into account traffic and obstacle information.

[1467] "Evaluation data collection means" refers to a function for receiving, collecting, and learning from evaluations from users.

[1468] "Vehicle" refers to a means of transportation for the purpose of moving a user, and examples include taxis and buses.

[1469] "Driver" means a person who drives a vehicle for the purpose of transporting users.

[1470] This invention is a system that matches the optimal vehicle and driver by inputting a user's basic information and desired conditions, and also includes an emotion engine that recognizes and processes the user's emotions in real time. Specific embodiments of this system are described below.

[1471] System configuration

[1472] The system includes the following components:

[1473] User information input method

[1474] Data Receiving Method

[1475] Analysis means

[1476] emotion recognition means

[1477] A database that stores vehicle and driver information

[1478] Matching Method

[1479] Vehicle dispatch information transmission means

[1480] Route Management Method

[1481] Evaluation data collection method

[1482] Hardware and Software Configuration

[1483] User

[1484] Using a smartphone application, users enter basic information and desired conditions, such as name, age, gender, and address, as well as detailed desired conditions such as "quick arrival," "no conversation required," and "multilingual support."

[1485] Terminal

[1486] It receives the information entered by the user and sends it to a server over the Internet, where the data is typically transmitted securely using the HTTPS protocol.

[1487] server

[1488] It works with a database and processes the received data using analytical and emotion recognition techniques, using machine learning and image processing libraries such as Python, TensorFlow, and OpenCV as specific software technologies.

[1489] The analysis means analyzes the data input by the user and extracts specific parameters. For example, if the user inputs a desire to arrive early, parameters based on this desire are extracted.

[1490] The emotion recognition means analyzes real-time data such as voice and facial expressions to evaluate the user's emotional state. For example, if the user has an anxious expression, the emotional state is recognized.

[1491] The matching means selects the most suitable vehicle and driver based on data from the analysis means and emotion recognition means, using an algorithm that references the driver's preferred routes and past evaluation data available in real time.

[1492] The vehicle dispatch information transmitting means transmits information about the most suitable vehicle and driver to the user's terminal, which displays information such as the driver's name, vehicle number, and estimated arrival time.

[1493] The route management tool tracks the location of selected vehicles in real time using GPS data and calculates the optimal route, taking into account traffic and obstacle information.

[1494] The evaluation data collection means collects evaluation data from users after the movement, and the collected data is used to improve the accuracy of the next matching.

[1495] Specific examples

[1496] 1. Users

[1497] To leave for work at 9 a.m., the user enters the conditions "I want to arrive early" and "No conversation" into the smartphone application.

[1498] 2. Terminal

[1499] Sends user input data to the server.

[1500] 3. Server

[1501] The analysis means analyzes the user's desired conditions, and the emotion recognition means evaluates the user's emotional state (e.g., no anxiety). Based on the analysis results and the emotional state, a driver who can provide a sense of security and speed is selected.

[1502] 4. Server

[1503] The driver's vehicle information is sent to the user's device.

[1504] 5. Users

[1505] The user reviews and accepts the proposed content.

[1506] 6. Server

[1507] The system issues dispatch instructions to the driver and manages routes. The emotion recognition system monitors the user's emotional state while traveling and sends warnings to the driver as necessary.

[1508] Prompt Sentence Examples

[1509] "I want to arrive early at 9 a.m. I'm looking for a driver who can comfortably travel without conversation."

[1510] "Designing a system to select the optimal driver and vehicle based on the user's emotional state"

[1511] As described above, the system starts with the user's basic information and desired conditions, recognizes their emotional state, selects the most suitable vehicle and driver, and performs real-time tracking and route management to provide a comfortable and safe travel experience.

[1512] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1513] System program processing flow

[1514] Step 1:

[1515] User

[1516] Users open the smartphone application and enter basic information (name, age, gender, address), followed by their desired travel conditions (e.g., "I want to arrive quickly," "No conversation required," "Multilingual support," "I want to share a ride," etc.).

[1517] Input: Basic information and desired conditions

[1518] Output: Basic information and desired conditions entered

[1519] Step 2:

[1520] Terminal

[1521] The device receives the basic information and desired conditions entered by the user and sends it to a server via the Internet, using the HTTPS protocol to ensure data security.

[1522] Input: Basic information and desired conditions

[1523] Output: User's basic information and preferences sent to the server

[1524] Step 3:

[1525] server

[1526] The server passes the received user information to an analysis module, which extracts parameters related to the user's specific preferences and lifestyle, using natural language processing (NLP) technology.

[1527] Input: Received basic information and desired conditions

[1528] Output: Extracted parameters

[1529] Step 4:

[1530] server

[1531] At the same time, the server uses emotion recognition means to analyze the user's emotional state, using technologies such as voice analysis and facial expression analysis, and determines the user's current emotional state based on real-time data.

[1532] Input: Real-time user voice and facial expression data

[1533] Output: Perceived emotional state

[1534] Step 5:

[1535] server

[1536] The server accesses the database based on data from the analysis and emotion recognition methods to select the most suitable vehicle and driver, using a machine learning algorithm to take into account the driver's past evaluations, preferred routes, driving quality, and other factors.

[1537] Input: extracted parameters and recognized emotional state

[1538] Output: Optimal vehicle and driver selection results

[1539] Step 6:

[1540] server

[1541] The server sends the selected vehicle and driver information to the user's terminal, including the driver's name, vehicle license plate number, estimated arrival time, etc.

[1542] Input: Optimal vehicle and driver selection results

[1543] Output: Vehicle and driver information sent to the user device

[1544] Step 7:

[1545] User

[1546] The user checks the vehicle and driver information displayed on the terminal and presses the OK button, which confirms the vehicle dispatch.

[1547] Input: Submitted vehicle and driver information

[1548] Output: Confirmation of dispatch

[1549] Step 8:

[1550] server

[1551] The server issues pickup instructions to the driver, tracks the vehicle's location in real time, calculates the optimal route taking into account traffic and obstacle information, and provides it to the driver and user.

[1552] Input: Trip confirmation and real-time GPS data

[1553] Output: Optimal route and location information

[1554] Step 9:

[1555] User

[1556] Users can check the driver's arrival time and get in the vehicle through the app. During the journey, users can check the route and estimated arrival time through the app.

[1557] Input: Real-time location

[1558] Output: Arrival confirmation and route information

[1559] Step 10:

[1560] server

[1561] The emotion recognition means monitors the user's emotional state while driving, and if the user shows signs of anxiety or discomfort, the server will provide warnings and guidance to the driver.

[1562] Input: Real-time user sentiment data

[1563] Output: Warnings and guidance to the driver

[1564] Step 11:

[1565] User

[1566] After completing a trip, users can rate the driver and vehicle through the app, in the form of a star rating and comments.

[1567] Input: Evaluation data after movement completion

[1568] Output: Collected evaluation data

[1569] Step 12:

[1570] server

[1571] The server analyzes the collected evaluation and emotion data and learns to improve matching accuracy for the next time, thereby strengthening the machine learning algorithm and increasing user satisfaction.

[1572] Input: Rating and sentiment data

[1573] Output: Improved matching algorithm

[1574] Through the above processing steps, the system achieves optimal vehicle and driver matching, taking into account the user's basic information, desired conditions, and even emotional state.

[1575] (Application example 2)

[1576] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1577] While conventional ride-hailing systems allow users to input their basic information and desired conditions, they do not take into account the user's emotional state when matching rides or monitor their emotions in real time while driving, limiting the improvement of the user experience. Furthermore, when using autonomous vehicles, the driving mode cannot be adjusted to match the user's emotional state while traveling, leaving issues in terms of safety and comfort.

[1578] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1579] In this invention, the server includes information input means for a user to input basic information and desired conditions, data receiving means for receiving information from the information input means, analysis means for analyzing the received user information and extracting the user's desired conditions, a database storing multiple vehicle and driver information, matching means for selecting the vehicle and driver most suitable for the user based on the user's desired conditions extracted by the analysis means, vehicle allocation information sending means for sending information on the selected vehicle and driver to the user, route management means for tracking the location information of the selected vehicle and driver and providing the optimal route, evaluation data collection means for receiving evaluations from users and collecting and learning the evaluation data, emotion recognition means for recognizing user emotion information in real time, and driving mode adjustment means for adjusting the driving mode during travel based on the emotion information obtained by the emotion recognition means. This enables optimal vehicle allocation matching according to the user's emotional state, thereby providing a safe and comfortable travel experience.

[1580] The "user information input means" is a means for a user to input basic information and desired conditions.

[1581] The "data receiving means" is a means for receiving information from the user information input means.

[1582] The "analysis means" is a means for analyzing the received user information and extracting the desired conditions of the user.

[1583] A "database" is a storage means for storing information on a plurality of vehicles and drivers.

[1584] The "matching means" is a means for selecting the vehicle and driver that are most suitable for the user based on the desired conditions of the user extracted by the analysis means.

[1585] The "vehicle allocation information transmission means" is a means for transmitting information about the selected vehicle and driver to the user.

[1586] The "route management means" is a means for tracking the location information of the selected vehicle and driver and providing the optimal route.

[1587] The "evaluation data collection means" is a means for receiving evaluations from users and collecting and learning the evaluation data.

[1588] The "emotion recognition means" is a means for recognizing the user's emotional information in real time.

[1589] The "driving mode adjustment means" is a means for adjusting the driving mode during movement based on the emotion information obtained by the emotion recognition means.

[1590] The present invention provides a system that matches the optimal vehicle and driver by inputting a user's basic information and desired conditions, as well as technology that recognizes the user's emotions in real time and adjusts the driving mode based on them.

[1591] Hardware and Software

[1592] Hardware:

[1593] Smartphone or tablet: A device where users can enter basic information and preferences. Emotional information is collected using audio and video input.

[1594] Server: Hardware for performing the following steps: data reception, analysis, matching, route management, evaluation data collection and learning, emotion recognition, and driving mode adjustment.

[1595] software:

[1596] User information input application: An application installed on a smartphone or tablet that allows users to enter basic information and desired conditions.

[1597] EmotionEngine: A software module used as an emotion recognition tool. It analyzes audio and video data to recognize the user's emotions.

[1598] Route Optimization Module: A software module used as a route management tool that calculates the optimal route based on traffic and accident information.

[1599] Processing flow

[1600] User:

[1601] Users open the smartphone application and enter their basic information (e.g., name, age, gender, address), as well as specific travel requests (e.g., "I want to arrive quickly," "No conversation required," "Multilingual support," etc.) into the application.

[1602] The user's audio and video data is also collected through the application and sent to the Emotion Engine.

[1603] server:

[1604] The server receives and analyzes the user's basic information and desired conditions, and extracts the user's desired conditions based on that data.

[1605] The analysis module selects the most suitable vehicle and driver from a database based on the analyzed information.

[1606] The emotion recognition means (Emotion Engine) analyzes the user's emotional information in real time and recognizes the corresponding emotional state.

[1607] The server sends information about the selected vehicle and driver to the user's terminal.

[1608] The route management module takes into account current traffic and accident information, calculates the optimal route and provides it to the vehicle and driver.

[1609] The emotion information is sent to the driving mode adjustment means to adjust the driving mode (e.g., speed, route selection, etc.) during travel.

[1610] After the move is complete, evaluation data from the user is received and used to improve the accuracy of the next match.

[1611] Specific examples

[1612] A specific example will be used to explain this.

[1613] A user inputs conditions such as "I want to arrive quickly" and "No conversation" and sends emotional information (e.g., audio and video data) through the application. The server analyzes this information and selects the self-driving vehicle and driver that best meets the desired conditions. The Emotion Engine recognizes the user's emotional state as "calm" and sets the appropriate driving mode. The route management module calculates the optimal route based on traffic information, providing safe and fast travel.

[1614] In this way, the present invention can provide an optimal travel experience according to the user's individual needs and emotional state.

[1615] Example prompt sentence:

[1616] Username: Yamada

[1617] Age: 40

[1618] Gender: Male

[1619] Address: Osaka Prefecture

[1620] Desired conditions: Arrive early, no conversation

[1621] Audio file: audio_sample.wav

[1622] Video file: video_sample.mp4

[1623] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1624] Step 1:

[1625] Entering user information

[1626] Input: The user launches the smartphone app and enters basic information (name, age, gender, address) and desired conditions (e.g., "I want to arrive quickly," "No conversation required," "Multilingual support," etc.).

[1627] What it does: As the user enters information, the application collects and formats the data for each field in the form.

[1628] Output: User's basic information and preferences are compiled in JSON format.

[1629] Step 2:

[1630] Sending data

[1631] Input: User information and desired conditions collected in the previous step.

[1632] Specific operation: The smartphone application sends the entered information to the server.

[1633] Output: The server receives the user's basic information and preferences.

[1634] Step 3:

[1635] Analysis of user information

[1636] Input: User's basic information and preferences received by the server.

[1637] Specific operation: The analysis module analyzes the received information and extracts the user's desired conditions.

[1638] Output: Extracted desired conditions.

[1639] Step 4:

[1640] Collecting emotional information

[1641] Input: Audio and video data from the user (e.g., "audio_sample.wav" and "video_sample.mp4").

[1642] Specific operation: The smartphone application collects audio and video data and sends it to the emotion recognition module (EmotionEngine).

[1643] Output: Audio and video data forwarded to the emotion recognition module.

[1644] Step 5:

[1645] Emotional information analysis

[1646] Input: Audio and video data collected in the previous step.

[1647] How it works: EmotionEngine analyzes audio and video data to recognize the user's emotional state in real time.

[1648] Output: Perceived emotional state of the user.

[1649] Step 6:

[1650] Vehicle and driver selection

[1651] Input: Extracted desired conditions and perceived emotional states.

[1652] Specific operation: The server refers to a database containing information on multiple vehicles and drivers, and selects the optimal vehicle and driver based on the desired conditions and emotional state.

[1653] Output: Information on the selected vehicle and driver.

[1654] Step 7:

[1655] Sending dispatch information

[1656] Input: Selected vehicle and driver information.

[1657] Specific operation: The server sends the selection information to the user's smartphone application.

[1658] Output: Vehicle and driver information displayed on the user's smartphone.

[1659] Step 8:

[1660] Route Optimization

[1661] Input: Vehicle and driver location and traffic information.

[1662] Specific operation: The server's route management module calculates the optimal route based on the received information.

[1663] Output: Optimized route information.

[1664] Step 9:

[1665] Adjusting the driving mode

[1666] Input: Perceived user emotional state and optimized route information.

[1667] Specific operation: The driving mode adjusting means adjusts the driving mode based on the emotional state to provide a safe and comfortable driving experience.

[1668] Output: Coordinated driving mode.

[1669] Step 10:

[1670] Collecting user ratings

[1671] Input: User feedback after the move is complete (e.g., star rating and comments).

[1672] Specific operation: The smartphone application collects ratings from users and sends them to the server.

[1673] Output: Collected evaluation data on the server.

[1674] Step 11:

[1675] Learning evaluation data

[1676] Input: Collected assessment data.

[1677] Specific operation: The server analyzes the evaluation data and learns to improve matching accuracy next time.

[1678] Output: Improved matching algorithm.

[1679] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1680] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1681] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1682] [Fourth embodiment]

[1683] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1684] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1685] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1686] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1687] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1688] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1689] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1690] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1691] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1692] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1693] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1694] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1695] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1696] The present invention is a system that matches users with the most suitable taxi vehicle and driver based on their basic information and desired conditions. The program processing of this system is explained below in natural language.

[1697] Entering and collecting user data

[1698] User

[1699] Users open the smartphone application and enter basic information such as their name, age, gender, and address.

[1700] The app allows users to input their specific travel needs, such as "I want to arrive quickly," "No conversation required," "Multilingual support," "I want to share a ride," "Car sickness prevention," and "Wheelchair and stroller access."

[1701] Data submission and initial analysis

[1702] Terminal

[1703] The information entered by the user is sent to a server via the Internet.

[1704] server

[1705] The server passes the received user information and requests to an analysis module.

[1706] The analytical module analyzes the user's specific needs and lifestyle and extracts relevant parameters.

[1707] Selection of the best vehicle and driver

[1708] server

[1709] The server checks the database of available taxi vehicles and drivers in real time.

[1710] The system uses data such as each driver's preferred routes, past ratings, and driving quality to select the most suitable driver and vehicle.

[1711] A matching algorithm is used to select the vehicle and driver that best suits the user's requirements.

[1712] Vehicle dispatch proposal

[1713] server

[1714] Information about the matched driver and vehicle is sent to the user's device.

[1715] Terminal

[1716] The user terminal displays driver and vehicle information (e.g., driver's name, vehicle number, estimated arrival time).

[1717] The user reviews and confirms the proposed vehicle and driver.

[1718] Movement initiation and path tracking

[1719] server

[1720] As the driver heads to the user's pickup location, the server tracks the vehicle's location in real time.

[1721] The server calculates the optimal route based on traffic and congestion information and provides it to the driver and user.

[1722] User

[1723] Users can check the driver's arrival on the app and then get in the car.

[1724] While traveling, users can check their route and estimated arrival time through the app.

[1725] Post-move evaluation and feedback

[1726] User

[1727] After completing a trip, users can rate the driver and vehicle through the app, including by leaving a star rating and comments.

[1728] server

[1729] The server passes the collected evaluation data to an analysis module, which then learns to improve matching accuracy for the next time.

[1730] This will improve future matching accuracy and increase user satisfaction.

[1731] Specific examples

[1732] 1. Users

[1733] Tanaka wants to leave for work at 9 a.m. He enters the conditions "I want to travel quickly" and "No conversation" into the app.

[1734] 2. Terminal

[1735] Tanaka's input data is sent to the server.

[1736] 3. Server

[1737] The AI ​​module analyzes Tanaka's request and selects a driver, Yamada, who can provide a quick and quiet trip.

[1738] 4. Server

[1739] Yamada's vehicle information is sent to Tanaka's device.

[1740] 5. Terminal

[1741] Tanaka checks the proposed content and approves it.

[1742] 6. Server

[1743] Gives pickup instructions to Yamada and manages the route.

[1744] 7. Users

[1745] After the trip is completed, Tanaka praises Yamada's driving and enters feedback.

[1746] 8. Server

[1747] The collected evaluation data will be analyzed by AI and used for the next match.

[1748] In this way, AI-powered dispatch systems will provide users with an optimal travel experience tailored to their individual needs, while also helping taxi companies improve their operational efficiency.

[1749] The processing flow will be explained below.

[1750] Step 1: Enter your user information and preferences

[1751] User

[1752] Users open the smartphone application and enter their basic information (e.g., name, age, gender, address).

[1753] Users input specific travel requests (e.g., early arrival, no conversation, multilingual support, ride-sharing, car sickness prevention, wheelchair and stroller accessibility).

[1754] Step 2: Send data

[1755] Terminal

[1756] The terminal sends the basic information and desired conditions entered by the user to the server.

[1757] Step 3: Receiving user information

[1758] server

[1759] The server receives the user information and desired conditions sent from the terminal.

[1760] Step 4: Analyze user information

[1761] server

[1762] The server transfers the received user information to the AI ​​analysis module.

[1763] The AI ​​analysis module extracts the user's desired conditions and generates analysis results.

[1764] Step 5: Vehicle and driver database matching

[1765] server

[1766] The server searches a database of available vehicles and drivers in real time.

[1767] The database stores information such as the driver's strengths and weaknesses in routes, past evaluations, and driving quality.

[1768] Step 6: Generate the best match

[1769] server

[1770] Based on the output of the AI ​​analysis module, the server selects the vehicle and driver that best meets the user's desired conditions.

[1771] A matching algorithm is used to create a final list of selected drivers and vehicles.

[1772] Step 7: Submit your trip

[1773] server

[1774] The server sends information about the selected vehicle and driver, as well as the estimated arrival time, to the user's terminal.

[1775] Step 8: Review the trip offer

[1776] Terminal

[1777] The user's device will display details of the ride offer (e.g., driver's name, vehicle number, estimated arrival time).

[1778] The user checks the proposed vehicle and driver information and presses the confirm button.

[1779] Step 9: Notification of arrival at pickup point

[1780] Terminal

[1781] The user's device is notified that the driver has arrived at the pickup location.

[1782] Users can check the driver's arrival via the app.

[1783] Step 10: Route Management and Tracking

[1784] server

[1785] The server tracks the vehicle's location in real time and calculates the optimal route based on traffic and congestion information.

[1786] The calculated optimal route is notified to the driver and user.

[1787] Step 11: Notification of completion of move

[1788] server

[1789] When the trip is complete, the server receives a notification from the driver that the trip is complete and notifies the user.

[1790] Step 12: Enter your rating

[1791] User

[1792] After completing the trip, users can rate the driver and vehicle through the app (e.g., star rating, comments).

[1793] Step 13: Collect evaluation data

[1794] server

[1795] The server transfers user evaluation data to an analysis module, which learns from it to help improve the matching process next time.

[1796] Example 1

[1797] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1798] In modern society, it is difficult to quickly and accurately match the optimal means of transportation and operators that meet the diverse needs of users, and there is a lack of mechanisms to respond to immediate and personalized requests. Furthermore, conventional systems have difficulty effectively utilizing collected evaluation data to improve the accuracy of the next match, which limits the ability to continuously improve the quality of the user experience.

[1799] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1800] In this invention, the server includes an information input means for a user to input basic information and desired conditions, a data receiving means for receiving information from the information input means, and an analysis means for analyzing the received user information and extracting the user's desired conditions. This enables quick and accurate matching of optimal transportation means and operators according to the user's diverse needs. Furthermore, by using a generative AI model to collect and learn evaluation data, the accuracy of the next match can be improved, and the quality of the user experience can be continuously improved.

[1801] The "information input means" is a means for the user to input basic information and desired conditions.

[1802] The "data receiving means" is a means for receiving information from the information input means.

[1803] The "analysis means" is a means for analyzing the received user information and extracting the user's desired conditions.

[1804] "Database" means data storage for storing multiple modes of transportation and operator information.

[1805] The "matching means" is a means for selecting the most suitable means of transportation and operator for the user based on the user's desired conditions extracted by the analysis means.

[1806] The "vehicle dispatch information transmission means" is a means for transmitting information about the selected transportation means and the driver to the user.

[1807] The "route management means" is a means for tracking the location information of the selected means of transportation and the operator, and providing the optimal route.

[1808] The "rating data collection means" is a means for receiving ratings from users and collecting and learning rating data.

[1809] A "generative AI model" is an artificial intelligence technology that analyzes and learns from evaluation data to improve matching accuracy the next time.

[1810] This invention is a system that matches the optimal means of transportation and operator based on the user's input of basic information and desired conditions. This system is composed of components of a server, a terminal, and a user, and is specifically implemented as follows.

[1811] A user opens a smartphone application and enters basic information such as name, age, gender, and address. For example, the user enters "Ichiro Tanaka" as the user name, "30 years old," "male," and "Shinjuku-ku, Tokyo" as the address. The user also enters specific travel requests. For example, from options such as "Arrive quickly," "No conversation," "Multilingual support," "Prefer carpooling," "Car sickness prevention," and "Wheelchair and stroller access," the user selects "Arrive quickly" and "No conversation."

[1812] The terminal automatically transmits the information entered by the user to the server via the Internet, at which point the terminal waits for a response to confirm that the data transmission was successful.

[1813] The server passes the user information and desired conditions received from the device to a dedicated analysis module. The analysis module analyzes the user's desired conditions and lifestyle and extracts relevant parameters. For example, if the user requests a "quick arrival," the server will issue instructions to prioritize the selection of drivers who can provide a fast route.

[1814] The server compares a database of available transportation methods and drivers in real time. The database includes each driver's location information, preferred routes, past evaluations, etc. The server narrows down the candidates based on each driver's preferred routes, past evaluations, and driving quality. For example, driver Taro Yamada is good at speedy driving, which meets the condition of "I want to arrive quickly." A matching algorithm is used to select the driver and transportation method that best suits the user's requirements. In this process, a generative AI model is used to analyze and learn from the evaluation data.

[1815] The server sends information about the selected driver and transportation means to the user's terminal. The information sent includes the driver's name, transportation means number, and estimated arrival time. The driver and transportation means information is displayed on the user's terminal. For example, it may show "Driver's name: Yamada Taro, Transportation means number: AB-1234, Estimated arrival time: 9:00." The user confirms the information presented and presses the "Confirm" button to indicate their consent.

[1816] As the driver heads to the user's pickup point, the server tracks the vehicle's location in real time. The driver's current location is monitored using GPS data. The server calculates the optimal route based on traffic and congestion information. This information is provided to the driver and user in real time. The user checks the driver's arrival in the app and waits at the specified pickup point. A notification is sent when the driver approaches the arrival point. The user gets into the driver's car and begins traveling. During the trip, the user can check the route and estimated arrival time through the app.

[1817] After completing a trip, the user can rate the driver and the means of transportation through the app. For example, they could rate the driver 5 stars and comment that the ride was very quick and quiet. The server then passes the collected rating data to an analysis module, which uses the data to learn how to improve the accuracy of the next match. The rating data is then analyzed by a generative AI model, which updates the driver's rating score. This improves future matching accuracy and continuously improves the quality of the user experience.

[1818] This system allows users to enjoy an optimal travel experience tailored to their individual needs, and also enables taxi companies and drivers to improve the quality of their services. Examples of prompt sentences include "Mr. Tanaka wishes to travel quickly," and "Mr. Yamada is good at driving quickly."

[1819] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1820] Step 1: Entering User Data

[1821] The user opens the smartphone application and enters basic information such as name, age, gender, and address, as well as desired conditions for travel. The specific data entered includes the user name "Tanaka Ichiro," age "30," gender "male," address "Shinjuku Ward, Tokyo," and desired conditions "arrive quickly" and "no conversation." This information is stored on the device as input data (basic information and desired conditions).

[1822] Step 2: Send data

[1823] The terminal sends the information entered by the user to the server via the Internet. The input is the user data stored in the previous step, and the output is sent to the server. At this time, the terminal waits for a response to confirm the successful data transmission.

[1824] Step 3: Initial analysis

[1825] The server passes the user data received from the device to the analysis module. The received data consists of the user's basic information and desired conditions. The analysis module analyzes the user's desired conditions and lifestyle and extracts related parameters. For example, based on the desired condition of "I want to arrive quickly," the module prepares to find a driver who can provide a fast route. The output is the extracted analysis results (parameters related to the user's desired conditions).

[1826] Step 4: Select the best transportation and driver

[1827] The server compares the analysis results with a database of available means of transportation and drivers in real time. The input data are the analysis results and database information. The server narrows down the most suitable candidates based on data such as each driver's location information, preferred routes, and past evaluations. For example, it confirms that driver "Yamada Taro" is good at speedy driving, which meets the condition of "wanting to arrive quickly." The output is the selection result of the most suitable means of transportation and driver.

[1828] Step 5: Ride proposal

[1829] The server sends the information of the selected driver and transportation means to the user's terminal. The input is the information of the selected driver and transportation means, and the output is the dispatch information (driver's name, transportation means number, estimated arrival time) sent to the user's terminal.

[1830] Step 6: View and confirm your trip information

[1831] The terminal receives the dispatch information from the server and displays it to the user. The displayed information is "Driver's name: Yamada Taro, Transportation number: AB-1234, Estimated arrival time: 9:00." The user confirms the information presented and presses the "Confirm" button to accept it. The input is the dispatch information from the server, and the output is the user's confirmation and confirmation action.

[1832] Step 7: Path tracing

[1833] The server tracks the vehicle's location in real time as the driver heads to the user's pickup point. The input is GPS data and traffic information, and the output is the calculation of the optimal route. The server calculates the optimal route based on traffic and congestion information and provides this information to the driver and user.

[1834] Step 8: Driver arrives and begins travel

[1835] The user checks the driver's arrival through the app. A notification is sent when the user approaches the destination. The user waits at the designated pickup point, gets into the driver's car, and begins their journey. During the journey, the user can check the route and estimated arrival time through the app. The input is the arrival notification and route information from the server, and the output is the user's journey start and route confirmation.

[1836] Step 9: Post-move evaluation and feedback

[1837] After completing the trip, the user evaluates the driver and the means of transportation through the app. For example, the user might enter "star rating: 5, comment: The drive was very quick and quiet." The input is the user's evaluation data, and the output is the transmission of the evaluation data to the server.

[1838] Step 10: Collect evaluation data and learn

[1839] The server passes the collected evaluation data to an analysis module, which then performs learning to improve the accuracy of the next match. The input is the user's evaluation data, and the output is the pilot's evaluation score analyzed and updated by the generative AI model. This improves future matching accuracy and continuously improves the quality of the user experience.

[1840] (Application example 1)

[1841] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1842] Conventional taxi dispatch systems have difficulty reflecting detailed user preferences and providing services that meet some customization requests. In particular, they lack accuracy in providing specific travel requests and optimal route guidance, leaving users wanting more. Similar challenges exist in the food delivery field, where it is difficult to efficiently select the optimal delivery person and route.

[1843] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1844] In this invention, the server includes: a user information input means for a user to input basic information and desired conditions; a data receiving means for receiving information from the user information input means; an analysis means for analyzing the received user information and extracting the user's desired conditions; a database storing information on multiple vehicles and drivers; a matching means for selecting the most suitable vehicle and driver for the user based on the user's desired conditions extracted by the analysis means; a dispatch information sending means for sending information on the selected vehicle and driver to the user; a route management means for tracking the location information of the selected vehicle and driver and providing the optimal route; an evaluation data collection means for receiving evaluations from users and collecting and learning the evaluation data; and a means for selecting the most suitable delivery person and route based on the basic information and desired conditions input by the user. This makes it possible to provide an optimal service that meets the user's detailed desired conditions and specific requests.

[1845] "User information input means" refers to a device or function that allows a user to input basic information and desired conditions.

[1846] The "data receiving means" is a device or function that receives information from the user information input means.

[1847] The "analysis means" is a device or function that analyzes the received user information and extracts the user's desired conditions.

[1848] A "database" is a storage device for storing information on a plurality of vehicles and drivers.

[1849] The "matching means" is a device or function that selects the most suitable vehicle and driver based on the user's desired conditions extracted by the analysis means.

[1850] The "vehicle dispatch information transmission means" is a device or function that transmits information about the selected vehicle and driver to the user.

[1851] The "route management means" is a device or function that tracks the location information of the selected vehicle and driver and provides the optimal route.

[1852] The "evaluation data collection means" is a device or function that receives evaluations from users and collects and learns evaluation data.

[1853] A "delivery staff member" is a person who delivers items along the optimal route based on the basic information and desired conditions entered by the user.

[1854] An "optimal route" is the best travel route calculated taking into account traffic conditions and the user's desired conditions.

[1855]

[1856] The system for implementing this invention includes a user information input means through which the user inputs basic information and desired conditions. This information is input via a mobile device such as a smartphone. Specifically, the user inputs their name, age, address, and desired conditions for travel (e.g., conditions such as "I want to arrive quickly," "No conversation," and "I want contactless delivery").

[1857] The entered data is sent to a server via the Internet. The server has a data receiving means for receiving this data. The received user information is analyzed using an analysis means, and the user's desired conditions are extracted. This analysis means includes an AI module using Python, which efficiently analyzes user information.

[1858] The analyzed data is compared with a database that stores information on multiple vehicles and drivers. The server's database contains detailed information on each driver, such as their preferred routes, past ratings, and driving quality. The server also has a matching means for selecting the optimal vehicle and driver. This matching means selects the vehicle and driver that best meets the user's desired conditions.

[1859] The selected vehicle and driver information will be sent to the user's terminal via the vehicle dispatch information sending means. The user will then confirm and confirm the proposed vehicle and driver information (e.g., driver's name, vehicle license plate number, estimated arrival time).

[1860] The server then tracks the location information of the vehicle and the driver using a route management means, which calculates the optimal route taking into account traffic and congestion information.

[1861] After completing a delivery, the user can rate the driver and vehicle via their device. This rating is in the form of a star rating or a comment, and is sent to the server via a data collection tool. The server then uses AI to analyze the collected rating data and use it to improve the accuracy of the next match.

[1862] For example, the user inputs requests such as "I want to arrive quickly" or "I want contactless delivery." This information is sent to the server, and the AI ​​module analyzes it, selecting the most suitable driver to deliver quickly and contactlessly. This driver's information is sent to the user's device, and the user confirms it, and the delivery begins. Below is an example of a prompt:

[1863] User: Name: Taro Tanaka, Age: 30, Address: Shibuya-ku, Tokyo

[1864] Desired conditions: Delivery by 12:00, no-contact delivery, early arrival

[1865] This will enable us to provide optimal services that meet the user's detailed requirements and specific requests.

[1866] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1867] Step 1:

[1868] The user starts the smartphone application and inputs their name, age, address, and desired conditions. Specific desired conditions include "quick arrival" and "contactless delivery." This input information is sent to the server by the data receiving means.

[1869] Input: User's basic information and desired conditions

[1870] Output: Data sent to the data receiver

[1871] Step 2:

[1872] The server's data receiving means acquires the received user information and passes it to the analyzing means, which extracts the user's desired conditions from the received data.

[1873] Input: Data sent to the data receiving means

[1874] Output: Extracted user preferences

[1875] Step 3:

[1876] The server's analytical means uses a generative AI model using Python to analyze the user's input data, and this data analysis extracts the user's desired conditions.

[1877] Input: Received user information

[1878] Output: Parsed desired conditions

[1879] Step 4:

[1880] The server's matching means selects the optimal delivery person and route from a database based on the desired conditions obtained from the analysis means. This database stores information such as each driver's preferred routes, past evaluations, and driving quality.

[1881] Input: Parsed desired conditions

[1882] Output: Selection of optimal delivery personnel and route

[1883] Step 5:

[1884] The server's dispatch information transmission means transmits the selected delivery person and route information to the user terminal, and the user confirms and approves the received information.

[1885] Input: Selection of optimal delivery personnel and route

[1886] Output: Information sent to the user's terminal

[1887] Step 6:

[1888] The server's route management means tracks the location information of selected drivers and delivery routes in real time and provides the optimal route, using an algorithm that takes into account traffic and accident information to optimize the route.

[1889] Input: Selected driver and delivery route information

[1890] Output: Optimal route information

[1891] Step 7:

[1892] After the delivery is completed, the user can rate the driver and the vehicle through the smartphone application, which will then be sent to the server's rating data collection means.

[1893] Input: User rating data

[1894] Output: Rating data sent to the server

[1895] Step 8:

[1896] The server's evaluation data collection means analyzes the collected evaluation data and trains the generative AI model to improve matching accuracy next time. This continuous learning process improves the quality of service and increases user satisfaction.

[1897] Input: User-submitted rating data

[1898] Output: The learning results of the generative AI model

[1899] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1900] The present invention is a system that combines a system that matches the optimal vehicle and driver by inputting the user's basic information and desired conditions with an emotion engine that recognizes and processes the user's emotions. Below, the program processing of this system is specifically explained in natural language.

[1901] Entering and collecting user data

[1902] User

[1903] Users open the smartphone application and enter their basic information (e.g., name, age, gender, address).

[1904] The app allows users to input their specific travel needs, such as "I want to arrive quickly," "No conversation required," "Multilingual support," "I want to share a ride," "Car sickness prevention," and "Wheelchair and stroller access."

[1905] Data submission and initial analysis

[1906] Terminal

[1907] The information entered by the user is sent to a server via the Internet.

[1908] server

[1909] The server passes the received user information and requests to the analysis module and emotion engine.

[1910] The analytical module analyzes the user's specific needs and lifestyle and extracts relevant parameters.

[1911] At the same time, the emotion engine recognizes the user's emotional state based on user input data and real-time interactions (e.g., voice, facial expressions, text).

[1912] Selection of the best vehicle and driver

[1913] server

[1914] The server checks the database of available taxi vehicles and drivers in real time.

[1915] The system uses data such as each driver's preferred routes, past ratings, and driving quality to select the most suitable driver and vehicle.

[1916] It also incorporates the output of the emotion engine to select the vehicle and driver that best suits the user's current emotional state.

[1917] Vehicle dispatch proposal

[1918] server

[1919] Information about the matched driver and vehicle is sent to the user's device.

[1920] Terminal

[1921] The user terminal displays driver and vehicle information (e.g., driver's name, vehicle number, estimated arrival time).

[1922] The user checks the proposed vehicle and driver information and presses the confirm button.

[1923] Movement initiation and path tracking

[1924] server

[1925] As the driver heads to the user's pickup location, the server tracks the vehicle's location in real time.

[1926] The server calculates the optimal route based on traffic and congestion information and provides it to the driver and user.

[1927] User

[1928] Users can check the driver's arrival on the app and then get in the car.

[1929] While traveling, users can check their route and estimated arrival time through the app.

[1930] Emotion Monitoring and Alerts

[1931] server

[1932] The emotion engine monitors the user's emotional state in real time while on the move.

[1933] If the server detects any emotion indicative of anxiety or discomfort from the user, it will provide warnings and guidance to the driver.

[1934] Post-move evaluation and feedback

[1935] User

[1936] After completing a trip, users can rate the driver and vehicle through the app, including by leaving a star rating and comments.

[1937] server

[1938] The server passes the collected evaluation data and emotion data to an analysis module, which then performs learning to improve matching accuracy for the next time.

[1939] This will improve future matching accuracy and increase user satisfaction.

[1940] Specific examples

[1941] 1. Users

[1942] Tanaka wants to leave for work at 9 a.m. He enters the conditions "I want to travel quickly" and "No conversation" into the app.

[1943] 2. Terminal

[1944] Tanaka's input data is sent to the server.

[1945] 3. Server

[1946] The analysis module analyzes Tanaka's desired conditions, and the emotion engine evaluates Tanaka's current emotional state. A driver, Yamada, who can provide a fast, quiet, and reassuring ride is selected.

[1947] 4. Server

[1948] Yamada's vehicle information is sent to Tanaka's device.

[1949] 5. Terminal

[1950] Tanaka checks the proposed content and approves it.

[1951] 6. Server

[1952] It issues pickup instructions to Yamada and manages the route. The emotion engine monitors Tanaka's emotional state and sends a warning to Yamada if anxiety is detected.

[1953] 7. Users

[1954] After the trip is completed, Tanaka praises Yamada's driving and enters feedback.

[1955] 8. Server

[1956] The collected evaluation and emotional data is analyzed by AI to help improve the accuracy of the next match.

[1957] In this way, a dispatch system that combines an emotion engine can provide an optimal travel experience tailored to the individual needs and emotional state of the user, while also helping to improve the operational efficiency of taxi companies.

[1958] The processing flow will be explained below.

[1959] Step 1: Enter your user information and preferences

[1960] User

[1961] Users open the smartphone application and enter their basic information (e.g., name, age, gender, address).

[1962] Users input specific travel requests (e.g., early arrival, no conversation, multilingual support, ride-sharing, car sickness prevention, wheelchair and stroller accessibility).

[1963] Step 2: Submit your user information and preferences

[1964] Terminal

[1965] The terminal sends the basic information and desired conditions entered by the user to the server.

[1966] Step 3: Receiving and analyzing user information

[1967] server

[1968] The server receives the user information and desired conditions sent from the terminal.

[1969] The server uses an AI analysis module to analyze the user's desired conditions and extract relevant parameters.

[1970] Step 4: Emotional state analysis by the emotion engine

[1971] server

[1972] The emotion engine in the server analyzes the user's input data and real-time interactions (e.g., voice, facial expressions, text) to assess the user's emotional state.

[1973] Step 5: Vehicle and driver database matching

[1974] server

[1975] The server searches a database of available vehicles and drivers in real time.

[1976] The database contains information such as the driver's preferred routes, past ratings, and driving quality.

[1977] Step 6: Selecting the best vehicle and driver

[1978] server

[1979] Based on the output of the AI ​​analysis module and the evaluation of the emotion engine, the server selects the vehicle and driver that best suits the user's desired conditions and emotional state.

[1980] Step 7: Submit your trip

[1981] server

[1982] The server sends information about the selected vehicle and driver, as well as the estimated arrival time, to the user's terminal.

[1983] Step 8: Review the trip offer

[1984] Terminal

[1985] The user's device will display details of the ride offer (e.g., driver's name, vehicle number, estimated arrival time).

[1986] The user checks the proposed vehicle and driver information and presses the confirm button.

[1987] Step 9: Notification of arrival at pickup point

[1988] Terminal

[1989] The user's device is notified that the driver has arrived at the pickup location.

[1990] Users can check the driver's arrival via the app.

[1991] Step 10: Route Management and Tracking

[1992] server

[1993] The server tracks the vehicle's location in real time and calculates the optimal route based on traffic and congestion information.

[1994] The calculated optimal route is notified to the driver and user.

[1995] Step 11: Emotion Monitoring and Alerts

[1996] server

[1997] The emotion engine monitors the user's emotional state in real time while on the move.

[1998] If the server detects any emotion indicative of anxiety or discomfort from the user, it will provide warnings and guidance to the driver.

[1999] Step 12: Notification of completion of move

[2000] server

[2001] When the trip is complete, the server receives a notification from the driver that the trip is complete and notifies the user.

[2002] Step 13: Enter your rating

[2003] User

[2004] After completing the trip, users can rate the driver and vehicle through the app (e.g., star rating, comments).

[2005] Step 14: Collect and analyze assessment data

[2006] server

[2007] The server transfers user evaluation and sentiment data to an analysis module, which learns from the data to help improve the matching process next time.

[2008] Example 2

[2009] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[2010] In modern mobility services, it is important to select a vehicle and driver that takes into account the user's basic information and desired conditions. Additionally, there is a need for a system that can recognize the user's emotional state in real time and respond appropriately. However, conventional systems have difficulty matching users while taking their emotions into account, limiting their ability to improve satisfaction. Furthermore, they are insufficient in utilizing real-time emotion monitoring and evaluation data. To address these issues, the present invention aims to provide a system that selects the optimal vehicle and driver, taking into account the user's emotional state.

[2011] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[2012] In this invention, the server includes: a user information input means for a user to input basic information and desired conditions; a data receiving means for receiving information from the user information input means; an analysis means for analyzing the received user information and extracting the user's desired conditions; an emotion recognition means for recognizing and processing the user's emotional state; a database storing multiple vehicle and driver information; a matching means for selecting the optimal vehicle and driver for the user based on the user's desired conditions and emotional state extracted by the analysis means and the emotion recognition means; a vehicle dispatch information sending means for sending information on the selected vehicle and driver to the user; a route management means for tracking the location information of the selected vehicle and driver and providing the optimal route; and an evaluation data collection means for receiving evaluations from users and collecting and learning the evaluation data. This enables the optimal vehicle and driver to be matched taking the user's emotional state into consideration, thereby improving user satisfaction.

[2013] "User information input means" refers to the device or software functions that allow users to input their own basic information and desired conditions.

[2014] "Data receiving means" refers to a function for receiving information sent from the user information input means.

[2015] "Analysis means" refers to a function for analyzing received user information and extracting the user's desired conditions.

[2016] "Emotion recognizer" refers to technology or software that recognizes and processes a user's emotional state.

[2017] "Database" refers to a data storage system that stores and retrieves information about multiple vehicles and drivers.

[2018] The "matching means" refers to a function that selects the most suitable vehicle and driver based on the user's desired conditions and emotional state extracted by the analysis means and emotion recognition means.

[2019] "Vehicle dispatch information transmission means" refers to a function for transmitting information about the selected vehicle and driver to the user.

[2020] "Route management means" refers to a function for tracking the location information of selected vehicles and drivers, and providing the optimal route taking into account traffic and obstacle information.

[2021] "Evaluation data collection means" refers to a function for receiving, collecting, and learning from evaluations from users.

[2022] "Vehicle" refers to a means of transportation for the purpose of moving a user, and examples include taxis and buses.

[2023] "Driver" means a person who drives a vehicle for the purpose of transporting users.

[2024] This invention is a system that matches the optimal vehicle and driver by inputting a user's basic information and desired conditions, and also includes an emotion engine that recognizes and processes the user's emotions in real time. Specific embodiments of this system are described below.

[2025] System configuration

[2026] The system includes the following components:

[2027] User information input method

[2028] Data Receiving Method

[2029] Analysis means

[2030] emotion recognition means

[2031] A database that stores vehicle and driver information

[2032] Matching Method

[2033] Vehicle dispatch information transmission means

[2034] Route Management Method

[2035] Evaluation data collection method

[2036] Hardware and Software Configuration

[2037] User

[2038] Using a smartphone application, users enter basic information and desired conditions, such as name, age, gender, and address, as well as detailed desired conditions such as "quick arrival," "no conversation required," and "multilingual support."

[2039] Terminal

[2040] It receives the information entered by the user and sends it to a server over the Internet, where the data is typically transmitted securely using the HTTPS protocol.

[2041] server

[2042] It works with a database and processes the received data using analytical and emotion recognition techniques, using machine learning and image processing libraries such as Python, TensorFlow, and OpenCV as specific software technologies.

[2043] The analysis means analyzes the data input by the user and extracts specific parameters. For example, if the user inputs a desire to arrive early, parameters based on this desire are extracted.

[2044] The emotion recognition means analyzes real-time data such as voice and facial expressions to evaluate the user's emotional state. For example, if the user has an anxious expression, the emotional state is recognized.

[2045] The matching means selects the most suitable vehicle and driver based on data from the analysis means and emotion recognition means, using an algorithm that references the driver's preferred routes and past evaluation data available in real time.

[2046] The vehicle dispatch information transmitting means transmits information about the most suitable vehicle and driver to the user's terminal, which displays information such as the driver's name, vehicle number, and estimated arrival time.

[2047] The route management tool tracks the location of selected vehicles in real time using GPS data and calculates the optimal route, taking into account traffic and obstacle information.

[2048] The evaluation data collection means collects evaluation data from users after the movement, and the collected data is used to improve the accuracy of the next matching.

[2049] Specific examples

[2050] 1. Users

[2051] To leave for work at 9 a.m., the user enters the conditions "I want to arrive early" and "No conversation" into the smartphone application.

[2052] 2. Terminal

[2053] Sends user input data to the server.

[2054] 3. Server

[2055] The analysis means analyzes the user's desired conditions, and the emotion recognition means evaluates the user's emotional state (e.g., no anxiety). Based on the analysis results and the emotional state, a driver who can provide a sense of security and speed is selected.

[2056] 4. Server

[2057] The driver's vehicle information is sent to the user's device.

[2058] 5. Users

[2059] The user reviews and accepts the proposed content.

[2060] 6. Server

[2061] The system issues dispatch instructions to the driver and manages routes. The emotion recognition system monitors the user's emotional state while traveling and sends warnings to the driver as necessary.

[2062] Prompt Sentence Examples

[2063] "I want to arrive early at 9 a.m. I'm looking for a driver who can comfortably travel without conversation."

[2064] "Designing a system to select the optimal driver and vehicle based on the user's emotional state"

[2065] As described above, the system starts with the user's basic information and desired conditions, recognizes their emotional state, selects the most suitable vehicle and driver, and performs real-time tracking and route management to provide a comfortable and safe travel experience.

[2066] The flow of the identification process in the second embodiment will be described with reference to FIG.

[2067] System program processing flow

[2068] Step 1:

[2069] User

[2070] Users open the smartphone application and enter basic information (name, age, gender, address), followed by their desired travel conditions (e.g., "I want to arrive quickly," "No conversation required," "Multilingual support," "I want to share a ride," etc.).

[2071] Input: Basic information and desired conditions

[2072] Output: Basic information and desired conditions entered

[2073] Step 2:

[2074] Terminal

[2075] The device receives the basic information and desired conditions entered by the user and sends it to a server via the Internet, using the HTTPS protocol to ensure data security.

[2076] Input: Basic information and desired conditions

[2077] Output: User's basic information and preferences sent to the server

[2078] Step 3:

[2079] server

[2080] The server passes the received user information to an analysis module, which extracts parameters related to the user's specific preferences and lifestyle, using natural language processing (NLP) technology.

[2081] Input: Received basic information and desired conditions

[2082] Output: Extracted parameters

[2083] Step 4:

[2084] server

[2085] At the same time, the server uses emotion recognition means to analyze the user's emotional state, using technologies such as voice analysis and facial expression analysis, and determines the user's current emotional state based on real-time data.

[2086] Input: Real-time user voice and facial expression data

[2087] Output: Perceived emotional state

[2088] Step 5:

[2089] server

[2090] The server accesses the database based on data from the analysis and emotion recognition methods to select the most suitable vehicle and driver, using a machine learning algorithm to take into account the driver's past evaluations, preferred routes, driving quality, and other factors.

[2091] Input: extracted parameters and recognized emotional state

[2092] Output: Optimal vehicle and driver selection results

[2093] Step 6:

[2094] server

[2095] The server sends the selected vehicle and driver information to the user's terminal, including the driver's name, vehicle license plate number, estimated arrival time, etc.

[2096] Input: Optimal vehicle and driver selection results

[2097] Output: Vehicle and driver information sent to the user device

[2098] Step 7:

[2099] User

[2100] The user checks the vehicle and driver information displayed on the terminal and presses the OK button, which confirms the vehicle dispatch.

[2101] Input: Submitted vehicle and driver information

[2102] Output: Confirmation of dispatch

[2103] Step 8:

[2104] server

[2105] The server issues pickup instructions to the driver, tracks the vehicle's location in real time, calculates the optimal route taking into account traffic and obstacle information, and provides it to the driver and user.

[2106] Input: Trip confirmation and real-time GPS data

[2107] Output: Optimal route and location information

[2108] Step 9:

[2109] User

[2110] Users can check the driver's arrival time and get in the vehicle through the app. During the journey, users can check the route and estimated arrival time through the app.

[2111] Input: Real-time location

[2112] Output: Arrival confirmation and route information

[2113] Step 10:

[2114] server

[2115] The emotion recognition means monitors the user's emotional state while driving, and if the user shows signs of anxiety or discomfort, the server will provide warnings and guidance to the driver.

[2116] Input: Real-time user sentiment data

[2117] Output: Warnings and guidance to the driver

[2118] Step 11:

[2119] User

[2120] After completing a trip, users can rate the driver and vehicle through the app, in the form of a star rating and comments.

[2121] Input: Evaluation data after movement completion

[2122] Output: Collected evaluation data

[2123] Step 12:

[2124] server

[2125] The server analyzes the collected evaluation and emotion data and learns to improve matching accuracy for the next time, thereby strengthening the machine learning algorithm and increasing user satisfaction.

[2126] Input: Rating and sentiment data

[2127] Output: Improved matching algorithm

[2128] Through the above processing steps, the system achieves optimal vehicle and driver matching, taking into account the user's basic information, desired conditions, and even emotional state.

[2129] (Application example 2)

[2130] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[2131] While conventional ride-hailing systems allow users to input their basic information and desired conditions, they do not take into account the user's emotional state when matching rides or monitor their emotions in real time while driving, limiting the improvement of the user experience. Furthermore, when using autonomous vehicles, the driving mode cannot be adjusted to match the user's emotional state while traveling, leaving issues in terms of safety and comfort.

[2132] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[2133] In this invention, the server includes information input means for a user to input basic information and desired conditions, data receiving means for receiving information from the information input means, analysis means for analyzing the received user information and extracting the user's desired conditions, a database storing multiple vehicle and driver information, matching means for selecting the vehicle and driver most suitable for the user based on the user's desired conditions extracted by the analysis means, vehicle allocation information sending means for sending information on the selected vehicle and driver to the user, route management means for tracking the location information of the selected vehicle and driver and providing the optimal route, evaluation data collection means for receiving evaluations from users and collecting and learning the evaluation data, emotion recognition means for recognizing user emotion information in real time, and driving mode adjustment means for adjusting the driving mode during travel based on the emotion information obtained by the emotion recognition means. This enables optimal vehicle allocation matching according to the user's emotional state, thereby providing a safe and comfortable travel experience.

[2134] The "user information input means" is a means for a user to input basic information and desired conditions.

[2135] The "data receiving means" is a means for receiving information from the user information input means.

[2136] The "analysis means" is a means for analyzing the received user information and extracting the desired conditions of the user.

[2137] A "database" is a storage means for storing information on a plurality of vehicles and drivers.

[2138] The "matching means" is a means for selecting the vehicle and driver that are most suitable for the user based on the desired conditions of the user extracted by the analysis means.

[2139] The "vehicle allocation information transmission means" is a means for transmitting information about the selected vehicle and driver to the user.

[2140] The "route management means" is a means for tracking the location information of the selected vehicle and driver and providing the optimal route.

[2141] The "evaluation data collection means" is a means for receiving evaluations from users and collecting and learning the evaluation data.

[2142] The "emotion recognition means" is a means for recognizing the user's emotional information in real time.

[2143] The "driving mode adjustment means" is a means for adjusting the driving mode during movement based on the emotion information obtained by the emotion recognition means.

[2144] The present invention provides a system that matches the optimal vehicle and driver by inputting a user's basic information and desired conditions, as well as technology that recognizes the user's emotions in real time and adjusts the driving mode based on them.

[2145] Hardware and Software

[2146] Hardware:

[2147] Smartphone or tablet: A device where users can enter basic information and preferences. Emotional information is collected using audio and video input.

[2148] Server: Hardware for performing the following steps: data reception, analysis, matching, route management, evaluation data collection and learning, emotion recognition, and driving mode adjustment.

[2149] software:

[2150] User information input application: An application installed on a smartphone or tablet that allows users to enter basic information and desired conditions.

[2151] EmotionEngine: A software module used as an emotion recognition tool. It analyzes audio and video data to recognize the user's emotions.

[2152] Route Optimization Module: A software module used as a route management tool that calculates the optimal route based on traffic and accident information.

[2153] Processing flow

[2154] User:

[2155] Users open the smartphone application and enter their basic information (e.g., name, age, gender, address), as well as specific travel requests (e.g., "I want to arrive quickly," "No conversation required," "Multilingual support," etc.) into the application.

[2156] The user's audio and video data is also collected through the application and sent to the Emotion Engine.

[2157] server:

[2158] The server receives and analyzes the user's basic information and desired conditions, and extracts the user's desired conditions based on that data.

[2159] The analysis module selects the most suitable vehicle and driver from a database based on the analyzed information.

[2160] The emotion recognition means (Emotion Engine) analyzes the user's emotional information in real time and recognizes the corresponding emotional state.

[2161] The server sends information about the selected vehicle and driver to the user's terminal.

[2162] The route management module takes into account current traffic and accident information, calculates the optimal route and provides it to the vehicle and driver.

[2163] The emotion information is sent to the driving mode adjustment means to adjust the driving mode (e.g., speed, route selection, etc.) during travel.

[2164] After the move is complete, evaluation data from the user is received and used to improve the accuracy of the next match.

[2165] Specific examples

[2166] A specific example will be used to explain this.

[2167] A user inputs conditions such as "I want to arrive quickly" and "No conversation" and sends emotional information (e.g., audio and video data) through the application. The server analyzes this information and selects the self-driving vehicle and driver that best meets the desired conditions. The Emotion Engine recognizes the user's emotional state as "calm" and sets the appropriate driving mode. The route management module calculates the optimal route based on traffic information, providing safe and fast travel.

[2168] In this way, the present invention can provide an optimal travel experience according to the user's individual needs and emotional state.

[2169] Example prompt sentence:

[2170] Username: Yamada

[2171] Age: 40

[2172] Gender: Male

[2173] Address: Osaka Prefecture

[2174] Desired conditions: Arrive early, no conversation

[2175] Audio file: audio_sample.wav

[2176] Video file: video_sample.mp4

[2177] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[2178] Step 1:

[2179] Entering user information

[2180] Input: The user launches the smartphone app and enters basic information (name, age, gender, address) and desired conditions (e.g., "I want to arrive quickly," "No conversation required," "Multilingual support," etc.).

[2181] What it does: As the user enters information, the application collects and formats the data for each field in the form.

[2182] Output: User's basic information and preferences are compiled in JSON format.

[2183] Step 2:

[2184] Sending data

[2185] Input: User information and desired conditions collected in the previous step.

[2186] Specific operation: The smartphone application sends the entered information to the server.

[2187] Output: The server receives the user's basic information and preferences.

[2188] Step 3:

[2189] Analysis of user information

[2190] Input: User's basic information and preferences received by the server.

[2191] Specific operation: The analysis module analyzes the received information and extracts the user's desired conditions.

[2192] Output: Extracted desired conditions.

[2193] Step 4:

[2194] Collecting emotional information

[2195] Input: Audio and video data from the user (e.g., "audio_sample.wav" and "video_sample.mp4").

[2196] Specific operation: The smartphone application collects audio and video data and sends it to the emotion recognition module (EmotionEngine).

[2197] Output: Audio and video data forwarded to the emotion recognition module.

[2198] Step 5:

[2199] Emotional information analysis

[2200] Input: Audio and video data collected in the previous step.

[2201] How it works: EmotionEngine analyzes audio and video data to recognize the user's emotional state in real time.

[2202] Output: Perceived emotional state of the user.

[2203] Step 6:

[2204] Vehicle and driver selection

[2205] Input: Extracted desired conditions and perceived emotional states.

[2206] Specific operation: The server refers to a database containing information on multiple vehicles and drivers, and selects the optimal vehicle and driver based on the desired conditions and emotional state.

[2207] Output: Information on the selected vehicle and driver.

[2208] Step 7:

[2209] Sending dispatch information

[2210] Input: Selected vehicle and driver information.

[2211] Specific operation: The server sends the selection information to the user's smartphone application.

[2212] Output: Vehicle and driver information displayed on the user's smartphone.

[2213] Step 8:

[2214] Route Optimization

[2215] Input: Vehicle and driver location and traffic information.

[2216] Specific operation: The server's route management module calculates the optimal route based on the received information.

[2217] Output: Optimized route information.

[2218] Step 9:

[2219] Adjusting the driving mode

[2220] Input: Perceived user emotional state and optimized route information.

[2221] Specific operation: The driving mode adjusting means adjusts the driving mode based on the emotional state to provide a safe and comfortable driving experience.

[2222] Output: Coordinated driving mode.

[2223] Step 10:

[2224] Collecting user ratings

[2225] Input: User feedback after the move is complete (e.g., star rating and comments).

[2226] Specific operation: The smartphone application collects ratings from users and sends them to the server.

[2227] Output: Collected evaluation data on the server.

[2228] Step 11:

[2229] Learning evaluation data

[2230] Input: Collected assessment data.

[2231] Specific operation: The server analyzes the evaluation data and learns to improve matching accuracy next time.

[2232] Output: Improved matching algorithm.

[2233] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[2234] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[2235] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[2236] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[2237] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[2238] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[2239] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[2240] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[2241] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[2242] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[2243] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[2244] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[2245] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[2246] 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.

[2247] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[2248] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[2249] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[2250] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[2251] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[2252] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[2253] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[2254] The following is further disclosed regarding the above embodiment.

[2255] (Claim 1)

[2256] a user information input means for the user to input basic information and desired conditions;

[2257] data receiving means for receiving information from the user information input means;

[2258] an analysis means for analyzing the received user information and extracting desired conditions of the user;

[2259] a database storing information on a plurality of vehicles and drivers;

[2260] a matching means for selecting a vehicle and a driver that are optimal for the user based on the desired conditions of the user extracted by the analysis means;

[2261] a vehicle dispatch information transmitting means for transmitting information on the selected vehicle and driver to the user;

[2262] a route management means for tracking the location information of the selected vehicle and driver and providing an optimal route;

[2263] a rating data collection means for receiving ratings from users and collecting and learning the rating data;

[2264] A system including:

[2265] (Claim 2)

[2266] 2. The system according to claim 1, wherein said user information input means allows a user to input specific travel needs.

[2267] (Claim 3)

[2268] 2. The system according to claim 1, wherein the route management means calculates the optimum route taking into account traffic information and accident information.

[2269] "Example 1"

[2270] (Claim 1)

[2271] an information input means for the user to input basic information and desired conditions;

[2272] data receiving means for receiving information from the information input means;

[2273] an analysis means for analyzing the received user information and extracting the user's desired conditions;

[2274] a database storing information on a plurality of means of transportation and operators;

[2275] a matching means for selecting the most suitable transportation means and operator for the user based on the desired conditions of the user extracted by the analysis means;

[2276] a vehicle dispatch information transmitting means for transmitting information on the selected transportation means and the operator to the user;

[2277] a route management means for tracking the location information of the selected means of transportation and the operator and providing an optimal route;

[2278] a rating data collection means for receiving ratings from users and collecting and learning rating data;

[2279] A learning method to improve the accuracy of the next match using the generative AI model;

[2280] A system including:

[2281] (Claim 2)

[2282] 2. The system according to claim 1, wherein said information input means allows a user to input specific travel needs.

[2283] (Claim 3)

[2284] 2. The system according to claim 1, wherein the route management means calculates the optimum route taking into account traffic information and obstacle information.

[2285] "Application Example 1"

[2286] (Claim 1)

[2287] a user information input means for the user to input basic information and desired conditions;

[2288] data receiving means for receiving information from the user information input means;

[2289] an analysis means for analyzing the received...

Claims

1. a user information input means for the user to input basic information and desired conditions; data receiving means for receiving information from the user information input means; an analysis means for analyzing the received user information and extracting desired conditions of the user; a database storing information on a plurality of vehicles and drivers; a matching means for selecting a vehicle and a driver that are optimal for the user based on the desired conditions of the user extracted by the analysis means; a vehicle dispatch information transmitting means for transmitting information on the selected vehicle and driver to the user; a route management means for tracking the location information of the selected vehicle and driver and providing an optimal route; a rating data collection means for receiving ratings from users and collecting and learning the rating data; A system including:

2. 2. The system according to claim 1, wherein said user information input means allows a user to input specific travel needs.

3. 2. The system according to claim 1, wherein said route management means calculates an optimal route taking into account traffic information and accident information.

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