system

Abstract

The system receives passenger location and destination information and analyzes real-time traffic data based on this information to provide optimal routes and fares. [Solution] A system comprising: communication means for receiving passenger location information and destination information; calculation means for collecting and analyzing real-time traffic data based on the received location information and destination information; calculation means for generating an optimal route and fare based on the analysis results; selection means for analyzing passenger profile information and proposing benefits and discounts; display means for presenting the generated route, fare, benefits, and discount information to the passenger; analysis means for collecting feedback from passengers and analyzing it using natural language processing; and improvement means for proposing service improvement measures based on the analysis results.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In modern means of transportation, the popularization of autonomous taxis has the potential to contribute to improving operation efficiency and user convenience. However, due to the lack of optimal route selection, fare presentation, and even individual privilege and discount proposals based on the user's location information, the transparency and satisfaction for users are insufficient. Also, in driverless autonomous vehicles, the issue is that appropriate communication means with users have not been established.

Means for Solving the Problems

[0005] This invention provides a system that receives passenger location and destination information and analyzes real-time traffic data based on this information to suggest the optimal route and fare. Furthermore, the system includes a function to analyze passenger profile information and suggest appropriate benefits and discounts. In addition, by presenting this information to passengers using a display means and analyzing the feedback using natural language processing, it is possible to continuously improve the service. This can improve user satisfaction and service transparency.

[0006] "Passenger location information" refers to geographical data used to identify the current location of a passenger.

[0007] "Destination information" refers to geographical data indicating the location where the passenger wishes to travel.

[0008] "Communication means" refers to mechanical or electronic devices or technologies that enable the transmission and reception of data.

[0009] "Real-time traffic data" refers to data that shows the current traffic situation, instantly updating road congestion levels and traffic conditions.

[0010] A "computational means" is a device or program used to perform analysis and calculations based on received data.

[0011] "Computation means" refers to a device or technology that performs optimization based on a specific algorithm in conjunction with computation means.

[0012] "Profile information" refers to individualized information generated based on a passenger's past behavior and preferences.

[0013] "Benefits and discounts" refer to monetary or non-monetary advantages or reductions offered to users.

[0014] A "selection method" is a technique or algorithm used to determine the optimal option based on specific criteria.

[0015] The "display means" is a device or technology for visually providing the user with calculation results and related information.

[0016] "Feedback" is the opinions and evaluations provided by the user after using the service.

[0017] "Natural language processing" is a technology for enabling a computer to understand and analyze human language.

[0018] The "improvement means" is a method or process for making the system or service better based on the collected data and analysis results.

Brief Description of Drawings

[0019] [Figure 1] It is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] It shows an emotion map to which a plurality of emotions are mapped. [Figure 10]Shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Embodiment 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Embodiment 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined.

Mode for Carrying Out the Invention

[0020] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0021] First, the language used in the following description will be explained.

[0022] In the following embodiments, a processor with a reference numeral (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of a plurality of arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of a plurality of types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0023] In the following embodiments, a RAM (Random Access Memory) with a reference numeral is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0024] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0025] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0026] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0027] [First Embodiment]

[0028] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0029] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0030] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0031] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0032] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0033] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0034] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0035] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0036] As shown in Figure 2, in the data processing device 12, specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0037] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0038] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0039] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0040] This invention provides a system for operating an autonomous taxi system that offers passengers optimal fares, routes, and even perks and discounts. In this system, the server, terminals, and users each have specific roles, and the overall service is provided through the operation of each role.

[0041] The server handles the primary processing, receiving location and destination information transmitted by passengers. This information is immediately analyzed and combined with real-time traffic data to calculate the optimal route. Furthermore, the server has the capability to select individual benefits and discounts based on passenger profile information. The calculated route, fare, and benefit information are then provided to the user via the terminal.

[0042] The terminal functions as an interface between the user and the server. When a user sends a taxi request using a smartphone or other device, the terminal transmits the user's input information to the server. In addition, the terminal has the function of visually presenting the user with the optimal route, fare, and special offers received from the server, allowing the user to review and select them.

[0043] Users initiate a taxi ride through their device and utilize the service by confirming the information displayed on their device. Once the ride is complete, users can provide feedback through their device. This feedback is collected and analyzed by the server and used to improve the service using natural language processing.

[0044] As a concrete example, this process runs smoothly when a user calls a taxi and heads to a specified destination. The server uses geographical information and traffic data to determine the optimal route and presents it to the user via the terminal. The user checks the presented fare and benefits, accepts, and boards the taxi. After the journey is complete, the user sends feedback, which the server analyzes and incorporates into the next service.

[0045] Thus, the present invention aims to improve the process of using autonomous taxis and provide users with an efficient and convenient means of transportation.

[0046] The following describes the processing flow.

[0047] Step 1:

[0048] The user enters a request to hail a taxi into the application using their terminal. This request includes information about the pickup location and destination.

[0049] Step 2:

[0050] The terminal collects location and destination information entered by the user and transmits it to the server. Data is transmitted quickly via communication means.

[0051] Step 3:

[0052] The server retrieves the latest traffic information from a real-time traffic database based on the received location information. This includes current road conditions and congestion information.

[0053] Step 4:

[0054] The server uses the acquired traffic information and the user's location information to run an optimization algorithm and calculate the optimal route and estimated fare.

[0055] Step 5:

[0056] The server analyzes the user's profile information (past usage history and preferences, etc.) and selects individually customized benefits and discounts.

[0057] Step 6:

[0058] The server sends the calculated optimal route, estimated fare, and information on special offers and discounts to the terminal.

[0059] Step 7:

[0060] The terminal displays information received from the server to the user. The user reviews the presented route, fare, and benefits.

[0061] Step 8:

[0062] The user confirms the taxi reservation and prepares to board by reviewing and approving the information presented.

[0063] Step 9:

[0064] The server, upon receiving approval from the user, sends commands to the autonomous vehicle, directing it to the pick-up location.

[0065] Step 10:

[0066] The user rides in an autonomous taxi until they reach their destination. After arriving at their destination, they fill out a feedback screen on their device to describe their experience.

[0067] Step 11:

[0068] The device sends the user's input to the server.

[0069] Step 12:

[0070] The server analyzes the collected feedback using natural language processing technology and extracts service elements that need improvement. It then generates service improvement measures and prepares them for implementation in the next operation.

[0071] (Example 1)

[0072] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0073] Modern transportation systems face challenges in providing passengers with real-time information on optimal routes and fares while they are traveling. Furthermore, the provision of individualized benefits and discounts tailored to the needs of each passenger is insufficient. Additionally, effectively collecting passenger feedback and utilizing it for service improvement is difficult. To address these challenges, a more efficient and flexible information delivery and analysis system is necessary.

[0074] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0075] In this invention, the server includes communication means for receiving location information and destination information, computing means for collecting and analyzing real-time geographic information and traffic data, and selection means for analyzing passenger history information and proposing benefits and discounts. This makes it possible to provide users with optimal route, fare, and benefit information in real time, and to effectively collect user feedback and reflect it in service improvements.

[0076] "Communication means" refers to the devices and protocols used to receive passenger location and destination information and transmit it to a server.

[0077] A "computational means" is a system that performs calculations to derive the optimal route based on received information and real-time geographical information and traffic data.

[0078] The "calculation means" is a system that calculates the appropriate fare based on passenger input data and traffic conditions.

[0079] A "selection method" refers to a function that analyzes the user's history information and profile to select the most suitable benefits and discounts.

[0080] A "display means" is an interface for visually presenting generated route, fare, and reward information to the user.

[0081] The "analysis method" refers to a mechanism that uses natural language processing technology to analyze passenger feedback and collect it as data.

[0082] "Improvement measures" refer to the process of proposing specific measures to improve the quality of service based on analyzed feedback.

[0083] This system is designed to provide an efficient autonomous taxi service through the collaboration of a server, terminal, and user.

[0084] The server is the primary platform for processing passenger requests. It receives location and destination information sent from the user's device and utilizes API services to obtain real-time geographic information. For example, a common map service can be selected for the geographic information system. Based on this information, the server calculates the optimal route for the user and determines the fare. The fare calculation uses an algorithm that combines historical data and traffic information.

[0085] The terminal is a device that the user directly interacts with, such as a smartphone or tablet. The terminal provides an interface to present the user with optimal route and fare information received from the server, as well as information on special offers. The user calls a taxi through the application on the terminal and provides a rating after the trip is completed.

[0086] Users contribute to the system by experiencing the service through their devices and providing feedback. During the process of a user calling a taxi and traveling to their destination, this information is exchanged between the server and the device in real time.

[0087] As a concrete example, consider a user commuting from home to work. The user enters their destination using the app, and the server, taking current traffic information into account, sends the fastest route to the user's device. Once the user confirms and accepts the presented information, the journey begins. Upon arrival, the user provides feedback through the app, helping to improve the service.

[0088] An example of a prompt might be, "After providing the optimal route to the airport and discount information, please describe specifically how the user will submit feedback." Through this example, it is possible to plan specifically how to support the user experience and collect information.

[0089] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0090] Step 1:

[0091] The terminal receives the user's current location and destination information as input. The user uses a smartphone app to enter this information into a text box and presses the send button. The terminal formats the entered data and prepares to transfer it to the server using a communication protocol.

[0092] Step 2:

[0093] The server receives location and destination information transmitted from the terminal as input. Based on the received data, the server calls a geographic information service API to collect real-time traffic information. The server also takes traffic data and weather information into consideration, calculates the optimal travel route through data calculations, and generates route information as output.

[0094] Step 3:

[0095] The server takes calculated route information as input and performs calculations to generate fares based on distance, time, and real-time traffic conditions. Furthermore, it uses a generative AI model to select appropriate benefits and discounts based on the user's past ride history. The server then generates fare and benefit information as output.

[0096] Step 4:

[0097] The server sends the generated route, fare, and benefits information to the terminal as output. The terminal processes the received information to display it in a view component. The user can review the displayed information and choose whether or not to use the taxi by accepting it.

[0098] Step 5:

[0099] Once the movement is complete, the user enters feedback via a smartphone app. The device sends the entered feedback data to a server. The server receives the feedback data as input and analyzes it using natural language processing.

[0100] Step 6:

[0101] The server aggregates the analysis results as input and generates insights for service improvement. The server prepares better suggestions and proposed solutions for detected problems for future use, contributing to the improvement of the autonomous taxi system's quality. These results are incorporated into the system and reflected in future service delivery.

[0102] (Application Example 1)

[0103] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0104] In autonomous vehicles, there is a need to efficiently suggest the optimal route and fare for each passenger. Furthermore, a system is required that effectively provides benefits and discounts based on passenger profile information, and incorporates real-time feedback for future use. Conventional systems have difficulty providing these functions comprehensively and smoothly.

[0105] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0106] In this invention, the server includes information transmission means for receiving passenger location information and destination information; calculation means for collecting and analyzing real-time traffic information based on the received location information and destination information; calculation means for generating the optimal route and fare based on the analysis results; selection means for analyzing passenger attribute information and proposing benefits and discounts; time management means for setting the user's desired arrival time and providing customized services based on it; and feedback management means for analyzing the post-travel experience and immediately reflecting the feedback in the next service. This enables the provision of efficient and personalized autonomous vehicle services.

[0107] "Information transmission means" refers to a device or process that has the function of accurately receiving passenger location information and destination information and transmitting it to other parts of the system.

[0108] "Computation means" refers to a device or method that has the ability to calculate the optimal travel route by collecting and analyzing real-time traffic information.

[0109] A "calculation mechanism" is a system that has the function of calculating the optimal fare for passengers based on collected data and analysis results.

[0110] A "selection method" is a system that analyzes passenger attribute information and proposes individually beneficial perks and discounts.

[0111] A "time management means" is a device or process that has the function of setting the user's desired arrival time and providing an optimized service accordingly.

[0112] A "feedback management system" is a device or method that has the function of collecting and analyzing passengers' experiences after their journey in order to quickly reflect those experiences in future services.

[0113] The system for realizing this invention is comprised of the interaction of a server, a terminal, and a user. The server functions as an information transmission means for receiving passenger location and destination information. Furthermore, the server collects and analyzes real-time traffic information and calculates the optimal route as a calculation means. Based on this, the calculation means generates the optimal fare, and the selection means analyzes passenger attribute information to propose benefits and discounts.

[0114] The terminal serves as both a user-server interface and a display tool. Pre-generated routes, fares, and reward information are presented to passengers via the terminal for them to review and select. Furthermore, the terminal acts as a feedback management tool, collecting user experience feedback after the trip. The server analyzes this feedback and immediately incorporates it into future service provision.

[0115] This system utilizes a smartphone (iOS or Android®) as hardware, React Native for the frontend, Node.js and Express for the backend, and MongoDB for the database. Google Maps API is used for obtaining real-time traffic information, and Stripe API is used for electronic payments.

[0116] For example, when a user uses an autonomous vehicle service, they specify their desired arrival time and make a request through the app. The server then calculates and provides an optimized route and fare for that time. Furthermore, if the experience was pleasant, that feedback is quickly used to improve the service, enabling it to provide a service that meets the user's needs.

[0117] An example of a prompt to input into the generated AI model is: "Generate a blueprint for an application that provides optimal routes and rewards for autonomous vehicles. Pay particular attention to setting rewards based on user profiles and utilizing real-time traffic data."

[0118] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0119] Step 1:

[0120] The user launches the application on their smartphone and enters their pickup location and destination. The entered location and destination information is transmitted to the server via a data transmission method. This information triggers the server to begin processing.

[0121] Step 2:

[0122] The server uses the Google Maps API to collect real-time traffic information based on the received location and destination information. The server uses this information as a computational tool, analyzing various traffic data to calculate the optimal route. This process takes into account traffic congestion, road construction, and other obstacles.

[0123] Step 3:

[0124] The server uses a calculation tool to determine the optimal fare based on the calculated travel route. This calculation takes into account time-of-day data and supply and demand balance. The calculated fare is temporarily stored.

[0125] Step 4:

[0126] The server retrieves the user's past usage history and profile information from a database and analyzes it using selection methods. Based on specific patterns and needs, the server determines the most suitable benefits or discounts for the user.

[0127] Step 5:

[0128] The server aggregates the above information and generates route, fare, benefits, and discount information. It then formats this information as display data and transmits it to the terminal.

[0129] Step 6:

[0130] The terminal displays the received data on the user interface. The user reviews and selects the presented route, fare, and benefits. The user's selection is immediately sent to the server, and the reservation is confirmed.

[0131] Step 7:

[0132] After using the taxi, users provide feedback about their experience through their device. This feedback is entered as voice or text and sent from the device to the server.

[0133] Step 8:

[0134] The server analyzes the received feedback using natural language processing technology. The analysis results are saved through a feedback management system as measures for improving the service in the future. This allows the system to be adjusted to provide a better experience for users.

[0135] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0136] This invention is a system that aims to further improve the user experience by combining an emotion engine with an autonomous driving taxi system. This system recognizes the user's emotions and uses them to suggest benefits and discounts, adjust the interface, and analyze feedback.

[0137] The server receives location and destination information sent by the user and collects real-time traffic data based on it. This data is analyzed to generate the optimal route and fare. The emotion engine identifies the user's emotions in real time from their voice and facial expressions and sends the results to the server. The server takes this emotion information into consideration and adjusts and proposes benefits tailored to the user.

[0138] The terminal presents information to the user appropriately based on data received from the server. Reflecting the results of the emotion engine, the display system adjusts the method and timing of information presentation. This adjustment ensures a comfortable information delivery experience that does not stress the user.

[0139] Users review the displayed rewards and route information and make selections as needed. At the final stage of their journey, users submit feedback via their device. This feedback, along with sentiment information, is analyzed on the server and used as input for service improvement.

[0140] For example, if a user taking a taxi during peak hours shows signs of dissatisfaction through the emotion engine during their journey, the server immediately adjusts the rewards to provide the user with a more positive experience. This results in greater user satisfaction and an overall improvement in the quality of the service.

[0141] The introduction of an emotion engine will enable the provision of flexible services tailored to the individual needs of each user, rather than simply providing a means of transportation. This will further improve user satisfaction and service transparency, realizing the future vision of autonomous taxis.

[0142] The following describes the processing flow.

[0143] Step 1:

[0144] The user uses a terminal to specify a destination and enter a request for an autonomous taxi. This allows the system to collect information about the pick-up location and destination.

[0145] Step 2:

[0146] The terminal transmits the entered location and destination information to the server. This communication provides the data necessary to arrange a taxi.

[0147] Step 3:

[0148] The server uses the received location information to collect real-time traffic conditions from a traffic database. This information includes road congestion levels and speed limits.

[0149] Step 4:

[0150] The server uses the acquired traffic information to calculate the optimal route and estimated fare. Weather and time-of-day data are also taken into consideration in the calculation.

[0151] Step 5:

[0152] The emotion engine analyzes the user's voice and facial expressions in real time to recognize their current emotional state. This information is sent to the server.

[0153] Step 6:

[0154] The server adjusts the content of rewards and discounts based on information from the emotion engine. For example, if a user is feeling stressed, it increases the rewards to provide a sense of security.

[0155] Step 7:

[0156] The server sends the calculated route, fare, and adjusted reward information to the terminal.

[0157] Step 8:

[0158] The terminal displays information received from the server to the user. This includes details on the optimal route and fares, as well as emotionally sensitive offers.

[0159] Step 9:

[0160] Users review the displayed information and approve their ride by selecting benefits and routes.

[0161] Step 10:

[0162] The server, upon user approval, instructs the self-driving taxi to proceed to the pickup location.

[0163] Step 11:

[0164] If a user has different requests via their device while riding, the emotion engine constantly monitors their emotional state and supports information adjustments as needed.

[0165] Step 12:

[0166] After arriving at their destination, users provide feedback on their travel experience through their device.

[0167] Step 13:

[0168] The device sends the user's feedback to the server.

[0169] Step 14:

[0170] The server analyzes the collected feedback along with sentiment data to generate improvements for the overall service. This allows for actions that will lead to higher customer satisfaction in future service deliveries.

[0171] (Example 2)

[0172] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0173] In autonomous taxi systems, there is a need to improve the passenger travel experience. Conventional systems are limited to providing traffic information and fare information, and do not adequately provide services that are tailored to the passenger's emotional state or offer perks based on real-time emotional information. As a result, it is difficult to adequately mitigate passenger dissatisfaction, and improving service satisfaction remains a challenge.

[0174] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0175] In this invention, the server includes recognition means for identifying the passenger's emotional state in real time, selection means for adjusting and proposing benefits and discounts based on the recognized emotional state and passenger profile, and generation means for generating special prompt sentences using a generative AI model. This enables the provision of benefits tailored to the passenger's emotional state and improves the travel experience.

[0176] "Communication means" refers to a device or function for transmitting passenger location information and destination information to a server.

[0177] "Computation means" refers to a device or function that collects and analyzes real-time traffic information based on received location information and destination information.

[0178] "Calculation means" refers to a device or function that generates the optimal travel route and fare based on the analysis results from the calculation means.

[0179] "Recognition means" refers to a device or function that analyzes the voice and facial expressions of passengers and identifies their emotional state in real time.

[0180] "Selection means" refers to a device or function that adjusts and proposes benefits and discounts based on the emotional state and passenger profile obtained by the recognition means.

[0181] "Generation means" refers to a device or function that uses a generative AI model to create prompt sentences appropriate to a specific situation.

[0182] "Display means" refers to a device or function that provides passengers with generated travel route, fare, benefits, and discount information visually or audibly.

[0183] "Analysis means" refers to a device or function that uses natural language processing technology to analyze feedback collected from passengers and utilize it to improve services.

[0184] "Improvement measures" refer to devices or functions that propose service improvement measures based on analysis results from analytical tools and emotional information.

[0185] This invention is a system for data communication and analysis between a server, terminal, and user in order to improve the passenger experience in an autonomous taxi. The main components include communication means, calculation means, computation means, recognition means, selection means, generation means, display means, analysis means, and improvement means.

[0186] The server uses cloud services to process data in real time. It uses common APIs (e.g., map service APIs) to collect traffic information. In the calculation process, it combines real-time location information from users with traffic information to calculate the optimal route and fare.

[0187] The terminals are devices that provide information to users, and smartphones and tablets are used. These terminals use cameras and microphones to input passengers' facial expressions and voices into the emotion engine. This emotion engine utilizes OpenCV for image processing and TENSORFLOW® for speech analysis, among other things.

[0188] Users board self-driving taxis and provide their departure and destination locations, as well as feedback, via a terminal. This data is sent to a server and processed in real time. The user experience is optimized, particularly by offering perks and discounts based on the user's emotional state.

[0189] As a concrete example, if a user's device detects they are experiencing stress while traveling, the server uses this information to enhance their benefits. Using a generative AI model, it creates and applies a prompt message such as, "The user is experiencing stress. Please offer a 10% discount coupon for their next visit." This example allows passengers to have a more comfortable experience.

[0190] This system not only offers autonomous driving capabilities but also provides personalized service based on the emotional state of passengers. This improves safety and satisfaction, paving the way for a new future of transportation.

[0191] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0192] Step 1:

[0193] The server receives location and destination information transmitted from the user via communication means. Using this information as input, it begins collecting real-time traffic data. Specifically, it obtains current road conditions using a map service API and analyzes this data using a computing device. The output is the analyzed traffic data.

[0194] Step 2:

[0195] The server generates the optimal travel route and fare based on collected traffic data. Inputs include the user's location, destination, and traffic data. The data is integrated, and the algorithm calculates the shortest route and cost. The output is the optimized travel route and fare information. Specifically, the calculation results are stored on the server.

[0196] Step 3:

[0197] The device uses its built-in camera and microphone to collect the user's voice and facial expressions, providing them as input data for emotion recognition. The emotion engine uses this data to recognize the user's emotional state. The input is audio and video data, and the output is the result of identifying the emotional state. In practice, the device continuously analyzes this data in real time.

[0198] Step 4:

[0199] The server adjusts rewards and discounts based on the recognized emotional state and user profile data. This is done by the selection mechanism. The input is emotional state and profile information, and a generative AI model is used to create prompt messages and generate rewards. The output is reward and discount information. For example, a prompt message such as "The user is feeling stressed. Please offer a 10% discount coupon for their next visit" might be used.

[0200] Step 5:

[0201] The terminal displays travel routes, fares, benefits, and discount information provided by the server to the user. Input is information from the server, and output is a visual display to the user. Specifically, detailed information is displayed on the screen, and voice guidance is provided as needed.

[0202] Step 6:

[0203] Users provide feedback via a terminal after their ride. This feedback is sent to a server via text or voice input. The server uses analytical tools to analyze the feedback information using natural language processing techniques. The output is the analysis results, which are useful for improving the service. Specifically, feedback data is recorded and analyzed on the server.

[0204] Step 7:

[0205] The server proposes service improvements based on the analysis results and sentiment information of the feedback. Improvement measures are used for this. The input is the analysis results, and the output is specific suggestions for improvement. For example, specific service adjustment proposals are generated.

[0206] (Application Example 2)

[0207] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0208] In the travel experience for passengers using autonomous vehicles, there is a demand not only for the function of transportation itself, but also for the provision of services that respond to passengers' emotions and individual needs. However, current technology has the challenge of not being able to effectively identify passenger emotions and dynamically adjust services based on that information. This limits improvements in passenger satisfaction and service quality.

[0209] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0210] In this invention, the server includes emotion recognition means for recognizing passengers' emotions, adjustment means for adjusting benefits and display methods based on the recognized emotion information, and analysis means for collecting feedback from passengers and analyzing it using natural language processing. This enables the provision of flexible services in response to passengers' emotions, thereby improving passenger satisfaction.

[0211] "Passenger location information" refers to geographical data that indicates where a passenger is currently located.

[0212] "Destination information" is data that indicates the final destination to which a passenger wishes to travel.

[0213] "Communication means" refers to technical means used to send and receive data, enabling communication between networks and devices.

[0214] "Computational means" refers to computing devices and algorithms used to process and analyze received data.

[0215] "Real-time traffic data" refers to dynamic data that shows the current traffic situation and is collected from traffic information systems.

[0216] "Computational means" refers to a device or means that performs a mathematical or algorithmic process to derive the optimal result from data.

[0217] "Profile information" refers to specific information about individual passengers, including past usage history and personal preferences.

[0218] "Benefits and discounts" refer to incentives or special treatments offered to passengers as part of the service provided to them, aimed at keeping prices down.

[0219] A "selection method" is an algorithm or process used to choose the appropriate option from multiple choices.

[0220] "Display means" refers to a device or technology for visually presenting information to passengers.

[0221] "Emotion recognition means" refers to a technology or process for identifying a passenger's emotional state, which may involve using voice analysis or image analysis.

[0222] "Adjustment means" refers to a process or device for changing the content or display method of service according to the emotions and circumstances of passengers.

[0223] "Feedback" refers to information such as opinions and evaluations provided by passengers after using a service.

[0224] Natural language processing is a branch of computer science that deals with processing and analyzing human language.

[0225] "Analytical means" refers to techniques or devices used to analyze data, extract meaningful information, and make decisions.

[0226] "Improvement measures" refer to strategies and devices used to improve service quality based on analysis results.

[0227] The system that realizes this invention consists of a server installed in an autonomous vehicle, a terminal carried by a passenger, and an emotion recognition device that works in conjunction with these.

[0228] The server receives location and destination information from passengers' devices and collects and analyzes real-time traffic data based on this information. High-performance processors and cloud-based analysis servers are used as computing tools to enable the processing of large amounts of data. Specifically, machine learning libraries such as Python and TensorFlow are used to analyze traffic patterns and calculate optimal routes and fares. Based on the analysis, passenger profile information and emotion recognition tools are used to identify passengers' emotions and tailor personalized benefits and services.

[0229] Emotion recognition utilizes passengers' smartphones and the vehicle's internal cameras and microphones, while emotion analysis is performed using deep learning models powered by PyTorch and other tools. Based on the emotion data obtained from this model, adjustment mechanisms function to fine-tune perks and experiences, ensuring that passengers maintain the most comfortable state during their ride.

[0230] The terminal visually presents this information to passengers through display means. The displayed information includes customized benefits and route information generated by a generative AI model, as well as a feedback collection interface. In particular, the terminal can directly receive feedback from users and analyze their opinions using natural language processing. This analysis utilizes NLP-enabled libraries (e.g., spaCy or NLTK).

[0231] For example, when the server recognizes stress from a passenger's emotions, it immediately enhances the perks or displays relaxation music or videos on the terminal to alleviate the passenger's dissatisfaction. The following prompts can be used in the generative AI model when presenting entertainment and discount information.

[0232] Example of a prompt:

[0233] "Identify the user's current emotional state. If dissatisfaction or stress is detected, generate appropriate rewards or positive suggestions."

[0234] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0235] Step 1:

[0236] The server receives location and destination information from the passenger's device. Based on this information, it calls an API to collect real-time traffic data and retrieves the necessary information by referring to a traffic database. This generates a dataset that reflects the current traffic situation.

[0237] Step 2:

[0238] The server uses Python and machine learning libraries (such as TensorFlow) to perform data calculations in order to analyze traffic data. This analysis generates the optimal travel route and fare plan. Real-time traffic data and historical route data are used as input, and the most efficient route suggestions and pricing information are output.

[0239] Step 3:

[0240] The server references passenger profile information and analyzes their emotional state using emotion recognition tools. Passenger voice and facial expression data are acquired as input via smartphone sensors and cameras and processed by a PyTorch-based emotion analysis algorithm. This identifies the passenger's current emotional state.

[0241] Step 4:

[0242] The server runs a generative AI model that adjusts the rewards and display methods based on the emotional information it receives. Using prompts, it generates specific rewards and entertainment options tailored to the user's emotions and sends them to the terminal. The generated output is information about rewards and promotions designed to soothe passengers.

[0243] Step 5:

[0244] The terminal displays information received from the server, visually presenting passengers with rewards and route information. The interface is designed to allow passengers to easily check information using the device's display and select and apply rewards on the spot.

[0245] Step 6:

[0246] Users provide feedback via their device at the end of their journey. This feedback is collected using voice input and converted into text data by an analysis tool equipped with natural language processing algorithms. The input feedback is added to a dataset for future service improvements.

[0247] Step 7:

[0248] The server analyzes the collected feedback and uses the information to suggest service improvements. These suggestions directly contribute to increased passenger satisfaction, and the feedback data is used as parameters for future services.

[0249] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0250] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0251] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0252] [Second Embodiment]

[0253] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0254] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0255] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0256] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0257] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0258] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0259] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0260] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0261] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0262] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0263] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0264] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0265] This invention provides a system for operating an autonomous taxi system that offers passengers optimal fares, routes, and even perks and discounts. In this system, the server, terminals, and users each have specific roles, and the overall service is provided through the operation of each role.

[0266] The server handles the primary processing, receiving location and destination information transmitted by passengers. This information is immediately analyzed and combined with real-time traffic data to calculate the optimal route. Furthermore, the server has the capability to select individual benefits and discounts based on passenger profile information. The calculated route, fare, and benefit information are then provided to the user via the terminal.

[0267] The terminal functions as an interface between the user and the server. When a user sends a taxi request using a smartphone or other device, the terminal transmits the user's input information to the server. In addition, the terminal has the function of visually presenting the user with the optimal route, fare, and special offers received from the server, allowing the user to review and select them.

[0268] Users initiate a taxi ride through their device and utilize the service by confirming the information displayed on their device. Once the ride is complete, users can provide feedback through their device. This feedback is collected and analyzed by the server and used to improve the service using natural language processing.

[0269] As a concrete example, this process runs smoothly when a user calls a taxi and heads to a specified destination. The server uses geographical information and traffic data to determine the optimal route and presents it to the user via the terminal. The user checks the presented fare and benefits, accepts, and boards the taxi. After the journey is complete, the user sends feedback, which the server analyzes and incorporates into the next service.

[0270] Thus, the present invention aims to improve the process of using autonomous taxis and provide users with an efficient and convenient means of transportation.

[0271] The following describes the processing flow.

[0272] Step 1:

[0273] The user inputs a request for calling a taxi into the application using the terminal. This request includes information on the boarding location and destination.

[0274] Step 2:

[0275] The terminal collects the location information and destination information input by the user and transmits it to the server. Data transmission is performed quickly by the communication means.

[0276] Step 3:

[0277] Based on the received location information, the server obtains the latest traffic information from the real-time traffic database. This includes the current road conditions and traffic congestion information.

[0278] Step 4:

[0279] The server executes an optimization algorithm using the obtained traffic information and the user's location information to calculate the optimal route and estimated fare.

[0280] Step 5:

[0281] The server analyzes the user's profile information (such as past usage history and preferences) and selects customized privileges and discounts.

[0282] Step 6:

[0283] The server transmits the calculated optimal route, estimated fare, privileges, and discount information to the terminal.

[0284] Step 7:

[0285] The terminal displays the information received from the server to the user. The user checks the presented route, fare, and privileges.

[0286] Step 8:

[0287] The user confirms the presented information and approves it to finalize the taxi arrangement and prepare for boarding.

[0288] Step 9:

[0289] Upon receiving the approval from the user, the server sends a command to the autonomous vehicle to make it head towards the boarding location.

[0290] Step 10:

[0291] The user boards the autonomous taxi until reaching the destination. After arriving at the destination, the user fills in the usage experience on the feedback screen through the terminal.

[0292] Step 11:

[0293] The terminal sends the feedback input by the user to the server.

[0294] Step 12:

[0295] The server analyzes the collected feedback using natural language processing technology and extracts service elements to be improved. It generates service improvement measures and prepares to reflect them in the next operation.

[0296] (Example 1)

[0297] Next, Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0298] In modern transportation systems, there are problems such as it being difficult for passengers to receive optimal route and fare information in real time during travel. Also, the provision of benefits and discounts according to the needs of individual passengers is insufficient. Furthermore, it is difficult to effectively collect feedback from passengers and utilize it for service improvement. To solve these problems, a more efficient and flexible information provision and analysis system is needed.

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

[0300] In this invention, the server includes a communication means for receiving position information and destination information, a calculation means for collecting and analyzing real-time geographical information and traffic data, and a selection means for analyzing the passenger's history information to propose benefits and discounts. As a result, it becomes possible to provide the user with optimal route, fare, and benefit information in real time, and it is also possible to effectively collect feedback from the user and reflect it in service improvement.

[0301] The "communication means" is a general term for devices and protocols for receiving the passenger's position information and destination information and transmitting them to the server.

[0302] The "calculation means" is a mechanism for performing calculations to derive an optimal route based on the received information, real-time geographical information, and traffic data.

[0303] The "calculation means" is a system for calculating an appropriate fare based on the passenger's input data and traffic conditions.

[0304] The "selection means" has a function of analyzing the user's history information and profile to select optimal benefits and discounts.

[0305] The "display means" is an interface for visually presenting the generated route, fare, and benefit information to the user.

[0306] The "analysis means" is a mechanism for analyzing the feedback from the passenger using natural language processing technology and collecting it as data.

[0307] The "improvement means" is a process for proposing specific measures for improving the quality of the service based on the analyzed feedback.

[0308] This system is designed to provide an efficient autonomous taxi service through the collaboration of a server, terminal, and user.

[0309] The server is the primary platform for processing passenger requests. It receives location and destination information sent from the user's device and utilizes API services to obtain real-time geographic information. For example, a common map service can be selected for the geographic information system. Based on this information, the server calculates the optimal route for the user and determines the fare. The fare calculation uses an algorithm that combines historical data and traffic information.

[0310] The terminal is a device that the user directly interacts with, such as a smartphone or tablet. The terminal provides an interface to present the user with optimal route and fare information received from the server, as well as information on special offers. The user calls a taxi through the application on the terminal and provides a rating after the trip is completed.

[0311] Users contribute to the system by experiencing the service through their devices and providing feedback. During the process of a user calling a taxi and traveling to their destination, this information is exchanged between the server and the device in real time.

[0312] As a concrete example, consider a user commuting from home to work. The user enters their destination using the app, and the server, taking current traffic information into account, sends the fastest route to the user's device. Once the user confirms and accepts the presented information, the journey begins. Upon arrival, the user provides feedback through the app, helping to improve the service.

[0313] An example of a prompt might be, "After providing the optimal route to the airport and discount information, please describe specifically how the user will submit feedback." Through this example, it is possible to plan specifically how to support the user experience and collect information.

[0314] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0315] Step 1:

[0316] The terminal receives the user's current location and destination information as input. The user uses a smartphone app to enter this information into a text box and presses the send button. The terminal formats the entered data and prepares to transfer it to the server using a communication protocol.

[0317] Step 2:

[0318] The server receives location and destination information transmitted from the terminal as input. Based on the received data, the server calls a geographic information service API to collect real-time traffic information. The server also takes traffic data and weather information into consideration, calculates the optimal travel route through data calculations, and generates route information as output.

[0319] Step 3:

[0320] The server takes calculated route information as input and performs calculations to generate fares based on distance, time, and real-time traffic conditions. Furthermore, it uses a generative AI model to select appropriate benefits and discounts based on the user's past ride history. The server then generates fare and benefit information as output.

[0321] Step 4:

[0322] The server sends the generated route, fare, and benefits information to the terminal as output. The terminal processes the received information to display it in a view component. The user can review the displayed information and choose whether or not to use the taxi by accepting it.

[0323] Step 5:

[0324] Once the movement is complete, the user enters feedback via a smartphone app. The device sends the entered feedback data to a server. The server receives the feedback data as input and analyzes it using natural language processing.

[0325] Step 6:

[0326] The server aggregates the analysis results as input and generates insights for service improvement. The server prepares better suggestions and proposed solutions for detected problems for future use, contributing to the improvement of the autonomous taxi system's quality. These results are incorporated into the system and reflected in future service delivery.

[0327] (Application Example 1)

[0328] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0329] In autonomous vehicles, there is a need to efficiently suggest the optimal route and fare for each passenger. Furthermore, a system is required that effectively provides benefits and discounts based on passenger profile information, and incorporates real-time feedback for future use. Conventional systems have difficulty providing these functions comprehensively and smoothly.

[0330] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0331] In this invention, the server includes information transmission means for receiving passenger location information and destination information; calculation means for collecting and analyzing real-time traffic information based on the received location information and destination information; calculation means for generating the optimal route and fare based on the analysis results; selection means for analyzing passenger attribute information and proposing benefits and discounts; time management means for setting the user's desired arrival time and providing customized services based on it; and feedback management means for analyzing the post-travel experience and immediately reflecting the feedback in the next service. This enables the provision of efficient and personalized autonomous vehicle services.

[0332] "Information transmission means" refers to a device or process that has the function of accurately receiving passenger location information and destination information and transmitting it to other parts of the system.

[0333] "Computation means" refers to a device or method that has the ability to calculate the optimal travel route by collecting and analyzing real-time traffic information.

[0334] A "calculation mechanism" is a system that has the function of calculating the optimal fare for passengers based on collected data and analysis results.

[0335] A "selection method" is a system that analyzes passenger attribute information and proposes individually beneficial perks and discounts.

[0336] A "time management means" is a device or process that has the function of setting the user's desired arrival time and providing an optimized service accordingly.

[0337] A "feedback management system" is a device or method that has the function of collecting and analyzing passengers' experiences after their journey in order to quickly reflect those experiences in future services.

[0338] The system for realizing this invention is comprised of the interaction of a server, a terminal, and a user. The server functions as an information transmission means for receiving passenger location and destination information. Furthermore, the server collects and analyzes real-time traffic information and calculates the optimal route as a calculation means. Based on this, the calculation means generates the optimal fare, and the selection means analyzes passenger attribute information to propose benefits and discounts.

[0339] The terminal serves as both a user-server interface and a display tool. Pre-generated routes, fares, and reward information are presented to passengers via the terminal for them to review and select. Furthermore, the terminal acts as a feedback management tool, collecting user experience feedback after the trip. The server analyzes this feedback and immediately incorporates it into future service provision.

[0340] This system utilizes a smartphone (iOS or Android) as hardware, and for software, it employs React Native for the frontend, Node.js and Express for the backend, and MongoDB for the database. Additionally, the Google Maps API is used to obtain real-time traffic information, and the Stripe API is used for electronic payments.

[0341] For example, when a user uses an autonomous vehicle service, they specify their desired arrival time and make a request through the app. The server then calculates and provides an optimized route and fare for that time. Furthermore, if the experience was pleasant, that feedback is quickly used to improve the service, enabling it to provide a service that meets the user's needs.

[0342] An example of a prompt to input into the generated AI model is: "Generate a blueprint for an application that provides optimal routes and rewards for autonomous vehicles. Pay particular attention to setting rewards based on user profiles and utilizing real-time traffic data."

[0343] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0344] Step 1:

[0345] The user launches the application on their smartphone and enters their pickup location and destination. The entered location and destination information is transmitted to the server via a data transmission method. This information triggers the server to begin processing.

[0346] Step 2:

[0347] The server uses the Google Maps API to collect real-time traffic information based on the received location and destination information. The server uses this information as a computational tool, analyzing various traffic data to calculate the optimal route. This process takes into account traffic congestion, road construction, and other obstacles.

[0348] Step 3:

[0349] The server uses a calculation tool to determine the optimal fare based on the calculated travel route. This calculation takes into account time-of-day data and supply and demand balance. The calculated fare is temporarily stored.

[0350] Step 4:

[0351] The server retrieves the user's past usage history and profile information from a database and analyzes it using selection methods. Based on specific patterns and needs, the server determines the most suitable benefits or discounts for the user.

[0352] Step 5:

[0353] The server aggregates the above information and generates route, fare, benefits, and discount information. It then formats this information as display data and transmits it to the terminal.

[0354] Step 6:

[0355] The terminal displays the received data on the user interface. The user reviews and selects the presented route, fare, and benefits. The user's selection is immediately sent to the server, and the reservation is confirmed.

[0356] Step 7:

[0357] After using the taxi, users provide feedback about their experience through their device. This feedback is entered as voice or text and sent from the device to the server.

[0358] Step 8:

[0359] The server analyzes the received feedback using natural language processing technology. The analysis results are saved through a feedback management system as measures for improving the service in the future. This allows the system to be adjusted to provide a better experience for users.

[0360] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0361] This invention is a system that aims to further improve the user experience by combining an emotion engine with an autonomous driving taxi system. This system recognizes the user's emotions and uses them to suggest benefits and discounts, adjust the interface, and analyze feedback.

[0362] The server receives location and destination information sent by the user and collects real-time traffic data based on it. This data is analyzed to generate the optimal route and fare. The emotion engine identifies the user's emotions in real time from their voice and facial expressions and sends the results to the server. The server takes this emotion information into consideration and adjusts and proposes benefits tailored to the user.

[0363] The terminal presents information to the user appropriately based on data received from the server. Reflecting the results of the emotion engine, the display system adjusts the method and timing of information presentation. This adjustment ensures a comfortable information delivery experience that does not stress the user.

[0364] Users review the displayed rewards and route information and make selections as needed. At the final stage of their journey, users submit feedback via their device. This feedback, along with sentiment information, is analyzed on the server and used as input for service improvement.

[0365] For example, if a user taking a taxi during peak hours shows signs of dissatisfaction through the emotion engine during their journey, the server immediately adjusts the rewards to provide the user with a more positive experience. This results in greater user satisfaction and an overall improvement in the quality of the service.

[0366] The introduction of an emotion engine will enable the provision of flexible services tailored to the individual needs of each user, rather than simply providing a means of transportation. This will further improve user satisfaction and service transparency, realizing the future vision of autonomous taxis.

[0367] The following describes the processing flow.

[0368] Step 1:

[0369] The user uses a terminal to specify a destination and enter a request for an autonomous taxi. This allows the system to collect information about the pick-up location and destination.

[0370] Step 2:

[0371] The terminal transmits the entered location and destination information to the server. This communication provides the data necessary to arrange a taxi.

[0372] Step 3:

[0373] The server uses the received location information to collect real-time traffic conditions from a traffic database. This information includes road congestion levels and speed limits.

[0374] Step 4:

[0375] The server uses the acquired traffic information to calculate the optimal route and estimated fare. Weather and time-of-day data are also taken into consideration in the calculation.

[0376] Step 5:

[0377] The emotion engine analyzes the user's voice and facial expressions in real time to recognize their current emotional state. This information is sent to the server.

[0378] Step 6:

[0379] The server adjusts the content of rewards and discounts based on information from the emotion engine. For example, if a user is feeling stressed, it increases the rewards to provide a sense of security.

[0380] Step 7:

[0381] The server sends the calculated route, fare, and adjusted reward information to the terminal.

[0382] Step 8:

[0383] The terminal displays information received from the server to the user. This includes details on the optimal route and fares, as well as emotionally sensitive offers.

[0384] Step 9:

[0385] Users review the displayed information and approve their ride by selecting benefits and routes.

[0386] Step 10:

[0387] The server, upon user approval, instructs the self-driving taxi to proceed to the pickup location.

[0388] Step 11:

[0389] If a user has different requests via their device while riding, the emotion engine constantly monitors their emotional state and supports information adjustments as needed.

[0390] Step 12:

[0391] After arriving at their destination, users provide feedback on their travel experience through their device.

[0392] Step 13:

[0393] The device sends the user's feedback to the server.

[0394] Step 14:

[0395] The server analyzes the collected feedback along with sentiment data to generate improvements for the overall service. This allows for actions that will lead to higher customer satisfaction in future service deliveries.

[0396] (Example 2)

[0397] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0398] In autonomous taxi systems, there is a need to improve the passenger travel experience. Conventional systems are limited to providing traffic information and fare information, and do not adequately provide services that are tailored to the passenger's emotional state or offer perks based on real-time emotional information. As a result, it is difficult to adequately mitigate passenger dissatisfaction, and improving service satisfaction remains a challenge.

[0399] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0400] In this invention, the server includes recognition means for identifying the passenger's emotional state in real time, selection means for adjusting and proposing benefits and discounts based on the recognized emotional state and passenger profile, and generation means for generating special prompt sentences using a generative AI model. This enables the provision of benefits tailored to the passenger's emotional state and improves the travel experience.

[0401] "Communication means" refers to a device or function for transmitting passenger location information and destination information to a server.

[0402] "Computation means" refers to a device or function that collects and analyzes real-time traffic information based on received location information and destination information.

[0403] "Calculation means" refers to a device or function that generates the optimal travel route and fare based on the analysis results from the calculation means.

[0404] "Recognition means" refers to a device or function that analyzes the voice and facial expressions of passengers and identifies their emotional state in real time.

[0405] "Selection means" refers to a device or function that adjusts and proposes benefits and discounts based on the emotional state and passenger profile obtained by the recognition means.

[0406] "Generation means" refers to a device or function that uses a generative AI model to create prompt sentences appropriate to a specific situation.

[0407] "Display means" refers to a device or function that provides passengers with generated travel route, fare, benefits, and discount information visually or audibly.

[0408] "Analysis means" refers to a device or function that uses natural language processing technology to analyze feedback collected from passengers and utilize it to improve services.

[0409] "Improvement measures" refer to devices or functions that propose service improvement measures based on analysis results from analytical tools and emotional information.

[0410] This invention is a system for data communication and analysis between a server, terminal, and user in order to improve the passenger experience in an autonomous taxi. The main components include communication means, calculation means, computation means, recognition means, selection means, generation means, display means, analysis means, and improvement means.

[0411] The server uses cloud services to process data in real time. It uses common APIs (e.g., map service APIs) to collect traffic information. In the calculation process, it combines real-time location information from users with traffic information to calculate the optimal route and fare.

[0412] Terminals are devices that provide information to users, and smartphones and tablets are used. These terminals use cameras and microphones to input passengers' facial expressions and voices into an emotion engine. This emotion engine utilizes technologies such as OpenCV for image processing and TensorFlow for speech analysis.

[0413] Users board self-driving taxis and provide their departure and destination locations, as well as feedback, via a terminal. This data is sent to a server and processed in real time. The user experience is optimized, particularly by offering perks and discounts based on the user's emotional state.

[0414] As a concrete example, if a user's device detects they are experiencing stress while traveling, the server uses this information to enhance their benefits. Using a generative AI model, it creates and applies a prompt message such as, "The user is experiencing stress. Please offer a 10% discount coupon for their next visit." This example allows passengers to have a more comfortable experience.

[0415] This system not only offers autonomous driving capabilities but also provides personalized service based on the emotional state of passengers. This improves safety and satisfaction, paving the way for a new future of transportation.

[0416] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0417] Step 1:

[0418] The server receives location and destination information transmitted from the user via communication means. Using this information as input, it begins collecting real-time traffic data. Specifically, it obtains current road conditions using a map service API and analyzes this data using a computing device. The output is the analyzed traffic data.

[0419] Step 2:

[0420] The server generates the optimal travel route and fare based on collected traffic data. Inputs include the user's location, destination, and traffic data. The data is integrated, and the algorithm calculates the shortest route and cost. The output is the optimized travel route and fare information. Specifically, the calculation results are stored on the server.

[0421] Step 3:

[0422] The device uses its built-in camera and microphone to collect the user's voice and facial expressions, providing them as input data for emotion recognition. The emotion engine uses this data to recognize the user's emotional state. The input is audio and video data, and the output is the result of identifying the emotional state. In practice, the device continuously analyzes this data in real time.

[0423] Step 4:

[0424] The server adjusts rewards and discounts based on the recognized emotional state and user profile data. This is done by the selection mechanism. The input is emotional state and profile information, and a generative AI model is used to create prompt messages and generate rewards. The output is reward and discount information. For example, a prompt message such as "The user is feeling stressed. Please offer a 10% discount coupon for their next visit" might be used.

[0425] Step 5:

[0426] The terminal displays travel routes, fares, benefits, and discount information provided by the server to the user. Input is information from the server, and output is a visual display to the user. Specifically, detailed information is displayed on the screen, and voice guidance is provided as needed.

[0427] Step 6:

[0428] Users provide feedback via a terminal after their ride. This feedback is sent to a server via text or voice input. The server uses analytical tools to analyze the feedback information using natural language processing techniques. The output is the analysis results, which are useful for improving the service. Specifically, feedback data is recorded and analyzed on the server.

[0429] Step 7:

[0430] The server proposes service improvements based on the analysis results and sentiment information of the feedback. Improvement measures are used for this. The input is the analysis results, and the output is specific suggestions for improvement. For example, specific service adjustment proposals are generated.

[0431] (Application Example 2)

[0432] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0433] In the travel experience for passengers using autonomous vehicles, there is a demand not only for the function of transportation itself, but also for the provision of services that respond to passengers' emotions and individual needs. However, current technology has the challenge of not being able to effectively identify passenger emotions and dynamically adjust services based on that information. This limits improvements in passenger satisfaction and service quality.

[0434] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0435] In this invention, the server includes emotion recognition means for recognizing passengers' emotions, adjustment means for adjusting benefits and display methods based on the recognized emotion information, and analysis means for collecting feedback from passengers and analyzing it using natural language processing. This enables the provision of flexible services in response to passengers' emotions, thereby improving passenger satisfaction.

[0436] "Passenger location information" refers to geographical data that indicates where a passenger is currently located.

[0437] "Destination information" is data that indicates the final destination to which a passenger wishes to travel.

[0438] "Communication means" refers to technical means used to send and receive data, enabling communication between networks and devices.

[0439] "Computational means" refers to computing devices and algorithms used to process and analyze received data.

[0440] "Real-time traffic data" refers to dynamic data that shows the current traffic situation and is collected from traffic information systems.

[0441] "Computational means" refers to a device or means that performs a mathematical or algorithmic process to derive the optimal result from data.

[0442] "Profile information" refers to specific information about individual passengers, including past usage history and personal preferences.

[0443] "Benefits and discounts" refer to incentives or special treatments offered to passengers as part of the service provided to them, aimed at keeping prices down.

[0444] A "selection method" is an algorithm or process used to choose the appropriate option from multiple choices.

[0445] "Display means" refers to a device or technology for visually presenting information to passengers.

[0446] "Emotion recognition means" refers to a technology or process for identifying a passenger's emotional state, which may involve using voice analysis or image analysis.

[0447] "Adjustment means" refers to a process or device for changing the content or display method of service according to the emotions and circumstances of passengers.

[0448] "Feedback" refers to information such as opinions and evaluations provided by passengers after using a service.

[0449] Natural language processing is a branch of computer science that deals with processing and analyzing human language.

[0450] "Analytical means" refers to techniques or devices used to analyze data, extract meaningful information, and make decisions.

[0451] "Improvement measures" refer to strategies and devices used to improve service quality based on analysis results.

[0452] The system that realizes this invention consists of a server installed in an autonomous vehicle, a terminal carried by a passenger, and an emotion recognition device that works in conjunction with these.

[0453] The server receives location and destination information from passengers' devices and collects and analyzes real-time traffic data based on this information. High-performance processors and cloud-based analysis servers are used as computing tools to enable the processing of large amounts of data. Specifically, machine learning libraries such as Python and TensorFlow are used to analyze traffic patterns and calculate optimal routes and fares. Based on the analysis, passenger profile information and emotion recognition tools are used to identify passengers' emotions and tailor personalized benefits and services.

[0454] Emotion recognition utilizes passengers' smartphones and the vehicle's internal cameras and microphones, while emotion analysis is performed using deep learning models powered by PyTorch and other tools. Based on the emotion data obtained from this model, adjustment mechanisms function to fine-tune perks and experiences, ensuring that passengers maintain the most comfortable state during their ride.

[0455] The terminal visually presents this information to passengers through display means. The displayed information includes customized benefits and route information generated by a generative AI model, as well as a feedback collection interface. In particular, the terminal can directly receive feedback from users and analyze their opinions using natural language processing. This analysis utilizes NLP-enabled libraries (e.g., spaCy or NLTK).

[0456] For example, when the server recognizes stress from a passenger's emotions, it immediately enhances the perks or displays relaxation music or videos on the terminal to alleviate the passenger's dissatisfaction. The following prompts can be used in the generative AI model when presenting entertainment and discount information.

[0457] Example of a prompt:

[0458] "Identify the user's current emotional state. If dissatisfaction or stress is detected, generate appropriate rewards or positive suggestions."

[0459] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0460] Step 1:

[0461] The server receives location and destination information from the passenger's device. Based on this information, it calls an API to collect real-time traffic data and retrieves the necessary information by referring to a traffic database. This generates a dataset that reflects the current traffic situation.

[0462] Step 2:

[0463] The server uses Python and machine learning libraries (such as TensorFlow) to perform data calculations in order to analyze traffic data. This analysis generates the optimal travel route and fare plan. Real-time traffic data and historical route data are used as input, and the most efficient route suggestions and pricing information are output.

[0464] Step 3:

[0465] The server references passenger profile information and analyzes their emotional state using emotion recognition tools. Passenger voice and facial expression data are acquired as input via smartphone sensors and cameras and processed by a PyTorch-based emotion analysis algorithm. This identifies the passenger's current emotional state.

[0466] Step 4:

[0467] The server runs a generative AI model that adjusts the rewards and display methods based on the emotional information it receives. Using prompts, it generates specific rewards and entertainment options tailored to the user's emotions and sends them to the terminal. The generated output is information about rewards and promotions designed to soothe passengers.

[0468] Step 5:

[0469] The terminal displays information received from the server, visually presenting passengers with rewards and route information. The interface is designed to allow passengers to easily check information using the device's display and select and apply rewards on the spot.

[0470] Step 6:

[0471] Users provide feedback via their device at the end of their journey. This feedback is collected using voice input and converted into text data by an analysis tool equipped with natural language processing algorithms. The input feedback is added to a dataset for future service improvements.

[0472] Step 7:

[0473] The server analyzes the collected feedback and uses the information to suggest service improvements. These suggestions directly contribute to increased passenger satisfaction, and the feedback data is used as parameters for future services.

[0474] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0475] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0476] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0477] [Third Embodiment]

[0478] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0479] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0480] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0481] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0482] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0483] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0484] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0485] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0486] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0487] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0488] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0489] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0490] This invention provides a system for operating an autonomous taxi system that offers passengers optimal fares, routes, and even perks and discounts. In this system, the server, terminals, and users each have specific roles, and the overall service is provided through the operation of each role.

[0491] The server handles the primary processing, receiving location and destination information transmitted by passengers. This information is immediately analyzed and combined with real-time traffic data to calculate the optimal route. Furthermore, the server has the capability to select individual benefits and discounts based on passenger profile information. The calculated route, fare, and benefit information are then provided to the user via the terminal.

[0492] The terminal functions as an interface between the user and the server. When a user sends a taxi request using a smartphone or other device, the terminal transmits the user's input information to the server. In addition, the terminal has the function of visually presenting the user with the optimal route, fare, and special offers received from the server, allowing the user to review and select them.

[0493] Users initiate a taxi ride through their device and utilize the service by confirming the information displayed on their device. Once the ride is complete, users can provide feedback through their device. This feedback is collected and analyzed by the server and used to improve the service using natural language processing.

[0494] As a concrete example, this process runs smoothly when a user calls a taxi and heads to a specified destination. The server uses geographical information and traffic data to determine the optimal route and presents it to the user via the terminal. The user checks the presented fare and benefits, accepts, and boards the taxi. After the journey is complete, the user sends feedback, which the server analyzes and incorporates into the next service.

[0495] Thus, the present invention aims to improve the process of using autonomous taxis and provide users with an efficient and convenient means of transportation.

[0496] The following describes the processing flow.

[0497] Step 1:

[0498] The user enters a request to hail a taxi into the application using their terminal. This request includes information about the pickup location and destination.

[0499] Step 2:

[0500] The terminal collects location and destination information entered by the user and transmits it to the server. Data is transmitted quickly via communication means.

[0501] Step 3:

[0502] The server retrieves the latest traffic information from a real-time traffic database based on the received location information. This includes current road conditions and congestion information.

[0503] Step 4:

[0504] The server uses the acquired traffic information and the user's location information to run an optimization algorithm and calculate the optimal route and estimated fare.

[0505] Step 5:

[0506] The server analyzes the user's profile information (past usage history and preferences, etc.) and selects individually customized benefits and discounts.

[0507] Step 6:

[0508] The server sends the calculated optimal route, estimated fare, and information on special offers and discounts to the terminal.

[0509] Step 7:

[0510] The terminal displays information received from the server to the user. The user reviews the presented route, fare, and benefits.

[0511] Step 8:

[0512] The user confirms the taxi reservation and prepares to board by reviewing and approving the information presented.

[0513] Step 9:

[0514] The server, upon receiving approval from the user, sends commands to the autonomous vehicle, directing it to the pick-up location.

[0515] Step 10:

[0516] The user rides in an autonomous taxi until they reach their destination. After arriving at their destination, they fill out a feedback screen on their device to describe their experience.

[0517] Step 11:

[0518] The device sends the user's input to the server.

[0519] Step 12:

[0520] The server analyzes the collected feedback using natural language processing technology and extracts service elements that need improvement. It then generates service improvement measures and prepares them for implementation in the next operation.

[0521] (Example 1)

[0522] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0523] Modern transportation systems face challenges in providing passengers with real-time information on optimal routes and fares while they are traveling. Furthermore, the provision of individualized benefits and discounts tailored to the needs of each passenger is insufficient. Additionally, effectively collecting passenger feedback and utilizing it for service improvement is difficult. To address these challenges, a more efficient and flexible information delivery and analysis system is necessary.

[0524] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0525] In this invention, the server includes communication means for receiving location information and destination information, computing means for collecting and analyzing real-time geographic information and traffic data, and selection means for analyzing passenger history information and proposing benefits and discounts. This makes it possible to provide users with optimal route, fare, and benefit information in real time, and to effectively collect user feedback and reflect it in service improvements.

[0526] "Communication means" refers to the devices and protocols used to receive passenger location and destination information and transmit it to a server.

[0527] A "computational means" is a system that performs calculations to derive the optimal route based on received information and real-time geographical information and traffic data.

[0528] The "calculation means" is a system that calculates the appropriate fare based on passenger input data and traffic conditions.

[0529] A "selection method" refers to a function that analyzes the user's history information and profile to select the most suitable benefits and discounts.

[0530] A "display means" is an interface for visually presenting generated route, fare, and reward information to the user.

[0531] The "analysis method" refers to a mechanism that uses natural language processing technology to analyze passenger feedback and collect it as data.

[0532] "Improvement measures" refer to the process of proposing specific measures to improve the quality of service based on analyzed feedback.

[0533] This system is designed to provide an efficient autonomous taxi service through the collaboration of a server, terminal, and user.

[0534] The server is the primary platform for processing passenger requests. It receives location and destination information sent from the user's device and utilizes API services to obtain real-time geographic information. For example, a common map service can be selected for the geographic information system. Based on this information, the server calculates the optimal route for the user and determines the fare. The fare calculation uses an algorithm that combines historical data and traffic information.

[0535] The terminal is a device that the user directly interacts with, such as a smartphone or tablet. The terminal provides an interface to present the user with optimal route and fare information received from the server, as well as information on special offers. The user calls a taxi through the application on the terminal and provides a rating after the trip is completed.

[0536] Users contribute to the system by experiencing the service through their devices and providing feedback. During the process of a user calling a taxi and traveling to their destination, this information is exchanged between the server and the device in real time.

[0537] As a concrete example, consider a user commuting from home to work. The user enters their destination using the app, and the server, taking current traffic information into account, sends the fastest route to the user's device. Once the user confirms and accepts the presented information, the journey begins. Upon arrival, the user provides feedback through the app, helping to improve the service.

[0538] An example of a prompt might be, "After providing the optimal route to the airport and discount information, please describe specifically how the user will submit feedback." Through this example, it is possible to plan specifically how to support the user experience and collect information.

[0539] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0540] Step 1:

[0541] The terminal receives the user's current location and destination information as input. The user uses a smartphone app to enter this information into a text box and presses the send button. The terminal formats the entered data and prepares to transfer it to the server using a communication protocol.

[0542] Step 2:

[0543] The server receives location and destination information transmitted from the terminal as input. Based on the received data, the server calls a geographic information service API to collect real-time traffic information. The server also takes traffic data and weather information into consideration, calculates the optimal travel route through data calculations, and generates route information as output.

[0544] Step 3:

[0545] The server takes calculated route information as input and performs calculations to generate fares based on distance, time, and real-time traffic conditions. Furthermore, it uses a generative AI model to select appropriate benefits and discounts based on the user's past ride history. The server then generates fare and benefit information as output.

[0546] Step 4:

[0547] The server sends the generated route, fare, and benefits information to the terminal as output. The terminal processes the received information to display it in a view component. The user can review the displayed information and choose whether or not to use the taxi by accepting it.

[0548] Step 5:

[0549] Once the movement is complete, the user enters feedback via a smartphone app. The device sends the entered feedback data to a server. The server receives the feedback data as input and analyzes it using natural language processing.

[0550] Step 6:

[0551] The server aggregates the analysis results as input and generates insights for service improvement. The server prepares better suggestions and proposed solutions for detected problems for future use, contributing to the improvement of the autonomous taxi system's quality. These results are incorporated into the system and reflected in future service delivery.

[0552] (Application Example 1)

[0553] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0554] In autonomous vehicles, there is a need to efficiently suggest the optimal route and fare for each passenger. Furthermore, a system is required that effectively provides benefits and discounts based on passenger profile information, and incorporates real-time feedback for future use. Conventional systems have difficulty providing these functions comprehensively and smoothly.

[0555] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0556] In this invention, the server includes information transmission means for receiving passenger location information and destination information; calculation means for collecting and analyzing real-time traffic information based on the received location information and destination information; calculation means for generating the optimal route and fare based on the analysis results; selection means for analyzing passenger attribute information and proposing benefits and discounts; time management means for setting the user's desired arrival time and providing customized services based on it; and feedback management means for analyzing the post-travel experience and immediately reflecting the feedback in the next service. This enables the provision of efficient and personalized autonomous vehicle services.

[0557] "Information transmission means" refers to a device or process that has the function of accurately receiving passenger location information and destination information and transmitting it to other parts of the system.

[0558] "Computation means" refers to a device or method that has the ability to calculate the optimal travel route by collecting and analyzing real-time traffic information.

[0559] A "calculation mechanism" is a system that has the function of calculating the optimal fare for passengers based on collected data and analysis results.

[0560] A "selection method" is a system that analyzes passenger attribute information and proposes individually beneficial perks and discounts.

[0561] A "time management means" is a device or process that has the function of setting the user's desired arrival time and providing an optimized service accordingly.

[0562] A "feedback management system" is a device or method that has the function of collecting and analyzing passengers' experiences after their journey in order to quickly reflect those experiences in future services.

[0563] The system for realizing this invention is comprised of the interaction of a server, a terminal, and a user. The server functions as an information transmission means for receiving passenger location and destination information. Furthermore, the server collects and analyzes real-time traffic information and calculates the optimal route as a calculation means. Based on this, the calculation means generates the optimal fare, and the selection means analyzes passenger attribute information to propose benefits and discounts.

[0564] The terminal serves as both a user-server interface and a display tool. Pre-generated routes, fares, and reward information are presented to passengers via the terminal for them to review and select. Furthermore, the terminal acts as a feedback management tool, collecting user experience feedback after the trip. The server analyzes this feedback and immediately incorporates it into future service provision.

[0565] This system utilizes a smartphone (iOS or Android) as hardware, and for software, it employs React Native for the frontend, Node.js and Express for the backend, and MongoDB for the database. Additionally, the Google Maps API is used to obtain real-time traffic information, and the Stripe API is used for electronic payments.

[0566] For example, when a user uses an autonomous vehicle service, they specify their desired arrival time and make a request through the app. The server then calculates and provides an optimized route and fare for that time. Furthermore, if the experience was pleasant, that feedback is quickly used to improve the service, enabling it to provide a service that meets the user's needs.

[0567] An example of a prompt to input into the generated AI model is: "Generate a blueprint for an application that provides optimal routes and rewards for autonomous vehicles. Pay particular attention to setting rewards based on user profiles and utilizing real-time traffic data."

[0568] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0569] Step 1:

[0570] The user launches the application on their smartphone and enters their pickup location and destination. The entered location and destination information is transmitted to the server via a data transmission method. This information triggers the server to begin processing.

[0571] Step 2:

[0572] The server uses the Google Maps API to collect real-time traffic information based on the received location and destination information. The server uses this information as a computational tool, analyzing various traffic data to calculate the optimal route. This process takes into account traffic congestion, road construction, and other obstacles.

[0573] Step 3:

[0574] The server uses a calculation tool to determine the optimal fare based on the calculated travel route. This calculation takes into account time-of-day data and supply and demand balance. The calculated fare is temporarily stored.

[0575] Step 4:

[0576] The server retrieves the user's past usage history and profile information from a database and analyzes it using selection methods. Based on specific patterns and needs, the server determines the most suitable benefits or discounts for the user.

[0577] Step 5:

[0578] The server aggregates the above information and generates route, fare, benefits, and discount information. It then formats this information as display data and transmits it to the terminal.

[0579] Step 6:

[0580] The terminal displays the received data on the user interface. The user reviews and selects the presented route, fare, and benefits. The user's selection is immediately sent to the server, and the reservation is confirmed.

[0581] Step 7:

[0582] After using the taxi, users provide feedback about their experience through their device. This feedback is entered as voice or text and sent from the device to the server.

[0583] Step 8:

[0584] The server analyzes the received feedback using natural language processing technology. The analysis results are saved through a feedback management system as measures for improving the service in the future. This allows the system to be adjusted to provide a better experience for users.

[0585] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0586] This invention is a system that aims to further improve the user experience by combining an emotion engine with an autonomous driving taxi system. This system recognizes the user's emotions and uses them to suggest benefits and discounts, adjust the interface, and analyze feedback.

[0587] The server receives location and destination information sent by the user and collects real-time traffic data based on it. This data is analyzed to generate the optimal route and fare. The emotion engine identifies the user's emotions in real time from their voice and facial expressions and sends the results to the server. The server takes this emotion information into consideration and adjusts and proposes benefits tailored to the user.

[0588] The terminal presents information to the user appropriately based on data received from the server. Reflecting the results of the emotion engine, the display system adjusts the method and timing of information presentation. This adjustment ensures a comfortable information delivery experience that does not stress the user.

[0589] Users review the displayed rewards and route information and make selections as needed. At the final stage of their journey, users submit feedback via their device. This feedback, along with sentiment information, is analyzed on the server and used as input for service improvement.

[0590] For example, if a user taking a taxi during peak hours shows signs of dissatisfaction through the emotion engine during their journey, the server immediately adjusts the rewards to provide the user with a more positive experience. This results in greater user satisfaction and an overall improvement in the quality of the service.

[0591] The introduction of an emotion engine will enable the provision of flexible services tailored to the individual needs of each user, rather than simply providing a means of transportation. This will further improve user satisfaction and service transparency, realizing the future vision of autonomous taxis.

[0592] The following describes the processing flow.

[0593] Step 1:

[0594] The user uses a terminal to specify a destination and enter a request for an autonomous taxi. This allows the system to collect information about the pick-up location and destination.

[0595] Step 2:

[0596] The terminal transmits the entered location and destination information to the server. This communication provides the data necessary to arrange a taxi.

[0597] Step 3:

[0598] The server uses the received location information to collect real-time traffic conditions from a traffic database. This information includes road congestion levels and speed limits.

[0599] Step 4:

[0600] The server uses the acquired traffic information to calculate the optimal route and estimated fare. Weather and time-of-day data are also taken into consideration in the calculation.

[0601] Step 5:

[0602] The emotion engine analyzes the user's voice and facial expressions in real time to recognize their current emotional state. This information is sent to the server.

[0603] Step 6:

[0604] The server adjusts the content of rewards and discounts based on information from the emotion engine. For example, if a user is feeling stressed, it increases the rewards to provide a sense of security.

[0605] Step 7:

[0606] The server sends the calculated route, fare, and adjusted reward information to the terminal.

[0607] Step 8:

[0608] The terminal displays information received from the server to the user. This includes details on the optimal route and fares, as well as emotionally sensitive offers.

[0609] Step 9:

[0610] Users review the displayed information and approve their ride by selecting benefits and routes.

[0611] Step 10:

[0612] The server, upon user approval, instructs the self-driving taxi to proceed to the pickup location.

[0613] Step 11:

[0614] If a user has different requests via their device while riding, the emotion engine constantly monitors their emotional state and supports information adjustments as needed.

[0615] Step 12:

[0616] After arriving at their destination, users provide feedback on their travel experience through their device.

[0617] Step 13:

[0618] The device sends the user's feedback to the server.

[0619] Step 14:

[0620] The server analyzes the collected feedback along with sentiment data to generate improvements for the overall service. This allows for actions that will lead to higher customer satisfaction in future service deliveries.

[0621] (Example 2)

[0622] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0623] In autonomous taxi systems, there is a need to improve the passenger travel experience. Conventional systems are limited to providing traffic information and fare information, and do not adequately provide services that are tailored to the passenger's emotional state or offer perks based on real-time emotional information. As a result, it is difficult to adequately mitigate passenger dissatisfaction, and improving service satisfaction remains a challenge.

[0624] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0625] In this invention, the server includes recognition means for identifying the passenger's emotional state in real time, selection means for adjusting and proposing benefits and discounts based on the recognized emotional state and passenger profile, and generation means for generating special prompt sentences using a generative AI model. This enables the provision of benefits tailored to the passenger's emotional state and improves the travel experience.

[0626] "Communication means" refers to a device or function for transmitting passenger location information and destination information to a server.

[0627] "Computation means" refers to a device or function that collects and analyzes real-time traffic information based on received location information and destination information.

[0628] "Calculation means" refers to a device or function that generates the optimal travel route and fare based on the analysis results from the calculation means.

[0629] "Recognition means" refers to a device or function that analyzes the voice and facial expressions of passengers and identifies their emotional state in real time.

[0630] "Selection means" refers to a device or function that adjusts and proposes benefits and discounts based on the emotional state and passenger profile obtained by the recognition means.

[0631] "Generation means" refers to a device or function that uses a generative AI model to create prompt sentences appropriate to a specific situation.

[0632] "Display means" refers to a device or function that provides passengers with generated travel route, fare, benefits, and discount information visually or audibly.

[0633] "Analysis means" refers to a device or function that uses natural language processing technology to analyze feedback collected from passengers and utilize it to improve services.

[0634] "Improvement measures" refer to devices or functions that propose service improvement measures based on analysis results from analytical tools and emotional information.

[0635] This invention is a system for data communication and analysis between a server, terminal, and user in order to improve the passenger experience in an autonomous taxi. The main components include communication means, calculation means, computation means, recognition means, selection means, generation means, display means, analysis means, and improvement means.

[0636] The server uses cloud services to process data in real time. It uses common APIs (e.g., map service APIs) to collect traffic information. In the calculation process, it combines real-time location information from users with traffic information to calculate the optimal route and fare.

[0637] Terminals are devices that provide information to users, and smartphones and tablets are used. These terminals use cameras and microphones to input passengers' facial expressions and voices into an emotion engine. This emotion engine utilizes technologies such as OpenCV for image processing and TensorFlow for speech analysis.

[0638] Users board self-driving taxis and provide their departure and destination locations, as well as feedback, via a terminal. This data is sent to a server and processed in real time. The user experience is optimized, particularly by offering perks and discounts based on the user's emotional state.

[0639] As a concrete example, if a user's device detects they are experiencing stress while traveling, the server uses this information to enhance their benefits. Using a generative AI model, it creates and applies a prompt message such as, "The user is experiencing stress. Please offer a 10% discount coupon for their next visit." This example allows passengers to have a more comfortable experience.

[0640] This system not only offers autonomous driving capabilities but also provides personalized service based on the emotional state of passengers. This improves safety and satisfaction, paving the way for a new future of transportation.

[0641] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0642] Step 1:

[0643] The server receives location and destination information transmitted from the user via communication means. Using this information as input, it begins collecting real-time traffic data. Specifically, it obtains current road conditions using a map service API and analyzes this data using a computing device. The output is the analyzed traffic data.

[0644] Step 2:

[0645] The server generates the optimal travel route and fare based on collected traffic data. Inputs include the user's location, destination, and traffic data. The data is integrated, and the algorithm calculates the shortest route and cost. The output is the optimized travel route and fare information. Specifically, the calculation results are stored on the server.

[0646] Step 3:

[0647] The device uses its built-in camera and microphone to collect the user's voice and facial expressions, providing them as input data for emotion recognition. The emotion engine uses this data to recognize the user's emotional state. The input is audio and video data, and the output is the result of identifying the emotional state. In practice, the device continuously analyzes this data in real time.

[0648] Step 4:

[0649] The server adjusts rewards and discounts based on the recognized emotional state and user profile data. This is done by the selection mechanism. The input is emotional state and profile information, and a generative AI model is used to create prompt messages and generate rewards. The output is reward and discount information. For example, a prompt message such as "The user is feeling stressed. Please offer a 10% discount coupon for their next visit" might be used.

[0650] Step 5:

[0651] The terminal displays travel routes, fares, benefits, and discount information provided by the server to the user. Input is information from the server, and output is a visual display to the user. Specifically, detailed information is displayed on the screen, and voice guidance is provided as needed.

[0652] Step 6:

[0653] Users provide feedback via a terminal after their ride. This feedback is sent to a server via text or voice input. The server uses analytical tools to analyze the feedback information using natural language processing techniques. The output is the analysis results, which are useful for improving the service. Specifically, feedback data is recorded and analyzed on the server.

[0654] Step 7:

[0655] The server proposes service improvements based on the analysis results and sentiment information of the feedback. Improvement measures are used for this. The input is the analysis results, and the output is specific suggestions for improvement. For example, specific service adjustment proposals are generated.

[0656] (Application Example 2)

[0657] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0658] In the travel experience for passengers using autonomous vehicles, there is a demand not only for the function of transportation itself, but also for the provision of services that respond to passengers' emotions and individual needs. However, current technology has the challenge of not being able to effectively identify passenger emotions and dynamically adjust services based on that information. This limits improvements in passenger satisfaction and service quality.

[0659] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0660] In this invention, the server includes emotion recognition means for recognizing passengers' emotions, adjustment means for adjusting benefits and display methods based on the recognized emotion information, and analysis means for collecting feedback from passengers and analyzing it using natural language processing. This enables the provision of flexible services in response to passengers' emotions, thereby improving passenger satisfaction.

[0661] "Passenger location information" refers to geographical data that indicates where a passenger is currently located.

[0662] "Destination information" is data that indicates the final destination to which a passenger wishes to travel.

[0663] "Communication means" refers to technical means used to send and receive data, enabling communication between networks and devices.

[0664] "Computational means" refers to computing devices and algorithms used to process and analyze received data.

[0665] "Real-time traffic data" refers to dynamic data that shows the current traffic situation and is collected from traffic information systems.

[0666] "Computational means" refers to a device or means that performs a mathematical or algorithmic process to derive the optimal result from data.

[0667] "Profile information" refers to specific information about individual passengers, including past usage history and personal preferences.

[0668] "Benefits and discounts" refer to incentives or special treatments offered to passengers as part of the service provided to them, aimed at keeping prices down.

[0669] A "selection method" is an algorithm or process used to choose the appropriate option from multiple choices.

[0670] "Display means" refers to a device or technology for visually presenting information to passengers.

[0671] "Emotion recognition means" refers to a technology or process for identifying a passenger's emotional state, which may involve using voice analysis or image analysis.

[0672] "Adjustment means" refers to a process or device for changing the content or display method of service according to the emotions and circumstances of passengers.

[0673] "Feedback" refers to information such as opinions and evaluations provided by passengers after using a service.

[0674] Natural language processing is a branch of computer science that deals with processing and analyzing human language.

[0675] "Analytical means" refers to techniques or devices used to analyze data, extract meaningful information, and make decisions.

[0676] "Improvement measures" refer to strategies and devices used to improve service quality based on analysis results.

[0677] The system that realizes this invention consists of a server installed in an autonomous vehicle, a terminal carried by a passenger, and an emotion recognition device that works in conjunction with these.

[0678] The server receives location and destination information from passengers' devices and collects and analyzes real-time traffic data based on this information. High-performance processors and cloud-based analysis servers are used as computing tools to enable the processing of large amounts of data. Specifically, machine learning libraries such as Python and TensorFlow are used to analyze traffic patterns and calculate optimal routes and fares. Based on the analysis, passenger profile information and emotion recognition tools are used to identify passengers' emotions and tailor personalized benefits and services.

[0679] Emotion recognition utilizes passengers' smartphones and the vehicle's internal cameras and microphones, while emotion analysis is performed using deep learning models powered by PyTorch and other tools. Based on the emotion data obtained from this model, adjustment mechanisms function to fine-tune perks and experiences, ensuring that passengers maintain the most comfortable state during their ride.

[0680] The terminal visually presents this information to passengers through display means. The displayed information includes customized benefits and route information generated by a generative AI model, as well as a feedback collection interface. In particular, the terminal can directly receive feedback from users and analyze their opinions using natural language processing. This analysis utilizes NLP-enabled libraries (e.g., spaCy or NLTK).

[0681] For example, when the server recognizes stress from a passenger's emotions, it immediately enhances the perks or displays relaxation music or videos on the terminal to alleviate the passenger's dissatisfaction. The following prompts can be used in the generative AI model when presenting entertainment and discount information.

[0682] Example of a prompt:

[0683] "Identify the user's current emotional state. If dissatisfaction or stress is detected, generate appropriate rewards or positive suggestions."

[0684] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0685] Step 1:

[0686] The server receives location and destination information from the passenger's device. Based on this information, it calls an API to collect real-time traffic data and retrieves the necessary information by referring to a traffic database. This generates a dataset that reflects the current traffic situation.

[0687] Step 2:

[0688] The server uses Python and machine learning libraries (such as TensorFlow) to perform data calculations in order to analyze traffic data. This analysis generates the optimal travel route and fare plan. Real-time traffic data and historical route data are used as input, and the most efficient route suggestions and pricing information are output.

[0689] Step 3:

[0690] The server references passenger profile information and analyzes their emotional state using emotion recognition tools. Passenger voice and facial expression data are acquired as input via smartphone sensors and cameras and processed by a PyTorch-based emotion analysis algorithm. This identifies the passenger's current emotional state.

[0691] Step 4:

[0692] The server runs a generative AI model that adjusts the rewards and display methods based on the emotional information it receives. Using prompts, it generates specific rewards and entertainment options tailored to the user's emotions and sends them to the terminal. The generated output is information about rewards and promotions designed to soothe passengers.

[0693] Step 5:

[0694] The terminal displays information received from the server, visually presenting passengers with rewards and route information. The interface is designed to allow passengers to easily check information using the device's display and select and apply rewards on the spot.

[0695] Step 6:

[0696] Users provide feedback via their device at the end of their journey. This feedback is collected using voice input and converted into text data by an analysis tool equipped with natural language processing algorithms. The input feedback is added to a dataset for future service improvements.

[0697] Step 7:

[0698] The server analyzes the collected feedback and uses the information to suggest service improvements. These suggestions directly contribute to increased passenger satisfaction, and the feedback data is used as parameters for future services.

[0699] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0700] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0701] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0702] [Fourth Embodiment]

[0703] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0704] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0705] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0706] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0707] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0708] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0709] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0710] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0711] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0712] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0713] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0714] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0715] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0716] This invention provides a system for operating an autonomous taxi system that offers passengers optimal fares, routes, and even perks and discounts. In this system, the server, terminals, and users each have specific roles, and the overall service is provided through the operation of each role.

[0717] The server handles the primary processing, receiving location and destination information transmitted by passengers. This information is immediately analyzed and combined with real-time traffic data to calculate the optimal route. Furthermore, the server has the capability to select individual benefits and discounts based on passenger profile information. The calculated route, fare, and benefit information are then provided to the user via the terminal.

[0718] The terminal functions as an interface between the user and the server. When a user sends a taxi request using a smartphone or other device, the terminal transmits the user's input information to the server. In addition, the terminal has the function of visually presenting the user with the optimal route, fare, and special offers received from the server, allowing the user to review and select them.

[0719] Users initiate a taxi ride through their device and utilize the service by confirming the information displayed on their device. Once the ride is complete, users can provide feedback through their device. This feedback is collected and analyzed by the server and used to improve the service using natural language processing.

[0720] As a concrete example, this process runs smoothly when a user calls a taxi and heads to a specified destination. The server uses geographical information and traffic data to determine the optimal route and presents it to the user via the terminal. The user checks the presented fare and benefits, accepts, and boards the taxi. After the journey is complete, the user sends feedback, which the server analyzes and incorporates into the next service.

[0721] Thus, the present invention aims to improve the process of using autonomous taxis and provide users with an efficient and convenient means of transportation.

[0722] The following describes the processing flow.

[0723] Step 1:

[0724] The user enters a request to hail a taxi into the application using their terminal. This request includes information about the pickup location and destination.

[0725] Step 2:

[0726] The terminal collects location and destination information entered by the user and transmits it to the server. Data is transmitted quickly via communication means.

[0727] Step 3:

[0728] The server retrieves the latest traffic information from a real-time traffic database based on the received location information. This includes current road conditions and congestion information.

[0729] Step 4:

[0730] The server uses the acquired traffic information and the user's location information to run an optimization algorithm and calculate the optimal route and estimated fare.

[0731] Step 5:

[0732] The server analyzes the user's profile information (past usage history and preferences, etc.) and selects individually customized benefits and discounts.

[0733] Step 6:

[0734] The server sends the calculated optimal route, estimated fare, and information on special offers and discounts to the terminal.

[0735] Step 7:

[0736] The terminal displays information received from the server to the user. The user reviews the presented route, fare, and benefits.

[0737] Step 8:

[0738] The user confirms the taxi reservation and prepares to board by reviewing and approving the information presented.

[0739] Step 9:

[0740] The server, upon receiving approval from the user, sends commands to the autonomous vehicle, directing it to the pick-up location.

[0741] Step 10:

[0742] The user rides in an autonomous taxi until they reach their destination. After arriving at their destination, they fill out a feedback screen on their device to describe their experience.

[0743] Step 11:

[0744] The device sends the user's input to the server.

[0745] Step 12:

[0746] The server analyzes the collected feedback using natural language processing technology and extracts service elements that need improvement. It then generates service improvement measures and prepares them for implementation in the next operation.

[0747] (Example 1)

[0748] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0749] Modern transportation systems face challenges in providing passengers with real-time information on optimal routes and fares while they are traveling. Furthermore, the provision of individualized benefits and discounts tailored to the needs of each passenger is insufficient. Additionally, effectively collecting passenger feedback and utilizing it for service improvement is difficult. To address these challenges, a more efficient and flexible information delivery and analysis system is necessary.

[0750] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0751] In this invention, the server includes communication means for receiving location information and destination information, computing means for collecting and analyzing real-time geographic information and traffic data, and selection means for analyzing passenger history information and proposing benefits and discounts. This makes it possible to provide users with optimal route, fare, and benefit information in real time, and to effectively collect user feedback and reflect it in service improvements.

[0752] "Communication means" refers to the devices and protocols used to receive passenger location and destination information and transmit it to a server.

[0753] A "computational means" is a system that performs calculations to derive the optimal route based on received information and real-time geographical information and traffic data.

[0754] The "calculation means" is a system that calculates the appropriate fare based on passenger input data and traffic conditions.

[0755] A "selection method" refers to a function that analyzes the user's history information and profile to select the most suitable benefits and discounts.

[0756] A "display means" is an interface for visually presenting generated route, fare, and reward information to the user.

[0757] The "analysis method" refers to a mechanism that uses natural language processing technology to analyze passenger feedback and collect it as data.

[0758] "Improvement measures" refer to the process of proposing specific measures to improve the quality of service based on analyzed feedback.

[0759] This system is designed to provide an efficient autonomous taxi service through the collaboration of a server, terminal, and user.

[0760] The server is the primary platform for processing passenger requests. It receives location and destination information sent from the user's device and utilizes API services to obtain real-time geographic information. For example, a common map service can be selected for the geographic information system. Based on this information, the server calculates the optimal route for the user and determines the fare. The fare calculation uses an algorithm that combines historical data and traffic information.

[0761] The terminal is a device that the user directly interacts with, such as a smartphone or tablet. The terminal provides an interface to present the user with optimal route and fare information received from the server, as well as information on special offers. The user calls a taxi through the application on the terminal and provides a rating after the trip is completed.

[0762] Users contribute to the system by experiencing the service through their devices and providing feedback. During the process of a user calling a taxi and traveling to their destination, this information is exchanged between the server and the device in real time.

[0763] As a concrete example, consider a user commuting from home to work. The user enters their destination using the app, and the server, taking current traffic information into account, sends the fastest route to the user's device. Once the user confirms and accepts the presented information, the journey begins. Upon arrival, the user provides feedback through the app, helping to improve the service.

[0764] An example of a prompt might be, "After providing the optimal route to the airport and discount information, please describe specifically how the user will submit feedback." Through this example, it is possible to plan specifically how to support the user experience and collect information.

[0765] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0766] Step 1:

[0767] The terminal receives the user's current location and destination information as input. The user uses a smartphone app to enter this information into a text box and presses the send button. The terminal formats the entered data and prepares to transfer it to the server using a communication protocol.

[0768] Step 2:

[0769] The server receives location and destination information transmitted from the terminal as input. Based on the received data, the server calls a geographic information service API to collect real-time traffic information. The server also takes traffic data and weather information into consideration, calculates the optimal travel route through data calculations, and generates route information as output.

[0770] Step 3:

[0771] The server takes calculated route information as input and performs calculations to generate fares based on distance, time, and real-time traffic conditions. Furthermore, it uses a generative AI model to select appropriate benefits and discounts based on the user's past ride history. The server then generates fare and benefit information as output.

[0772] Step 4:

[0773] The server sends the generated route, fare, and benefits information to the terminal as output. The terminal processes the received information to display it in a view component. The user can review the displayed information and choose whether or not to use the taxi by accepting it.

[0774] Step 5:

[0775] Once the movement is complete, the user enters feedback via a smartphone app. The device sends the entered feedback data to a server. The server receives the feedback data as input and analyzes it using natural language processing.

[0776] Step 6:

[0777] The server aggregates the analysis results as input and generates insights for service improvement. The server prepares better suggestions and proposed solutions for detected problems for future use, contributing to the improvement of the autonomous taxi system's quality. These results are incorporated into the system and reflected in future service delivery.

[0778] (Application Example 1)

[0779] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0780] In autonomous vehicles, there is a need to efficiently suggest the optimal route and fare for each passenger. Furthermore, a system is required that effectively provides benefits and discounts based on passenger profile information, and incorporates real-time feedback for future use. Conventional systems have difficulty providing these functions comprehensively and smoothly.

[0781] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0782] In this invention, the server includes information transmission means for receiving passenger location information and destination information; calculation means for collecting and analyzing real-time traffic information based on the received location information and destination information; calculation means for generating the optimal route and fare based on the analysis results; selection means for analyzing passenger attribute information and proposing benefits and discounts; time management means for setting the user's desired arrival time and providing customized services based on it; and feedback management means for analyzing the post-travel experience and immediately reflecting the feedback in the next service. This enables the provision of efficient and personalized autonomous vehicle services.

[0783] "Information transmission means" refers to a device or process that has the function of accurately receiving passenger location information and destination information and transmitting it to other parts of the system.

[0784] "Computation means" refers to a device or method that has the ability to calculate the optimal travel route by collecting and analyzing real-time traffic information.

[0785] A "calculation mechanism" is a system that has the function of calculating the optimal fare for passengers based on collected data and analysis results.

[0786] A "selection method" is a system that analyzes passenger attribute information and proposes individually beneficial perks and discounts.

[0787] A "time management means" is a device or process that has the function of setting the user's desired arrival time and providing an optimized service accordingly.

[0788] A "feedback management system" is a device or method that has the function of collecting and analyzing passengers' experiences after their journey in order to quickly reflect those experiences in future services.

[0789] The system for realizing this invention is comprised of the interaction of a server, a terminal, and a user. The server functions as an information transmission means for receiving passenger location and destination information. Furthermore, the server collects and analyzes real-time traffic information and calculates the optimal route as a calculation means. Based on this, the calculation means generates the optimal fare, and the selection means analyzes passenger attribute information to propose benefits and discounts.

[0790] The terminal serves as both a user-server interface and a display tool. Pre-generated routes, fares, and reward information are presented to passengers via the terminal for them to review and select. Furthermore, the terminal acts as a feedback management tool, collecting user experience feedback after the trip. The server analyzes this feedback and immediately incorporates it into future service provision.

[0791] This system utilizes a smartphone (iOS or Android) as hardware, and for software, it employs React Native for the frontend, Node.js and Express for the backend, and MongoDB for the database. Additionally, the Google Maps API is used to obtain real-time traffic information, and the Stripe API is used for electronic payments.

[0792] For example, when a user uses an autonomous vehicle service, they specify their desired arrival time and make a request through the app. The server then calculates and provides an optimized route and fare for that time. Furthermore, if the experience was pleasant, that feedback is quickly used to improve the service, enabling it to provide a service that meets the user's needs.

[0793] An example of a prompt to input into the generated AI model is: "Generate a blueprint for an application that provides optimal routes and rewards for autonomous vehicles. Pay particular attention to setting rewards based on user profiles and utilizing real-time traffic data."

[0794] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0795] Step 1:

[0796] The user launches the application on their smartphone and enters their pickup location and destination. The entered location and destination information is transmitted to the server via a data transmission method. This information triggers the server to begin processing.

[0797] Step 2:

[0798] The server uses the Google Maps API to collect real-time traffic information based on the received location and destination information. The server uses this information as a computational tool, analyzing various traffic data to calculate the optimal route. This process takes into account traffic congestion, road construction, and other obstacles.

[0799] Step 3:

[0800] The server uses a calculation tool to determine the optimal fare based on the calculated travel route. This calculation takes into account time-of-day data and supply and demand balance. The calculated fare is temporarily stored.

[0801] Step 4:

[0802] The server retrieves the user's past usage history and profile information from a database and analyzes it using selection methods. Based on specific patterns and needs, the server determines the most suitable benefits or discounts for the user.

[0803] Step 5:

[0804] The server aggregates the above information and generates route, fare, benefits, and discount information. It then formats this information as display data and transmits it to the terminal.

[0805] Step 6:

[0806] The terminal displays the received data on the user interface. The user reviews and selects the presented route, fare, and benefits. The user's selection is immediately sent to the server, and the reservation is confirmed.

[0807] Step 7:

[0808] After using the taxi, users provide feedback about their experience through their device. This feedback is entered as voice or text and sent from the device to the server.

[0809] Step 8:

[0810] The server analyzes the received feedback using natural language processing technology. The analysis results are saved through a feedback management system as measures for improving the service in the future. This allows the system to be adjusted to provide a better experience for users.

[0811] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0812] This invention is a system that aims to further improve the user experience by combining an emotion engine with an autonomous driving taxi system. This system recognizes the user's emotions and uses them to suggest benefits and discounts, adjust the interface, and analyze feedback.

[0813] The server receives location and destination information sent by the user and collects real-time traffic data based on it. This data is analyzed to generate the optimal route and fare. The emotion engine identifies the user's emotions in real time from their voice and facial expressions and sends the results to the server. The server takes this emotion information into consideration and adjusts and proposes benefits tailored to the user.

[0814] The terminal presents information to the user appropriately based on data received from the server. Reflecting the results of the emotion engine, the display system adjusts the method and timing of information presentation. This adjustment ensures a comfortable information delivery experience that does not stress the user.

[0815] Users review the displayed rewards and route information and make selections as needed. At the final stage of their journey, users submit feedback via their device. This feedback, along with sentiment information, is analyzed on the server and used as input for service improvement.

[0816] For example, if a user taking a taxi during peak hours shows signs of dissatisfaction through the emotion engine during their journey, the server immediately adjusts the rewards to provide the user with a more positive experience. This results in greater user satisfaction and an overall improvement in the quality of the service.

[0817] The introduction of an emotion engine will enable the provision of flexible services tailored to the individual needs of each user, rather than simply providing a means of transportation. This will further improve user satisfaction and service transparency, realizing the future vision of autonomous taxis.

[0818] The following describes the processing flow.

[0819] Step 1:

[0820] The user uses a terminal to specify a destination and enter a request for an autonomous taxi. This allows the system to collect information about the pick-up location and destination.

[0821] Step 2:

[0822] The terminal transmits the entered location and destination information to the server. This communication provides the data necessary to arrange a taxi.

[0823] Step 3:

[0824] The server uses the received location information to collect real-time traffic conditions from a traffic database. This information includes road congestion levels and speed limits.

[0825] Step 4:

[0826] The server uses the acquired traffic information to calculate the optimal route and estimated fare. Weather and time-of-day data are also taken into consideration in the calculation.

[0827] Step 5:

[0828] The emotion engine analyzes the user's voice and facial expressions in real time to recognize their current emotional state. This information is sent to the server.

[0829] Step 6:

[0830] The server adjusts the content of rewards and discounts based on information from the emotion engine. For example, if a user is feeling stressed, it increases the rewards to provide a sense of security.

[0831] Step 7:

[0832] The server sends the calculated route, fare, and adjusted reward information to the terminal.

[0833] Step 8:

[0834] The terminal displays information received from the server to the user. This includes details on the optimal route and fares, as well as emotionally sensitive offers.

[0835] Step 9:

[0836] Users review the displayed information and approve their ride by selecting benefits and routes.

[0837] Step 10:

[0838] The server, upon user approval, instructs the self-driving taxi to proceed to the pickup location.

[0839] Step 11:

[0840] If a user has different requests via their device while riding, the emotion engine constantly monitors their emotional state and supports information adjustments as needed.

[0841] Step 12:

[0842] After arriving at their destination, users provide feedback on their travel experience through their device.

[0843] Step 13:

[0844] The device sends the user's feedback to the server.

[0845] Step 14:

[0846] The server analyzes the collected feedback along with sentiment data to generate improvements for the overall service. This allows for actions that will lead to higher customer satisfaction in future service deliveries.

[0847] (Example 2)

[0848] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0849] In autonomous taxi systems, there is a need to improve the passenger travel experience. Conventional systems are limited to providing traffic information and fare information, and do not adequately provide services that are tailored to the passenger's emotional state or offer perks based on real-time emotional information. As a result, it is difficult to adequately mitigate passenger dissatisfaction, and improving service satisfaction remains a challenge.

[0850] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0851] In this invention, the server includes recognition means for identifying the passenger's emotional state in real time, selection means for adjusting and proposing benefits and discounts based on the recognized emotional state and passenger profile, and generation means for generating special prompt sentences using a generative AI model. This enables the provision of benefits tailored to the passenger's emotional state and improves the travel experience.

[0852] "Communication means" refers to a device or function for transmitting passenger location information and destination information to a server.

[0853] "Computation means" refers to a device or function that collects and analyzes real-time traffic information based on received location information and destination information.

[0854] "Calculation means" refers to a device or function that generates the optimal travel route and fare based on the analysis results from the calculation means.

[0855] "Recognition means" refers to a device or function that analyzes the voice and facial expressions of passengers and identifies their emotional state in real time.

[0856] "Selection means" refers to a device or function that adjusts and proposes benefits and discounts based on the emotional state and passenger profile obtained by the recognition means.

[0857] "Generation means" refers to a device or function that uses a generative AI model to create prompt sentences appropriate to a specific situation.

[0858] "Display means" refers to a device or function that provides passengers with generated travel route, fare, benefits, and discount information visually or audibly.

[0859] "Analysis means" refers to a device or function that uses natural language processing technology to analyze feedback collected from passengers and utilize it to improve services.

[0860] "Improvement measures" refer to devices or functions that propose service improvement measures based on analysis results from analytical tools and emotional information.

[0861] This invention is a system for data communication and analysis between a server, terminal, and user in order to improve the passenger experience in an autonomous taxi. The main components include communication means, calculation means, computation means, recognition means, selection means, generation means, display means, analysis means, and improvement means.

[0862] The server uses cloud services to process data in real time. It uses common APIs (e.g., map service APIs) to collect traffic information. In the calculation process, it combines real-time location information from users with traffic information to calculate the optimal route and fare.

[0863] Terminals are devices that provide information to users, and smartphones and tablets are used. These terminals use cameras and microphones to input passengers' facial expressions and voices into an emotion engine. This emotion engine utilizes technologies such as OpenCV for image processing and TensorFlow for speech analysis.

[0864] Users board self-driving taxis and provide their departure and destination locations, as well as feedback, via a terminal. This data is sent to a server and processed in real time. The user experience is optimized, particularly by offering perks and discounts based on the user's emotional state.

[0865] As a concrete example, if a user's device detects they are experiencing stress while traveling, the server uses this information to enhance their benefits. Using a generative AI model, it creates and applies a prompt message such as, "The user is experiencing stress. Please offer a 10% discount coupon for their next visit." This example allows passengers to have a more comfortable experience.

[0866] This system not only offers autonomous driving capabilities but also provides personalized service based on the emotional state of passengers. This improves safety and satisfaction, paving the way for a new future of transportation.

[0867] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0868] Step 1:

[0869] The server receives location and destination information transmitted from the user via communication means. Using this information as input, it begins collecting real-time traffic data. Specifically, it obtains current road conditions using a map service API and analyzes this data using a computing device. The output is the analyzed traffic data.

[0870] Step 2:

[0871] The server generates the optimal travel route and fare based on collected traffic data. Inputs include the user's location, destination, and traffic data. The data is integrated, and the algorithm calculates the shortest route and cost. The output is the optimized travel route and fare information. Specifically, the calculation results are stored on the server.

[0872] Step 3:

[0873] The device uses its built-in camera and microphone to collect the user's voice and facial expressions, providing them as input data for emotion recognition. The emotion engine uses this data to recognize the user's emotional state. The input is audio and video data, and the output is the result of identifying the emotional state. In practice, the device continuously analyzes this data in real time.

[0874] Step 4:

[0875] The server adjusts rewards and discounts based on the recognized emotional state and user profile data. This is done by the selection mechanism. The input is emotional state and profile information, and a generative AI model is used to create prompt messages and generate rewards. The output is reward and discount information. For example, a prompt message such as "The user is feeling stressed. Please offer a 10% discount coupon for their next visit" might be used.

[0876] Step 5:

[0877] The terminal displays travel routes, fares, benefits, and discount information provided by the server to the user. Input is information from the server, and output is a visual display to the user. Specifically, detailed information is displayed on the screen, and voice guidance is provided as needed.

[0878] Step 6:

[0879] Users provide feedback via a terminal after their ride. This feedback is sent to a server via text or voice input. The server uses analytical tools to analyze the feedback information using natural language processing techniques. The output is the analysis results, which are useful for improving the service. Specifically, feedback data is recorded and analyzed on the server.

[0880] Step 7:

[0881] The server proposes service improvements based on the analysis results and sentiment information of the feedback. Improvement measures are used for this. The input is the analysis results, and the output is specific suggestions for improvement. For example, specific service adjustment proposals are generated.

[0882] (Application Example 2)

[0883] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0884] In the travel experience for passengers using autonomous vehicles, there is a demand not only for the function of transportation itself, but also for the provision of services that respond to passengers' emotions and individual needs. However, current technology has the challenge of not being able to effectively identify passenger emotions and dynamically adjust services based on that information. This limits improvements in passenger satisfaction and service quality.

[0885] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0886] In this invention, the server includes emotion recognition means for recognizing passengers' emotions, adjustment means for adjusting benefits and display methods based on the recognized emotion information, and analysis means for collecting feedback from passengers and analyzing it using natural language processing. This enables the provision of flexible services in response to passengers' emotions, thereby improving passenger satisfaction.

[0887] "Passenger location information" refers to geographical data that indicates where a passenger is currently located.

[0888] "Destination information" is data that indicates the final destination to which a passenger wishes to travel.

[0889] "Communication means" refers to technical means used to send and receive data, enabling communication between networks and devices.

[0890] "Computational means" refers to computing devices and algorithms used to process and analyze received data.

[0891] "Real-time traffic data" refers to dynamic data that shows the current traffic situation and is collected from traffic information systems.

[0892] "Computational means" refers to a device or means that performs a mathematical or algorithmic process to derive the optimal result from data.

[0893] "Profile information" refers to specific information about individual passengers, including past usage history and personal preferences.

[0894] "Benefits and discounts" refer to incentives or special treatments offered to passengers as part of the service provided to them, aimed at keeping prices down.

[0895] A "selection method" is an algorithm or process used to choose the appropriate option from multiple choices.

[0896] "Display means" refers to a device or technology for visually presenting information to passengers.

[0897] "Emotion recognition means" refers to a technology or process for identifying a passenger's emotional state, which may involve using voice analysis or image analysis.

[0898] "Adjustment means" refers to a process or device for changing the content or display method of service according to the emotions and circumstances of passengers.

[0899] "Feedback" refers to information such as opinions and evaluations provided by passengers after using a service.

[0900] Natural language processing is a branch of computer science that deals with processing and analyzing human language.

[0901] "Analytical means" refers to techniques or devices used to analyze data, extract meaningful information, and make decisions.

[0902] "Improvement measures" refer to strategies and devices used to improve service quality based on analysis results.

[0903] The system that realizes this invention consists of a server installed in an autonomous vehicle, a terminal carried by a passenger, and an emotion recognition device that works in conjunction with these.

[0904] The server receives location and destination information from passengers' devices and collects and analyzes real-time traffic data based on this information. High-performance processors and cloud-based analysis servers are used as computing tools to enable the processing of large amounts of data. Specifically, machine learning libraries such as Python and TensorFlow are used to analyze traffic patterns and calculate optimal routes and fares. Based on the analysis, passenger profile information and emotion recognition tools are used to identify passengers' emotions and tailor personalized benefits and services.

[0905] Emotion recognition utilizes passengers' smartphones and the vehicle's internal cameras and microphones, while emotion analysis is performed using deep learning models powered by PyTorch and other tools. Based on the emotion data obtained from this model, adjustment mechanisms function to fine-tune perks and experiences, ensuring that passengers maintain the most comfortable state during their ride.

[0906] The terminal visually presents this information to passengers through display means. The displayed information includes customized benefits and route information generated by a generative AI model, as well as a feedback collection interface. In particular, the terminal can directly receive feedback from users and analyze their opinions using natural language processing. This analysis utilizes NLP-enabled libraries (e.g., spaCy or NLTK).

[0907] For example, when the server recognizes stress from a passenger's emotions, it immediately enhances the perks or displays relaxation music or videos on the terminal to alleviate the passenger's dissatisfaction. The following prompts can be used in the generative AI model when presenting entertainment and discount information.

[0908] Example of a prompt:

[0909] "Identify the user's current emotional state. If dissatisfaction or stress is detected, generate appropriate rewards or positive suggestions."

[0910] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0911] Step 1:

[0912] The server receives location and destination information from the passenger's device. Based on this information, it calls an API to collect real-time traffic data and retrieves the necessary information by referring to a traffic database. This generates a dataset that reflects the current traffic situation.

[0913] Step 2:

[0914] The server uses Python and machine learning libraries (such as TensorFlow) to perform data calculations in order to analyze traffic data. This analysis generates the optimal travel route and fare plan. Real-time traffic data and historical route data are used as input, and the most efficient route suggestions and pricing information are output.

[0915] Step 3:

[0916] The server references passenger profile information and analyzes their emotional state using emotion recognition tools. Passenger voice and facial expression data are acquired as input via smartphone sensors and cameras and processed by a PyTorch-based emotion analysis algorithm. This identifies the passenger's current emotional state.

[0917] Step 4:

[0918] The server runs a generative AI model that adjusts the rewards and display methods based on the emotional information it receives. Using prompts, it generates specific rewards and entertainment options tailored to the user's emotions and sends them to the terminal. The generated output is information about rewards and promotions designed to soothe passengers.

[0919] Step 5:

[0920] The terminal displays information received from the server, visually presenting passengers with rewards and route information. The interface is designed to allow passengers to easily check information using the device's display and select and apply rewards on the spot.

[0921] Step 6:

[0922] Users provide feedback via their device at the end of their journey. This feedback is collected using voice input and converted into text data by an analysis tool equipped with natural language processing algorithms. The input feedback is added to a dataset for future service improvements.

[0923] Step 7:

[0924] The server analyzes the collected feedback and uses the information to suggest service improvements. These suggestions directly contribute to increased passenger satisfaction, and the feedback data is used as parameters for future services.

[0925] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0926] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0927] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0928] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0929] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0930] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0931] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0932] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0933] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0934] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0935] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0936] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0937] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0938] 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.

[0939] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0940] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0941] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0942] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0943] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0944] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0945] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted as being incorporated by reference.

[0946] The following is further disclosed regarding the embodiments described above.

[0947] (Claim 1)

[0948] A communication means for receiving passenger location information and destination information,

[0949] A computing means for collecting and analyzing real-time traffic data based on received location and destination information,

[0950] A calculation means that generates the optimal travel route and fare based on the analysis results,

[0951] A selection method that analyzes passenger profile information to propose benefits and discounts,

[0952] A display means for presenting the generated route, fare, benefits, and discount information to passengers,

[0953] An analytical method that collects feedback from passengers and analyzes it using natural language processing,

[0954] An improvement method that proposes service improvement measures based on the analysis results.

[0955] Includes system.

[0956] (Claim 2)

[0957] The system according to claim 1, which takes weather data and time-of-day data into consideration when determining the optimal route and fare.

[0958] (Claim 3)

[0959] The system according to claim 1, which collects passenger feedback using voice input means.

[0960] "Example 1"

[0961] (Claim 1)

[0962] A communication means for receiving passenger location information and destination information,

[0963] A computing means for collecting and analyzing real-time geographic information and traffic data based on received location information and destination information,

[0964] A calculation means for generating the optimal route and fare based on the analysis results,

[0965] A selection method that analyzes user history information to propose benefits and discounts,

[0966] A display means for presenting generated route, fare, benefits, and discount information to the user,

[0967] An analytical method that collects user feedback and analyzes it using natural language processing,

[0968] Based on the analysis results, we propose improvement measures for transportation services.

[0969] Includes system.

[0970] (Claim 2)

[0971] The system according to claim 1, which takes weather information and time-of-day information into consideration when determining the optimal route and fare.

[0972] (Claim 3)

[0973] The system according to claim 1, which collects user evaluations using a voice input device.

[0974] "Application Example 1"

[0975] (Claim 1)

[0976] Information transmission means for receiving passenger location information and destination information,

[0977] A computing means for collecting and analyzing real-time traffic information based on received location and destination information,

[0978] A calculation means that generates the optimal travel route and fare based on the analysis results,

[0979] A selection method that analyzes passenger attribute information to propose benefits and discounts,

[0980] A display means for presenting the generated route, fare, benefits, and discount information to the passenger,

[0981] An analytical means that collects responses from passengers and analyzes them using natural language processing,

[0982] An improvement method that proposes service improvement measures based on the analysis results,

[0983] A time management system that allows users to set their desired arrival time and provides customized services based on that time,

[0984] A feedback management system that analyzes the post-travel experience and immediately incorporates that feedback into the next service.

[0985] A system that includes this.

[0986] (Claim 2)

[0987] The system according to claim 1, which takes weather data and time-of-day information into consideration when determining the optimal route and fare.

[0988] (Claim 3)

[0989] The system according to claim 1, which collects passenger responses using voice input means.

[0990] "Example 2 of combining an emotion engine"

[0991] (Claim 1)

[0992] A communication means for receiving passenger location information and destination information,

[0993] A computing means for collecting and analyzing real-time traffic information based on received location and destination information,

[0994] A calculation means that generates the optimal travel route and fare based on the analysis results,

[0995] A recognition means for identifying the emotional state of passengers in real time,

[0996] A selection mechanism that adjusts and proposes benefits and discounts based on recognized emotional states and passenger profiles,

[0997] A generation means that generates special prompt sentences using a generative AI model,

[0998] A display means for providing passengers with generated travel routes, fares, benefits, and discount information,

[0999] An analytical method that collects feedback from passengers and analyzes it using natural language processing technology,

[1000] An improvement method that proposes service improvement measures based on analysis results and emotional information.

[1001] Includes system.

[1002] (Claim 2)

[1003] The system according to claim 1, which takes weather information and time-of-day information into consideration when determining the optimal travel route and fare.

[1004] (Claim 3)

[1005] The system according to claim 1, which collects passenger feedback using voice input means and analyzes the emotional state of passengers.

[1006] "Application example 2 when combining with an emotional engine"

[1007] (Claim 1)

[1008] A communication means for receiving passenger location information and destination information,

[1009] A computing means for collecting and analyzing real-time traffic data based on received location and destination information,

[1010] A calculation means that generates the optimal travel route and fare based on the analysis results,

[1011] A selection method that analyzes passenger profile information to propose benefits and discounts,

[1012] A display means for presenting the generated route, fare, benefits, and discount information to passengers,

[1013] A means of recognizing passengers' emotions,

[1014] An adjustment mechanism that adjusts benefits and display methods based on recognized emotional information,

[1015] An analytical method that collects feedback from passengers and analyzes it using natural language processing,

[1016] An improvement method that proposes service improvement measures based on the analysis results.

[1017] Includes system.

[1018] (Claim 2)

[1019] The system according to claim 1, which takes weather data and time-of-day data into consideration when determining the optimal route and fare.

[1020] (Claim 3)

[1021] The system according to claim 1, which collects passenger feedback using voice input means. [Explanation of Symbols]

[1022] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A communication means for receiving passenger location information and destination information, A computing means for collecting and analyzing real-time traffic data based on received location and destination information, A calculation means that generates the optimal travel route and fare based on the analysis results, A selection method that analyzes passenger profile information to propose benefits and discounts, A display means for presenting the generated route, fare, benefits, and discount information to passengers, An analytical method that collects feedback from passengers and analyzes it using natural language processing, An improvement method that proposes service improvement measures based on the analysis results. Includes system.

2. The system according to claim 1, which takes weather data and time-of-day data into consideration when determining the optimal route and fare.

3. The system according to claim 1, which collects passenger feedback using voice input means.

Images